J Allen

D Almeida

F Arias

E Avila

D Baeta

C Barrio Amoros

D Baudain

M Blanc

L Bonora

S Cally

A Camacho

Pristimantis_aff.bipunctatus S Castroviejo-Fisher

O Chaline

J C Chaparro

M Chouteau

J Chretien

E Courtois

S Couto

L Crisley

A Cristinoi

Dal Vechio

V T De Carvalho

R De Fraga

S M De Souza

J Devillechabrolle

M Dewynter

J Dias Lima

F A Dubyna Esteves

R Ernst

A Fouquet

G Gagliardi Urrutia

R Gaiga

P Gaucher

J M B Ghellere

F Glaw

M Gordo

T Grant

L Groeneveld

M Guerra

M Panaijo

R Holting

M Jairam

P Jansen

A P Kok

J J Lima

J R Lopez Rojas

Q Martin

C Martinez

M Marty

R Medina

J Mesa

B P Moravec

P Noonan

M Nunes

D Odicio

M E Oliveira

J M Ortega

P Padial

J Peloso

J Perez

K Perez-Marin

C Pineau

E Poixblanc

I Pontes

V Prates

J Premel

R Raysky

M T Recoder

B Rodrigues

R Rogel

F Rojas

S Rojas-Runjaic

V Ron

D Rufray

R Salazar

M Salvatierra

A Sena

P I Snyder

L Solimoes

I Storti

S Tapia

M Taricuarima

E Teixeira

E Toral

J P Twomey

N Vacher

L Vidal

B Viegas

R Villette

Vogt

Andes-A Ajc2270 Leptodactylus_Diedrus

Andes-A Ajc2271

Pristimantis_Vilarsi S Castroviejo-Fisher

Andes-A Ajc2391

Andes-A Ajc2405 Leptodactylus_Riveroi

Andes-A Ajc2409

Pristimantis_Vilarsi S Castroviejo-Fisher

Andes-A Ajc2410 Leptodactylus_Riveroi

Andes-A Ajc2411 Leptodactylus_Riveroi

Andes-A Ajc2412 Leptodactylus_Riveroi

Andes-A Ajc2413 Leptodactylus_Riveroi

Andes-A Ajc2414 Leptodactylus_Riveroi

Andes-A Ajc2414 Leptodactylus_Riveroi

Andes-A Ajc2416

Allobates_Marchesianus S Castroviejo-Fisher

Andes-A Ajc2434

Pristimantis_Vilarsi S Castroviejo-Fisher

Andes-A Ajc2435

Pristimantis_Vilarsi S Castroviejo-Fisher

Andes-A Ajc2443

Andes-A Ajc2444

Andes-A Ajc2461

Andes-A Ajc2462

Andes-A Ajc2477

Andes-A Ajc2493 Leptodactylus_Riveroi

Andes-A Ajc2494 Leptodactylus_Riveroi

Andes-A Ajc2496 Leptodactylus_Riveroi

Andes-A Ajc2497 Leptodactylus_Riveroi

Andes-A Ajc2498

Andes-A Ajc2527 Leptodactylus_Diedrus

Andes-A Ajc2528 Leptodactylus_Riveroi

Andes-A Ajc2577

Leptodactylus_Spgrpodib R Mesa

Andes-A Ajc2578

Leptodactylus_Spgrpodib R Mesa

Andes-A Ajc2579

Leptodactylus_Spgrpodib R Mesa

Andes-A Ajc2580

Leptodactylus_Spgrpodib R Mesa

Andes-A Ajc2589

R Mesa

Andes-A Ajc2594

Leptodactylus_Leptodactyloides R Mesa

Andes-A Ajc2597

Andes-A Ajc2602

Leptodactylus_Colombiensis R Mesa

Andes-A Ajc2614 Leptodactylus_Griseigularis

Andes-A Ajc2615 Leptodactylus_Griseigularis

Andes-A Ajc2616 Leptodactylus_Griseigularis

Evan Twomey

Andes-A Ajc2836

Andes-A Ajc2837

Andes-A Ajc2838

Andes-A Ajc2842

Leptodactylus_Leptodactyloides R Mesa

F Rojas-Runjaic

C Barrio-Amoros

Xxxxxxxxxxxam002 Am002

Leptodactylus_Spgrpodic M Dewynter

K Pineau

Xxxxxxxxam016 Am016

Pristimantis_Chiastonotus M Dewynter

Zonig31013 Amnh20032

Pristimantis_Koehleri S Castroviejo-Fisher

P Peloso

P I Solimoes

Bpn1746 Xxxxxxxxxxxbpn1746

Brice P Leptodactylus_Spgrpodib

Noonan

Xxxxxxxxbpn1880 Bpn1880

Pristimantis_Zeuctotylus Brice

Bpn2958 Xxxxxxxxxxxbpn2958

Xxxxxxxxbpn3719 Bpn3719

Pristimantis_Koehleri A Snyder

Xxxxxxxxcaam24 Caam24

Pristimantis_Chiastonotus O Chaline

Xxxxxxxxht5896 Ht5896

Pristimantis_Koehleri D Oliveira

S Marques

Xxxxxxxxht6725 Ht6725

Pristimantis_Koehleri E Pontes

L Viegas

Xxxxxxxxht6792 Ht6792

Xxxxxxxxht7354 Ht7354

J M Padial

R Salvatierra

Allobates_Insperatus / Juami

J Perez- Marin

Jmp084 Zonig24013

Mnk-A

Pristimantis_Aff Koehleri

Jmp123 Zonib18212

J M Leptodactylus_Leptodactyloides

Padial

Jmp1232 Zonib16012

I would also like to thank all the collectors that helped me gather such an extensive dataset

ACKNOWLEDGEMENTS

Here we are, after three years working on this PhD project and four years studying Amazonian amphibians. Three years gathering memories, learning how to do science, but also learning myself; and experiencing emotional rollercoaster (thank you COVID). Although it was a long road, I wasn't on my own and I would like to thanks all the people that contributed in very different ways to make this thesis what it is. I hope I won't forget anyone, if so please pardon me, as you can imagine I am not fully rested when writing these final lines.

First of all, I would like to thank my supervisors, Antoine Fouquet and Jérôme Chave; for giving me the opportunity to do this PhD and leaving me lot of flexibility in the development of the different chapters. Jérôme, ta culture scientifique est vraiment impressionnante ; tes remarques constructives et ta vivacité d'esprit m'ont beaucoup aidé à améliorer la qualité générale de ce manuscrit, ainsi que mes compétences de rédaction. Antoine, cela fait maintenant quatre ans que l'on travaille ensemble et après tout ce temps je suis toujours impressionné par ta connaissance de l'herpétofaune Amazonienne. Merci de m'avoir donné l'opportunité d'explorer et de me former à toutes les étapes de la recherche en biogéographie tropicale : du terrain au Pérou (dont je garde un très bon souvenir) aux analyses derrière l'ordinateur, en passant par la biologie moléculaire (même si tu sais bien que ce n'est pas ma partie préférée). Merci d'avoir été à l'écoute et très présent au cours de ses quatre années, je ne me suis jamais senti seul. I would like to thank Alexandre Antonelli and Camila Ribas for accepting to be referees for my PhD, as well as the other members that accepted to be part of the jury, Hélène Morlon, Fernanda Werneck and Thierry Oberdorff.

I also thank Fabien Condamine, Jérôme Murienne and Josselin Cornuault for being part of my two PhD committees. Fabien, merci pour ta disponibilité en dehors des comités et pour avoir pris le temps de m'expliquer le fonctionnement de diverses méthodes et scripts. Jérôme, merci pour tous les conseils distillés depuis le stage de master 2. Josselin, merci pour toutes ses réunions en dehors des comités où tu as pris le temps de m'expliquer comment fonctionne BEAST et les modèles de reconstruction d'aires ancestrales. I would also like to thank the heads of the EDB laboratory: Jean-Louis Hemptinne, Jérôme Chave and Géraldine Loot; for welcoming me.

I would like to thank all of my collaborators and co-authors. More specifically I would like to thank Fernanda Werneck, Leandro Moraes and the people that helped organizing the COMBIA meeting in Manaus. Leandro, it has been a pleasure welcoming you at EDB for few weeks and working with you on Amazophrynella biogeography, I wish you all the best in your coming PhD project. I also thank Diego Ortiz for tackling the Osteocephalus biogeography; Diego, I wish you good luck for your defense. I would like to thank Raíssa Rainha for her work on the Boana calcarata-fasciata complex and for including me in her project.

Paulo, Departamento de Zoologia, Brazil; Universidade Federal do Rio de Janeiro, Museu Nacional, Brazil.

Over the years we also hosted several interns that either directly help me for preliminary analyses or worked on side projects in which I was involved and I would like to thank them: Kilian Leblanc and Marlène Framit for your work on Synapturanus; Léa Demay for initiating the historical biogeography of Amazophrynella; Johann Chrétien for the work on Rhinella; and Emily Gibson for your help with the Leptodactylus gr. podicipinus.

I would like to thank Sophie Manzi for explaining and teaching me the basics of molecular biology and for her help at the different steps of the sequencing. I also thank Amaia Irribar-Pelozuelo, Uxue Suescun and Yves Cuenot for their help in DNA extraction and PCR.

I took part in a field sampling mission my first year of PhD and I would like to thank once again Antoine Fouquet for this once in a lifetime opportunity. I would also like to thank Matthieu Chouteau for welcoming us at Tarapoto and for helping us in the sampling along with Vincent Prémel, Johann Chrétien and Josh Allen.

During two years I have been a PhD student representative at the SEVAB doctoral school and I warmly thank Dominique Pantalacci and Claude Marange for giving me this opportunity. Dominique, tu es vraiment le pilier de l'école doctorale, toujours disponible et à l'écoute que se soit pour un problème administratif ou une simple demande d'information ; merci beaucoup pour toutes les discussions et ton aide au cours de ces trois années. Claude, avec ton implication sans faille et ton empathie tu fais un très grand directeur d'école doctorale qui se plie en quatre pour que les doctorants passent leur thèse dans les meilleures conditions possibles, ça a été un plaisir de travailler avec toi.

Devenir représentant m'a aussi permis de rencontrer et de nouer des amitiés avec des doctorants d'autres laboratoires que je n'aurai probablement pas connus autrement ; merci aux autres représentants des doctorants pour toutes ces réunions de travail au Dubli et ces moments funs passés à organiser les journées des doctorants, promis un jour je me déguiserais en Rambo. Bon courage dans la dernière ligne droite à Sevan, Manon et Marie-Aurore ; ainsi qu'aux autres à qui il reste plus de temps ! I would also like to thank Sergine Ponsard, Emilie Lecompte and Pierrick Blanchard for giving me the opportunity to teach in their classes. Pierrick, j'avais adoré participer à l'UE APEE en tant qu'étudiant et j'ai pris autant de plaisir à y revenir deux ans après en tant qu'enseignant ; merci beaucoup pour toute ton énergie positive.

I warmly thank Alexandre Riberon that accompanied me for my first steps in research through three internships during my undergrad studies. Alexandre, je me souviendrais longtemps de ces longues discussions à refaire le monde dans ton bureau ; merci de m'avoir fait confiance et d'avoir aider à développer mon intérêt pour la recherche et mon regard critique.

Speaking of mentor figure, I would also like to thank David Duneau. David, merci pour toutes les conversations sur la science, mais aussi sur les choix de carrière et ce depuis le master. Je n'ai pas oublié que tu m'as beaucoup aidé à préparer le concours SEVAB en passant tous les soirs dans la salle des stagiaires pour jeter un oeil sur ma diapo et me donner des petits conseils ; encore merci.

To finish with the professional oriented acknowledgements, I would like to thank all the people of the EDB lab, especially the non-permanent community. Merci aux anciens doctorants pour m'avoir accueilli et intégré au labo dès le début de la thèse, avec entre autres le weekend d'intégration.

Mention spéciale à Kévin Cilleros, le coeur sur la main et toujours prêt à aider tout le monde sans rien attendre en retour. Merci à Eglantine et Thibault pour toutes les pauses café / clope. Merci également aux collègues de bureau, Juan, Isa et Luana pour la bonne humeur et longues discussions. Juan le « papa » du bureau, toujours le sourire, empli de sagesse et d'empathie, j'espère que tu te plairas à Leipzig, je tâcherais de t'y rendre visite. Merci aussi à Iris, mon autre « camarade de promotion », pour le soutien moral. Merci à E-ping d'avoir eu le courage de reprendre le journal club presque tout seul et d'avoir aussi repris le flambeau pour les représentants des doctorants ; bon courage pour la dernière année. Merci à Ricardo pour les séances de kickboxing qui m'ont permis de me défouler pendant la rédaction du manuscrit. Merci Opale, Déborah et Pauline pour les quelques randos et les nombreuses soirées. Merci Thomas de m'encourager dans mon humour. Et merci à tous les autres doctorants (anciens et actuels) pour la bonne ambiance qui a régné au labo pendant ces 3 ans : Jan, Céline, Lucie, Seb, Félix, Marine, Fabian, Maxime, Maëva, Sandra, Quentin, Sean et les nouveaux venus que je n'ai pas eu l'occasion de rencontrer avec le COVID et la rédaction. Merci aussi aux post docs : David, Josselin, Jessica, Jordi, Nico et Julian.

Merci aux amis en dehors du labo pour leur soutien et m'avoir permis de me changer les idées : Thibault, « friendly fire » ; Gaby « tu n'es pas comme tous les autres enfants » ; Lexou, « camouflage bambou » ; Adam, « le charbong » ; Benoît, « jeudi Dubli » ; Farès, «ya gonna loose » ; Aurèle, « pastèque » ; Steve and Scarlett, « cornichon » ; Valérie, « I didn't like your discussion AT ALL » ; ainsi qu'à la team INSA (Thomas E., Alex, Charlotte, Sevan, Manon, Marie-Aurore, Kévin), les anciens stagiaires d'EDB (Julia, Axel, Quentin, Kilian, Léa et Lisa) et les gens du CRCA (Flora, Guillaume, Louis, Nour, Ana, Greg, Floflo, Stéphane, Mathieu, Iulia, Hugo, Ben, Christophe et Matthias). Merci aussi à l'équipe du Dubli, je m'y suis toujours senti le bienvenu (Seb, Romane et Jérem').

J'aimerai remercier mes parents, Gilles et Pascale, pour m'avoir soutenu et encouragé pendant toutes ses longues années d'études et permis de m'y consacrer pleinement. Je remercie également ma soeur, Charlotte, pour sa bonne humeur et sa positive attitude. J'aimerai aussi remercier mon grand-père, Marcel, qui a su m'éveiller à la biologie et à l'écologie dès mon plus jeune âge en regardant des documentaires avec moi et en m'apprenant le nom de tout un tas d'espèces marines et terrestres. Pour finir, j'aimerai remercier Louise qui partage ma vie et me supporte au jour le jour ; merci pour ton soutient infaillible.

CHAPTER I: Thesis introduction 1| BIOGEOGRAPHY 1.1| Biogeographical context

Current Amazonian diversity

With more than 6 million square kilometers, Amazonia hosts the largest tract of lowland tropical rainforest in the world and a large portion of the global terrestrial diversity (Gibson et al., 2011;Jenkins et al., 2013). This diversity is up to 1500 bird species (IUCN, 2020), 600 amphibian species (Mayer et al., 2019); and around 16,000 plant species [START_REF] Ter Steege | Hyperdominance in the Amazonian tree flora[END_REF]. However, these numbers might still be largely underestimated for most taxonomic groups (e.g. [START_REF] De Oliveira | Cryptic diversity in the lizard genus Plica (Squamata): phylogenetic diversity and Amazonian biogeography[END_REF][START_REF] Fernandes | Multilocus phylogeography of the Wedge-billed Woodcreeper Glyphorynchus spirurus (Aves, Furnariidae) in lowland Amazonia: Widespread cryptic diversity and paraphyly reveal a complex diversification pattern[END_REF]Funk et al., 2012). For example, species delimitation based on molecular data of frogs located in the Northeastern Amazonian region, known as the Guiana Shield, estimated that the number of species in this region could in fact reach twice as much as currently established (Vacher et al., 2020).

Amazonia is mainly composed of lowland rainforest, but it also harbors a mosaic of different habitats such as savannas, swamps and inselbergs [START_REF] Olson | Terrestrial Ecoregions of the World: A New Map of Life on EarthA new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity[END_REF][START_REF] Prance | Islands in Amazonia[END_REF]. Forest habitats themselves are very heterogeneous notably with unflooded forests (or terra-firme forests) and seasonally flooded forests. In Amazonia, terra-firme forests are dominant and regroup a variety of habitats with different soil characteristics [START_REF] Quesada | Soils of Amazonia with particular reference to the RAINFOR sites[END_REF] and distinct community assemblages, for example soils rich in quartz form white-sand forests [START_REF] Tuomisto | Dispersal, environment, and floristic variation of Western Amazonian forests[END_REF]. Flooded forests originated from the low relief of the riverine basins, and cover around 20% of Amazonia, with great variations in flooding parameters such as duration, water characteristics or periodicity along Amazonia (Junk & Piedade, 2010). They are categorized in varzéa habitats that originate from Andean nutrient rich "white" waters; and igapó habitats derived from "black/clear" waters flooding, which originates from forest runoff, mostly run on cratons and is relatively poor in nutrients (Junk, 1989).

These different habitats generally host very distinct community assemblages of plants [START_REF] Draper | Dominant tree species drive beta diversity patterns in western Amazonia[END_REF], birds [START_REF] Borges | Bird Communities in Amazonian White-Sand Vegetation Patches: Effects of Landscape Configuration and Biogeographic Context[END_REF] and amphibians (von May et al., 2010), suggesting that species' evolutionary histories are strongly linked to the histories of their habitats whose spatial fluctuations and connectivity through time likely had a major influence in shaping the current diversity.

For example terra-firme species distributions are often limited by major Amazonian rivers as they constitute barriers to dispersal for them (Moraes et al., 2016;[START_REF] Ribas | A palaeobiogeographic model for biotic diversification within Amazonia over the past three million years[END_REF], whereas species that are associated with flooded forests often occur on both margins of major rivers (e.g. Lynch Alfaro et al., 2015;[START_REF] Naka | Avian biogeography of Amazonian flooded forests in the Rio Branco Basin, Brazil[END_REF].

Major geological and hydrological historical changes within Amazonia

The origin of the Amazonian rainforest remains poorly known due to the paucity of palynological and fossil records dating as far back as the Cretaceous in Amazonia [START_REF] Jaramillo | The origin of the modern Amazon rainforest: implications of the palynological and palaeobotanical record[END_REF]. However, subsequent pollen and macrobotanical fossils suggest that the basic composition of current plant community assemblages was already existing during the Paleocene (66-56 Mya; Wing et al., 2009); with a diversity that increased throughout Neogene (da Silva-Caminha et al., 2010). Mammalian fossil records suggest similar patterns with an important turn-over in species composition at the Paleogene/Neogene transition in western Amazonia (Antoine et al., 2017;Campbell & others, 2004).

For passerine birds and frogs, that have scarce fossil records, time-calibrated phylogenetic reconstructions indicate that most Neotropical families formed during the Paleogene and most genera diversified throughout the Neogene (Hime et al., 2020;Selvatti et al., 2015).

At a global scale, Neogene appears as a key period, during which most of the current landscapes and biotic composition was formed [START_REF] Potter | Global Miocene tectonics and the modern world[END_REF]. Andean orogeny is undoubtedly the most important consequence of Neogene tectonism in the Neotropics, because it induced multiple and complex climatic, hydrological and geological changes throughout the continent notably in Amazonia (Hoorn et al., 2010;[START_REF] Sundell | Implications of variable late Cenozoic surface uplift across the Peruvian central Andes[END_REF]. These changes were heterogeneous in Amazonia, with radical landscape changes in western Amazonia due to the proximity of the Andes (Shephard et al., 2010). One of these major transformations is related to the formation of the Pebas system, a large freshwater system initially connected to the Caribbean Sea; covering up to 13% of the current Amazonian surface (800,000 km²; Albert et al., 2018). The Pebas system supposedly formed during early Miocene (23 Mya) and occupied most of western Amazonian lowlands until around 10-9 Mya, when this system started to be drained eastward into the Atlantic Ocean (Hoorn et al., 2017).

The dating of this connection between the western and eastern Amazonian watersheds is estimated from the first tracks of Andean sediments in the Amazon fan, yet the amount of these Andean sediments highly increased after 5 Mya, suggesting a long and complex process (Figueiredo et al., 2009). During the Pliocene and the Pleistocene, additional important hydrological changes occurred, such as river avulsions and capture (Albert et al., 2018). Western Amazonian rivers of Andean origin carry high sediment loads and are more subject to meandering (Constantine et al., 2014); they consequently appear highly dynamic compared to the more channeled rivers running on the cratons (e.g. Tapajós, Xingú, Tocantins and Negro). This river instability likely affected the permeability over time, allowing dispersal and isolation among neighboring interfluves (Naka & Brumfield, 2018;Pirani et al., 2019) whereas both ancient and fine scale structures are commonly reported for the rivers of the Guiana (Fouquet et al., 2015;Fouquet, Recoder, et al., 2012) and the Brazilian Shield [START_REF] Moraes | Biotic and Landscape Evolution in an Amazonian Contact Zone: Insights from the Herpetofauna of the Tapajós River Basin, Brazil[END_REF].

It is important to note that there are high uncertainties regarding to the dating, magnitude and sometimes the very existence of the different hydrological and geological events in Amazonia. For instance, [START_REF] Bicudo | Andean tectonics and mantle dynamics as a pervasive influence on Amazonian ecosystem[END_REF] used a set of models to reconstruct very distinct paleogeographic scenarios of Amazonia over the past 27 Mya, where the existence of the Pebas system is recovered only in models accounting for dynamic topography. Furthermore, the consequences of these largescaled hydrological changes on biogeography and diversification are difficult to assess and can greatly differ according to organisms' life history traits and ecological niches (Hoorn et al., 2010). For instance, mollusk fossil records suggest a radiation of aquatic species (155 aquatic species, 3 terrestrial species) during the Pebas system era; followed by a massive extinction when the megawetland disappeared around 10-9 Mya (Wesselingh et al., 2006). Oppositely, the Pebas system 23 Mya likely coincides with a decrease in terra-firme habitats, leading to high extinction rates and high species turnover for terrestrial Mammalian species (Antoine et al., 2017). In turn, several phylogenetic reconstructions of terrestrial groups suggested intense diversification following the Pebas system disappearance (e.g. Chazot et al., 2019;Waddell et al., 2018), which could be associated to an expansion of terrestrial habitats in western Amazonia (Wesselingh & Salo, 2006). Additionally, the Pebas system likely affected the connectivity between Amazonia and the Andes for terrestrial taxa, by acting as a barrier to dispersal between 23 and 10 Mya (Antonelli et al., 2009;De-Silva et al., 2016).

Neotropical biotic interchange and consequences on Amazonian diversity

At the continental scale, Amazonia is one of the three distinct areas predominantly covered by moist forest along with the Atlantic Forest and the trans-Andean forests. The trans-Andean forests are isolated from Amazonia by the northern Andes, while the Atlantic Forest is separated from Amazonia by a belt of open formations-the dry diagonal-composed of the Caatinga, the Cerrado and the Chaco (Batalha-Filho et al., 2013).

Although the dry diagonal acts as an environmental barrier to dispersal for forestrial species, there have been multiple biotic exchanges between Amazonia and the Atlantic Forest during Neogene and Quaternary (e.g. Batalha-Filho et al., 2013;Fouquet et al., 2012;[START_REF] Pavan | Patterns of diversification in two species of short-tailed bats (Carollia Gray, 1838): the effects of historical fragmentation of Brazilian rainforests[END_REF]. Ledo & Colli (2017) notably illustrated through ecological niche modeling that quaternary climatic fluctuations likely influenced the expansion and contraction of suitable habitats for different species regarding to their environmental preferences (i.e. thermophilous species being favored during interglacials); possibly affecting connectivity between Amazonia and the Atlantic Forest. However, much more ancient biotic exchanges, some predating or coinciding with early Neogene, have been evidenced notably in frogs (Fouquet et al., 2012a(Fouquet et al., , 2012b)). Similarly, the Northern Andes were probably more permeable for lowland species during the early Miocene, since orogeny has been a gradual process from South to North, punctuated by several intensive phases, notably around 12 and 4.5 Mya (Hoorn et al., 2010).

Hence, connectivity between Amazonia and the trans-Andean forests may have progressively reduced until the present. It is illustrated by a handful of lowland species that are currently distributed on each side of the Andes (e.g. Boana boans, Cochranella resplendens, Atractus occipitoalbus; [START_REF] Köhler | Amphibians of Central America, Herpeton[END_REF]Marín et al., 2017;[START_REF] Molina-Zuluaga | Out of Amazonia: the unexpected trans-Andean distribution of Cochranella resplendens (Lynch and Duellman, 1978)(Anura: Centrolenidae)[END_REF]; while most have been more or less recently isolated on one or the other (e. g. Bessa-Silva et al., 2020;Weir & Price, 2011).

Case studies documented recurrent ecological adaptation from Andean highlands towards Amazonian lowlands during the Neogene, suggesting that the Andes also directly influenced the current composition of Amazonian communities (e.g. Mendoza et al., 2015;[START_REF] Musher | Why is Amazonia a 'source'of biodiversity? Climate-mediated dispersal and synchronous speciation across the Andes in an avian group (Tityrinae)[END_REF]Santos et al., 2009). Similarly, several open habitat lineages have expanded their distribution from the open habitats of the dry diagonal towards the forest habitats of Amazonia [START_REF] Pinto-Ledezma | The geographical diversification of Furnariides: the role of forest versus open habitats in driving species richness gradients[END_REF][START_REF] Rheindt | Habitat shifts in the evolutionary history of a Neotropical flycatcher lineage from forest and open landscapes[END_REF]. A recent meta-analysis conducted by Antonelli et al. (2018) highlighted that habitat switches were more frequent than previously thought in the Neotropics, with around 2100 events inferred for 4450 species from six major clades across the tree of life (angiosperms, birds, ferns, frogs, mammals, and squamates).

So far, studies focusing on more than a species complex or genus were mainly conducted at the scale of the entire Neotropical region (Antonelli et al., 2018;[START_REF] Meseguer | The origin and drivers of Neotropical plant and tetrapod diversification[END_REF], with little regard for the processes at stake within Amazonia. Although there are many historical biogeography case studies at Amazonian scale (e.g. [START_REF] Ferreira | Biogeography of the Neotropical genus Malacoptila (Aves: Bucconidae): the influence of the Andean orogeny, Amazonian drainage evolution and palaeoclimate[END_REF][START_REF] Lynch Alfaro | Biogeography of squirrel monkeys (genus Saimiri): South-central Amazon origin and rapid pan-Amazonian diversification of a lowland primate[END_REF]Pirani et al., 2020), we lack a broader perspective to fully understand the mechanisms underlying diversification within this region. This can be at least partially explained by the fact that it is difficult and costly to thoroughly sample an area as wide as Amazonia; as for example, sampling a single Amazonian snake has been estimated at 120 USD [START_REF] Fraga | The Costs of Evaluating Species Densities and Composition of Snakes to Assess Development Impacts in Amazonia[END_REF]. Consequently, there is a lack of basic knowledge about species boundaries, occurrence or even ecology in Amazonia (Hortal et al., 2015), which can affect the quality of the biogeographical inferences.

1.2| Inferences in historical biogeography

Beginnings of ancestral area estimation

The estimation of the center of origin as always been a central preoccupation in historical biogeography [START_REF] Nelson | From Candolle to Croizat: Comments on the History of Biogeography[END_REF]. Back in the 1970's, centers of origin as well as dispersal histories were limited to a narrative approach without a solid framework to test and compare different biogeographic scenarios [START_REF] Nelson | Vicariance Biogeography -A critique[END_REF]. A model of vicariance was then suggested to infer the center of origin of a group, in which the ancestral area was considered to be the sum of all current distributions, neglecting dispersals [START_REF] Platnick | A Method of Analysis for Historical Biogeography[END_REF]. This model revealed to be problematic for groups with a cosmopolitan distribution such as Asteraceae because, it inherently yielded a cosmopolitan distribution for the common ancestor of the group. To overcome this issue [START_REF] Bremer | Ancestral areas: A cladistic reinterpretation of the center of origin concept[END_REF] developed a parsimony-based model with dispersal events, enabling the possibility to infer a center of origin that can be more circumscribed than the sum of all current distributions. Unfortunately, this model failed at estimating widespread ancestors and did not consider polymorphism (a state composed of several area) for current species [START_REF] Ronquist | Ancestral Areas Revisited[END_REF]. Ronquist (1997) consequently developed a model based on vicariant speciation that aims at minimizing dispersal and extinction events and that was specifically designed to allow widespread ancestors.

Maximum likelihood implementation of ancestral area reconstruction models

Following the development of maximum-likelihood based approaches in comparative phylogenetic methods [START_REF] Blomberg | Testing for phylogenetic signal in comparative data: Behavioral traits are more labile[END_REF][START_REF] Pagel | Detecting correlated evolution on phylogenies: a general method for the comparative analysis of discrete characters[END_REF], Ree & Smith (2008) developed the first model-based inference of ancestral area reconstruction; the dispersal-extinction cladogenesis model (DEC). This model distinguishes range evolution at nodes (cladogenetic event) and along the branches (anagenetic event). At cladogenesis, the model considers vicariance, sympatry and subset sympatry (Fig. I.1), by using an overall prior for each range inheritance scenario [START_REF] Ree | A likelihood framework for inferring the evolution of geographic range on phylogenetic trees[END_REF]. Along the branches, the model allows dispersal and extinction, forming a transition matrix composed of dispersal and extinction rates between each pair of area (Fig.

I.2-A).

In this DEC model, the colonization and local extinction are modeled within a set of discrete areas as a continuous-time Markov process with a state space consisting of all possible geographicrange. The probability of transitions among the states/ranges for anagenetic processes is calculated through matrix exponentiation, while the [START_REF] Felsenstein | Journal of Molecular Evolution Evolutionary Trees from DNA Sequences: A Maximum Likelihood Approach[END_REF] pruning algorithm is used to account for the cladogenetic events. Combining matrix exponentiation and the Felsenstein pruning algorithm allow the likelihood to account for all possible scenarios of range expansion and local extinction that could have resulted in the current geographic distribution of species.

The BioGeoBEARS super model of ancestral area reconstruction

The different ancestral area reconstruction models in BioGeoBEARS are based on different assumptions involving different changes in range/area distribution of ancestral nodes (Fig. I.1). These assumptions usually lead to very different results of ancestral node inferences and it is therefore often difficult to identify which biogeographic processes best fit a given dataset [START_REF] Lamm | Reconstructing ancestral ranges in historical biogeography: properties and prospects[END_REF]. Matzke (2013) consequently developed a super model of ancestral area reconstruction in a maximum likelihood framework similar to the DEC implementation. Under this super model, it is possible to fit the two iconic models: the dispersal-extinction and cladogenesis (DEC) and dispersal vicariance (DIVA); as well as a more recently developed model, BayArea, that relies only on sympatry at cladogenesis (Landis et al., 2013). In an effort to limit computation time, the dispersal D and extinction E parameters are implemented following the simplified version of the DEC model in BioGeoBEARS, leaving only two free parameters to be estimated by maximum likelihood instead of n² parameters, n being the number of areas (Fig. I.2-B). Another specificity of the BioGeoBEARS super model is the implementation of a new cladogenetic event, the founder or jump dispersal event, that is estimated as a single free parameter, creating the "DEC+J", "DIVAlike+J" and "BayArea+J" models [START_REF] Matzke | Model selection in historical biogeography reveals that founder-event speciation is a crucial process in island clades[END_REF]. The classic pipeline described in the scripts associated to the BioGeoBEARS R package fits these six different ancestral area reconstruction models to a dataset composed of a time-calibrated phylogeny and a presence/absence matrix of geographical range for current species. All models are compared based on Akaike Information Criterion (AIC), it is also possible to compare basic models to their +J counterpart (e.g. DEC and DEC+J) with a likelihood ratio test (LRT) as these are considered nested models [START_REF] Anderson | Avoiding Pitfalls When Using Information-Theoretic Methods[END_REF].

An interesting feature added to the BioGeoBEARS R package is to simulate biogeographical evolution under any model, using Biogeographical Stochastic Mappings (BSM; Dupin et al., 2017;[START_REF] Matzke | Stochastic mapping under biogeographical models[END_REF]. Based on parameters estimations of a given model, the BSM approach can estimate biogeographic scenarios ('realizations') composed of cladogenetic and anagenetic events that are constrained to produce the observed data given the phylogeny. By averaging over many realizations, it is possible to obtain a count for each type of biogeographic event supported by the given model.

Recently, Ree & Sanmartin (2018) emitted several criticisms against model implementation and testing in BioGeoBEARS. According to Ree & Sanmartin (2018), the main conceptual flaw results from the fact that BioGeoBEARS estimates likelihood of anagenetic events under a continuous-time, while the cladogenetic events are estimated without any time-dependent probability. They argue that ignoring this probability factor artificially inflates the contribution of cladogenetic events to the likelihood, and minimizes the contribution of anagenetic, time-dependent range evolution. This is even more problematic for models allowing the founder event, as it is basically a cladogenetic event that combines range expansion and range contraction (D + E). It is illustrated by the fact that most case studies find the "+J" models to better fit the dataset while estimates of dispersal and extinction rates are close or equal to zero (e.g. [START_REF] Leliaert | Patterns and drivers of species diversity in the Indo-Pacific red seaweed Portieria[END_REF][START_REF] Matzke | Model selection in historical biogeography reveals that founder-event speciation is a crucial process in island clades[END_REF]. Considering these implementation issues of DEC-based models in BioGeoBEARS, Ree & Sanmartin (2018) suggested that ancestral area reconstruction models should no more be selected by comparing model likelihoods, but rather based on empirical considerations (e.g. biology of the focal group, geological history), and always exclude the models allowing founder events.

Through this section, we have seen that recent methodological developments offer the possibility to answer key questions in historical biogeography (e.g. Where is the center of origin?; How many distinct dispersal events were emitted by an area towards another?). However, they are very sensitive to the completeness and robustness of phylogenetic reconstructions since missing species, poorly supported relationships or large incertitudes on divergence times can strongly affect these inferences [START_REF] Diniz-Filho | Darwinian shortfalls in biodiversity conservation[END_REF]Hortal et al., 2015).

Phylogenetic and divergence time uncertainties are a problem in ancestral area reconstruction as most methods perform the analysis on a fixed tree topology, assuming that the tree is known without error (Landis et al., 2013;Ree & Smith, 2008;Ronquist, 1997). However, it is possible to account for such uncertainties by performing the ancestral area reconstruction on a set of sampled trees (e.g. [START_REF] Harris | Estimating ancestral distributions of lineages with uncertain sister groups: a statistical approach to Dispersal-Vicariance Analysis and a case using Aesculus L. (Sapindaceae) including fossils[END_REF][START_REF] Nylander | Accounting for Phylogenetic Uncertainty in Biogeography: A Bayesian Approach to Dispersal-Vicariance Analysis of the Thrushes (Aves: Turdus)[END_REF]. Concerning missing species, we can differentiate two sorts: those that have already been described but could not be included in the study because no sample was available; and those that we do not know yet and which remain to be described. While it can be easy to account for the first category; the second is trickier as it requires to identify and reduce the discrepancy between known species and existing species, i.e. overcome the Linnaean shortfall [START_REF] Bini | Challenging Wallacean and Linnean shortfalls: knowledge gradients and conservation planning in a biodiversity hotspot[END_REF]; we will detail this in the next section.

1.3| Species concepts and species delimitation

Towards a unified species concept

Nearly all biological sciences rely on species as a fundamental unit such as conservation, evolution, physiology or genetics. Despite the omnipresent use of the species, the precise definition of its underlying concept has been deeply debated.

The biological species concept of [START_REF] Mayr | Systematics and the Origin of Species[END_REF], who defined species as: "groups of actually or potentially interbreeding natural populations which are reproductively isolated from other such groups".

According to this concept, a species is therefore a group of reproducing natural populations that are unable to effectively breed or mate with other sympatric groups [START_REF] Bisby | Biodiversity from a taxonomic and evolutionary perspective[END_REF][START_REF] Mayr | The growth of biological thought: Diversity, evolution, and inheritance[END_REF]. While this theory appears simple and generic, it is by essence applicable neither onto asexual organisms nor allopatric populations [START_REF] Cronquist | Once again, what is a species? Biosystematics in agriculture[END_REF].

Ever since, a multitude of species concepts have been formulated with even more alternative definitions [START_REF] Mayden | A hierarchy of species concepts: the denouement in the saga of the species problem[END_REF]. Reaching a consensus in these species concepts was thus crucial to assess the problem of species delimitation in a more straightforward way, meaning focusing on the methodological problems of delimiting species boundaries and not in the conceptual problem of defining the species categories. From this postulate, De Queiroz (2007) suggested a unified species concept that conserves the element that are common to all contemporary species concepts-the general concept of species as separately evolving metapopulation lineages-and treats this property as the only necessary property to define species. Consequently, lineages do not have to be intrinsically reproductively isolated, monophyletic, ecologically distinct or anything else to be considered as distinct species; they only have to be evolving independently from other lineages.

Nevertheless, this unified concept does not simply discard the different element that were used for species categorization; it rather implies that they can be used as criterions in the species delimitation phase, as long as they provide evidence of independent evolutionary trajectories among lineages.

Molecular-based species delimitation

With the progresses made in molecular biology over the last decades, generating single locus datasets became rather straightforward and affordable. These sequences were quickly used as a taxonomic tool to identify specimens (barcoding), but also to delimit species boundaries (Hebert et al., 2003a(Hebert et al., , 2003b)). Although molecular species delimitation was enthusiastically welcomed by ecologists [START_REF] Janzen | Now is the time[END_REF], taxonomists warned about the dangers of delimiting species based on single DNAlocus criterion alone [START_REF] Blaxter | The promise of a DNA taxonomy[END_REF]. Indeed, the single locus chosen for this type of species delimitation is often a small sequence of mitochondrial DNA (mtDNA). MtDNA has many advantages for species delimitation but also caveats that should be kept in mind such as a male-biased gene flow, incomplete lineage sorting and introgression [START_REF] Ballard | The incomplete natural history of mitochondria[END_REF]. Recent advances in producing genomic data made it possible to acquire large molecular datasets through next generation sequencing (e.g. [START_REF] Mccormack | Applications of next-generation sequencing to phylogeography and phylogenetics[END_REF]. These genomic data changed deeply our perception of species boundaries, notably unraveling that past events of introgression or horizontal transfers could in fact be relatively frequent (e.g. [START_REF] Frantz | Genome sequencing reveals fine scale diversification and reticulation history during speciation in Sus[END_REF][START_REF] Gogarten | Horizontal gene transfer, genome innovation and evolution[END_REF]. For instance, genomic data allowed to detect important introgression among all species of Cod (Gadus) and even suggest that newly acquired alleles have been positively selected, affecting species abilities to expand into new habitats [START_REF] Árnason | CodWeb: Whole-genome sequencing uncovers extensive reticulations fueling adaptation among Atlantic, Arctic, and Pacific gadids[END_REF].

Although several studies show very distinct results between reconstructions performed with mtDNA only and multilocus genomic DNA (e.g. [START_REF] Árnason | CodWeb: Whole-genome sequencing uncovers extensive reticulations fueling adaptation among Atlantic, Arctic, and Pacific gadids[END_REF][START_REF] Hailer | Nuclear genomic sequences reveal that polar bears are an old and distinct bear lineage[END_REF]; one can wonder if these are the rule or the exception, as reconstructions are also often congruent (e.g. [START_REF] Hughes | Comprehensive phylogeny of ray-finned fishes (Actinopterygii) based on transcriptomic and genomic data[END_REF]. Despite the aforementioned caveats and all being considered, the increasing availability of mtDNA data over the past 30 years helped develop the fields of species delimitation and phylogenetics (e.g. Hebert, et al., 2003b;Kocher et al., 1989;[START_REF] Rubinoff | Between Two Extremes: Mitochondrial DNA is neither the Panacea nor the Nemesis of Phylogenetic and Taxonomic Inference[END_REF], leading to major perception changes in species boundaries. For instance, single locus species delimitation provided an inexpensive measure of biodiversity [START_REF] Smith | DNA barcoding for effective biodiversity assessment of a hyperdiverse arthropod group: the ants of Madagascar[END_REF]; and unraveled unsuspected high levels of cryptic diversity, especially in tropical areas (Funk et al., 2012).

These single locus species delimitation methods can be categorized in two groups, the genetic distance-based ones and the coalescent based ones. Methods relying on genetic distance basically cluster sequences based on a cut-off value of genetic distance that can be either arbitrary or estimated (e.g. Puillandre et al., 2012). These methods are interesting to analyse large datasets because they are not time consuming. However, genetic distance thresholds are not biologically meaningful and the evolutionary relationships between the sequences are ignored. Coalescent based methods use a coalescent and a speciation model (or parameters) to determine the transition point from a betweenspecies to a within-species branching process [START_REF] Fujisawa | Delimiting Species Using Single-Locus Data and the Generalized Mixed Yule Coalescent Approach: A Revised Method and Evaluation on Simulated Data Sets[END_REF]Pons et al., 2006;Zhang et al., 2013). These methods do account for evolutionary relationships between sequences and have biologically meaningful parameters but are generally more time consuming. Recent methodological developments greatly improved the coalescent based methods, allowing for instance multiple coalescent processes for a single species-tree (Kapli et al., 2017) or multi-locus data [START_REF] Rannala | Improved reversible jump algorithms for Bayesian species delimitation[END_REF][START_REF] Yang | Bayesian species delimitation using multilocus sequence data[END_REF].

Despite the existence of multi-locus species delimitation methods, it remains often challenging to gather a sufficient of genomic data for a large number of individuals at a species complex or genus scale. Typically, in studies were the taxonomy is already stabilized and questions of delimitation concern a relatively limited number of species, a larger genomic sampling is preferred (e.g. [START_REF] Georges | Genomewide <scp>SNP</scp> markers breathe new life into phylogeography and species delimitation for the problematic short-necked turtles (Chelidae: Emydura ) of eastern Australia[END_REF][START_REF] Pedraza-Marrón | Genomics overrules mitochondrial DNA, siding with morphology on a controversial case of species delimitation[END_REF]. Oppositely, when trying to assess the actual diversity of a species complex or a genus, authors tend to include as much individuals as possible and are constrained to use a small number of loci; mainly mtDNA that can be coupled to a few nuclear loci (e.g. Fouquet et al., 2014;[START_REF] Leliaert | Patterns and drivers of species diversity in the Indo-Pacific red seaweed Portieria[END_REF]. Hence, the design of most delimitation studies faces a trade-off between inclusiveness of the extant diversity (taxonomical and geographical sampling) and the genomic sampling.

2| AMAZONIAN AMPHIBIANS IN HISTORICAL BIOGEOGRAPHY

2.1| Why studying amphibians?

Amphibians are undergoing the most drastic population declines among vertebrates, with at least 41% of all species that are considered as threatened (VU+EN+CR) or extinct (IUCN, 2020) notably because of land use, climate change and fungi infections; making them a priority group in terms of conservation [START_REF] Hof | Additive threats from pathogens, climate and land-use change for global amphibian diversity[END_REF]. Moreover, amphibians form a very ancient and diverse group in which the number of recognized species has increased greatly in the last few decades, especially in the Neotropics (Jenkins et al., 2013), and now exceeds 8000 described species worldwide (Frost, 2020).

These simultaneous trends of extinction and species discovery suggest that many species may disappear even before we know they exist [START_REF] Crawford | Epidemic disease decimates amphibian abundance, species diversity, and evolutionary history in the highlands of central Panama[END_REF]Vacher et al., 2020); urging the necessity to describe this diversity and to improve our understanding of its origins and maintenance [START_REF] Ricklefs | Community diversity: relative roles of local and regional processes[END_REF].

Amphibians are an incredibly diverse vertebrate group with a large variety of ecomorphs, including terrestrial, arboreal, aquatic and even fossorial species (Moen et al., 2016;[START_REF] Moen | Microhabitat and climatic niche change explain patterns of diversification among frog families[END_REF]. It is also the vertebrate group displaying the largest number of reproductive modes, with up to 39 modes according to Haddad & Prado (2005)'s classification, with 31 found in Neotropical Anurans.

Although the inferred ancestral state for Anurans and most species are oviparous, with eggs hatching into exotrophic and aquatic tadpoles (55%); some lineages transitioned to eggs with directdevelopment i.e. that hatch into froglets, assuring an independence from bodies of water for reproduction (at least 19 distinct transitions); there are even intermediate stages where eggs are laid in terrestrial habitats but require an aquatic tadpole development phase (at least 50 distinct transitions; [START_REF] Gomez-Mestre | Phylogenetic analyses reveal unexpected patterns in the evolution of reproductive modes in frogs[END_REF]. Within these large categories, species present a lot of variations in different components of the reproductions such as habitats for oviposition (e.g. in lentic or lotic water bodies, terrestrial or aerial clutches), parental care (e.g. active transport of tadpoles by adults, foam nest to protect the eggs; Haddad & Prado, 2005). This large diversity of life history traits is linked to patterns of diversification and biogeographical histories between Amphibian clades [START_REF] Gomez-Mestre | Phylogenetic analyses reveal unexpected patterns in the evolution of reproductive modes in frogs[END_REF][START_REF] Moen | Microhabitat and climatic niche change explain patterns of diversification among frog families[END_REF]. For instance, Hutter et al. (2017) found a positive relationship between the rates of change in elevational distributions and diversification rates in Neotropical Amphibians.

Overall, Amphibians are often seen as species with low vagility, which can be illustrated by a stronger barrier effects of rivers than in more vagile vertebrates such as birds (Moraes et al., 2016;Naka & Brumfield, 2018). There is however strong variance in dispersal abilities of Amphibian species that can be linked to life history traits, with smaller and strictly terrestrial species being more affected by these riverine barrier effects (Fouquet et al., 2015). Moreover, [START_REF] Meseguer | The origin and drivers of Neotropical plant and tetrapod diversification[END_REF] highlighted that similarly diverse clades of Neotropical ectotherms generally have an older crown-age than endotherms; and that both groups have very distinct diversification drivers, with ectotherms being strongly affected by the Andean uplift. However, these results are based on a small number of comparable phylogenies (16 for Amphibians and 32 for birds; 150 in total) and more work is required to confirm these tendencies and to investigate variation within Amphibians.

2.2| Diversity of Amazonian amphibians

Hyloidea: the major clade of Amazonian amphibians Amphibians belong to three orders, Anurans that are cosmopolitan but mainly distributed in the tropics (~7200 species, 56 species), Caudata that occur mainly in temperate ecosystems (~760 species, nine families), and Gymnophiona that are only tropical (~210 species, 10 families); with around half of the extent diversity distributed in the Neotropics (Frost, 2020). Most of the Neotropical amphibian species are encompassed in the Anuran clade of Hyloidea, accounting for 17 families ~53% of all frog species (81% of Neotropical frogs). That clade is notably formed by iconic families such as Bufonidae (true toads), Hylidae (treefrogs) and Dendrobatidae (dart-poison frogs) [START_REF] Pyron | A large-scale phylogeny of Amphibia including over 2800 species, and a revised classification of extant frogs, salamanders, and caecilians[END_REF]Frost, 2020).

Until recently, phylogenetic relationships between these 17 families were poorly understood (e.g., [START_REF] Roelants | Global patterns of diversification in the history of modern amphibians[END_REF]Zhang et al., 2013b), but studies using large genomic datasets recently managed to clarify their relationships (e.g. Feng et al., 2017;[START_REF] Streicher | Evaluating methods for phylogenomic analyses, and a new phylogeny for a major frog clade (Hyloidea) based on 2214 loci[END_REF]Hime et al., 2020). [START_REF] Streicher | Evaluating methods for phylogenomic analyses, and a new phylogeny for a major frog clade (Hyloidea) based on 2214 loci[END_REF] identified three clades within Hyloidea (Fig. I.4). The Neoaustrana clade is distributed essentially in the southern part of the Neotropics, while the Amazorana and the Commutabirana clades are cosmopolitan but originated and mostly diversified in the tropical regions of the South American continent. Both clades encompass a large diversity of natural history and reproductive modes such as direct development (e.g. Eleutherodactylus; [START_REF] Elinson | Secondary coverage of the yolk by the body wall in the direct developing frog, Eleutherodactylus coqui: an unusual process for amphibian embryos[END_REF], foam nest building (e.g. Pleurodema; [START_REF] Faivovich | A phylogenetic analysis of Pleurodema (Anura: Leptodactylidae: Leiuperinae) based on mitochondrial and nuclear gene sequences, with comments on the evolution of anuran foam nests[END_REF], and species with parental care (e.g.

Ranitomeya;

Von [START_REF] May | The tadpole of the bamboobreeding poison frog Ranitomeya biolat (anura: Dendrobatidae)[END_REF].

Reltionships among families within Amazorana and Commutabirana are strongly supported, but the phylogenetic relationships as well as the divergence time between genera and/or species groups remain largely ambiguous, as most studies did not include all species in their reconstructions and that several nodes remain unsupported (e.g. Santos et al., 2009;Waddell et al., 2018). However, the large diversity of species and life history traits encompassed in these clades provide an interesting canvas for comparative biogeography in Amazonia, with very distinct evolutive histories (e.g. Pirani et al., 2020;Rojas et al., 2018).

3| THESIS

3.1| Aim

Previous works on Amazonian biogeography mainly consist of single clade studies including ancestral area reconstructions and spatially circumscribed phylogeographic studies; notably investigating the effects of rivers and geological events on Amazonian diversification (e.g. [START_REF] Marques-Souza | Reconquering the water: Evolution and systematics of South and Central American aquatic lizards (Gymnophthalmidae)[END_REF]Moraes et al., 2020;Pirani et al., 2019Pirani et al., , 2020)). Antonelli et al. (2018) realized the first and only comparative study by regrouping these independent case studies, with a special emphasis on dispersal at a Neotropical scale. This study identified the central role played by Amazonia among Neotropical regions, but left unexplored the patterns of historical biogeography within Amazonia. In this thesis, the first goal was to design a methodology to produce comparable ancestral area reconstructions of Amazonian groups that accounted for uncertainties in species boundaries and distribution ranges; and use it to produce case studies investigating the historical biogeography of several species' groups. The second goal was then to compare these group-centered ancestral area reconstructions to infer patterns of historical biogeography within Amazonia, such as in-situ speciation and dispersal events.

3.2| How did we study historical biogeography in Amazonian amphibians?

Pyron (2014) performed an ancestral area reconstruction for living amphibians, relying on a phylogeny with many poorly supported branches and a largely incomplete taxonomic sampling (40-45%) and using a few chimeric and misidentified sequences. Although this type analysis provides important information at a global scale, the aforementioned caveats hampers any conclusion at thinner patterns, notably within Amazonia. As mentioned above, sampling extensively the diversity within six million square kilometers is time consuming and costly [START_REF] Fraga | The Costs of Evaluating Species Densities and Composition of Snakes to Assess Development Impacts in Amazonia[END_REF]; and even though the progresses made over the last 20 years are impressive, large gaps still remain (Mayer et al., 2019). In this PhD we benefited from a sampling throughout Amazonia and sequencing effort to gather an improved taxonomic sampling for all of the species complexes we studied; ranging between 79% (Pristimantis gr. conspicillatus) and 100% (Osteocephalus) of described species included.

We selected six species groups that have diversified extensively within Amazonia during Neogene according to the current distribution of nominal species and previous phylogenetic investigations and are representative of distinct Hyloidea lineages (Allobates, Santos et al., 2009;Amazophrynella, Rojas et al., 2018;Pristimantis gr. conspicillatus, Waddell et al., 2018;Boana gr. albopunctata, Caminer & Ron, 2014;Leptodactylus gr. podicipinus, de Sá et al., 2014;and Osteocephalus, Chasiluisa et al., 2020). The Allobates genus (Anura: Aromobatidae) is a group of small-bodied, diurnal, and terrestrial frogs that, at a few exceptions, deposit their clutches in the forest leaf litter where they hatch; tadpoles are then carried into lentic waters by adults (Lötters & Mutschmann, 2007). This genus contains 55 recognized species distributed mostly in Amazonia but also occurring, although more marginally, in the Atlantic Forest, Cerrado, Northern Andes and trans-Andean lowland forests (Frost, 2020). The Amazophrynella genus (Anura: Bufonidae) is also a group of small-bodied, diurnal and nocturnal, and terrestrial frogs, that lay their eggs directly into lentic aquatic environments where they develop into exotrophic tadpoles (Menin et al., 2020). It encompasses 13 recognized species distributed at low altitudes throughout Amazonia (Frost, 2020).

The Boana gr. albopunctata (Anura: Hylidae) is composed of 15 species with arboreal and nocturnal habits, laying eggs that develop in exotrophic tadpoles in lentic or lotic water [START_REF] Faivovich | Systematic review of the frog family Hylidae, with special reference to Hylinae: Phylogenetic analysis and taxonomic revision[END_REF]. This species group occurs mainly across Amazonia but also in the Atlantic Forest and in the open habitats of the dry diagonal (Frost, 2020). The clade formed by the genera Osteocephalus, Dryaderces and Tepuihyla (Anura: Hylidae) account for 26, two and nine recognized species respectively.

Osteocephalus is distributed throughout Amazonia; Dryaderces occurs mostly in the Brazilian Shield within Amazonia; and Tepuihyla is mainly distributed in Pantepui, with a few species found in western Amazonia (Frost et al., 2020). These genera are composed of nocturnal and arboreal species that lay eggs in lentic or lotic water bodies, and also in dendrotelms (tree holes) or phytotelms (bromeliaceae); later developing into exotrophic tadpoles (Jungfer et al., 2013). The 17 recognized species belonging to the Leptodactylus gr. podicipinus (Anura: Leptodactylidae) are occurring in a variety of open (e.g. savanna) and forest habitats (Frost, 2020). The eggs are protected by a foam nest deposited either in constructed basins or directly in the ponds; they later hatch into exotrophic tadpoles that are actively defended by the female [START_REF] Jiménez-Robles | Reproductive traits associated with species turnover of amphibians in Amazonia and its Andean slopes[END_REF]Prado et al., 2002). Finally, the Pristimantis gr. conspicillatus (Anura: Craugastoridae) encompasses 38 recognized species of terrestrial and direct developing frogs; that occur mainly in Amazonia but also in the Andes, Atlantic Forest, Tobago and trans-Andean forests (Frost et al., 2020).

Moreover, these six groups can be categorized in two ecotypes, the 'eco-conserved' species that diversified mainly in terra-firme habitats (Allobates, Amazophrynella and Pristimantis gr. conspicillatus) have small distribution ranges often limited by major Amazonian rivers; and the 'ecodiverse' (Boana gr. albopunctata, Leptodactylus gr. podicipinus and Osteocephalus) that are adapted to a wider variety of habitats including flooded forests. The species of these last groups have wider distribution ranges that often covers both banks of a major Amazonian river. These differences in habitat preferences and distribution pattern denote of important differences in dispersal abilities (Fouquet et al., 2015;Moraes et al., 2016), and likely influenced biogeographical histories by setting distinct evolutive trajectories for these categories (e.g. Pirani et al., 2020;Rojas et al., 2018), Comparing the historical biogeography of these six species complexes is a first step to improve our understanding of Amazonian amphibian diversification. However, as we discussed earlier in the 1.3 section, our perception of species boundaries can be far from the reality, particularly in tropical areas [START_REF] Bini | Challenging Wallacean and Linnean shortfalls: knowledge gradients and conservation planning in a biodiversity hotspot[END_REF]Funk et al., 2012). Impressive efforts have been made to reduce this gap of knowledge, as just for the genus Allobates (Aromobatidae) 14 species were described during the past decade, representing 25% of the known diversity (Frost, 2020); which might still represent an underestimation of the extant diversity (Melo-Sampaio et al., 2020). Consequently, we chose to perform a molecular species delimitation analysis previous to the time-calibrated phylogenetic reconstructions of the six species complexes, in order to reduce the risks of not sampling species.

3.3| Molecular species delimitation as a tool to estimate species number.

There is a wide variety of species delimitation methods that can yield different estimations in the number of molecular clusters [START_REF] Luo | Comparison of Methods for Molecular Species Delimitation Across a Range of Speciation Scenarios[END_REF]. We chose to remain cautious and adopted a majorityrule consensus between the results of three different methods of single-locus species delimitation methods that rely on different principles to compensate for each other's weaknesses (i.e. a lineage is considered as being a molecular cluster if supported by at least two of the three methods; [START_REF] Pentinsaari | Algorithmic single-locus species delimitation: effects of sampling effort, variation and nonmonophyly in four methods and 1870 species of beetles[END_REF]. The Approximate Barcode Gap Discovery (ABGD) is a genetic-distance based method that uses different threshold values to materialize the difference between an intra and an inter specific genetic distance (Puillandre et al., 2012). The General Mixed Yule Coalescent (GMYC) method is based on a combination of a neutral coalescent model [START_REF] Hudson | Gene genealogies and the coalescent process[END_REF]) and a pure-birth speciation model [START_REF] Yule | A mathematical theory of evolution, based on the conclusions of Dr. JC Willis[END_REF], it infers a threshold by maximum likelihood where branching events switch from a within species to a between species process [START_REF] Fujisawa | Delimiting Species Using Single-Locus Data and the Generalized Mixed Yule Coalescent Approach: A Revised Method and Evaluation on Simulated Data Sets[END_REF]. The multi-rate Poisson Tree Processes (mPTP) method is also a coalescent based method but that allows multiple coalescent processes across a species tree. It is based on a model composed of a coalescent and a speciation parameter and consider that each substitution has a small probability to produce a speciation event (Kapli et al., 2017).

We acknowledge that this type of species delimitation presents many caveats and that additional information on reproductive isolation, phenotypic and genomic variation are needed to evaluate if the molecular cluster can be considered as species on independent evolutionary trajectories (Hickerson et al., 2006).

3.4| Methodological choices in ancestral area reconstruction

The main results produced during this PhD were obtained through ancestral area reconstruction. As discussed earlier, there are a lot of methodological choices required to run these models including which framework should be used for the reconstruction (e.g. Matzke, 2013;[START_REF] Fitzjohn | Diversitree: comparative phylogenetic analyses of diversification in R[END_REF][START_REF] Goldberg | Tempo and mode in plant breeding system evolution[END_REF], which type of models should be tested (e.g. Landis et al., 2013;Ree & Smith, 2008;Ronquist, 1997); or even how to define the areas. Through this part we will explain what are the important choices we made and why, as well as discuss the methodological changes between the chapters.

Ancestral area reconstruction method

BioGeoBEARS ancestral area reconstruction models are conditioned by the completion of the phylogeny; the observed range data and the implicit Yule process assumption; meaning it cannot account for unsampled or extinct lineages. We have also seen in the previous section that the maximum likelihood implementation of these model is flawed to a certain extent (Ree & Sanmartin, 2018).

It is important to note that the ClaSSE supermodel overcome most of BioGeoBEARS caveats as it can account for missing lineages and it is possible to implement time-dependent cladogenetic processes [START_REF] Fitzjohn | Diversitree: comparative phylogenetic analyses of diversification in R[END_REF][START_REF] Goldberg | Tempo and mode in plant breeding system evolution[END_REF]. While ClaSSE offers interesting perspectives for the future of historical biogeography, more development is required before it can be as easily and being as computationally efficient as BioGeoBEARS [START_REF] Matzke | Model selection in historical biogeography reveals that founder-event speciation is a crucial process in island clades[END_REF]Ree & Sanmartin, 2018).

Consequently, we chose to realize all of the historical biogeography inferences of the thesis with the BioGeoBEARS super model (Matzke, 2013).

During the PhD, my position on the use of the founder event parameter evolved. For the first two chapters we favored the "classic" model comparison approach of BioGeoBEARS and included the models with founder event [START_REF] Matzke | Model selection in historical biogeography reveals that founder-event speciation is a crucial process in island clades[END_REF]. In the third chapter we adopted the position defended by Ree & Sanmartin (2018) and used only the DEC model given it is more suitable for continental systems and was used to infer Neotropical biogeographical history of many different taxonomical groups, including frogs (Antonelli et al., 2018).

Ancestral area reconstruction is an interesting tool to investigate historical biogeography, but it remains a modeling approach and it would require empirical evidences to support results (i.e. fossils of ancestral species to establish past distributions). Although fossil data can be implemented in BioGeoBEARS as an additional source of information (Matzke, 2013), they are nearly inexistant for Amazonian amphibians. To limit the influence of biogeographic uncertainties we used a simulation approach, the Biogeographical Stochastic Mapping, that allows to extract averaged number for each type of biogeographical events supported by the model (the method is presented in the previous section; Dupin et al., 2017;[START_REF] Matzke | Stochastic mapping under biogeographical models[END_REF]. These event counts are very useful to answer important questions of historical biogeography, such as: Where in-situ diversification is the most important? Is this area a source or a sink of diversification?

Area delimitation

Ancestral area reconstructions consider geographic entities as discrete characters coded in the form of a presence/absence matrix for all species. Defining the boundaries of these areas is therefore crucial and even small differences in the delimitation can lead to very different biogeographical reconstructions. There is no consensus in the area delimitation and this process depends mainly on the biogeographic question addressed with the ancestral area reconstruction; e.g. to investigate the role of habitat switching, one must delimit areas based on habitat types (Antonelli et al., 2018).

To identify processes at different spatiotemporal scales, we adopted two contrasting biogeographic partitioning schemes during this thesis. They differ within Amazonia but similarly include five non-Amazonian Neotropical areas: trans-Andean forests (Tf), northern Andes (nA), dry diagonal (DD), Atlantic forest (Af) and Lesser Antilles (lA).

The first Amazonian partitioning differentiates major geological features within Amazonia: western Amazonia, the Brazilian Shield and the Guiana Shield; for a total of eight Neotropical areas . The western Amazonia underwent dynamic hydrological and geological changes throughout Neogene, while the Brazilian and Guiana shields remained more stable (Albert et al., 2018;Hoorn et al., 2010). Modern riverine barriers roughly delimit boundaries for these three Amazonian areas: the Madeira River, the Negro River and the lower course of the Amazon River; and correspond to the large biogeographic regions known as Wallace's districts (Hoorn et al., 2010;Wallace, 1854).

These districts were recently confirmed as major breaks of species composition in bird (Oliveira et al., 2017) and amphibians (Godinho & da Silva, 2018), strengthening their status of biogeographic region.

The second Amazonian partitioning is based on the major Amazonian interfluves, recognized as the nine Amazonian areas of endemism (AAE) by Cracraft (1985). They have been used in many other studies investigating the historical biogeography of taxa that diversified during late Neogene notably because of riverine barriers (e.g., Smith et al., 2014). We used this partitioning to identify patterns of diversification within the previously described larger Amazonian areas. In this second scheme, following Smith et al. (2014), western Amazonia is subdivided into four areas (Napo, Inambari, Huallaga and Imeri) as well as the Brazilian Shield (Rondônia, Tapajos, Xingu, Belém), for a total of 14 Neotropical areas (Fig.

I.3-B).
In the first and second chapters that are case studies of historical biogeography on two of the species complexes, we used both area delimitations because they are complementary and inform us about different processes. For the third chapter that includes the six groups and have a more comparative nature, we chose to keep only the simpler area delimitation with three Amazonian areas.

This choice was motivated by simplification purposes as the other delimitation was too thin (i.e. several species complexes do not occur in all of the Amazonian interfluves) and preliminary results were complex and difficult to interpret.

3.5| Thesis conduct and structure

This thesis is organized in three chapters. In the first and second chapters, we assessed the number of species of two different groups belonging to the terra-firme specialist category (Allobates and the Pristimantis gr. conspicillatus), based on 16S sequences (see Table I.2 for a list of newly sequenced 16S and their origins); reconstructed time-calibrated phylogenies with complete mitochondrial genomes; and we used these phylogenies to reconstruct ancestral areas. In both cases, the highest diversity is found in western Amazonia and our first objective is to test whether this diversity originated in-situ or through numerous independent dispersal events; i.e. identify whether western Amazonia acted as a species pump or as a diversity sink [START_REF] Jablonski | Out of the tropics: Evolutionary dynamics of the latitudinal diversity gradient[END_REF]. Considering that both of these groups are terra-firme specialists and have supposedly limited dispersal abilities, we expect western Amazonia to have acted as a species pump. Then, we will investigate whether the diversity of these groups accumulated constantly over time or was time-dependent. Because Amazonian habitats were heavily transformed over time due to Andean orogeny (Hoorn et al., 2010), and that these changes influenced diversification of amphibians at the Neotropical scale [START_REF] Meseguer | The origin and drivers of Neotropical plant and tetrapod diversification[END_REF]; we expect that it will also influence the diversification of amphibian at an Amazonian scale.

In the third chapter, we compared the historical biogeography patterns among six different groups belonging to two ecological categories: eco-conserved species, that mainly occur in terra-firme habitats; and eco-diverse species that have adapted to a wider range of habitats. The first aim of this chapter is to identify general tendencies in historical biogeography for Amazonian amphibians. In other words, we will investigate whether species belonging to the same ecological categories display similar biogeographical patterns in terms of diversification and dispersal. In our first and second chapter, we have determined that two of the three species complexes belonging to the eco-conserved groups share similar histories and expect similar congruences when including a third group. For eco-diverse species, we also expect to find similarities but with greater variance between groups as this category appears more heterogeneous. Then, we will investigate whether these ecological categories are biogeographically meaningful; i.e. if they present distinct specific patterns of diversification and dispersals or not. Considering the differences in dispersal abilities we expect to detect higher dispersal rates for eco-diverse species, and possibly higher diversification rates as they have more habitat availability than eco-conserved species. (Matzke et al., 2013). The name of each type of event is followed by the associated parameter in the BioGeoBEARS super model, in italic. Explanatory schemes are displayed as example for each type of biogeographic event, followed by a short description. The DEC, DIVA and BAYAREA columns are colored in dark grey when an event type is supported by the dispersal-extinction cladogenesis (DEC), dispersal-vicariance (DIVA) and BayArea ancestral area reconstruction models, respectively. The Founder event is a specific case and is colored in light grey for the three models as it is an optional feature specific to the BioGeoBEARS supermodel [START_REF] Matzke | Model selection in historical biogeography reveals that founder-event speciation is a crucial process in island clades[END_REF]. Table I.1: Diversity of the species groups chosen for the comparative biogeography of Amazonian amphibians. The Family to which belong each species groups is indicated in the second column. The known reproductive modes (sensu Haddad & Prado, 2005) are listed for each species groups in the third column. The habitat type is depicted in the fourth column, while the microhabitat type can be found in the fifth column. References are listed in the sixth column.

Species groups

1| INTRODUCTION

The number of species found in the Neotropics exceeds that of the other tropical regions [START_REF] Qian | Global concordance in diversity patterns of vascular plants and terrestrial vertebrates[END_REF] and the origins of this astonishing diversity have puzzled biologists for over two centuries (Wallace, 1854). Within the Neotropics, lineages of Amazonian origin frequently dispersed into other Neotropical regions, positioning Amazonia as a major source of diversity for the entire continent (Antonelli et al., 2018). However, processes responsible for diversification within Amazonia remain poorly understood. Speciation within Amazonia has been hypothesized to be linked to climatic fluctuations during the Quaternary with successive stages of forest fragmentation promoting allopatric speciation [START_REF] Haffer | Speciation in Amazonian forest birds[END_REF][START_REF] Hooghiemstra | Neogene and Quaternary development of the neotropical rain forest: The forest refugia hypothesis, and a literature overview[END_REF], though recent empirical evidences tend to refute this hypothesis [START_REF] Wang | Hydroclimate changes across the Amazon lowlands over the past 45,000 years[END_REF]. Andean orogeny during the Neogene may also have created the conditions for in situ diversification (Hoorn et al., 2010;Santos et al., 2009) notably by modifying the hydrological system of Amazonia (Shephard et al., 2010). Phylogenetic analyses suggest that both Neogene and Quaternary events have contributed to Neotropical diversification, but they vary in importance across Amazonian regions and taxa (Hoorn et al., 2010;[START_REF] Turchetto-Zolet | Phylogeographical patterns shed light on evolutionary process in South America[END_REF].

During the Neogene, the Brazilian and the Guiana Shields remained geologically stable.

However, Quaternary climatic fluctuations appear to have had an important influence on the biota in these regions (Fouquet et al., 2012b;[START_REF] Wang | Wet periods in northeastern Brazil over the past 210 kyr linked to distant climate anomalies[END_REF]. Western Amazonia, in contrast, underwent profound landscape changes during the Neogene (Hoorn et al., 2010;Shephard et al., 2010). One of these major transformations is related to the formation of the Pebas system, a freshwater system the size of Western Europe initially connected to the Caribbean Sea. The Pebas system supposedly occupied most of western Amazonia during a period ranging from early Miocene (23 Mya) to around 10-9 Mya, when this system started to be drained eastward into the Atlantic Ocean (Albert et al., 2018). Subsequent hydrological changes have frequently taken place during the Pliocene and the Pleistocene as well, particularly in western Amazonia [START_REF] Rossetti | Mid-Late Pleistocene OSL chronology in western Amazonia and implications for the transcontinental Amazon pathway[END_REF][START_REF] Pupim | Chronology of Terra Firme formation in Amazonian lowlands reveals a dynamic Quaternary landscape[END_REF], with various consequences on Amazonian diversification, such as promoting allopatric speciation and increasing extinction risk or even increasing anagenetic dispersal (Albert et al., 2018). These different temporal and spatial contexts may have had distinctive contributions to the diversification of Amazonian flora and fauna. Moreover, different taxonomic groups with distinct life history traits are likely to have responded differently to the dramatic landscape changes [START_REF] Paz | Testing the role of ecology and life history in structuring genetic variation across a landscape: A trait-based phylogeographic approach[END_REF].

Continued scientific exploration of Amazonia and large international collaborative efforts have made available samples throughout Amazonia and shed light on the highly taxon-specific patterns of diversification (e.g., Fouquet et al., 2012a). For example, many biogeographical studies within Amazonia have focused on birds, arguably the best-known group of vertebrates in terms of Amazonian biogeography. The consensus emerging from these studies is that Amazon bird diversification patterns throughout Amazonia during the last 6 Mya have resulted from a complex interplay of vicariance and dispersals events, promoted by Amazonian rivers (Smith et al., 2014) and ecological gradients (Silvia et al., 2019). Bird lineages diversified rapidly throughout Amazonia, while similarly distributed frog lineages display much older crown ages, suggesting that drivers promoting these diversifications might be different [START_REF] Antonelli | Molecular studies and phylogeography of Amazonian tetrapods and their relation to geological and climatic models[END_REF][START_REF] Rull | Neotropical biodiversity: Timing and potential drivers[END_REF].

Several studies have sought to unravel the phylogenetic relationships among Neotropical anurans, they suggest that certain clades diversified within Amazonia during Neogene (Fouquet et al., 2012a;Santos et al., 2009). Here, we focus on one of these clades, the genus Allobates (Anura:

Aromobatidae), a group of small-bodied, diurnal, and territorial frogs that deposit their clutches in the forest leaf litter. This genus contains 55 recognized species distributed mostly in Amazonia but occurring also in the Atlantic Forest, Cerrado, Northern Andes and trans-Andean lowland forests. With 25 described species occurring in Western Amazonia, this region seems to harbor more species (25 species) than the Brazilian (11 species) and the Guiana Shields (six species; Table II.S1; IUCN, 2019;[START_REF] Frost | Amphibian species of the world: an online reference[END_REF]. This pattern is probably underestimated because recent molecular phylogenetic studies suggest that the species diversity in this genus remains underestimated, with many species still awaiting formal description (e.g. Melo-Sampaio et al., 2020). Given their habitat and their reproductive biology, Allobates species have limited dispersal abilities, which is consistent with marked range boundaries and the profound intraspecific genetic structure documented for several species at regional scale, notably throughout large rivers [START_REF] Kaefer | The early stages of speciation in Amazonian forest frogs: Phenotypic conservatism despite strong genetic structure[END_REF][START_REF] Maia | Not just the river: genes, shapes, and sounds reveal population-structured diversification in the Amazonian frog Allobates tapajos (Dendrobatoidea)[END_REF].

Previous phylogenetic studies proposed a crown age of approximately 26 Million years (My) (CI 95%: 18-35.4 My) for Allobates (excluding the A. olfersioides clade; Santos et al., 2009).

Subsequent studies improved taxonomic coverage but left virtually unexplored the temporal and spatial context of Allobates diversification within Amazonia (e.g. Melo-Sampaio et al., 2020). We speculate that Allobates diversification was likely influenced by the important hydrological and geomorphological changes that took place especially in western Amazonia. These changes were related to the existence of the Pebas system until 10 Mya and subsequently to the dynamic river courses (e.g., [START_REF] Wesselingh | Lake Pebas: a palaeoecological reconstruction of a Miocene, long-lived lake complex in western Amazonia[END_REF]Albert et al., 2018). Before 10 Mya, we hypothesize that the Pebas system could have either fostered diversification by providing a mosaic of fragmented habitats or prevented it by creating unsuitable habitats. The subsequent transformation of western Amazonia may have provided opportunity for in situ diversification but also for dispersals from the Brazilian Shield and Guiana Shield toward western Amazonia. In this complex biogeographic context, we aim to test whether the higher Allobates diversity in western Amazonia results predominantly from: (1) dispersals from neighboring provinces; (2) in situ diversification; or (3) a combination of both processes. We also investigate whether most of Allobates Amazonian diversification preceded the transition from the Pebas to the modern Amazonian hydrological system (14-10 Mya), postdated this event, or was continuous throughout the Neogene. To answer these questions, we evaluated boundaries among Allobates species, reconstructed a time-calibrated phylogeny of the genus and estimated patterns of diversification through time and space.

2| MATERIALS AND METHODS

2.1| Species delimitation

Our first objective was to delimit all major mitochondrial DNA (mtDNA) lineages. Our sampling included 74 Allobates tissue samples, obtained through fieldwork and loans from collaborators. We sequenced a fragment of the 16S rDNA region, a classical marker for Neotropical amphibian taxonomy and systematics (Vences et al., 2005). We also retrieved homologous sequences from GenBank (859 accessions). In total, we obtained 932 16S mitochondrial sequences for this study (sequencing protocols can be found in Appendix S1 and database details are provided in Table II.S2).

These samples span the entire range of distribution of the genus Allobates. DNA sequence alignment was conducted on the MAFFT online server under the E-INS-i option with default parameters, which is designed for sequences with multiple conserved domains and long gaps (Katoh et al., 2017).

We first clustered DNA sequences into taxonomic units, which may represent valid species.

To this end, we applied three DNA-based single-locus species delimitation approaches: (a) a distancebased method, the Automated Barcode Gap Discovery (ABGD; Puillandre et al., 2012); (b) a singlethreshold coalescent-based method, the Generalized Mixed Yule Coalescent approach (single threshold GMYC; Monaghan et al., 2009;Pons et al., 2006); and (c) a multi-rate coalescent based method, the multi-rate Poisson Tree Processes model approach (mPTP; Kapli et al., 2017).

Operational Taxonomic Units (OTUs) were defined using a majority-rule consensus from the results of the three methods, i.e., a lineage is considered as being an OTU if supported by at least two of the three methods. Details of the species delimitation analyses are provided in Appendix S1.

2.2|Time-calibrated species phylogeny

We selected one representative for each OTU (n=50, see Results) for complete mitogenome sequencing in order to infer the topology and obtain time calibrated relationships. These mitogenomic sequences were obtained through low-coverage shotgun sequencing. They were combined with available mtDNA sequences in GenBank for OTU for which we could not obtain tissue samples (see database details in Table II.S2).

To reconstruct a phylogenetic tree, we recovered mitochondrial genome assemblies for 32 OTU representatives (see Appendix S2 for details regarding mitogenomes sequencing, assembling and annotation). We then extracted 12S, 16S, and all coding DNA sequences (CDS), thus removing D-loop and tRNAs from complete mitogenomes. For the remaining 18 OTUs, we gathered all the available mitochondrial DNA loci (12S, 16S, ND1, COI and Cytb) from GenBank; six of these OTUs were represented by the 16S region only (12%). We also selected ten species as outgroups (three with complete mitogenomes), including one for each genus of Aromobatidae and six genera within Dendrobatidae, the sister clade of Aromobatidae (Table II.S3).

We then aligned each locus independently using the MAFFT7 online server. For rRNA genes we chose the E-INS-i strategy, which is designed for sequences with multiple conserved domain and long gaps, while for the protein-coding genes, we chose the G-INS-i strategy, recommended for sequences with global homology (Katoh et al., 2017). Coding regions were realigned considering reading frame and concatenated in Geneious V9.1.8.

We selected the best-fit partition scheme and model of evolution for each partition using PartitionFinder V2.1.1 (Lanfear et al., 2016), according to the Bayesian Information Criterion (BIC). We predefined four data blocks, one for rRNA genes (12S and 16S) and one for each codon position of concatenated CDS regions.

We reconstructed a time-calibrated gene tree using a birth-death tree prior in BEAST 2.5 that accounts for extinction processes and incomplete sampling. We parametrized unlinked substitution models according to the estimates obtained in the PartitionFinder V2.1.1 analysis (Lanfear et al., 2016). Time calibration was implemented using an uncorrelated relaxed lognormal clock model of the distribution of rates among branches for each partition (Drummond et al., 2006). In the absence of a Dendrobatoidea fossil record, we relied on a secondary calibration point. The alternative, to expand the matrix to distantly related lineages, may lead to an overestimation of calibration dates (Molak & Ho, 2015;Papadopoulou et al., 2010). We based our secondary calibration on the results of Feng et al. (2017), whom relied on an extensive nuclear genomic dataset (88 kb) of anurans including all major frog lineages and inferring estimates for the crown age of Dendrobatoidea. We consequently constrained our analysis based on the Feng et al. (2017) estimates. Specifically, we assumed a normal prior distribution (Mean = 34.6 Mya, SD = 3 Mya), a time range consistent with several other studies (i.e. [START_REF] Roelants | Anuran radiations and the evolution of tadpole morphospace[END_REF]Hedges et al., 2015). We acknowledge that our phylogenetic reconstruction is based only on mtDNA sequences only and can lead to an overestimate speciation rate (e.g., McCormack et al., 2011;Near et al., 2012). We emphasize that future work, e.g. using nuclear genomic approaches may lead to slightly different time estimates. Since the availability of material remains challenging gathering nuDNA data for all the terminals included herein was out of reach. However, we could prioritize spatial and taxonomic completeness over genomic coverage.

In the BEAST2 tree reconstruction, the Markov chain Monte Carlo (MCMC) parameters were set to four independent runs of 100 million iterations each, recording every 10,000 th iteration and discarding the first 10% of iterations as burn-in. We combined the log files of the independent runs using LogCombiner 2.5 (Bouckaert et al., 2014) and checked the convergence of our parameters via time-series plots and confirming that all parameters achieved an effective sample size above 200. We extracted the maximum clade credibility tree using Tree annotator 2.5 (Bouckaert et al., 2014).

2.3| Biogeographic analysis

Biogeographic inferences assumed the resulting time-calibrated phylogeny and were conducted using the BioGeoBEARS R package (Matzke, 2013). BioGeoBEARS infers the geographic distributions of ancestral species and explores for each node the relative probabilities of potential biogeographic events based on a maximum likelihood algorithm. We compared three models: (i) a likelihood version of the Dispersal-Vicariance model (DIVALIKE; Ronquist, 1997) which would be selected in case of a prevalence of vicariance events, such as newly formed riverine barriers; (ii) a likelihood version of the BayArea (BBM) model (Landis et al., 2013) that would be favored in case of prevalence of in situ diversification within biogeographic areas, such as a radiation in newly formed environments; and (iii)

the Dispersal Extinction Cladogenesis model (DEC; Ree & Smith, 2008) which would be favored if both vicariance and in situ diversification occurred. We also compared versions of these models allowing jump dispersal as described by the J parameter (Matzke, 2013), though there is an ongoing debate around the use of this parameter (Klaus & Matzke, 2020;Ree & Sanmartín, 2018). We modified the list of geographic ranges in order to forbid state transitions between non-adjacent areas.

Models were compared with the Akaike Information Criterion (AIC). We ran the BioGeoBEARS biogeographical stochastic mapping 50 times, to determine biogeographical event counts for the bestfit model (Dupin et al., 2017).

To identify processes at different spatiotemporal scales, we adopted two contrasting biogeographic partitioning schemes that differed within Amazonia but similarly included five non-Amazonian Neotropical areas: trans-Andean forests (Tf), northern Andes (nA), dry diagonal (DD), Atlantic forest (Af) and Lesser Antilles (lA).

The first Amazonian partitioning differentiates major geological features within Amazonia:

western Amazonia, the Brazilian Shield and the Guiana Shield; for a total of eight Neotropical areas (referred hereafter as the 8A analysis). The western Amazonia underwent dynamic hydrological and geological changes throughout Neogene, while the Brazilian and Guiana shields remained more stable (Hoorn et al., 2010;Albert et al., 2018). Boundaries for these three Amazonian areas are roughly delimited by modern riverine barriers: the Madeira River, the Negro River and the lower course of the Amazon River and also correspond to the large biogeographic regions known as Wallace's districts (Hoorn et al., 2010;Wallace, 1854). These districts were recently confirmed as major breaks of species composition in bird (Oliveira et al., 2017) and amphibians (Godinho & da Silva, 2018), strengthening their status of biogeographic region. This spatial partitioning of Amazonia will be used to investigate the early Neogene East/West and North/South divergences expected in Allobates species according to their distributions and phylogenetic relationships (Melo-Sampaio et al., 2020).

The second Amazonian partitioning is based on the major Amazonian interfluves, recognized as the nine Amazonian areas of endemism (AAE) by Cracraft (1985). They have been used in many other studies investigating the historical biogeography of taxa that diversified during late Neogene notably because of riverine barriers (e.g., Smith et al., 2014). We used this partitioning to identify patterns of diversification within the previously described larger Amazonian areas (8A analysis). In this second scheme, following Smith et al. (2014), western Amazonia is subdivided into four areas (Napo, Inambari, Huallaga and Imeri) as well as the Brazilian Shield (Rondônia, Tapajos, Xingu, Belém), for a total of 14 Neotropical areas (referred hereafter as the 14A analysis; see Appendix S3 for additional details on area boundaries).

2.4| Diversification through time

To investigate diversification through time in Allobates, we used two complementary approaches. First, we tested whether diversification of Allobates varied through time and was significantly different from a

Yule pure-birth model accounting for randomly distributed missing taxa, using a Monte Carlo Constant Rate (MCCR) test implemented in the package LASER V2.4.1 (Pybus & Harvey, 2000;[START_REF] Rabosky | LASER: A Maximum Likelihood toolkit for detecting temporal shifts in diversification rates from molecular phylogenies[END_REF].

Once we assured that diversification of Allobates varied through time, we explored this variation by fitting seven models of diversification through time using the same package. They include two constant-rate models (pure-birth and birth-death), two density-dependent models (DDX and DDL), and three variants of the Yule-n-rate model (respectively with two, three and four speciation rates categories across the tree). Adjustments of these models were compared with the Akaike Information Criterion (AIC).

Finally, we computed a lineage through time (LTT) plot using APE V5.3 package and compared it to a Yule-pure birth model with a 95% CI [START_REF] Paradis | APE: analyses of phylogenetics and evolution in R language[END_REF], to confirm whether and when Allobates diversification varied through time.

3| RESULTS

3.1| Species delimitation

Of the three tested methods of species delimitation, the mPTP method was found to be the most conservative, delimiting 45 candidate species while GMYC and ABGD delimited 51 and 66 candidate species, respectively. Results were largely congruent among methods, as expected from previous studies [START_REF] Paz | Molecular-based rapid inventories of sympatric diversity: A comparison of DNA barcode clustering methods applied to geography-based vs clade-based sampling of amphibians[END_REF]

4| DISCUSSION

As discussed below, the results presented here are consistent with the role of landscape changes in western Amazonia in the rapid diversification of the genus Allobates before 10 Mya. After 10 Mya, diversification rate decreased and a complex combination of dispersal and isolation throughout rivers as well as ecological opportunities seem to have played a major role in Allobates diversification, paralleling the situation found in birds (Silva et al., 2019;Smith et al., 2014). Although the lack of fossils of aromobatids and the use of mitogenomic data alone (see Material and Methods) limit the accuracy of time estimates, our study still represents one of the most thorough in terms of spatial and taxonomic sampling of a vertebrate clade in lowland Amazonia. As such, our results contribute to the understanding of diversification in the Amazonian lowlands.

4.1| Species delimitation and distribution pattern

We used mtDNA to delimit major extant lineages within Allobates. Our dataset includes 16S DNA sequences of 932 specimens of which, about a third were newly generated (Table II.S2). We identified 50 OTUs and confirmed that Allobates species richness is higher in western Amazonia than in the rest of the region (Table II.S1; IUCN, 2019;[START_REF] Frost | Amphibian species of the world: an online reference[END_REF].

Although our delimitation clarified some species boundaries, Allobates diversity remains undersampled. Considering that we included 41 of the 55 currently recognized taxa, we estimate that the genus may approach 70 species, which represents 21% of missing diversity. This figure still likely represents an underestimation, since most Allobates species have small ranges and large parts of Amazonia remains poorly sampled (Mayer et al., 2019). Additional information on reproductive isolation, phenotypic and genomic variation are needed to evaluate if our proposed OTUs correspond to species on independent evolutionary trajectories (Hickerson et al., 2006). Nevertheless, we are confident that our study will contribute to foster such extended studies. We briefly refer to the taxonomic implications of our results in Appendix S1.

4.2| In situ western Amazonian diversification during the Pebas system era

The early diverging lineages of Allobates are distributed in the Brazilian Shield, with the A. masniger clade, and along the Andean foothills with the A. femoralis and A. kingsbury clades. Geographical origins of lineages arising between 20 and 15 Mya remain largely ambiguous as well as the potential number of vicariance or dispersal events. Nevertheless, the current spatial distribution of these early diverging lineages suggests that during early Miocene the genus had a disjunct distribution in the Andean foothills and the Brazilian shield, with a gap in the western Amazonian lowlands. Moreover, biogeographical inferences suggest a large amount of in situ diversification in western Amazonia (8A; Complementary biogeographic inferences considering 14 Neotropical areas (14A) suggest that central Andean foothills (particularly Huallaga) acted as a diversification source for Allobates (similar findings were obtained in different frog taxa; e.g. Santos et al., 2009;Guayasamin et al., 2017). Furthermore, the Andean foothills remained largely unflooded during the Neogene (Shephard et al., 2010), while a large part of western Amazonia was likely unsuitable for most terrestrial organisms and likely endured high extinction rates and species turnover following the setup of the Pebas system around 23 Mya (Antoine et al., 2017). Consequently, the central Andean foothills might have acted as a refuge during the Pebas system era for Allobates (~23-10 Mya). These findings also suggest that the Pebas system prevented Allobates diversification in western Amazonia by constituting an unsuitable habitat.

Most of the in situ diversification in western Amazonia occurred between 14 and 10 Mya and is highlighted by a rapid accumulation of lineages in the A. trilineatus clade, which was followed by a decrease in diversification rate (Fig. II.1a-c). In the A. trilineatus clade, this 14-10 Mya period encompasses 14 out of 20 nodes, among which 12 correspond to western Amazonian in situ speciation events (Fig. II.1a). This represents approximately 72% of the total western Amazonian in situ speciation events inferred by our analysis (Fig. . This 14-10 Mya period coincides with the last phase of the Pebas system that led to the formation of new terra-firme habitats, as well as the setup of the modern Amazon watershed (Albert et al., 2018;[START_REF] Boonstra | Marine connections of Amazonia: Evidence from foraminifera and dinoflagellate cysts (early to middle Miocene, Colombia/Peru)[END_REF]. We speculate that this period might have promoted speciation through an increased availability and diversity of habitats and niches (see below) and reconfiguration of riverine barriers (Hoorn et al., 2010;[START_REF] Rull | Neotropical biodiversity: Timing and potential drivers[END_REF].

Previous studies suggested that the Pebas system might also have fostered the diversification in other amphibians through similar processes (e.g. Waddell et al., 2018).

4.3| Allopatric speciation as a driver of diversification

Given the drastic hydrological reconfiguration of western Amazonia during the 14-10 Mya period, i.e., the transition from the Pebas system to the modern Amazon drainage (Hoorn et al., 2010), the role of rivers as dispersal barriers in the context of the more ancient history of the genus Allobates remains unclear. Many Allobates species ranges are currently circumscribed to single major interfluves, suggesting that rivers may act as barriers, limiting the distribution ranges of species pointing to the role of rivers in the recent diversification of the genus [START_REF] Kaefer | The early stages of speciation in Amazonian forest frogs: Phenotypic conservatism despite strong genetic structure[END_REF]Moraes et al., 2016) Conversely, we observe that western Amazonian rivers appear to play a modest role as barriers, a pattern already suggested by the classic phylogeographic study on Allobates femoralis (Lougheed et al., 1999). Many species are found on both sides of the Madeira River for example, which marks the transition between the Brazilian Shield and western Amazonia (A. caerulodactylus, A. flaviventris, A. masniger/nidicola, A. paleovarzensis, A. conspicuus/subfolionidificans and A. femoralis/hodli; Fig.

II.1a

). In addition, we recovered numerous dispersal events from and towards the two interfluves bordered by the Madeira River (Table II.S8). Courses of western Amazonian rivers, such as the Juruá and Purús Rivers, have high sediment loads that increase river meandering rates, making them highly dynamic compared to the more channeled rivers with lower sediment loads running on the cratons (Tapajos, Xingu, Tocantins and Negro) (Constantine et al., 2014). In addition, western Amazonian rivers are narrower and therefore less effective dispersal barriers. These characteristics might partly explain why these western Amazonian rivers have been more permeable than cratonic rivers, whose courses have remained much more stable over time (Albert et al., 2018). Quaternary events of river avulsions and captures in the Amazon drainage may help explain part of the dispersal and species accumulation in particular interfluves (Rosseti et al., 2015;[START_REF] Pupim | Chronology of Terra Firme formation in Amazonian lowlands reveals a dynamic Quaternary landscape[END_REF]). Overall, the major Amazonian rivers seem to have acted as semipermeable barriers for Allobates, allowing dispersal and isolation among neighboring interfluves, as reported for other taxa (Naka & Brumfield, 2018;Pirani et al, 2019).

CONCLUSION

In Allobates, western Amazonia acted as a source of diversification for the rest of Amazonia, and did so mainly before the end of the Pebas system. Subsequently (last 10 My), riverine semi permeable barriers may have been the main cause of speciation. This study is among only a few to document a history of diversification spanning the last 23 My of the Neogene within Amazonia. The prevalence of the roles of geological and hydrological changes throughout Neogene in driving the diversification of many other organisms within Amazonia remains to be investigated. , we present values of the within-area speciation events for each region (values within rectangles) as well as the anagenetic dispersal events and founder speciation events between each region (values within ellipses). Values correspond to the mean number of inferred events of a specific type acro ss the 50 independent BSMs. Anagenetic dispersals events and founder events are represented only for intra-Amazonian events with a mean of > 0.

APPENDIX II.S1: Details regarding molecular data acquisition, species delimitation analyses and taxonomic implications

Molecular data acquisition

We extracted DNA from liver or muscle tissue (thigh or toe-clip) of the 73 samples using the Wizard Genomic extraction protocol (Promega; Madison, WI, USA). We targeted a c.a. 400bp fragment of the 16S rDNA. We used primers N16R and N16F (Salducci et al., 2005), to which we added NNN + 8nucleotide labels (hereafter designated as "tags") for sample identification as all resulting PCR products were mixed into single libraries: 32 tags for forward primer (N16R) and 36 tags for reverse primer (N16F). The final volume of PCRs was 20 µl, and contained 2 µl of DNA extract diluted 10 fold, 10 µl of Amplitaq Gold® 360 Master Mix (Life Technologies, Carlsbad, CA, USA), 5.84 µl of Nuclease Free Water Ambion (Thermo Fisher Scientific, Massachusetts, USA), 0.25 µM of each primer and 3.2 µg of Bovine Serum Albumin (Roche Diagnostic, Basel, Switzerland). We ran the PCR in duplicate for each sample, and we included blank PCR controls in the analysis using nuclease-free water as template. First, the mixture was denaturated at 95°C for 10 min, followed by 40 cycles of 30 sec at 95°C, 30 sec at 55°C and 1 min at 72°C; followed by a final step of 7 min at 72°C. Libraries of mixed PCR products were sequenced using 2 x 250 paired-end reads sequencing technology through MiSeq high throughput sequencing (Illumina) at the Génopole (Toulouse, France). The resulting outputs we re analysed with the OBITOOLS software suite (Boyer et al., 2016a). Paired-end reads were assembled and merged, and we used the tag attached to the primer to assign each reads to its label. Then we removed low quality reads (alignment scores <50, containing Ns or shorter than 50 bp). The resulting batch of reads was dereplicated while keeping the coverage information (number of reads merged). All sequences <100 bp were discarded. Eventually, every sequences that we included in our dataset were all >380 bp long.

Species delimitation

The ABGD delimitation was performed with a prior of intraspecific divergence P between 0.001 and 0.1, a proxy for minimum relative gap width, X, of 1, and a number of steps n equal to 30. We kept the partition such that P = 0.0127, as it corresponds to the end of a plateau for group number and it is close to the 1% threshold of intraspecific divergence proposed in other vertebrates' delimitation studies using 16S barcodes (Puillandre et al., 2012). For mPTP delimitation we first reconstructed a ML tree with RAxML v. 8.2.4 (Stamatakis, 2014) using the GTR + G model. We used ten outgroups representing most Dendrobatoidea genera (Table S2 in Appendix S4). The mPTP delimitation was undertaken on the rooted tree, with 5 million Markov chain Monte Carlo (MCMC) iterations, sampling every 10,000 th iteration and discarding initial 10% iterations as burn-in. For the GMYC delimitation, we reconstructed a time-calibrated phylogeny using BEAST 2.5 (Bouckaert et al., 2014). We used a birth and death population model to account for extinction processes and incomplete sampling. We included the same ten outgroups used in RAXML reconstruction. We used a single partition with a GTR + G + I substitution model, with an uncorrelated relaxed lognormal clock model of rate variation among branches (Drummond et al., 2006). The root age, corresponding to the common ancestor of the Dendrobatoidea superfamily, was estimated based on the time-calibrated phylogeny of Feng et al. (2017) and was consequently constrained with a normal prior distribution (M = 34.6 Mya, SD = 3 Mya).

We consider it the best prior because Feng et al. (2017) included a comprehensive dataset of nuclear loci for an extensive sampling of anurans and used many reliable fossils as calibration points. For the MCMC parameters, we used four independent chains of 100 million iterations, recording every 10,000 th iteration and discarding the first 10% iterations as burn-in. We combined the log files of the independent runs using LogCombiner 2.5 (Bouckaert et al., 2014) and checked the convergence of our parameters, confirmed by all ESS being above 200. Then, we extracted the maximum clade credibility tree using Tree annotator 2.5 with a burn-in of 10% (Bouckaert et al., 2014). After retrieving outgroups, we performed a GMYC delimitation on the ultrametric tree using the GMYC function of the {splits} R package (Ezard et al., 2009), with a threshold interval between 0 and 10 Mya and by using the single threshold method. Operational Taxonomic Units (OTUs) were defined using a majority-rule consensus from the results of the three methods, i.e., a lineage is considered as being an OTU if supported by at least two of the three methods.

All but five occurrence records were georeferenced and used to create distribution maps with convex polygons under QGIS 2.14 (Team & others, 2015). OTUs were assigned to taxa based on the field/museum identification and sometimes corrected in accordance with type localities and known distribution [START_REF] Frost | Amphibian species of the world: an online reference[END_REF]IUCN, 2019).

Taxonomic implications

Our results are in overall agreement with the currently recognized species and distribution ranges.

Operational taxonomic units were assigned to species based on initial specimen identification by collectors and comparison of population against type localities. Out of the 50 OTUs delimited, 29

OTUs were assigned to a recognized species, while six OTUs were assigned to a pair of recognized (Simões et al., 2010), but they are known to hybridize in a specific contact zone (Simões et al., 2012); leading to potential bias in DNA based delimitation analyses; (ii) A. insperatus and A. juami have similar but distinct advertisement calls and can be distinguished by external morphological characters [START_REF] Simões | A new species of nurse-frog (Aromobatidae, Allobates) from the Juami River basin, northwestern Brazilian Amazonia[END_REF]; and (iii) the description paper of A. pittieri distinguish A. pittieri and A. humilis on the base of external morphological characters (La [START_REF] Marca | Taxonomic revision of the Colostethus from northern Venezuela for long time mistaken for C. brunneus/Revision taxonomica del Colostethus del norte de Venezuela confundido durante largo tiempo con C. brunneus[END_REF], to our knowledge there has been no acoustic comparative study between these taxa.

Conversely, two nominal species were split into as many as four OTUs. For instance, Allobates gr.

olfersioides is comprised of four highly divergent OTUs with a crown age around 16 Mya, and four nominal species are currently considered synonyms of A. olfersioides on the basis of external morphometric characters (Verdade & Rodrigues, 2007), although some of them display clear molecular [START_REF] Grant | Phylogenetic systematics of Dart-Poison Frogs and their relatives revisited (Anura: Dendrobatoidea)[END_REF] and acoustic [START_REF] Forti | The acoustic repertoire of the Atlantic Forest Rocket Frog and its consequences for taxonomy and conservation (Allobates, Aromobatidae)[END_REF] We interpret these cases as potential true positives that strongly suggest that the delimited OTUs correspond to candidate species remaining to be described.

We ended up with 15 OTUs that were not assigned to any recognized species, of which 12 are closely related to recognized species (referred as aff.) and further three that could not be linked to any nominal species (referred as sp.). Part of these undescribed lineages have already been identified in recent Allobates phylogenetic studies (e.g. [START_REF] Moraes | A new nurse frog of Allobates masnigernidicola complex (Anura, Aromobatidae) from the east bank of Tapajós River, eastern Amazonia[END_REF]. We hope that our work will help identifying part of the taxonomic gaps remaining in the Allobates genus.

APPENDIX II.S2: Mitogenome sequencing, assembling and annotation

For complete mitochondrial sequencing we used 200 ng of genomic DNA for library construction.

These libraries were constructed using the Illumina TruSeq Nano DNA Sample Prep kit following supplier instructions at the Genotoul-Get-PlaGe core facility (Toulouse, France). Genomic DNA was fragmented by sonication, fragments were size-selected (50 -400 bp), were adenylated and ligated to indexed sequencing adapters. Eight cycles of Polymerase Chain Reaction (PCR) were applied to amplify libraries before library quantification and validation. 48 libraries were multiplexed to be sequenced on one lane on an Illumina Hiseq3000 flow cell (Illumina Inc., San Diego, CA). Read assembly was performed using the ORGanelle ASseMbler (Boyer et al., 2016), a python program developed especially for organelle and ribosomal DNA reconstructions from a genome skimming dataset. The first step is to sort and index the reads with the oa index command. Then, with the oa buildgraph command the program use a seed (reference sequence) to assemble the reads and creates an assembling graph with all the different fragments assembled. Finally, the oa unfold command finds an optimal path in the assembling graph and allows the sequence reconstruction (Boyer et al., 2016).

For the oa index command, we used two options: (i) -estimate-length 0.9, which estimates the reads' length using 90% of the reads; and (ii) -bypass-filtering when the read indexing failed due to an over-filtering that resulted in an empty index. For the oa buildgraph command we used default settings in most cases, except if the assembly graph was empty due to errors in coverage estimation. In these cases, we modified the -coverage value to be slightly lower than the estimate. For the oa unfold command we used the default options as long as it was able to find the optimal assembly path. If not, we checked the assembling graph with the yEd Graph Editor (organic layout, default parameters) and selected the green fragments (that correspond to similar seed fragments) and extracted them with the --path--option in the oa unfold command. The fragments were then mapped onto a seed with the Geneious (R9.1.7; Biomatters Ltd, Auckland, New Zealand) map to reference function with mediumlow sensitivity and one iteration. The mapping consensus was extracted and considered as a mitochondrial genome assembly.

For annotation, sequences were aligned on MAFFT7 online server under default parameters except the E-INS-i strategy which is designed for sequences with multiple conserved domain and long gaps (Katoh et al., 2017). Then, with the Geneious R9.1.7 transfer annotations function we annotate all the alignments sequences from a reference sequence (Anomaloglossus baeobatrachus; NC_030054). Finally, we checked for all the sequences that each CDS began with a methionine (ATA or ATG codon for mitochondrial vertebrates' genomes) and ended with a STOP codon.

APPENDIX II.S3: Delimitation of the nine Neotropical biogeographical areas.

For the 14 Neotropical biogeographic areas analysis (14A), we divided Amazonia in nine areas corresponding to the areas of endemism described by Cracraft (1985), they are the major Amazonian interfluvia. We chose to reproduce the limits displayed in Smith et al. (2014) with: Napo (Na) -that is west of the Japurá River and north of the Amazon river; Imerí (Im) -which is west of the Negro River to east of the Japurá River; Guiana (G) -delineated westward by the Negro River, it corresponds to the Eastern Amazonia area of the 8A analysis; Huallaga (Hu) -delineated northward by the Amazon river and eastward as well as southward by the Amazon river; Inambari (In) -the area east of the Huallaga River, south of the Amazon River, and west of the Madeira River; Rondônia (Ro)delineated westward by the Madeira River, eastward by the Tapajós River and northward by the Amazon river; Tapajós (Ta) -that is located at the east of the Tapajós River, at the west of the Xingú River and at the south of the Amazon river; Xingú (Xi) -the region east of the Xingu River, west of the Tocantins River and south of the Amazon River; and finally, Belém (Be) -the area east of the upper Tocantins River. These areas have been argued to match range boundaries of many vertebrates (e.g., Avila-Pires, 1995;de Castro Godinho & da Silva, 2018) and also largely correspond to Allobates species range boundaries.

The other non-Amazonian Neotropical regions that we considered are: (i) The transandean moist lowland forests (Tf); (ii) The northern Andes (nA); (iii) The dry diagonal (open habitats; D); (iv)

The Atlantic Forest (historical range; Af); and (v) The lesser Antilles archipelago (lA). We delineate all these regions with QGIS using the world ecoregions retrieved from www.worldwildlife.org and we assigned subregions to species according to their geographical distributions (Team & others, 2015).

Given that A. femoralis occur in all Amazonian areas of the 14A analysis except Imeri (Im) and Napo (Na), Huallaga (Hu) and Inambari (In). II.S1 in Appendix II.S4.

APPENDIX II.S4: Supplementary tables

1| INTRODUCTION

Amazonia has been found to represent the primary source of diversity for the entire Neotropics (Antonelli et al., 2018). Nevertheless, some Amazonian lineages originated from Andean dispersals (e.g. Antonelli et al., 2009;Mendoza et al., 2015;[START_REF] Musher | Why is Amazonia a 'source'of biodiversity? Climate-mediated dispersal and synchronous speciation across the Andes in an avian group (Tityrinae)[END_REF]Santos et al., 2009), the Pantepui region [START_REF] Marques-Souza | Reconquering the water: Evolution and systematics of South and Central American aquatic lizards (Gymnophthalmidae)[END_REF]Fouquet et al., in press) and other dispersed more recently from the Atlantic Forest (e.g. Dal Vechio et al., 2018;[START_REF] Prates | Local adaptation in mainland anole lizards: Integrating population history and genome-environment associations[END_REF]Batalha-Filho et al., 2013) and the Cerrado (e.g. Fouquet et al. 2014); suggesting that the role of Amazonia as a source may be a general trend that is in fact lineage and time dependent and needs to be investigated further.

Moreover, the central role that Amazonia played within the Neotropics reinforces the importance of the question of the diversification within Amazonia which remains largely unexplored, notably because of the challenging task of gathering a comprehensive taxonomic and spatial sampling of the organisms of this region. The Andes currently act as a barrier to dispersal, isolating Amazonian organisms from the trans-Andean moist forests such as the Chocó. Yet, the Andean uplift was a relatively long process that intensified from South to North and was punctuated by several intensive phases, notably around 12 and 4.5 Mya (Hoorn et al., 2010). This gradual process is exemplified by some lowland species that are currently distributed on both sides of northern Andes (e.g. Boana boans, Atractus occipitoalbus; [START_REF] Köhler | Amphibians of Central America, Herpeton[END_REF]Marín et al., 2017), while some have only been recently isolated on each sides of the Andes (e. g. Bessa- Silva et al., 2020;Weir & Price, 2011); and others display ancient divergence that dates back to Neogene (e.g. Réjaud et al., 2020).

The Andes also had an indirect role on Amazonian diversification, by promoting complex hydrological and geological changes during Neogene (Hoorn et al., 2010). These changes were heterogeneous in Amazonia with a relative stability in eastern Amazonia, while western Amazonian landscapes were heavily transformed (Shephard et al., 2010). For instance, the Andean foothills, located in the westernmost part of Amazonia, appear as a complex landscape that might have fostered diversification through geographic isolation (Fjeldså et al., 2012) and the nearby lowlands were heavily transformed by hydrological transformation notably the Pebas system. This large megawetland system initially connected to the Carribean Sea formed around 23 Million years ago (Mya) and occupied most of western Amazonia, until 10-9 Mya when it started to be drained towards the East into the Atlantic Ocean (Figueiredo et al., 2009;Hoorn et al., 2010;2017). Subsequently, during the Pliocene and the Pleistocene additional hydrological events occurred in western Amazonia, such as river capture (Albert et al., 2018) while climatic fluctuations have probably strongly impacted the ecosystems of the Eastern part of Amazonia (Guiana and Brazilian Shields) (Fouquet et al., 2012b) and probably the ecotonal transition with the dry diagonal [START_REF] Cheng | Climate change patterns in Amazonia and biodiversity[END_REF].

The dry diagonal has been considered an important restraint to species migration between Amazonia and the Atlantic Forest (Batalha-Filho et al., 2013;[START_REF] Mori | Distribution patterns and conservation of eastern Brazilian coastal forest tree species[END_REF]. Many genera or even families of vertebrates are endemic to one or the other region; and only a few species groups occur in both forest biomes consequently to late Tertiary or Quaternary dispersals favored by recent forest expansion and climatic fluctuations (Ledo & Colli, 2017). However, ancient dispersals from Amazonia toward the Atlantic Forest have also been hypothesized notably in anurans (Fouquet et al., 2012a(Fouquet et al., , 2012c) ) and more rarely in the other direction [START_REF] De Sá | Multiple connections between Amazonia and Atlantic Forest shaped the phylogenetic and morphological diversity of Chiasmocleis Mehely, 1904 (Anura: Microhylidae: Gastrophryninae)[END_REF].

The frog genus Pristimantis (Anura: Craugastoridae) are poorly understood notably in terms of diversity and evolutionary history but seem to share with Dendrobatids a pattern of diversification centered on the Andes and western Amazonia (Mendoza et al., 2015;[START_REF] Rivera-Correa | A new small golden frog of the genus Pristimantis (Anura: Craugastoridae) from an Andean cloud forest of Colombia[END_REF]Waddell et al., 2018). This group represents the richest genus among vertebrates with 553 recognized species distributed throughout the Neotropics. Pristimantis species are direct-developing frogs with a high endemism [START_REF] Rivera-Correa | A new small golden frog of the genus Pristimantis (Anura: Craugastoridae) from an Andean cloud forest of Colombia[END_REF]. The genus is divided into 11 species groups [START_REF] Padial | Molecular systematics of terraranas (Anura: Brachycephaloidea) with an assessment of the effects of alignment and optimality criteria[END_REF]. One of these groups, the Pristimantis gr. conspicillatus, encompasses 38 valid taxa occurring mainly at low and mid elevation in Amazonia but also in the Andean foothills, the Atlantic Forest and the trans-Andean forests (Frost et al., 2020). The species richness of the groups seems particularly high in Western Amazonia notably in the Andean foothills. However, the actual number of species remains probably largely underestimated as suggested by recent taxonomic studies (e.g. [START_REF] Taucce | A New Rupicolous Species of the Pristimantis conspicillatus Group (Anura: Brachycephaloidea: Craugastoridae) from Central Bahia, Brazil[END_REF]Vacher et al., 2020). Consequently, phylogenetic relationships, divergence times remain speculative but nevertheless indicate a diversification initiating during early Neogene; as the crown age of the genus has been estimated to date back some 29.0 Mya (95% HDP =21. 6 -37.3 Mya, Waddell et al., 2018;Mendoza et al., 2015).

Pristimantis species are direct developers that have narrow bioclimatic niches and generally display small ranges and high endemism. They are thus assumed to disperse with little efficien cy and to have been strongly influence by historical climatic and landscape changes. According to de Oliveira et al. (2020), the eastern Amazonian species of the group mostly originated in-situ, suggesting limited connectivity between the different biogeographical areas notably within Amazonia. Conversely, recent phylogenetic reconstructions suggested that the Atlantic forest species of the P. conspicillatus group form an ancient clade that could date back to Paleogene [START_REF] Trevisan | Cryptic diversity and ancient diversification in the northern Atlantic Forest Pristimantis (Amphibia, Anura, Craugastoridae)[END_REF]. This mirrors the pattern found in several Neotropical frog clades (Fouquet et al., 2012a(Fouquet et al., , 2012c(Fouquet et al., , 2014;;Réjaud et al., 2020) and suggests ancient connectivity between Amazonia and the Atlantic Forest. Moreover, the higher species richness of the group in western Amazonia suggests an intense in situ diversification in this region. We speculate that the Pebas system, that occupied most of western Amazonia between 23 and 10 Mya, likely acted as a barrier for dispersal between western Amazonia and the Brazilian and the Guiana Shields (Antonelli et al., 2009;De-Silva et al., 2016) and that diversification took mostly place within each of these regions during Neogene.

In this complex biogeographic context, we aim at testing temporality and directionality of the biotic exchanges between the different biogeographic areas. We also investigated the origin of the western Amazonian diversity i.e. Whether the diversification is spatially homogeneous, centered along the Andean foothills or the lowlands? To answer these questions, we evaluated boundaries among the P. gr. conspicillatus species, reconstructed a time-calibrated phylogeny of the group and estimated patterns of diversification through time and space.

2| MATERIALS AND METHODS

2.1| Species delimitation

First, we delimited all major mtDNA lineages, using a sampling that included 53 Pristimantis conspicillatus group tissue samples, obtained through fieldwork and loans from collaborators. We chose to sequence a fragment of the 16S rDNA region, because it is a classical marker for Neotropical amphibian taxonomy and systematics and was available for many additional populations (Vences et al., 2005). We completed our sampling with all the available homologous sequences that we retrieved from GenBank (524 accessions). In total, we obtained 577 16S sequences for this study (sequencing protocols can be found in Appendix III.S1 and database details are provided in Table III (Katoh et al., 2017).

Three DNA-based single-locus species delimitation approaches were applied on this dataset:

(a) a distance-based method, the Automated Barcode Gap Discovery (ABGD; Puillandre et al., 2012);

(b) a single-threshold coalescent-based method, the Generalized Mixed Yule Coalescent approach (single threshold GMYC; Monaghan et al., 2009;Pons et al., 2006); and (c) a multi-rate coalescent based method, the multi-rate Poisson Tree Processes model approach (mPTP; Kapli et al., 2017). We defined Operational Taxonomic Units (OTUs) using a majority-rule consensus from the results of the three methods, i.e., a lineage is considered as being an OTU if supported by at least two of the three methods. Details of the species delimitation analyses are provided in Appendix III.S1.

2.2| Time-calibrated species phylogeny

One representative for each OTU (n=59, see Results) was selected to build-up a mitogenomic matrix in order to obtain highly supported and time calibrated relationships. High-quality mitochondrial genome assemblies were recovered for 28 OTU representatives (see Appendix III.S2 for details regarding mitogenomes sequencing, assembling and annotation). We also included representatives for three lineages not accounted in our species delimitation: P. sequences from GenBank for all the terminals included in our reconstruction (Table III.S3).

12S, 16S and all coding sequence regions (CDS) were extracted from complete mitogenomes (thus removing D-loop and t-RNAs) and were subsequently aligned with the GB sequences. The

The Markov Chain Monte Carlo (MCMC) parameters were set with four independent runs of 100 million iterations, recording every 10,000 th iteration and discarding the first 10% iterations as burn-in.

We combined the log files of the independent runs using LogCombiner 2.5 (Bouckaert et al., 2014) and checked the convergence of our parameters, confirmed by all ESS being above 200. Then, we extracted the maximum clade credibility tree using Tree annotator 2.5 (Bouckaert et al., 2014).

2.3| Biogeographic analysis

Biogeographic inferences were based on the obtained time-calibrated phylogeny using the BioGeoBEARS R package (Matzke, 2013). BioGeoBEARS infers ancestral geographic distributions of ancestral species and explores the role of each biogeographic event with a maximum likelihood algorithm. We compared three types of models: (i) a likelihood version of the Dispersal-Vicariance model (DIVALIKE; Ronquist, 1997); (ii) a likelihood version of the BayArea (BBM) model (Landis et al., 2013); and (iii) the Dispersal Extinction Cladogenesis model (DEC; Ree & Smith, 2008). We also compared versions of these models allowing jump dispersal, using the +J parameter, though there is an ongoing debate around the use of this parameter (Klaus & Matzke, 2020;Ree & Sanmartín, 2018).

We modified the list of geographic ranges in order to forbid state transitions between disjunct areas.

Models were compared with the Akaike Information Criterion (AIC). We ran the BioGeoBEARS biogeographical stochastic mapping (BSM) 50 times, to determine biogeographical event counts for the best-fit model (Dupin et al., 2017).

We chose to adopt two contrasting biogeographic partitioning that only differ within Amazonia in order to identify processes at different spatio-temporal scales. Both included five non-Amazonian Neotropical areas; Atlantic forest (Af), dry diagonal (D), northern Andes (nA), Lesser Antilles (lA) and trans-Andean forests (Tf).

The first Amazonian partitioning distinguish major geological features of Amazonia and corresponds to the large biogeographic regions known as Wallace's districts (Hoorn et al., 2010;Wallace, 1865). They include Brazilian Shield (BS), Guiana Shield (GS) and western Amazonia (WA);

for a total of eight Neotropical areas (referred hereafter as the 8A analysis). It is noteworthy that these subdivisions into West-East-South have been recently confirmed to correspond to major breaks in species composition in bird (Oliveira et al., 2017) and amphibian (Godinho & da Silva, 2018). This

We identified three major clades within the Pristimantis conspicillatus species group (P. ramagii; The biogeographical analyses inferred a large number of speciation events within Amazonia, mostly within western Amazonia with an average of 17.4 speciation events. Conversely, there were on average only 10.4 and 2.9 of similar within-area speciations within the Brazilian Shield and the Guiana Very few additional dispersal events occurred from western Amazonia toward the Brazilian Shield and the Guiana Shield (AD = 0.9 and 1.4 respectively); Fig. III.1b). On the opposite, we inferred several recent (<5 Mya) dispersal events towards western Amazonia from both the Brazilian Shield e.g. within P. fenestratus (FE = 1; and 1.9 anagenetic dispersals, or AD, in average) and the Guiana Shield e.g. within P. vilarsi (AD = 1.2) (Fig. III.1b). At a regional scale, the Upper Amazon and Madeira rivers were the most permeable rivers with 4.62 and 3.56 dispersal events on average respectively, nearly twice as much as across the other rivers that represented boundaries among regions considered herein (Table III.2).

3.4| Diversification through time

The LTT plot and the MCCR test both suggest significant difference from a pure-birth model (γ = -3.15, p-value = 8.16e-04; Fig.

III.S3

). This is consistent with the comparison of diversification models conducted using Treepar that selected the one rate shift model with a decrease of diversification around 1.6 Mya as the best model (Table III.1).

4| DISCUSSION

We delimited 59 OTUs in the Pristimantis conspicillatus group which strongly suggest a vast underestimation of the species richness in the group considering the 577 specimens were initially considered to belong to 30 taxa and confirmed that this richness is higher in western Amazonia than in the rest of the distribution (Table III.S1). We also identified three major clades within the P. gr.

conspicillatus, each occurring in distinct regions across the Neotropics and within Amazonia. Further analyses allowed us to conclude that Amazonian diversity in this group originated mainly in-situ i.e. in isolation across regions, with a few ancient (>10 Mya) dispersal events from the Atlantic Forest, toward trans-Andean forest and between the Brazilian and the Guiana Shields and a few recent ones (<5 Mya) across the Madeira, the Amazon and the Negro.

4.1| Species delimitation

Our delimitation clarified several species boundaries and highlighted that the Pristimantis conspicillatus group diversity remains largely undocumented, as already suggested by other studies (de Oliveira et al., 2020;[START_REF] Taucce | A New Rupicolous Species of the Pristimantis conspicillatus Group (Anura: Brachycephaloidea: Craugastoridae) from Central Bahia, Brazil[END_REF]. Considering that we included 30 of the 38 currently recognized taxa (79%) assigned to that group (Frost, 2020), we estimate that the genus may approach 67 species, which represents 43% of missing diversity in the current taxonomy. This rough extrapolation might still represent an underestimation given most of the P. conspicillatus group species are narrowly distributed (Frost, 2020) and large parts of Amazonia and the Andes remains poorly sampled (Mayer et al., 2019). Yet, further work including information on acoustic, phenotypic and genomic variation are needed to test whether these OTUs actually correspond to species on independent evolutionary trajectories [START_REF] Padial | The integrative future of taxonomy[END_REF]. Nevertheless, we are confident that our study will contribute to foster such extended studies. We briefly refer to the taxonomic implications of our results in Appendix III.S1.

4.2| Connectivity between the different Neotropical areas

The species of the Pristimantis gr. conspicillatus from the Atlantic forest do not form a monophyletic group since species from the Guiana Shield are nested within it, suggesting a single or two distinct ancient dispersal events from the Atlantic forest (Fig. III.1). These results are in contradiction with those of [START_REF] Trevisan | Cryptic diversity and ancient diversification in the northern Atlantic Forest Pristimantis (Amphibia, Anura, Craugastoridae)[END_REF] that did not include all Amazonian representatives of the P.

conspicillatus group; and according to which all Atlantic forest species form a clade. The dispersal times appear relatively concomitant with those other Neotropical frog complexes in late Paleogene / early Neogene (Fouquet et al., 2012c(Fouquet et al., , 2014;;Réjaud et al., 2020). However, the two dispersal events that we recovered were from the Atlantic forest towards Amazonia; while in most other frogs, the dispersals where in the opposite direction (at the exception of Chiasmocleis; de Sá et al., 2019).

These results suggest that a dispersal route might have existed between Amazonia and the Atlantic forest at this period for frogs and other vertebrates (Ledo & Colli, 2017); allowing connectivity between Amazonia and the Atlantic Forest and not only from Amazonia to the Atlantic forest, but bidirectionally.

The Andes represent a formidable barrier to dispersal between Amazonia and the trans-Andean forests. The Andean orogeny has been an ongoing process over the last 40 Mya punctuated by several intensive phases (23, 12 and 4.5 Mya;[START_REF] Mora | Tectonic history of the Andes and Subandean zones: Implications for the development of the Amazon drainage basin Tectonic history of the Andes and sub-Andean zones: implications for the development of the Amazon drainage basin[END_REF]Hoorn et al., 2010). In this study, we identified two congruent events of allopatric speciation between these regions (P.

4.3| The Andean foothills as promoters of Amazonian diversification in the Pristimantis conspicillatus group

Dispersals from the Andes toward Amazonia have previously been identified as an important process in the diversification of Pristimantis (Mendoza et al., 2015) as in Dendrobatoidea (Santos et al, 2009) and Centrolenidae [START_REF] Castroviejo-Fisher | Neotropical diversification seen through glassfrogs[END_REF]. However, our results suggest that this hypothesis can be rejected for Pristimantis gr. conspicillatus since only one dispersal from a non-Amazonian area toward Amazonia was recovered in the group and that Amazonian diversity originated exclusively in-situ (Fig.

III.1b).

This in-situ Amazonian diversification appears relatively continuous through time but most intense along the Andean foothills (Fig. III.1d). The Andean orogeny induced important climatic changes in this area, notably by altering the moisture transports and precipitations [START_REF] Insel | Influence of the Andes Mountains on South American moisture transport, convection, and precipitation[END_REF].

These climatic changes coupled with an elevational gradient created a mosaic of new environments, a context highly favorable to speciation (Fjeldså et al., 2012). Additionally, the complex topography with partially isolated valleys creates high potential for speciation by geographic isolation [START_REF] Muñoz-Ortiz | Of peaks and valleys: Testing the roles of orogeny and habitat heterogeneity in driving allopatry in mid-elevation frogs (Aromobatidae: Rheobates) of the northern Andes[END_REF]. We acknowledge that our analyses cannot clearly identify the diversification drivers of the P. conspicillatus group within the Andean foothills, notably disentangle between geographic isolation and ecological adaptation (Wiens & Graham, 2005). However, OTUs occurring in the foothills display small distribution ranges that are not overlapping among closely related species (Fig.

III.S1)

suggesting a striking pattern of speciation by geographic isolation. Guayasamin et al. (2017) hypothesized that the low vagility as well as the high mutation rate of Pristimantis species could explain why geographic isolation is such an important diversification driver for this genus. The limited proportion of sympatric/ecological speciation in amphibians is similarly observed as an overall niche conservatism (Rolland et al., 2018) which is particularly striking in the P. conspicillatus group.

4.4| Role of the hydrological changes in the P. conspicillatus group diversification

The current diversity within western Amazonian lowlands mainly originated through numerous dispersals from adjacent areas that mainly postdated 10 Mya (Fig. III.1d), suggesting that the Pebas system did prevent dispersals during a period ranging between its formation and its conversion to Overall, western Amazonian rivers appear more permeable than rivers running on the cratons (Tapajos, Xingu, Tocantins and Negro; Table III.2), possibly due to differences in river stability (Albert et al., 2018;[START_REF] Ruokolainen | Geologically recent rearrangements in central Amazonian river network and their importance for the riverine barrier hypothesis[END_REF]). Yet, all major Amazonian rivers but the Japura and Tocantins rivers were crossed by dispersals of species from the P. conspicillatus group (Table III.2), suggesting that they seem to have acted as semipermeable barriers, allowing dispersal and isolation among neighboring interfluves, as reported for other taxa (Pirani et al., 2019;Réjaud et al., 2020).

CONCLUSION

We recovered three major clades that diversified simultaneously in distinct biogeographical areas and that exhibit very low connectivity, even within Amazonia. Most of the group diversity formed in western Amazonia and the Andeans foothills appear to have played a central role in the diversification. Indeed, the profound geomorphological changes induced an alteration of climatic conditions and landscape, setting a favorable context to speciation in the foothills. Conversely, the setup of the Pebas system seem to have prevented dispersal across main Amazonian regions as well as diversification in the floodplain until today. Subsequently, western Amazonia was continuously colonized by multiple dispersal events, suggesting that riverine semi permeable barriers may have been the main cause of speciation over the past 10 My for these frog associated with terra firme forests. Pie charts on nodes show the most likely reconstructions of ancestral areas, the size of each slice proportional to the maximum likelihood. Colors corresponding to the different geographical distributions are depicted on the left. In the summaries of the BSMs results, we present values of the within area speciation events for each region (values within rectangles) as well as the anagenetic dispersal events and founder speciation events between each region (values within ellipses). Values correspond to the mean number of inferred events of a specific type across the 50 independent BSMs.

Anagenetic dispersals events and founder events are represented only for intra-Amazonian events with a mean is > 0.8. Red arrows highlight dispersals emitted by major dispersal hubs.

Taxonomic implications

Our results are in overall agreement with the currently recognized species and distribution ranges.

Operational taxonomic units were assigned to species based on initial specimen identification by collectors and comparison of population against type localities (Frost, 2020). Out of the 62 OTUs included, 28 OTUs were assigned to a recognized species.

In total, 13 nominal species were split into as many as six OTUs, but mostly into two OTUs (5 out of 13 cases; Fig. III. S1). A striking example of cryptic diversity highlighted by our molecular delimitation is the Pristimantis fenestratus species complex, it is comprised of 12 highly divergent OTUs, with an estimated crown age around 13.1 Mya (CI 95% 11. [START_REF] Puillandre | ABGD, Automatic Barcode Gap Discovery for primary species delimitation[END_REF][4][START_REF] Simoes | The description of a cryptic species related to the pan-Amazonian frog Allobates femoralis (Boulenger 1883)(Anura: Aromobatidae)[END_REF][START_REF] Von May | Amphibian community structure as a function of forest type in Amazonian Peru[END_REF][7][START_REF] Moraes | The combined influence of riverine barriers and flooding gradients on biogeographical patterns for amphibians and squamates in south-eastern Amazonia[END_REF][9][10][11][12][13][14][15]. Among these 12 OTUs, five were recently described as species (P. latro, P. pictus, P. giorgii, P. pluvian and P. moa; [START_REF] De Oliveira | A new species of Pristimantis from eastern Brazilian Amazonia (Anura, Craugastoridae)[END_REF]de Oliveira et al., , 2020)), suggesting that the diversity within this complex is still widely underestimated despite the recent taxonomic efforts as suggested in other studies (e.g., [START_REF] Motta | When the unknown lives next door: a study of central Amazonian anurofauna[END_REF]. Another interesting example lies with P. vilarsi species complex, it encompasses five divergent OTUs, with a crown age dated The Pristimantis iiap and P. aff. iiap OTUs appear more closely related to P. danae rather than species of the P. conspicillatus group (Fig. III. S3), suggesting either that the specimens we identified as P. iiap have been misidentified or that this species has been wrongly attributed to the P. conspicillatus group.

We ended up with 34 OTUs that were not assigned to any recognized species, of which 29 are closely linked to recognized species (referred as aff.) and five that could not be related to any nominal species (referred as sp.). We hope that our work will help identifying part of the taxonomic gaps remaining in the P. conspicillatus group.

1| INTRODUCTION

Amazonia hosts around 10% of the world's terrestrial species richness for a surface of more than 6

Mkm 2 (Gibson et al., 2011;Jenkins et al., 2013). The origin of this outstanding diversity has been a topic of wonderment since 19 th century (Wallace, 1854). The timing of this diversification remains debated, however. While there is evidence of the presence of a tropical forest biome in South America back to the Paleocene (66-56 Mya; Wing et al., 2009), plant diversification was high during the Neogene (23-2.6 Ma; da Silva-Caminha et al., 2010). Mammalian fossil records also suggest important diversification during Neogene, with a pronounced species turnover during the Paleogene/Neogene transition in Western Amazonia (Antoine et al., 2017;Campbell & others, 2004).

For passerine birds and frogs, that have limited Amazonian fossil records compared to mammals, time-calibrated phylogenetic analyses also suggest that Neotropical families diversified during the Neogene even though they originated during the Paleogene (Hime et al., 2020;Selvatti et al., 2015).

These evidences converge in suggesting that the Neogene represents a crucial period for the establishment of the species community we currently observe in Amazonia.

During the Neogene, tectonism and climate played important roles in the process of species diversification in the Neotropics [START_REF] Finarelli | Diversity dynamics of Miocene mammals in relation to the history of tectonism and climate[END_REF]. The Andean orogeny had multiple and complex consequences on the hydrology and topography throughout the continent and notably in Amazonia (Hoorn et al., 2010). These changes were particularly important on the Western Amazonian landscapes while Eastern Amazonia, on the cratonic Guiana and Brazilian Shields, remained little affected (Shephard et al., 2010). A major transformation is related to the formation of the Pebas system, a large freshwater system initially connected to the Caribbean Sea, that may have covered a surface of up to 13% of current Amazonia (800,000 km²; Albert et al., 2018). The Pebas system supposedly formed during the early Miocene (23 Mya) and occupied most of the Western Amazonian lowlands until around 10-9 Mya, when this system was progressively drained eastward into the Atlantic Ocean and transitioned into the modern Amazon watershed (Albert et al., 2018;Hoorn et al., 2017). The consequences of these large hydrological changes on biogeography and diversification are difficult to assess and can greatly differ among taxonomic groups, depending on life history traits and ecological niches (Hoorn et al., 2010). For instance, the mollusk fossil record suggests a radiation of aquatic species during the Pebas system era; followed by a massive extinction after the demise of the Pebas system (Wesselingh et al., 2006). In contrast, the proportion of terra firme habitats decreased during the Pebas system era, associated with high extinction rates and high species turnover for Mammalian species (Antoine et al., 2017). High diversification has been inferred for terrestrial groups following the demise of the Pebas system (e.g. Chazot et al., 2019;Réjaud et al., 2020).

Amphibians include >8000 species worldwide and they occur in a large variety of habitats, including terrestrial (e.g. Amazophrynella), arboreal (e.g. Boana), aquatic (e.g. Pipa) and even fossorial species (e.g. Synapturanus). Most Neotropical amphibian species belong to Hyloidea, a vast clade accounting for 17 families ~53% of all frog species (81% of Neotropical frogs) that diversified mostly within the Neotropics since the early Paleogene [START_REF] Pyron | Biogeographic analysis reveals ancient continental vicariance and recent oceanic dispersal in amphibians[END_REF]. It is also the vertebrate group presenting the most variation in reproductive modes, with no less than 39 modes according to Haddad & Prado (2005)'s classification, 31 of which are found in Neotropical Anurans. This large diversity of life history traits might have led to distinct biogeographical histories during the important hydrological and landscape changes that took place during the Neogene (e.g. Fouquet et al., 2014;Réjaud et al., 2020).

Biogeographic analyses of species groups that diversified within terra firme habitats, 'ecoconserved' groups, generally display a pattern of ancient in-situ diversification, with limited exchanges across Amazonian areas (Carvalho et al., 2020;Fouquet et al. 2012Fouquet et al. , 2014, in press;, in press;Jaramillo et al., 2020;Réjaud et al., 2020) with overall small distribution ranges often limited by major Amazonian rivers. In contrast, generalist taxa, those that have adapted to a wider array of arboreal and terrestrial habitats including flooded forests, open habitats or phytotelmata ('eco-diverse' groups), have generally undergone numerous dispersal events throughout Amazonia and surrounding areas (Caminer & Ron, 2020;de Sá et al., 2014;Jungfer et al., 2013;Pirani et al., 2020). However, this observation is based on scant evidence and a multitaxa comparison is poised to unravel the patterns of Neogene diversification within Amazonia for terrestrial vertebrates. Comparative studies have been undertaken

for Amazonian birds but temporally spans relatively recent periods, and revealed a high connectivity between the different Amazonian regions with Western Amazonia identified as the principal source of Amazonian diversity (Naka & Brumfield, 2018;Silva et al., 2019;Smith et al., 2014).

Herein we undertake a comparative biogeographic approach in Amazonian amphibians, aiming at testing whether Amazonian frogs display distinct biogeographic patterns depending on habitat use and degrees of niche conservatism. We hypothesize that species groups that are mainly restricted to terra firme forest (ecologically conservative) display ancient diversification with low connectivity between the different Amazonian areas. In contrast, we hypothesize that species groups with a higher propension to diversify ecologically, notably adapting to flooded forest environments, display more recent diversification and a high connectivity between Amazonian regions, a pattern more similar to the one seen in birds.

2| MATERIALS AND METHODS

2.1| Taxonomic groups included

We selected six species groups that have diversified extensively within Amazonia during the Neogene according to the current distribution of nominal species and previous phylogenetic investigations (Allobates, Réjaud et al., 2020;Amazophrynella, Rojas et al., 2018;Pristimantis gr. conspicillatus, Waddell et al., 2018;Boana gr. albopunctata, Caminer & Ron, 2014;Leptodactylus gr. podicipinus, de Sá et al., 2014;and Osteocephalus, Jungfer et al., 2013; see Table 1 andAppendix S2). They are representative of distinct Hyloidea lineages and display various ecological characteristics and reproductive modes. Nevertheless, three of these groups can be considered 'eco-conserved' (Allobates, Amazophrynella and Pristimantis gr. conspicillatus); the other three are 'eco-diverse' groups (Boana gr. albopunctata, Leptodactylus gr. podicipinus and Osteocephalus). The 'eco-diverse' groups species have wider distribution ranges that often encompass both banks of a major Amazonian rivers, denoting important differences in dispersal abilities (Fouquet et al., 2015;Moraes et al., 2016) Time-calibrated phylogenies were already available for five groups Allobates (Réjaud et al., 2020), Amazophrynella (Moraes et al., in prep.), Boana gr. albopunctata (Fouquet et al., sub.) ). These phylogenies were obtained from similar datasets and similar methodology of species delimitation and time-calibrated phylogenetic reconstructions described in Réjaud et al. (2020). We completed these data by conducting a similar species delimitation and phylogenetic reconstruction for the Leptodactylus gr. podicipinus (see methods and results in Appendix S1), thus totaling six groups.

the anagenetic and cladogenetic event tables to obtain stratified BSMs over three periods: anterior to 10 Mya, between 10 and 5 Mya, and posterior to five Mya. The mean number of events as well as the variance were transformed into rates through time by dividing with the branch length of the period, so as to obtain rates in mean number of events per My. We used the iGraph R package to plot biogeographic networks for each group, with nodes corresponding to the in-situ speciation rates of an area and edges corresponding to dispersal rates from an area to another. We averaged the networks for all groups and also into for the eco-conserved and eco-diverse groups.

3| RESULTS

3.1| General pattern of interchange and in-situ diversification

For the six groups combined, the biogeographical analyses inferred high in-situ speciation rates within Amazonia, especially in Western Amazonia, with an average of 0.067 events per Million years (My).

Conversely, speciation rates within the Brazilian and the Guiana Shields were about four times lower (0.017 and 0.012 events.My -1 on average respectively). As expected, since the focal groups only marginally diversified outside of Amazonia, we recovered very low speciation rates <0.004 in the non-Amazonian areas included in the analysis (Fig. 1). Within Amazonia, dispersal rates from Western Amazonia towards the Brazilian Shield and the Guiana Shield were over twice higher than the dispersal rates emitted from the Brazilian Shield and the Guiana Shield (0.007 vs. 0.002-0.004 events.My -1 respectively; Fig. 1). Dispersal rates between the Brazilian and the Guiana Shields were also low (0.002-0.003 events.My -1 ). The only noteworthy dispersal rate involving a non-Amazonian area originates from the Brazilian Shield and is directed towards the dry diagonal (0.002 dispersal events.My -1 ). Ancestral area reconstructions and BSMs results for each group are available in Appendix S3.

3.2| Predictors of dispersals within Amazonia

We inferred a total of 12 dispersal events between the Brazilian and the Guiana Shields; 16 between Western Amazonia and the Guiana Shield; and 18 between Western Amazonia and the Brazilian Shield. We identified a break in the temporal distribution of the dispersal events between the Brazilian and the Guiana Shields during the Neogene, whereas dispersals between the two other pairs of regions occurred throughout the period. This break also discriminates eco-conserved and eco-diverse groups since all the dispersal datings of the former are older than 10 Mya while that of the latter are younger than 5 Mya (Fig. 2-A). Overall, we detected a significant effect of ecotypes on the Amazonian dispersal times (Fig. 2-B), terra-firme species exhibit older times of dispersal than the eco-diverse groups (mean around 12.8 Mya, vs. 5.3 Mya) (Fig. 2-C).

3.3| Variations of dispersal and speciation rates through time

Overall, eco-diverse groups displayed higher in-situ speciation and dispersal rates (0.131 speciation events per My; 0.01 dispersal events per My) than eco-conserved groups (0.065 speciation events per My; 0.01 dispersal events per My). The period prior to 10 Mya, is characterized by relatively low dispersal rates compared to the other time-frames, with higher speciation rate and dispersal rate for terra-firme species than eco-diverse groups (Fig. 3). The time period 10-5 Mya had by far the highest in situ speciation rates for both groups, with speciation rates being more than three times higher than the overall (>10-0) mean rate. This 10-5 Mya epoch is also associated with a large increase of dispersal rates for eco-diverse groups (Fig. 3). The 5-0 Mya epoch is characterized by a sharp decrease of speciation rates (Fig. 3). Dispersal rates also decreased for eco-conserved groups and eco-diverse groups, but to a lesser extent (Fig. 3). Speciation rates could not be detected in the Brazilian and the Guiana Shield for the 5-0 Mya epoch (Fig. 3). Overall, Western Amazonia was identified as the principal dispersal source, except in the 5-0 Mya epoch for the eco-conserved group (Fig. 3).

4| DISCUSSION

4.1| Diversification within Amazonian regions

Our study tested the role of habitat specialization on patterns of speciation of dispersal in Amazonian amphibians. To test the hypothesis that terra firme clades were less likely to diversify across bioregions than generalist clades, we selected six clades of Amazonian amphibians, three of terra firme specialists (called eco-conserved), and three spanning a broad range of habitats (eco-diverse).

The highest in situ speciation rates were found in Western Amazonia overall, a bioregion representing 68% of the detected Amazonian speciation events (Fig. 1). Thus Western Amazonia has played a key role in Amazonian diversification during the Neogene at least for the six selected clades, as already suggested for birds a pattern already suggested for amphibians by several case studies (e.g. Réjaud et al., 2020;Waddell et al., 2018); and by a comparative study for birds (Silva et al., 2019).

Diversification rates peaked during the 10-5 Mya epoch (late Miocene) across Amazonia, but especially in Western Amazonia (Fig. 3). This intense diversification phase coincides with the end of the Pebas system, that occupied a large portion of Western Amazonia in the early to mid-Miocene, between 23 and 10 Mya (Hoorn et al., 2010). The post-Pebas period likely corresponds to an increased extension of terra-firme and flooded forest habitats [START_REF] Gomes | Compositional and diversity comparisons between the palynological records of the Neogene (Solimões Formation) and Holocene sediments of Western Amazonia[END_REF]Wesselingh & Salo, 2006). Previous studies hypothesized that the Pebas system acted as a barrier to dispersal between the Andean foothills and the rest of Amazonia for terra-firme taxa (Antonelli et al., 2009;De-Silva et al., 2016). This is also the case for all six of our groups, which dispersed across Amazonia mostly after the demise of the Pebas. During and after isolation, the Andean foothills possibly fostered Western Amazonian diversification by geographic isolation between the numerous valleys or by adaptation along the elevational gradient (Fjeldså et al., 2012). Furthermore, the post-Pebas hydrological configuration led to major changes in the courses of Andean rivers, drained eastward into the Atlantic Ocean (Figueiredo et al., 2009). These rivers that run across Western Amazonia carry high sediment loads, which affects the river stability by increasing the occurrence of meandering events (Constantine et al., 2014). Hence, river capture and avulsions are frequent in Western Amazonia watersheds since 10 Mya, while the courses of river running on the Brazilian and Guiana Shields remained more stable over time (Albert et al., 2018). Such river instability in Western Amazonia likely affected the permeability to dispersal over time, allowing dispersal and isolation among neighboring interfluves (Naka & Brumfield, 2018;Pirani et al., 2020). Consequently, the post-Pebas system context appears favorable to diversification for both terra-firme and groups that could adapt to different habitats and could partly explain why we inferred higher diversification rates in this area during the 10-5 Mya time interval. While this biogeographic context of habitat diversity and river dynamism appears to be consistent over the 5-0 Mya period (Albert et al., 2018, Hoorn et al., 2010), it is not associated a diversification slowdown (Fig. 3). Considering that Amphibians display important niche conservatism (Rolland et al., 2018) and that they diversified extensively during the 10-5 Mya interval (Fig. 3), we hypothesize that the decrease in diversification rate after 5 Mya is possibly a result of niche filling [START_REF] Moen | Why does diversification slow down? Trends in Ecology and Evolution[END_REF].

4.2| Amazonian interconnectivity

Our analyses identified Western Amazonia as a source of diversity for other Amazonian regions which conversely acted more as sinks (Fig. 1), similar to the situation for Amazonian birds (Silva et al., 2019). However, we identified important differences between ecotypes (Fig. 3 & Fig. S10 in Appendix S3). Western Amazonia seems to act as a source during the entire 10-0 Mya interval for eco-diverse groups while this is the case only between 10 and 5 Mya for terra-firme specialists, with both high insitu diversification rates and high dispersal outside of the region (Fig. 2). During the 5-0 Mya epoch, in-situ diversification of terra-firme specialist clades strongly decreased and a species dispersed from the Brazilian Shield, turning Western Amazonia into a diversity sink (Fig. 2). Previous studies documented that rivers represent more effective barriers for terra-firme specialists than for species adapted to riverine environments (Fouquet et al., 2015;Lougheed et al., 1999;Moraes et al., 2016), which could partly explain differences in biogeographical histories. It is likely that the biogeographical histories of Amazonian amphibians depend on whether clades are specialists or generalists. For instance, species belonging to eco-diverse groups can adapt to new habitats more easily than ecoconserved (Appendix S2). It suggests a lower niche conservatism in eco-diverse groups (Wiens & Graham, 2005), which may favor the coexistence of related eco-diverse species as they possibly occupy distinct niches [START_REF] Mayfield | Opposing effects of competitive exclusion on the phylogenetic structure of communities[END_REF]. Consequently, niche saturation might be reached later in eco-diverse groups and it may partly explain why they have overall higher diversification rates than eco-conserved groups.

4.3| The riverine barrier effect

The modern configuration of the Amazon River formed about 10 Mya, into one of the largest rivers in the world, flowing into the Atlantic Ocean (Hoorn et al., 2017). Its tributaries have also acted as biogeographical barriers notably the Madeira River isolating the Brazilian Shield from Western Amazonia, and the Amazon itself isolating the Brazilian Shield from the Guiana Shield (Albert et al., 2018). In spite of the existence of these riverine barriers, dispersals events were inferred after 10 Mya that involved the Brazilian Shield, in both ecotype groups (Fig. 2A & 3). Continuous river captures progressively increased the flow of the Amazon river (Albert et al., 2018;[START_REF] Stokes | Ongoing River Capture in the Amazon[END_REF], reaching an intermediate development phase marked by an increase of sedimentary rates and river entrenchment around 6.8 Mya (Figueiredo et al., 2009). This suggests that the proto-Amazon River only progressively became an effective barrier to dispersal for terra-firme species. The Madeira River

was not an effective barrier to dispersal for either terra-firme or habitat generalist species, possibly because this river and its tributaries were highly unstable even during Quaternary (e.g. [START_REF] Hayakawa | Late quaternary dynamics in the Madeira river basin, southern Amazonia (Brazil), as revealed by paleomorphological analysis[END_REF][START_REF] Rossetti | The role of Late Pleistocene-Holocene tectono-sedimentary history on the origin of patches of savanna vegetation in the middle Madeira River, southwest of the Amazonian lowlands[END_REF]. Similarly, the Rio Negro does not seem to constitute an effective barrier to dispersal as we observe dispersal events between Western Amazonia and the Guiana Shield even after 5 My a (Fig. 3); possibly because this river formed less than 5 Mya (Albert et al., 2018;[START_REF] Boubli | Spatial and temporal patterns of diversification on the Amazon: A test of the riverine hypothesis for all diurnal primates of Rio Negro and Rio Branco in Brazil[END_REF]. Therefore, although the riverine barrier hypothesis is an attractive hypothesis for explaining the current patterns of species distribution across Amazonia, we here found little evidence for major rivers to have acted as effective barriers to dispersal, hence promoting allopatric speciation.

CONCLUSION

This study offers a comparative biogeography approach at the Amazonian scale and provides new insights on diversification for Amazonian amphibians during the Neogene. We identified Western Amazonia as the principal source of diversification for the six selected groups of Amazonian amphibians, with high in-situ diversification events and high dispersal rates towards other Amazonian areas. Contrasting eco-conserved and eco-diverse clades, we found that dispersal and diversification rates were higher for eco-diverse species. Rivers appear to have isolated primarily eco-conserved species. These important differences between the two ecotypes suggest that amphibian diversification is directly related to ecological strategies. A) Time of dispersal events across areas in Million years ago (Mya), mean dispersal time and confidence intervals are illustrated. Color correspond to the species group, while the shape around the mean time correspond to the ecotype (a circle for eco-diverse and a square for eco-conserved species). B) Effects of the predictor variables, significant effect is noted when the confidence interval of the estimate does not cross zero, which is materialized with a red vertical line. C) Boxplot of the node ages per ecotype (Mya). command we used the default options as long as it was able to find the optimal assembly path. If not, we checked the assembling graph with the yEd Graph Editor (organic layout, default parameters) and selected the green fragments (that correspond to similar seed fragments) and extracted them with the --path--option in the oa unfold command. The fragments were then mapped onto a seed with the Geneious (R9. 1.7;Biomatters Ltd,Auckland,New Zealand) map to reference function with mediumlow sensitivity and one iteration. The mapping consensus was extracted and considered as a mitochondrial genome assembly.

For annotation, sequences were aligned on MAFFT7 online server under default parameters except the E-INS-i strategy which is designed for sequences with multiple conserved domain and long gaps (Katoh et al., 2017). Then, with the Geneious R9.1.7 transfer annotations function we annotate all the alignments sequences from a reference sequence. Finally, we checked for all the sequences that each CDS began with a methionine (ATA or ATG codon for mitochondrial vertebrates' genomes)

and ended with a STOP codon.

We then extracted 12S, 16S, and all coding DNA sequences (CDS), thus removing D-loop and tRNAs from complete mitogenomes. For the remaining 29 OTUs, we gathered all the available mitochondrial DNA loci (12S, 16S, ND1, COI and Cytb) from GenBank; six of these OTUs were represented by the 16S region only (12%). We also selected 15 species as outgroups (12 with complete mitogenomes), including six representatives of other Leptodactylus species groups, one of each other genus of Leptodactylinae (Adenomera, Hydrolaetare and Lithodytes), as well as three representatives of Leiuperinae and two of Paratelmatobiinae (Table S3).

Phylogenetic reconstruction

We then aligned each locus independently using the MAFFT7 online server. For rRNA genes we chose the E-INS-i strategy, which is designed for sequences with multiple conserved domain and long gaps, while for the protein-coding genes, we chose the G-INS-i strategy, recommended for sequences with global homology (Katoh et al., 2017). Coding regions were realigned considering reading frame and concatenated in Geneious V9.1.8.

We selected the best-fit partition scheme and model of evolution for each partition using PartitionFinder V2.1.1 (Lanfear et al., 2016), according to the Bayesian Information Criterion (BIC). We predefined four data blocks, one for rRNA genes (12S and 16S) and one for each codon position of concatenated CDS regions.

We reconstructed a time-calibrated gene tree using a birth-death tree prior in BEAST 2.5 that accounts for extinction processes and incomplete sampling. We parametrized unlinked substitution models according to the estimates obtained in the PartitionFinder V2.1.1 analysis (Lanfear et al., 2016). Time calibration was implemented using an uncorrelated relaxed lognormal clock model of the distribution of rates among branches for each partition/span (Drummond et al., 2006). In the absence of a Dendrobatoidea fossil record, we relied on a secondary calibration point. The alternative, to expand the matrix to distantly related lineages, may lead to an overestimation of calibration dates (Molak & Ho, 2015;Papadopoulou et al., 2010). We based our secondary calibration on the results of Feng et al. (2017), whom relied on an extensive nuclear genomic dataset (88 kb) of anurans including all major frog lineages and inferring estimates for the crown age of Leptodactylinae. We consequently constrained our analysis based on the Feng et al. (2017) estimates. Specifically, we assumed a normal prior distribution (Mean = 36.2 Million years ago; Mya, SD = 3.29 Mya), a time range consistent with several other studies (i.e. Hedges et al., 2015;Moen et al., 2016). We acknowledge that our phylogenetic reconstruction is based only on mtDNA sequences only and can lead to an overestimate speciation rate (e.g. McCormack et al., 2011;Near et al., 2012). We emphasize that future work, e.g. using nuclear genomic approaches may lead to slightly different time estimates. The availability of material remains challenging at Amazonian scale and gathering nuDNA data for all terminals included herein was out of reach. Therefore, we chose to prioritize spatial and taxonomic completeness over genomic coverage.

In the BEAST2 tree reconstruction, the Markov chain Monte Carlo (MCMC) parameters were set to four independent runs of 100 million iterations each, recording every 10,000 th iteration and discarding the first 10% of iterations as burn-in. We combined the log files of the independent runs using LogCombiner 2.5 (Bouckaert et al., 2014) and checked the convergence of our parameters via time-series plots and confirming that all parameters achieved an effective sample size above 200. We extracted the maximum clade credibility tree using Tree annotator 2.5 (Bouckaert et al., 2014). Biogeographical stochastic mappings (BSMs) indicate that the L. gr. podicipinus extensively diversified in Western Amazonia, with 15.7 in-situ speciation events in average; while only 5.3 and speciation events in average were recovered for the Guiana Shield and the Brazilian Shield respectively. Western Amazonia also appears as the principal source of Amazonian dispersals, with numerous dispersal events emitted toward the Guiana and the Brazilian Shields (5 and 4.3 dispersal events in average, respectively). Conversely, the Brazilian shield emitted only 1.1 dispersal event in average towards Western Amazonia; while the Guiana Shield emitted dispersals only towards non-Amazonian areas (0.7 events in average towards the dry diagonal and the Atlantic forest).

3| DISCUSSION

We used mtDNA to delimit major extant lineages within Leptodactylus gr. podicipinus. Our dataset includes 16S DNA sequences of 603 specimens and allowed us to delineate 50 OTUs (Table S2). Our delimitation clarified some species boundaries and revealed high levels of cryptic diversity, as it is often the case in Neotropical Anurans (e.g. Funk et al., 2012). Indeed, considering that we included of the 17 currently recognized taxa, we estimate that the genus may approach 52 species, which is about three times higher than the currently described species diversity (de Sá et al., 2014;Frost, 2020). On the one hand, since large parts of Amazonia remains poorly sampled this Figure IV.may still represent an underestimation of the actual number of species (Mayer et al., 2019). On the other hand, some of these OTUs may represent deep conspecific lineages [START_REF] Vieites | Vast underestimation of Madagascar's biodiversity evidenced by an integrative amphibian inventory[END_REF] (false positives) whereas some may correspond to more than a single species (false negatives). Additional information on reproductive isolation, phenotypic and genomic variation are needed to evaluate if our proposed or groups of related OTUs (colors) that are grouped together when they are related to the same described species, known localities are represented with different symbols matching those of the Pantepui and the Atlantic forest [START_REF] Kattan | Biological diversification in a complex region: a spatial analysis of faunistic diversity and biogeography of the Andes of Colombia[END_REF][START_REF] Kok | Islands in the sky: species diversity, evolutionary history, and patterns of endemism of the Pantepui Herpetofauna[END_REF]. Terra firme habitat generally implies terrestrial reproduction (direct/endotrophic development) or reproduction in small water bodies (temporary ponds). Riverine habitat and flooded forest imply reproduction directly in the bed of the rivers, either directly in lotic water but more often in ponds formed in the direct vicinity of the flowing water. These types of habitats and associated breeding sites are hosting very distinct communities.

Open habitats and altitude dwellers can also breed in very distinct habitats but form very distinct communities as well. We also considered an additional forest category: (v) phytotelmata, which are small water-filled cavities in terrestrial plants (e.g. bromeliad, bamboo, fruit husk, and tree hole) [START_REF] Lehtinen | Phytotelm-breeding anurans: past, present and future research[END_REF][START_REF] Tonini | Specialized breeding in plants affects diversification trajectories in Neotropical frogs[END_REF]. Phytotelmous species can occur in flooded/riverine and in terra-firme habitat, even in open highlands such as on top of Tepuis. Since it implies drastically distinct ecological requirements, we considered meaningful to consider this last category. Habitat of the different species was based Frost (2020) and IUCN assessment (IUCN, 2020) as well as completed by eild observation and expert knowledge for the undescribed species. We also ran the BioGeoBEARS biogeographical stochastic mapping 500 times for each group, to determine biogeographical event counts for the DEC model (Dupin et al., 2017).

2| RESULTS

2.1| Distribution area

The Welch two sample t-test detected a significant difference (P value = 0.008) between the mean species distribution area of eco-conserved species (mean = 129533 km²) and the eco-diverse species (mean = 394814 km²) (Fig. IV.S3). Taken individually, all eco-conserved groups had a lower mean species area distribution than any eco-diverse group (Table IV.S3).

2.2| Ecological shifts

Terra firme and flooded forests are the habitat hosting most OTUs for the eco-conserved and ecodiverse groups, respectively. All groups occur in terra firme habitat, but Amazophrynella is the only ). Eco-diverse groups all have species that occur in two or more habitats, while all species are restricted to a single habitat in eco-conserved groups. Counts of environmental shifts are much higher for eco-diverse than for eco-conserved groups (Table IV.S4).

However, the DEC model does not support widespread sympatry which might lead to an overestimation of the counts of environmental shifts for clades composed of species that occur in two or more habitats.

CONCLUSION

Eco-diverse species have larger area of distribution than eco-conserved species. Eco-conserved species are mainly restricted to terra firme habitat, with rare shifts to mountainous and flooded forest habitats. Oppositely, eco-diverse species are often found in two habitats but mainly in flooded forest;

and habitat shifts are relatively frequent

ORIGIN AND DIVERSITY OF AMAZONIAN AMPHIBIANS

With more than six million square kilometers, Amazonia hosts the largest tract of lowland tropical rainforest in the world and a large portion of the global terrestrial diversity. However, the temporal and spatial origins of this diversity remain poorly understood and need to be better comprehended to identify the processes responsible for this tremendous diversification. Amphibians are a particularly adequate group for investigating patterns of biogeographical history within Amazonia because they extensively diversified within the region and present important disparities in habitat use and dispersal abilities across groups. We first investigated the historical biogeography of the terra-firme genus

Allobates and identified western Amazonia as an important source of diversification between 14 and 10 million years ago (Mya). This spatio-temporal pattern was coinciding with the existence of the Pebas system, a mega-wetland system that occupied most of western Amazonia during this period, that was unsuitable for terra-firme species. The Pebas system discharge was likely followed by an extension of terra-firme forests that likely fostered Allobates diversification. Our results also suggested that western Amazonia rivers might have subsequently (after 10 Mya) promoted diversification, by acting as semi-permeable barriers allowing speciation by dispersal and isolation. Secondly, we investigated the biogeographical history of the Pristimantis conspicillatus group which, instead, presented a continuous diversification throughout Neogene. This group displays a striking spatial pattern of diversification with four ancient clades that have diversified concomitantly in distinct areas in Amazonia and the Atlantic Forest, with much fewer dispersal events between areas than in Allobates.

These differences suggest that amphibian species display differences in dispersal abilities that can be related to their life history traits. Finally, we compared the biogeographic histories of six frog clades, including the two aforementioned ones, that share comparable crown ages and span the Amazonian frog diversity in terms of life history traits, taxonomy, habitat use and reproduction modes. We identified western Amazonia as the principal source of diversification for Amazonian amphibians, although it acted as such only after 10 Mya for the groups that have adapted to various types of habitats; and only between 10 and 5 Mya for the ecologically conservative groups. This suggest that species with lower habitat availability reach niche filling more rapidly than ecologically adaptive species, resulting in shorter diversification phases. Our results also suggest that riverine barrier effect seems to have affected solely conservative groups particularly when the river course is stable over time. While these results were obtained by considering only a fraction of Amazonian diversity, they provide interesting insights on the influence of niche conservatism upon Amazonian evolutive trajectories, which will hopefully foster further and more ample research in this direction. Avec une surface de plus de six millions de kilomètres carrés, l'Amazonie abrite la plus grande forêt tropicale humide au monde, ainsi qu'une grande partie de la diversité terrestre. Cependant, les origines spatiales et temporelles de cette diversité demeurent mal comprises et nécessitent d'être plus étudiées pour identifier les causes de cette impressionnante diversification. Les amphibiens sont un groupe taxonomique particulièrement adapté à l'étude de la biogéographie historique en Amazonie car ils ont diversifié de manière importante dans la région et présentent d'importante variations d'utilisation de l'habitat ainsi que de capacités de dispersion entre groupes. Nous avons d'abord reconstruit l'histoire biogéographique du genre Allobates qui se rencontre principalement dans des forêts de terra-firme et pour lequel l'Amazonie de l'Ouest a joué un rôle important de source de diversification il y a entre 14 et 10 Millions d'années. Ce patron spatio-temporel coïncide avec une période marquée par la présence d'un système de méga-marécage qui recouvrait la plupart de l'Amazonie de l'Ouest il y a entre 23 et 10 Millions d'années. La décharge progressive de ce système marécageux a été suivie par une expansion des forêts de terra-firme en Amazonie de l'Ouest, une disponibilité en habitat favorable qui a probablement contribué à la diversification d'Allobates. Nos résultats suggèrent également que les rivières de l'Ouest Amazonien ont pu promouvoir la diversification au cours des 10 derniers Millions d'années en agissant comme une barrière semiperméable à la dispersion, permettant la spéciation par dispersion suivie d'isolation géographique. Dans un second temps, nous avons reconstruit l'histoire biogéographique du groupe Pristimantis conspicillatus qui a diversifié de manière continue au cours du temps. Ce groupe présente un patron spatial de diversification particulier, avec quatre clades anciens ayant diversifié dans différentes aires simultanément, avec relativement peu d'évènements de dispersion comparé à Allobates. Ces importantes différences suggèrent que les espèces d'amphibiens ont des capacités de dispersion très variables qui sont probablement liées à leurs traits d'histoire de vie. Pour finir, nous avons comparé les histoires biogéographiques de six clades d'amphibiens partageant des temps d'origines comparables et qui sont représentatifs de la diversité Amazonienne en termes de taxonomie, de traits d'histoire de vie, d'utilisation de l'habitat et de type de reproduction. L'Amazonie de l'Ouest a été identifiée comme la principale source de diversification pour les amphibiens Amazonien, bien qu'elle ne se soit comportée comme tel qu'à partir d'il y a 10 Millions d'années pour les groupes présentant de l'adaptabilité dans la disponibilité de l'habitat ; et seulement entre il y a 10 et 5 Millions d'années pour les groupes conservatifs dans la disponibilité de l'habitat. Cela suggère que les groupes conservatifs dans la disponibilité de l'habitat remplissent la niche écologique plus rapidement que les espèces présentant de l'adaptativité, ce qui se traduit par des phases de diversification plus courtes. Nos résultats suggèrent également que les rivières agissent comme des barrières à la dispersion uniquement pour les groupes conservatifs dans l'utilisation de l'habitat. Nous tenons à souligner que ces résultats doivent être considérés avec prudence car ils ont été obtenus en étudiant une petite fraction de la diversité Amazonienne, ils apportent néanmoins de nouveaux éléments l'influence du conservatisme de niche sur les trajectoires évolutives en Amazonie, ce qui permettra peut-être d'inciter un plus grand effort de recherche sur cette thématique.

MOTS-CLES : Amazonie, Amphibiens, Biogéographie historique, Phylogénie, Délimitation

Figure I. 1 :

 1 Figure I.1: Biogeographical events supported by the different ancestral area reconstruction in the BioGeoBEARS super model (Matzke et al., 2013). The name of each type of event is followed

Figure I. 2 :

 2 Figure I.2: Example of dispersal-extinction cladogenesis (DEC) transition matrix for anagenetic events with three areas (A, B and C). A) Is the transition matrix for the classic DEC model, with anindependent rate for each type of extinction event, i.e. one per area; as well as an independent and directional dispersal rate between each pair of areas, i.e. a total of n² -n dispersal rates, where n is the number of areas. Parameters are displayed in the matrix as Dij, the rate of dispersal from area i to area j, and Ei, the rate of local extinction in area i. B) Is the transition matrix for the simplified DEC model, with a unique dispersal rate and a unique extinction rate for all type of dispersal events, i.e. a total of two parameters for any number of areas.

Figure I. 3 :

 3 Figure I.3: Neotropical area delimitations used in this thesis. A) The 8 areas delimitation, with only three Amazonian areas: western Amazonia, the Guiana shield and the Brazilian shield. B) The 14

Figure I. 4 :

 4 Figure I.4: Phylogenetic relationships of the 17 Hyloidea families according to Streicher et al. (2018). Families occurring in Amazonia are colored in red and families included in our comparative study are in bold. Numbers between brackets indicates the number of described species for each family. The Boana gr. albopunctata as well as the genera Osteocephalus, Dryaderces and Tepuihyla are in the Hylidae family. The Pristimantis gr. conspicillatus belong to the Craugastoridae family, while the Leptodactylus gr. podicipinus is part of the Leptodactylidae family. Finally, the Allobates and Amazophrynella genera belong to the Aromobatidae and Bufonidae families, respectively.

Fig

  Fig. II.1b). In contrast, we identified only few dispersal events from the Brazilian Shield towards western Amazonia and none from the Guiana Shield during the same period (8A; Fig. II.1b).

Figure II. 1 :

 1 Figure II.1: Ancestral area reconstruction of the genus Allobates using BioGeoBears assuming a DEC + J model, with: a) and c) being most likely biogeographical scenarios plotted on the Maximum clade credibility chronogram obtained with BEAST 2.5, for the 8 Neotropical biogeographical areas and the 14 Neotropical biogeographical areas respectively; with b) and d) being summaries of the 50 biogeographical stochastic mappings (BSMs) for the 8 Neotropical biogeographical areas and the 14 Neotropical biogeographical areas respectively; and e) lineage through time (LTT) plot performed on the same tree. Numbers on branches are posterior clade probabilities, those ≥ 0.95 are indicated with an asterisk. Node bars indicate the 95% highest posterior distributions of node dates. Squares on the tips of the trees indicate the geographical distribution o f extant species sampled in the phylogeny. Pie charts on nodes show the most likely reconstructions of ancestral areas, the size of each slice being proportional to the maximum likelihood. Colours corresponding to the different geographical distributions are depicted on the left. Black dotted lines represent changes in diversification rate through time according to the Yule thre e-rate model. In the summaries of the BSMs results, we present values of the within-area speciation events for each region (values within rectangles) as well as the anagenetic dispersal events and founder speciation events between each region (values within ellipses). Values correspond to the mean number of inferred events of a specific type acro ss the 50 independent BSMs. Anagenetic dispersals events and founder events are represented only for intra-Amazonian events with a mean of > 0.8. Red arrows highlight dispersals emitted by major dispersal hubs. The different colour ranges on the LTT plot indicates different confidence intervals of lineage accumulation through time predicted by a Yule pure-birth diversification model where the black line represents empirical data. Dotted vertical black lines indicate the dates of mean diversification rate changes according to the Yule three -rate model, and associated diversification rates are displayed on top of the LTT plot.

8 .

 8 Figure II.1: Ancestral area reconstruction of the genus Allobates using BioGeoBears assuming a DEC + J model, with: a) and c) being most likely biogeographical scenarios plotted on the Maximum clade credibility chronogram obtained with BEAST 2.5, for the 8 Neotropical biogeographical areas and the 14 Neotropical biogeographical areas respectively; with b) and d) being summaries of the 50 biogeographical stochastic mappings (BSMs) for the 8 Neotropical biogeographical areas and the 14 Neotropical biogeographical areas respectively; and e) lineage through time (LTT) plot performed on the same tree. Numbers on branches are posterior clade probabilities, those ≥ 0.95 are indicated with an asterisk. Node bars indicate the 95% highest posterior distributions of node dates. Squares on the tips of the trees indicate the geographical distribution o f extant species sampled in the phylogeny. Pie charts on nodes show the most likely reconstructions of ancestral areas, the size of each slice being proportional to the maximum likelihood. Colours corresponding to the different geographical distributions are depicted on the left. Black dotted lines represent changes in diversification rate through time according to the Yule thre e-rate model. In the summaries of the BSMs results, we present values of the within-area speciation events for each region (values within rectangles) as well as the anagenetic dispersal events and founder speciation events between each region (values within ellipses). Values correspond to the mean number of inferred events of a specific type acro ss the 50 independent BSMs. Anagenetic dispersals events and founder events are represented only for intra-Amazonian events with a mean of > 0.8. Red arrows highlight dispersals emitted by major dispersal hubs. The different colour ranges on the LTT plot indicates different confidence intervals of lineage accumulation through time predicted by a Yule pure-birth diversification model where the black line represents empirical data. Dotted vertical black lines indicate the dates of mean diversification rate changes according to the Yule three -rate model, and associated diversification rates are displayed on top of the LTT plot.

  Belem (Be), we would have to allow a maximum range size equal to seven in biogeographic inferences, leading to computational issues due to a very high number of possible states. The other OTUs belonging to the clade formed by the A. femoralis and A. kingsburyi clades are occurring only in the Napo (Na) area(A. zaparo, A. kingsburyi and A. fratisenescus; Fig. II.S1), thus in our 14A analysis we chose to restrict the distribution of A. femoralis to the areas where it occurs in western Amazonia:

Figure

  Figure II.S1: Results from species delimitation illustrated with the Maximum Clade Credibility chronogram obtained from BEAST and distribution of each OTU of the genus Allobates. In order to facilitate representation, the chronogram is divided into three panels, with the overall topology on the

Figure

  Figure II.S2: DNA based delineation results of the Generalized Mixed Yule Coalescent (GMYC), Approximate Barcode Gap Delineation (ABGD) and multi-rate Poisson Tree Processes (mPTP) methods performed on the 932 16S sequences of the genus Allobates. Colored clusters indicate the boundaries of each OTU delineated by a single method, green is for GMYC, blue for ABGD and red for mPTP. The black bars next to the results per method indicate consensus between the three methods.

Figure II. S3 :

 S3 Figure II.S3: Maximum clade credibility tree of the genus Allobates reconstructed using BEAST 2.5, including outgroups. Black dots in tip labels indicate the amount of available data for each species, one round is for terminals with at least 3000 bp and two rounds is for terminals with complete mitogenome. Posterior probabilities higher than 0.95 are indicated with an asterisk, while values below 0.95 are indicated in red. Node bars indicate the 95% credibility interval for node heights. Node boxes indicates node number. Mean height as well as 95% height credibility intervals can be found on the Table II.S1 in Appendix II.S4.

  Shield, respectively (Fig.III.1b). Diversification within western Amazonia occurred mainly along the Andean foothills notably in the Napo area and to a lesser extent in the Huallagua and Imeri areas(7.7, 2.9 and 1.4 events in average, respectively; Fig. III.1d). Outside of Amazonia, most in-situ speciation events were recovered in the Atlantic Forest, while only few occurred in the trans-Andean forests and the northern Andes (8.3, 2.2 and 2 in-situ speciation events in average; respectively).We recovered one ancient (>15 Mya) potential dispersal event from the Atlantic Forest toward the Guiana Shield from the BSMs (0.7 founder event or FE, in average; Fig. III.1b); while the ancestral area reconstruction suggests two distinct ancient events i.e. P. vilarsi subclade and P. gutturalis+P. charlottevillensis (Fig. III.1a). Low supports for the relationships within this clade let possible the existence of a Guiana Shield (+Trinidad) clade and a single dispersal.

  forestrial ecosystems(23-10 Mya; Hoorn et al., 2010). Most of these dispersals are recent range expansions from the Brazilian shield within the P. chiastonotus clade (P. koehleri, P. aff. koehleri and P. fenestratus/pluvian; Fig. III.1d), suggesting that the Madeira river played a limited role as a barrier to dispersal and that these western lowland habitats might only be recently fitted for Pristimantis gr. conspicillatus species. We comparatively recovered numerous dispersals across the upper Amazon river notably from the Napo interfluve towards the Huallagua and Inambari interfluves (Fig. III.1d).

Figure III. 1 :

 1 Figure III.1: Ancestral area reconstruction of the Pristimantis conspicillatus group using BioGeoBears assuming a DEC + J model, with: a) and c) being most likely biogeographical scenarios plotted on the Maximum clade credibility chronogram obtained with BEAST 2.5, for the eight Neotropical biogeographic areas and the 14 Neotropical biogeographic areas respectively; with b) and d) being summaries of the 50 biogeographical stochastic mappings

around 5 . 8

 58 Mya (CI 95% 4.5-7.1), all OTUs are located along the Orinoco watershed (Fig. III. S2). This area remains largely undersampled at the Amazonian scale, explaining the high level of cryptic diversity detected by our molecular delimitation (Fig. III. S2; Mayer et al., 2019).

Figure

  Figure III.S1: Results from species delimitation illustrated with the Maximum Clade Credibility chronogram obtained from BEAST and distribution of each OTU of the Pristimantis conspicillatus group. In order to facilitate representation, the chronogram is divided into five panels, with the overall topology on the top left indicating each panel. Polygons correspond to the distribution range of single or groups of related OTUs (colors) that are grouped together when they are related to the same described species, known localities are represented with different symbols matching those of the terminals in the tree. Numbers on branches are posterior clade probabilities, those ≥ 0.95 are represented with an asterisk. See details of species assignations and coordinates in Table III-S2.

Figure III. S2 :

 S2 Figure III.S2: Lineage through time plot with CI 95% for a yule pure-birth model. The black line represents the acumulation of lineages through time. The grey pixels represent the CI 95% based on a hundred random trees, to account for phylogenetic uncertainties. The red line represent the expected lineage accumulation under a yule pure-birth model for a given root age and number of lineages.

Figure

  Figure III.S3: Maximum clade credibility tree of the Pristimantis gr. conspicillatus reconstructed using BEAST 2.5, including outgroups. Dots in tip labels indicate the amount of available data for each species, one white round indicates that terminal have available nuDNA, one black round is for terminals with at least 3000 bp of mtDNA and two black rounds is for terminals with complete mitogenome. Posterior probabilities higher than 0.95 are indicated with an asterisk, while values below 0.95 are indicated in red. Node bars indicate the 95% credibility interval for node heights.

Figure IV. 1 :

 1 Figure IV.1: Mean dispersal and in-situ speciation rates for all species groups across all time. Mean dispersal rate from an area toward another is materialized with an arrow; while the mean in-situ speciation rate within an area is represented by circles. Dispersal rate values are written next to the arrows and in-situ speciation rate values can be found within the circles. The color of the circles matches those of the areas displayed on the map.

Figure IV. 2 :

 2 Figure IV.2: Results of the linear mixed effects model testing the predictors of dispersal times.

Figure IV. 3 :

 3 Figure IV.3: Mean dispersal and in-situ speciation rates through time per ecotype. Mean dispersal rate from an area toward another is materialized with an arrow; while the mean in-situ speciation rate within an area is represented by circles. Dispersal rate values are written next to the arrows and in-situ speciation rate values can be found within the circles. The color of the circles matches those of the areas displayed on the map. Rates have been averaged for species groups composing the ecotypes, with Allobates, Pristimantis gr. conspicillatus and Amazophrynella being ecoconserved groups; while Boana gr. albopunctata, Osteocephalus/Dryaderces/Tepuihyla and Leptodactylus gr. podicipinus are eco-diverse groups. Rates are displayed for across all time as well as for three time-frames including anterior to 10 Million years ago (Mya), between 10 and 5 Mya and posterior to 5 Mya. Dispersal rates and in-situ speciation rates are summed for each time-frame to obtain the Amazonian rates displayed in bold.

  distribution, while the three others are distributed in the dry diagonal and the Atlantic forest. The five OTUs composing the L. nesiotus clade diversified over the last 9 Mya in the Guiana Shield. Its sister clade, the L. natalensis clade, formed 10 Mya and encompasses five OTUs that are mainly distributed in the Brazilian Shield, but also in the Atlantic forest, the dry diagonal, and Western Amazonia; leaving the spatial origin of this clade ambiguous. Finally, the L. sp.grpodiB clade extensively diversified in all Amazonian areas and possibly originated from western Amazonia; its crown age is estimated around 10 Mya and it encompasses 18 OTUs (Fig. IV. SXa).

Figure IV. S1 :

 S1 Figure IV.S1: Results from species delimitation illustrated with the Maximum Clade Credibility chronogram obtained from BEAST and distribution of each OTU of the Leptodactylus gr. podicipinus. In order to facilitate representation, the chronogram is divided into four panels, with the overall topology on the top left indicating each panel. Polygons correspond to the distribution range of single

  one to be strictly restricted to this habitat (Fig. IV.S5. While all eco-diverse groups occur in flooded forest habitat, there is only a single eco-conserved species (Allobates paleovarzensis) that inhabit this habitat (Fig. IV.S4). Boana gr. albopunctata and Leptodactylus gr. podicipinus are the only groups to occur in open area habitats (Figures IV.S6 & IV.S7), while Osteocephalus is the single one adapted to phytotelmata habitats (Fig. IV.S8). Mountainous habitat is the one with less diversity, hosting a single species of Allobates (A. aff. undulatus) and few species of Osteocephalus and Pristimantis gr. conspicillatus (Figures IV.S4, IV.S8 & IV.S9

Figure IV. S3 :

 S3 Figure IV.S3: Species distribution area per ecotypes (km²). Distribution area is plotted in log for clarity reasons, for the two ecotypes: eco-conserved (Allobates, Amazophrynella and Pristimantis gr. conspicillatus) and eco-diverse species (Boana gr. albopunctata, Leptodactylus gr. podicipinus and Osteocephalus).

Figure IV. S5 :

 S5 Figure IV.S5: Ancestral area reconstruction of the genus Amazophrynella using BioGeoBears assuming a DEC model, with the most likely biogeographical scenarios plotted on the Maximum clade credibility chronogram obtained with BEAST 2.5, for the ecological shift analysis. Numbers on branches are posterior clade probabilities, those ≥ 0.95 are indicated with an asterisk. Node bars indicate the 95% highest posterior distributions of node dates. Squares on the tips of the trees indicate the geographical distribution of extant species sampled in the phylogeny. Pie charts on nodes show the most likely reconstructions of ancestral areas, the size of each slice proportional to the maximum likelihood. Colors corresponding to the different habitats are depicted on the left.

Figure IV. S6 :

 S6 Figure IV.S6: Ancestral area reconstruction of the genus Boana gr. albopunctata using BioGeoBears assuming a DEC model, with the most likely biogeographical scenarios plotted on the Maximum clade credibility chronogram obtained with BEAST 2.5, for the ecological shift analysis. Numbers on branches are posterior clade probabilities, those ≥ 0.95 are indicated with an asterisk. Node bars indicate the 95% highest posterior distributions of node dates. Squares on the tips of the trees indicate the geographical distribution of extant species sampled in the phylogeny. Pie charts on nodes show the most likely reconstructions of ancestral areas, the size of each slice proportional to the maximum likelihood. Colors corresponding to the different habitats are depicted on the left.

Figure IV. S7 :

 S7 Figure IV.S7: Ancestral area reconstruction of the genus Leptodactylus gr. podicipinus using BioGeoBears assuming a DEC model, with the most likely biogeographical scenarios plotted on the Maximum clade credibility chronogram obtained with BEAST 2.5, for the ecological shift analysis. Numbers on branches are posterior clade probabilities, those ≥ 0.95 are indicated with an asterisk. Node bars indicate the 95% highest posterior distributions of node dates. Squares on the tips of the trees indicate the geographical distribution of extant species sampled in the phylogeny. Pie charts on nodes show the most likely reconstructions of ancestral areas, the size of each slice proportional to the maximum likelihood. Colors corresponding to the different habitats are depicted on the left.

Figure IV. S8 :

 S8 Figure IV.S8: Ancestral area reconstruction of the clade formed by the genera Osteocephalus, Dryaderces and Tepuihyla using BioGeoBears assuming a DEC model, with the most likely biogeographical scenarios plotted on the Maximum clade credibility chronogram obtained with BEAST 2.5, for the ecological shift analysis. Numbers on branches are posterior clade probabilities, those ≥ 0.95 are indicated with an asterisk. Node bars indicate the 95% highest posterior distributions of node dates. Squares on the tips of the trees indicate the geographical distribution of extant species sampled in the phylogeny. Pie charts on nodes show the most likely reconstructions of ancestral areas, the size of each slice proportional to the maximum likelihood. Colors corresponding to the different habitats are depicted on the left.

Figure IV. S9 :

 S9 Figure IV.S9: Ancestral area reconstruction of the Pristimantis gr. conspicillatus using BioGeoBears assuming a DEC model, with the most likely biogeographical scenarios plotted on the Maximum clade credibility chronogram obtained with BEAST 2.5, for the ecological shift analysis. Numbers on branches are posterior clade probabilities, those ≥ 0.95 are indicated with an asterisk. Node bars indicate the 95% highest posterior distributions of node dates. Squares on the tips of the trees indicate the geographical distribution of extant species sampled in the phylogeny. Pie charts on nodes show the most likely reconstructions of ancestral areas, the size of each slice proportional to the maximum likelihood. Colors corresponding to the different habitats are depicted on the left.

Figure

  Figure IV.S11: Ancestral area reconstruction of the genus Allobates using BioGeoBears assuming a DEC model, with the being most likely biogeographical scenarios plotted on the Maximum clade credibility chronogram obtained with BEAST 2.5, for the eight Neotropical biogeographic areas. Numbers on branches are posterior clade probabilities, those ≥ 0.95 are indicated with an asterisk. Node bars indicate the 95% highest posterior distributions of node dates. Squares on the tips of the trees indicate the geographical distribution of extant species sampled in the phylogeny. Pie charts on nodes show the most likely reconstructions of ancestral areas, the size of each slice proportional to the maximum likelihood. Colors corresponding to the different geographical distributions are depicted on the left.

Figure IV. S12 :

 S12 Figure IV.S12: Ancestral area reconstruction of the genus Amazophrynella using BioGeoBears assuming a DEC model, with the being most likely biogeographical scenarios plotted on the Maximum clade credibility chronogram obtained with BEAST 2.5, for the eight Neotropical biogeographic areas. Numbers on branches are posterior clade probabilities, those ≥ 0.95 are indicated with an asterisk. Node bars indicate the 95% highest posterior distributions of node dates. Squares on the tips of the trees indicate the geographical distribution of extant species sampled in the phylogeny. Pie charts on nodes show the most likely reconstructions of ancestral areas, the size of each slice proportional to the maximum likelihood. Colors corresponding to the different geographical distributions are depicted on the left.

Figure IV. S13 :

 S13 Figure IV.S13: Ancestral area reconstruction of the genus Boana gr. albopunctat using BioGeoBears assuming a DEC model, with the being most likely biogeographical scenarios plotted on the Maximum clade credibility chronogram obtained with BEAST 2.5, for the eight Neotropical biogeographic areas. Numbers on branches are posterior clade probabilities, those ≥ 0.95 are indicated with an asterisk. Node bars indicate the 95% highest posterior distributions of node dates. Squares on the tips of the trees indicate the geographical distribution of extant species sampled in the phylogeny. Pie charts on nodes show the most likely reconstructions of ancestral areas, the size of each slice proportional to the maximum likelihood. Colors corresponding to the different geographical distributions are depicted on the left.

Figure IV. S14 :

 S14 Figure IV.S14: Ancestral area reconstruction of the clade formed by the genera Osteocephalus, Dryaderces and Tepuihyla, using BioGeoBears assuming a DEC model, with the being most likely biogeographical scenarios plotted on the Maximum clade credibility chronogram obtained with BEAST 2.5, for the eight Neotropical biogeographic areas. Numbers on branches are posterior clade probabilities, those ≥ 0.95 are indicated with an asterisk. Node bars indicate the 95% highest posterior distributions of node dates. Squares on the tips of the trees indicate the geographical distribution of extant species sampled in the phylogeny. Pie charts on nodes show the most likely reconstructions of ancestral areas, the size of each slice proportional to the maximum likelihood. Colors corresponding to the different geographical distributions are depicted on the left.

Figure IV. S15 :

 S15 Figure IV.S15: Ancestral area reconstruction of the Pristimantis gr. conspicillatus, using BioGeoBears assuming a DEC model, with the being most likely biogeographical scenarios plotted on the Maximum clade credibility chronogram obtained with BEAST 2.5, for the eight Neotropical biogeographic areas. Numbers on branches are posterior clade probabilities, those ≥ 0.95 are indicated with an asterisk. Node bars indicate the 95% highest posterior distributions of node dates. Squares on the tips of the trees indicate the geographical distribution of extant species sampled in the phylogeny. Pie charts on nodes show the most likely reconstructions of ancestral areas, the size of each slice proportional to the maximum likelihood. Colors corresponding to the different geographical distributions are depicted on the left.

AUTEUR

  et diversité des amphibiens d'Amazonie DIRECTEUR DE THESE : Antoine FOUQUET LIEU ET DATE DE SOUTENANCE : 29/01/2021 à Toulouse, France.

Table I .2: List of newly sequenced 16S samples and their origins.

 I The name of the sequence is in the first column, with the associated field number and voucher in the second and third column respectively. Species name is in the fourth column. List of collectors is available in the fifth column, while the museum or temporary collection from which the tissue sample used for sequencing originates is in the sixth column. Sequence names beginning by ZONI were sequenced by the museum or temporary collection where they came from; while other sequences were sequenced at UMR5174 EDB, Toulouse, France.

	MT524105 ZONIA05912 XXXXXXXXXXXCAAM40 MT524168	AF4393 AJC2962 CAAM40 LAT 118	MHNLS 19910 MHNC8329	Allobates_ornatus Pristimantis_vilarsi Leptodactylus_spgrpodiC Allobates_aff.trilineatus_1	A. Fouquet, A. Rejaud, J. Chretien, V. Premel, M. Chouteau F. Rojas-Runjaic, C. Barrio-Amoros, S. Castroviejo-Fisher O. Chaline, M. Dewynter	UMR5174 EDB, Toulouse, France Museo de Historia Natural La Salle, Venezuela UMR5174 EDB, Toulouse, France Museo de Historia Natural de la Universidad Nacional de San Antonio Abad del Cusco, Peru
	MT524106 ZONIA06012 MT524117 MT524169	AF4402 AJC2963 CM388 LAT 119	MHNLS 19911 MHNC8330	Allobates_ornatus Pristimantis_vilarsi Allobates_femoralis/hodli Allobates_aff.trilineatus_1	A. Fouquet, A. Rejaud, J. Chretien, V. Premel, M. Chouteau F. Rojas-Runjaic, C. Barrio-Amoros, S. Castroviejo-Fisher C. Marty	UMR5174 EDB, Toulouse, France Museo de Historia Natural La Salle, Venezuela UMR5174 EDB, Toulouse, France Museo de Historia Natural de la Universidad Nacional de San Antonio Abad del Cusco, Peru
	MT524107 ZONIA06112 MT524141 MT524170	AF4403 AJC2964 FGZC3247 LAT 128	MHNLS 19912 FGZC3247 MHNC8339	Allobates_ornatus Pristimantis_vilarsi Allobates_aff.trilineatus_1 Allobates_aff.trilineatus_1	A. Fouquet, A. Rejaud, J. Chretien, V. Premel, M. Chouteau F. Rojas-Runjaic, C. Barrio-Amoros, S. Castroviejo-Fisher F. Glaw	UMR5174 EDB, Toulouse, France Museo de Historia Natural La Salle, Venezuela Universidad Nacional Mayor de San Marcos, Museo de Historia Natural, Peru Museo de Historia Natural de la Universidad Nacional de San Antonio Abad del Cusco, Peru
	MT524108 ZONIA08012 MT524142 MT524171	AF4404 AJC3000 FGZC3272 LAT 138	MHNLS 19946 FGZC3272 MHNC8349	Allobates_ornatus Pristimantis_vilarsi Allobates_aff.trilineatus_1 Allobates_aff.trilineatus_1	A. Fouquet, A. Rejaud, J. Chretien, V. Premel, M. Chouteau F. Rojas-Runjaic, C. Barrio-Amoros, S. Castroviejo-Fisher F. Glaw	UMR5174 EDB, Toulouse, France Museo de Historia Natural La Salle, Venezuela Universidad Nacional Mayor de San Marcos, Museo de Historia Natural, Peru Museo de Historia Natural de la Universidad Nacional de San Antonio Abad del Cusco, Peru
	MT524109 ZONIA08112 MT524143 MT524172	AF4406 AJC3001 FGZC3276 LAT 153	MHNLS 19947 FGZC3276 MHNC8364	Allobates_ornatus Pristimantis_vilarsi Allobates_sp."Huanuco" Allobates_aff.trilineatus_1	A. Fouquet, A. Rejaud, J. Chretien, V. Premel, M. Chouteau F. Rojas-Runjaic, C. Barrio-Amoros, S. Castroviejo-Fisher F. Glaw	UMR5174 EDB, Toulouse, France Museo de Historia Natural La Salle, Venezuela Universidad Nacional Mayor de San Marcos, Museo de Historia Natural, Peru Museo de Historia Natural de la Universidad Nacional de San Antonio Abad del Cusco, Peru
	MT524110 ZONIA09312 MT524144 MT524173 MT524155	AF4482 AJC3024 FGZC3279 LAT 156 PEP529	MHNLS 19970 FGZC3279 MHNC8367 PEP529	Allobates_femoralis/hodli Pristimantis_vilarsi Allobates_conspicuus/subfolionidificans Allobates_aff.trilineatus_1 Allobates_paleovarzensis	A. Fouquet, A. Rejaud, J. Chretien, V. Premel, M. Chouteau F. Rojas-Runjaic, C. Barrio-Amoros, S. Castroviejo-Fisher F. Glaw	UMR5174 EDB, Toulouse, France Museo de Historia Natural La Salle, Venezuela Universidad Nacional Mayor de San Marcos, Museo de Historia Natural, Peru Museo de Historia Natural de la Universidad Nacional de San Antonio Abad del Cusco, Peru Museo Nacional de Ciencias Naturales, Madrid, Spain
	GB / Seq name MT524111 MT524125 MT524145 MT524174 MT524156	Field Numbers AF4493 AJC3032 FGZC3348 LAT 160 PEP530	Voucher MHNLS 19978 FGZC3348 MHNC8371 PEP530	Species Allobates_trilineatus Allobates_undulatus Allobates_sp."Huanuco" Allobates_aff.trilineatus_1 Allobates_conspicuus/subfolionidificans	Collectors A. Fouquet, A. Rejaud, J. Chretien, V. Premel, M. Chouteau F. Rojas-Runjaic, C. Barrio-Amoros, S. Castroviejo-Fisher F. Glaw	Museum or temporary collection UMR5174 EDB, Toulouse, France Museo de Historia Natural La Salle, Venezuela Universidad Nacional Mayor de San Marcos, Museo de Historia Natural, Peru Museo de Historia Natural de la Universidad Nacional de San Antonio Abad del Cusco, Peru Museo Nacional de Ciencias Naturales, Madrid, Spain
	Family AF0655 AF4494 AJC3033 FTA33 LAT 161 PEP531 Aromobatidae XXXXXXXXAF0655 XXXXXXXXAF4494 MT524126 XXXXXXXXFTA33 MT524175 MT524157 Allobates Amazophrynella Bufonidae XXXXXXXXAF0692 AF0692 XXXXXXXXXXXAF743 AF0743 XXXXXXXXAF0830 AF0830 XXXXXXXXAF1119 AF1119 XXXXXXXXAF1362 AF1362 XXXXXXXXAF1411 AF1411 XXXXXXXXAF1423 AF1423 XXXXXXXXXXXAF1577 AF1577 XXXXXXXXAF4505 AF4505 XXXXXXXXAF4506 AF4506 XXXXXXXXAF4516 AF4516 XXXXXXXXAF4521 AF4521 XXXXXXXXAF4523 AF4523 MT524112 AF4526 XXXXXXXXAF4561 AF4561 XXXXXXXXAF4580 AF4580 MT524127 AJC3034 XXXXXXXXFTA44 FTA44 XXXXXXXXXXXMJ1235 MJ1235 MT524158 PEP532 MT524128 AJC3036 MT524116 AJC3040 ZONIA10612 AJC3049 ZONIA10912 AJC3052 ZONIA11212 AJC3061 XXXXXXXXXXXAJC3154 AJC3154 MT524166 GGU1010 MT524167 GGU1040 MT524151 MTKD47771 XXXXXXXXXXXPK1516 PK1516 MT524137 GGU684 MT524150 MTKD47884 MT524118 PK3798 MT524138 GGU895 MT524154 MTR10146 MT524119 SC9487 MT524139 GGU946 MT524153 MTR10147 XXXXXXXXQCAZ41868 SC17442 MT524140 GGU970 MT524152 MTR10259 MT524120 SC25368 XXXXXXXXQCAZ51154 SC37136 XXXXXXXXXXXH0056 H0056 XXXXXXXXQCAZ51916 SC38638 XXXXXXXXXXXAF1578 AF1578 MT524121 SC34585	Reproductive mode Pristimantis_chiastonotus Pristimantis_aff.conspicillatus Allobates_undulatus Pristimantis_chiastonotus Allobates_aff.trilineatus_1 Allobates_aff.trilineatus_1 Eggs and exotrophic tadpoles in lentic water (mode A. Fouquet, A. Rejaud, J. Chretien, V. Premel, M. Main habitat type A. Fouquet Chouteau MHNLS 19979 F. Rojas-Runjaic, C. Barrio-Amoros, S. Castroviejo-Fisher FTA33 J. Dias Lima MHNC8372 Museo de Historia Natural de la Universidad Nacional Microhabitat References UMR5174 EDB, Toulouse, France UMR5174 EDB, Toulouse, France Museo de Historia Natural La Salle, Venezuela IEPA, Macapa, Brazil de San Antonio Abad del Cusco, Peru PEP531 Museo Nacional de Ciencias Naturales, Madrid, Spain type Terra firme forest Terrestrial Caldwell & Lima, 2003; Lötters & Mutschmann, 2007; Test, 1956. 1) Terra firme forest Terrestrial Pristimantis_chiastonotus A. Fouquet, P. Gaucher UMR5174 EDB, Toulouse, France Leptodactylus_spgrpodiB A. Fouquet, D. Baudain, A. Cristinoi UMR5174 EDB, Toulouse, France Pristimantis_chiastonotus A. Fouquet, V. Rufray, O. Chaline UMR5174 EDB, Toulouse, France Pristimantis_chiastonotus A. Fouquet, K. Pineau UMR5174 EDB, Toulouse, France Pristimantis_chiastonotus A. Fouquet, B. Villette, Q. Martinez UMR5174 EDB, Toulouse, France Pristimantis_chiastonotus A. Fouquet, J. Devillechabrolle UMR5174 EDB, Toulouse, France Pristimantis_chiastonotus M. Dewynter UMR5174 EDB, Toulouse, France Leptodactylus_nesiotus A. Fouquet, C. Poixblanc UMR5174 EDB, Toulouse, France Pristimantis_aff.conspicillatus A. Fouquet, A. Rejaud, J. Chretien, V. Premel, M. Chouteau UMR5174 EDB, Toulouse, France Pristimantis_aff.conspicillatus A. Fouquet, A. Rejaud, J. Chretien, V. Premel, M. Chouteau UMR5174 EDB, Toulouse, France Pristimantis_aff.conspicillatus A. Fouquet, A. Rejaud, J. Chretien, V. Premel, M. Chouteau UMR5174 EDB, Toulouse, France Pristimantis_aff.conspicillatus A. Fouquet, A. Rejaud, J. Chretien, V. Premel, M. Chouteau UMR5174 EDB, Toulouse, France Pristimantis_aff.conspicillatus A. Fouquet, A. Rejaud, J. Chretien, V. Premel, M. Chouteau UMR5174 EDB, Toulouse, France Allobates_ornatus A. Fouquet, A. Rejaud, J. Chretien, V. Premel, M. Chouteau UMR5174 EDB, Toulouse, France Pristimantis_aff.conspicillatus A. Fouquet, A. Rejaud, J. Chretien, V. Premel, M. Chouteau UMR5174 EDB, Toulouse, France Pristimantis_aff.conspicillatus A. Fouquet, A. Rejaud, J. Allen UMR5174 EDB, Toulouse, France MHNLS 19980 Allobates_undulatus F. Rojas-Runjaic, C. Barrio-Amoros, S. Museo de Historia Natural La Salle, Venezuela FTA44 Pristimantis_chiastonotus J. Dias Lima IEPA, Macapa, Brazil Leptodactylus_leptodactyloides M. Jansen Senckenberg Research Institute, Frankfurt, Germany PEP532 Allobates_aff.trilineatus_1 Museo Nacional de Ciencias Naturales, Madrid, Spain Castroviejo-Fisher MHNLS 19982 Allobates_undulatus F. Rojas-Runjaic, C. Barrio-Amoros, S. Castroviejo-Fisher Museo de Historia Natural La Salle, Venezuela Allobates_undulatus F. Rojas-Runjaic, C. Barrio Amoros, S. Castroviejo-Fisher Museo de Historia Natural La Salle, Venezuela MHNLS 19995 Leptodactylus_riveroi F. Rojas-Runjaic, C. Barrio-Amoros, S. Castroviejo-Fisher Museo de Historia Natural La Salle, Venezuela MHNLS 19998 Pristimantis_aff.vilarsi_4 F. Rojas-Runjaic, C. Barrio-Amoros, S. Castroviejo-Fisher Museo de Historia Natural La Salle, Venezuela MHNLS 20006 Pristimantis_aff.vilarsi_4 F. Rojas-Runjaic, C. Barrio-Amoros, S. Castroviejo-Fisher Museo de Historia Natural La Salle, Venezuela MHNLS 20090 Leptodactylus_validus F. Rojas-Runjaic, C. Barrio-Amoros, S. Museo de Historia Natural La Salle, Venezuela GGU1010 Allobates_aff.trilineatus_1 G. Gagliardi Urrutia Universidad Nacional de la Amazonia Peruana, Museo de Zoologia, Peru GGU1040 Allobates_insperatus/juami G. Gagliardi Urrutia MTKD47771 Allobates_sumtuosus R. Ernst Senckenberg Natural History Collections Dresden, Museum of Zoology, Germany PK1516 Leptodactylus_spgrpodiB Royal Belgian Institute of Natural Sciences, Brussels, P. Kok Belgium Universidad Nacional de la Amazonia Peruana, Museo de Zoologia, Peru GGU684 Allobates_sp."Ucuyali" G. Gagliardi Urrutia MTKD47884 Allobates_amissiblis R. Ernst, M. Holting Senckenberg Natural History Collections Dresden, Museum of Zoology, Germany PK3798 Allobates_amissibilis Royal Belgian Institute of Natural Sciences, Brussels, P. Kok Belgium Universidad Nacional de la Amazonia Peruana, Museo de Zoologia, Peru GGU895 Allobates_conspicuus/subfolionidificans G. Gagliardi Urrutia MTR10146 Allobates_caeruleodactylus MT Rodrigues Universidade de Sao Paulo, Departamento de Zoologia, QCAZA35236 Allobates_talamancae I. Tapia Museo de Zoologia QCAZ, Quito, Ecuador Brazil Universidad Nacional de la Amazonia Peruana, Museo de Zoologia, Peru GGU946 Allobates_conspicuus/subfolionidificans G. Gagliardi Urrutia MTR10147 Allobates_masniger/nidicola MT Rodrigues Universidade de Sao Paulo, Departamento de Zoologia, QCAZA41868 Pristimantis_citriogaster D. Salazar Museo de Zoologia QCAZ, Quito, Ecuador Brazil Universidad Nacional de la Amazonia Peruana, Museo de Zoologia, Peru GGU970 Allobates_conspicuus/subfolionidificans G. Gagliardi Urrutia MTR10259 Allobates_sumtuosus Universidade de Sao Paulo, Departamento de Zoologia, QCAZA44577 Allobates_insperatus/juami S. Ron Museo de Zoologia QCAZ, Quito, Ecuador MT Rodrigues Brazil Universidad Nacional de la Amazonia Peruana, Museo QCAZA51154 Pristimantis_aff.malkini_2 E. Toral Museo de Zoologia QCAZ, Quito, Ecuador de Zoologia, Peru Castroviejo-Fisher H0057 Leptodactylus_spgrpodiB M Teixeira Jr, F Dal Vechio, M Sena, L Storti, R Gaiga, P Dias, P Nunes, P Sanchez, L Rizzati, Universidade de Sao Paulo, Sao Paulo, Brazil QCAZA51916 Pristimantis_aff.condor_1 S. Ron Museo de Zoologia QCAZ, Quito, Ecuador Menin et al., 2020. Leptodactylus_nesiotus A. Fouquet, C. Poixblanc UMR5174 EDB, Toulouse, France JMB Ghelleri, AM Neto, J Cassimiro QCAZA52435 Allobates_insperatus/juami S. Ron Museo de Zoologia QCAZ, Quito, Ecuador
	Pristimantis gr. conspicillatus XXXXXXXXAF1859 Craugastoridae AF1859 MT524113 AF4608 XXXXXXXXHT0656 MT524122 HMOA1703 Boana gr. albopunctata Hylidae XXXXXXXXXXXAF2407 AF2407 MT524114 AF4609 XXXXXXXXHT1613 XXXXXXXXQCAZ57584 SC47116 XXXXXXXXXXXAF2585 AF2585 MT524115 AF4635 XXXXXXXXHT1769 XXXXXXXXQCAZ63391 SC48415 XXXXXXXXAF2664 AF2664 XXXXXXXXAF4655 AF4655 XXXXXXXXXXXHT1787 MT524123 SC56956 Osteocephalus / Dryaderces / Tepuihyla Hylidae XXXXXXXXXXXAF3745 AF3745 XXXXXXXXAF4659 AF4659 XXXXXXXXXXXHT352 XXXXXXXXAF2806 AF2806 XXXXXXXXAF2923 AF2923 XXXXXXXXAF4656 AF4656 XXXXXXXXAF4657 AF4657 XXXXXXXXXXXHT342 XXXXXXXXXXXHT2768 MT524124 RCV2243	Direct development of terrestrial eggs or eggs hatching into froglets (modes 23 & 27). Pristimantis_chiastonotus Allobates_conspicuus/subfolionidificans HT0656 Pristimantis_chiastonotus QCAZA53540 Allobates_insperatus/juami Eggs and exotrophic tadpoles in lentic or lotic water A. Fouquet, A. Rejaud, J. Allen Terra firme forest J.P. Vacher V.T. de Carvalho M. Ortega (Modes 1 & 2) Leptodactylus_spgrpodiC A. Fouquet, R. Jairam Allobates_conspicuus/subfolionidificans A. Fouquet, A. Rejaud, J. Allen HT1613 Pristimantis_zeuctotylus V.T. de Carvalho QCAZA57584 Pristimantis_aff.condor_3 S. Ron Flooded and Terra-firme forest, Leptodactylus_podicipinus D. Baudain Allobates_femoralis/hodli A. Fouquet, A. Rejaud, J. Allen HT1769 Pristimantis_aff.zeuctotylus V.T. de Carvalho QCAZA63391 Pristimantis_aff.condor_1 S. Ron Pristimantis_chiastonotus J.P. Vacher, S. Cally Pristimantis_peruvianus A. Fouquet, V. Premel, M. Chouteau HT1787 Leptodactylus_spgrpodiB V.T. de Carvalho QCAZA68477 Allobates_kingsburyi D. Almeida open habitats Eggs and exotrophic tadpoles in lentic or lotic water, as well as water in tree holes or aerial plants (Modes 1, 2 & 6) Flooded and Leptodactylus_spgrpodiB A Fouquet, R Jairam, S Cally Pristimantis_peruvianus A. Fouquet, V. Premel, M. Chouteau HT352 Leptodactylus_validus F. A. Dubyna Esteves; M. Gordo; M. E. Oliveira, V. T. de Carvalho Terra-firme forest Pristimantis_gutturalis A. Fouquet, M. Dewynter, N. Vidal Pristimantis_chiastonotus A. Fouquet, P. Nunes Pristimantis_peruvianus A. Fouquet, V. Premel, M. Chouteau Pristimantis_peruvianus A. Fouquet, V. Premel, M. Chouteau HT342 Leptodactylus_validus V.T. de Carvalho HT2768 Leptodactylus_spgrpodiB A. P. Lima Allobates_masniger/nidicola Universidade de Sao Paulo, Departamento de Zoologia, Scansorial UMR5174 EDB, Toulouse, France UMR5174 EDB, Toulouse, France INPA, Manaus, Brazil Museo de Zoologia QCAZ, Quito, Ecuador Jiménez-Robles et al., 2017. UMR5174 EDB, Toulouse, France UMR5174 EDB, Toulouse, France INPA, Manaus, Brazil Museo de Zoologia QCAZ, Quito, Ecuador Arboreal UMR5174 EDB, Toulouse, France UMR5174 EDB, Toulouse, France INPA, Manaus, Brazil Museo de Zoologia QCAZ, Quito, Ecuador de Sá et al., 2014. UMR5174 EDB, Toulouse, France UMR5174 EDB, Toulouse, France INPA, Manaus, Brazil Museo de Zoologia QCAZ, Quito, Ecuador UMR5174 EDB, Toulouse, France UMR5174 EDB, Toulouse, France INPA, Manaus, Brazil Arboreal Jungfer et al., 2013. UMR5174 EDB, Toulouse, France UMR5174 EDB, Toulouse, France UMR5174 EDB, Toulouse, France INPA, Manaus, Brazil UMR5174 EDB, Toulouse, France INPA, Manaus, Brazil R. Vogt Brazil
	Leptodactylus gr. podicipinus XXXXXXXXXXXAF3754 Leptodactylidae AF3754 XXXXXXXXAF4778 AF4778 XXXXXXXXXXXHT4065 APL 17168 XXXXXXXXXXXAF3810 AF3810 XXXXXXXXXXXAG013 AG013 XXXXXXXXXXXHT482 XXXXXXXXXXXAF3829 AF3829 XXXXXXXXXXXAF3924 AF3924 XXXXXXXXXXXAF3930 AF3930 XXXXXXXXXXXAG442 AG442 ZONIF09412 AJC2260 XXXXXXXXXXXHT497 ZONIF09512 AJC2261 XXXXXXXXXXXHT520 XXXXXXXXXXXHT552	Foam nest floating on water accumulated in constructed basins or on ponds (modes 11 & 13) Leptodactylus_spgrpodiB Pristimantis_gutturalis HT4065 Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiB HT482 Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_podicipinus Leptodactylus_spgrpodiB HT497 Leptodactylus_spgrpodiB ANDES-A AJC2260 Pristimantis_aff.vilarsi_2 HT520 Leptodactylus_spgrpodiB ANDES-A AJC2261 Pristimantis_aff.vilarsi_2 HT552 Leptodactylus_aff.discodactylus	Flooded and Terra-firme forest, A Fouquet, R Jairam, S Cally Courtois E. Dewynter M. A. P. Lima A Fouquet, R Jairam, S Cally M. Blanc V.T. de Carvalho, R. de Fraga open habitats A Fouquet, R Jairam, S Cally B. Villette M. Blanc V.T. de Carvalho, R. de Fraga S. Castroviejo-Fisher V.T. de Carvalho, L. Bonora S. Castroviejo-Fisher V.T. de Carvalho, L. Bonora	Terrestrial UMR5174 EDB, Toulouse, France UMR5174 EDB, Toulouse, France INPA, Manaus, Brazil Jiménez-Robles et al., 2017; Prado et al., 2002. UMR5174 EDB, Toulouse, France UMR5174 EDB, Toulouse, France INPA, Manaus, Brazil UMR5174 EDB, Toulouse, France UMR5174 EDB, Toulouse, France UMR5174 EDB, Toulouse, France UMR5174 EDB, Toulouse, France INPA, Manaus, Brazil Universidad de los Andes, Bogota, Colombia INPA, Manaus, Brazil Universidad de los Andes, Bogota, Colombia INPA, Manaus, Brazil
							60

Mainly eggs hatching into exotrophic tadpoles that are carried to water by adult (mode 20). Eggs and exotrophic tadpoles in water in tree holes or aerial plants (mode 6; A. bromelicola). Eggs hatching into endotrophic tadpoles that complete their development in the nest

(mode 21; A. masniger and A. nidicola, A. chalcopis)

  . The majority-rule consensus of the three methods led to the delimitation of 50 OTUs in the genus Allobates. Closely related OTUs are rarely spatially overlapping

	(Fig. II.S1) and these allopatric ranges are generally separated by major Amazonian rivers. Following From the 8A analysis we inferred a majority of in situ speciation events, western Amazonia had an
	OTU delimitation we confirmed that the diversity in Amazonian Allobates species is heterogeneously average of 16.6 speciation events; versus in average only 7 and 2.3 of such events within the Brazilian
	distributed, having a higher diversity in western Amazonia (22 OTUs) than in the Brazilian Shield (15 Shield and the Guiana Shield, respectively (Fig. II.1b). At a finer scale it appears that in situ
	OTUs) and the Guiana Shield (9 OTUs; Table II.S1). Results of species assignation for each method diversification occurred notably along the Andean foothills within western Amazonia as well as in the
	are available in Table II.S2 and plotted in Fig. II.S2. Rondônia area within the Brazilian Shield (Fig. II.1d). In the 8A analysis, we recovered few dispersal
	events towards western Amazonia from the Brazilian Shield (1.1 founder events, or FE, and 2.2
	3.2| Time-calibrated species phylogeny
	anagenetic dispersals, or AD, on average) and none from the Guiana Shield (Fig. II.1b). Similarly, few
	Our final matrix totalized 60 taxa and 14,167 aligned nucleotide sites. Among these taxa, 33 were dispersal events occurred from western Amazonia toward the Guiana Shield (FE = 1.9; AD = 0.8) and
	complete and 49 comprised at least 3,000 nucleotides. The inferred mitogenome-based phylogenetic Brazilian Shield (FE = 1.7; AD = 2.8), as well as from the Brazilian Shield toward the Guiana Shield
	tree had clade posterior probabilities above 0.95 for 53 of the 59 nodes. We estimated the crown age (FE = 1.5; Fig. II.1b). At a finer scale, it appears that the Inambari and Rondônia areas played a major
	of Allobates to be 28.8 My (CI 95%: 23.3-34.2 Mya). The genus comprises one Atlantic forest clade role as hubs of Amazonian dispersal, with respectively a total of 17.6 and 13.2 dispersal events
	(A. olfersioides) with an onset of diversification at 16.1 Mya (CI 95%: 10.8-24.5 Mya) and its sister emitted/received on average, twice as much as other areas (Table II.S7).
	encompassing all other Amazonian Allobates species, which started to diversify 25.6 Mya (CI 95%:
	20.8-30.5 Mya) (Table II.S4; Fig. II.S3). The topology was consistent with the supported relationships 3.4| Diversification through time
	recovered by Melo-Sampaio et al. (2020), except for the placement of A. granti. In our analysis, A. The MCCR test suggested an overall decrease of diversification rate across time (γ = -5.429, p-value
	tinae is the supported sister species of a supported clade formed by A. granti+A. aff. granti (Fig 1a-c). = 2.831 x 10 -8 ). The diversification through time model that best fit Allobates phylogenetic
	reconstruction is the Yule pure-birth 3-rate model, with a high diversification rate until 10 Mya, followed
	3.3| Ancestral area reconstructions
	by a decrease (Table II.S8, Fig. II.1c). The second-best model was a Yule pure birth 4-rate model that
	Model comparisons identified DEC+J as the best model for each geographic partitioning (Tables II.S5 further detected a diversification peak around 10 Mya (Table II.S8). The LTT plot illustrates this rapid
	& II.S6), suggesting that vicariance, sympatry (within our areas) and jump dispersal played a role in accumulation of lineages at approximatively 10 Mya (Fig. II.1c).
	the history of diversification in Allobates. The ancestral range of the most recent common ancestor
	(MRCA) of all extant Allobates was ambiguous (Fig. II.1a), as all options included two or more areas,
	but most include the Guiana Shield. Initial diversification of Amazonian Allobates was inferred to have
	taken place in the Guiana Shield, around 25 Mya (Fig. II.1a). Around 22 Mya, Amazonian and trans-
	Amazonian sister clades (Chocó + Central America) diverged. This Amazonian clade encompasses
	four large clades, as follows. The A. masniger clade, likely initiated its diversification in the Brazilian
	Shield around 17 Mya (Fig. II.1a). A major Allobates group, the A. trilineatus clade, comprising 21
	OTUs, formed around 14 Mya in western Amazonia (Fig. II.1a). The origin of the A. caerulodactylus
	clade was uncertain, either in western Amazonia, the Brazilian Shield, or both (Fig. II.1a). Finally, we
	recovered a poorly supported node comprising the A. kingsburyi and A. femoralis clades, with a crown
	age of 18 Mya and possibly originating from western Amazonia (Fig. II.1a).

  differences. Another striking example of the cryptic diversity within Allobates is A. tapajos. This nominal species has a crown age estimated at 5.8 Mya and encompasses four OTUs that are each distributed in different interfluvia (Figures II.1c & II.S1).

Table II .

 II S1: Alpha diversity of Allobates species in the large Neotropical biogeographic areas. Biogeographic areas correspond to those used in the eight Neotropical areas analysis (8A). Alpha diversity has been calculated for Allobates species, based on IUCN (2019) and[START_REF] Frost | Amphibian species of the world: an online reference[END_REF]; as well as for the OTUs we delimited. Numbers into brackets in the IUCN/AOW Alpha diversity column indicate the number of missing species in our study. The species occurring in each area are listed in the Species list column, species in bold are not included in our study.

		Alpha diversity
	Biogeographic areas IUCN/AOW Réjaud et al.	Species list
	Western Amazonia	24 (3)	22
	Brazilian Shield	12 (3)	15
	Guiana Shield	6 (1)
	Northern Andes	6 (3)	2	Allobates algorei, Allobates bromelicola, Allobates humilis, Allobates mandelorum, Allobates mcdiarmidi, Allobates pittieri.
	trans-Andean forests	4	2	Allobates ignotus, Allobates niputidea, Allobates talamancae, Allobates wayuu.
	Cerrado	1	1	Allobates goianus.
	lesser Antilles	1	1	Allobates chalcopis.
	Atlantic forest	1	4	Allobates olfersioides.

Allobates caerulodactylus, Allobates conspicuus, Allobates femoralis, Allobates flaviventris, Allobates fratisenescus, Allobates fuscellus, Allobates gasconi, Allobates hodli, Allobates insperatus, Allobates juami, Allobates juanii, Allobates kingsburyi, Allobates marchesianus, Allobates melanolaemus, Allobates myersi, Allobates nidicola, Allobates ornatus, Allobates paleovarzensis, Allobates ranoides, Allobates subfolonidificans, Allobates tinae, Allobates trilineatus, Allobates vanzolinius, Allobates zaparo. Allobates bacurau, Allobates brunneus, Allobates carajas, Allobates crombiei, Allobates femoralis, Allobates fuscellus, Allobates grillisimilis, Allobates magnussoni, Allobates masniger, Allobates nunciatus, Allobates pacaas, Allobates tapajos. 9 Allobates amissibilis, Allobates femoralis, Allobates granti, Allobates sanmartini, Allobates sumtuosus, Allobates undulatus.

Table II .

 II S2: 16S DNA sequence information and delimitation results for the genus Allobates. GeneBank accession numbers are in the column labelled GB. Specimen numbers are indicated in the voucher column. Specimen identification prior and posterior to delimitation analyses are indicated in the ID_initial and ID_post_delimitation columns respectively. Species assignation of each sequence per delimitation method are indicated in columns GMYC, ABGD_P17 and mPTP. The consensus of the three delimitation methods is indicated in the consensus column. Locality, country and state are indicated in said columns. Spatial coordinates corresponding to localities are found in the latitude and longitude columns.

	KDQF01001236 KU495120 MT524158 MT524154 KDQF01000397 DQ502220 GU017435 GU017466 JF689948 JF689979 JF690010 JN690960 KDQF01000263 KDQF01001259 KDQF01002648 KF310914 KF310945 KF310976 KF311007 MF624177 JN690923 MT524136 HQ290963 KDQF01003215 JQ966837 KDQF01003326 DQ502057 AY364579	AF3095 CFBHT01191 PEP532 MTR10146 AF1097 MPEG12021 AbuE2220 Acre12938 MorriD2137 Democ12616 Manic12558 VOGT2050 AF0832 AF3137 HJ390 APL14109 APL14439 APL12269 APL15681 TG1855 BPN1054 IDLAR8200 ???? MTR12717 INPAH29071 MTR15537 362 QCAZ16604	Allobates_granti Allobates_olfersioides Allobates_trilineatus Colostethus_sp. Allobates_crombiei Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_granti Allobates_sp. Allobates_kingsburyi Allobates_masniger Allobates_paleovarzensis Allobates_sp.Neblina Allobates_talamancae Allobates_zaparo	Allobates_aff.granti Allobates_aff.olfersioides_1 Allobates_aff.trilineatus_1 Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_granti Allobates_insperatus/juami Allobates_kingsburyi Allobates_masniger/nidicola Allobates_paleovarzensis Allobates_sp."Neblina" Allobates_talamancae Allobates_zaparo	16 42 22 31 33 1 4 4 1 1 1 1 1 1 4 1 1 1 1 2 17 11 37 10 24 40 5 3	Group[34] Group[46] Group[49] Group[35] Group[22] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[34] Group[31] Group[1] Group[20] Group[6] Group[42] Group[38] Group[33]	Species9 Species41 Species13 Species24 Species27 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species10 Species 3 Species29 Species31 Species17 Species40 Species32 Species 1	17 30 47 6 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 16 20 22 26 34 36 41 50	Mitan G Passo de Camarajibe Nva. Arequipa. Dto. Inambari. Prov. Tambopata Cachoeira das Pombas, Margem direita Rio Aripuana Babaculandia Rio Curua-Una, 101 km S and 15 km E Santarem Abuna Brasileia Morrinhos direita madeira Democracia Manicore Boca do Juma, M.E. Aripuana Chutes Voltaire Mitan D UHE Jirau, Mutum BR-230 road, west of Pacaja Near ferry harbor across the Xingu River, Altamira Fazenda Treviso, Belterra Vila de Balbina, Presidente Figueiredo Caqueta, San Jose del Fragua, La Esmeralda Lely Mountain Arboretum de la UNAM Zamora Chinchipe: Río Chicaña Sao Sebastiao, Rio Abacaxis Careiro Sao Gabriel da Cachoeira, Pico da Neblina Napo Ahuano	France Brazil Peru Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil France Brazil Brazil Brazil Brazil Brazil Brazil Colombia Suriname Peru Ecuador Brazil Brazil Brazil Nicaragua Ecuador	French_Guiana AL Madre_de_Dios AM TO PA RO AC RO AM AM AM French_Guiana AP RO PA PA PA AM Colombia Suriname Loreto Ecuador AM AM AM Nicaragua Ecuador	2.6284 -9.235 -12.907 -6.417609 -7.20472222 -3.150000 -9.516 -10.9653 -9.0158 -5.8058 -5.823100 -6.01472222 5.0291 2.62764 -9.591318 -3.577222 -3.140000 -3.15770 -1.91972 1.296183 4.2666667 -3.831 -3.764430 -4.308889 -3.3740 0.7195 10.933175 -1.055671	-52.5540 -35.483 -69.992 -60.358057 -47.7569444 -54.833333 -65.3249 -68.7331 -64.0914 -61.4453 -61.298600 -60.1791666 -54.0893 -52.54195 -65.049181 -51.04583 -51.74222 -54.85950 -59.41611 -76.074967 -54.7333333 -73.373 -78.765378 -58.636389 -59.8684 -66.087842 -84.300110 -77.548267
	KDQF01001239 KU495121 MT524166 DQ502100 KDQF01002964 DQ502228 GU017436 GU017467 JF689949 JF689980 JF690011 JN690961 KDQF01000339 KDQF01001272 KDQF01002651 KF310915 KF310946 KF310977 KF311008 MT524117 JN690924 MT524146 DQ502047 KDQF01003228 JQ966838 MT524137 DQ502102 DQ502022	AF3100 CFBHT07821 GGU1010 MPEG13809 MPEG33388 LSU12798 AbuE2231 Acre12939 MorriD2139 Democ12517 Manic12561 RCV2245 AF0942 AF3156 HJ416 APL14110 APL14440 APL12296 APL15682 CM388 BPN1055 NMP6V711401 OMNH34473 MTR13039 APL12631 GGU684 408 USNM546404	Allobates_granti Allobates_olfersioides Allobates_trilineatus Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_granti Allobates_sp. Allobates_magnussoni Allobates_masniger Allobates_paleovarzensis Allobates_sp. Allobates_talamancae Allobates_zaparo	Allobates_aff.granti Allobates_aff.olfersioides_1 Allobates_aff.trilineatus_1 Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_granti Allobates_insperatus/juami Allobates_magnussoni Allobates_masniger/nidicola Allobates_paleovarzensis Allobates_sp."Ucuyali" Allobates_talamancae Allobates_zaparo	16 42 22 31 33 2 4 4 1 1 1 1 1 1 4 1 1 1 1 1 17 11 8 10 24 45 5 3	Group[34] Group[46] Group[49] Group[35] Group[57] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[34] Group[31] Group[19] Group[20] Group[6] Group[15] Group[38] Group[33]	Species9 Species41 Species13 Species25 Species27 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species10 Species 3 Species34 Species31 Species17 Species7 Species32 Species 1	17 30 47 6 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 16 20 23 26 34 38 41 50	Mitan G Campo Alegre Nva. Arequipa. Dto. Inambari. Prov. Tambopata Castanho, ca. 40 km S Manaus APA Tapajos, Projeto Tocantizinho Cuyabeno Abuna Brasileia Morrinhos direita madeira Democracia Manicore Itapinima, Margem Direita Rio Madeira Matoury , Lac des Americains Mitan D UHE Jirau, Mutum Assentamento Raio de Sol, east of Pacaja Near ferry harbor across the Xingu River, Altamira Fazenda Treviso, Belterra Vila de Balbina, Presidente Figueiredo Lely Mountain Arboretum de la UNAM Rio Curua-Una, 101 km S and 15 km E Santarem Pacamiri, Rio Abacaxis Careiro Tamaya. Provincia Imiria	France Brazil Peru Brazil Brazil Ecuador Brazil Brazil Brazil Brazil Brazil Brazil France Brazil Brazil Brazil Brazil Brazil Brazil France Suriname Peru Brazil Brazil Brazil Peru Nicaragua Ecuador	French_Guiana AL Madre_de_Dios AM PA Ecuador RO AC RO AM AM AM French_Guiana AP RO PA PA PA AM French_Guiana Suriname Loreto PA AM AM Ucayali Nicaragua Ecuador	2.6284 -9.777 -12.907 -3.619444 -6.061700 -0.0886 -9.516 -10.9653 -9.0158 -5.8058 -5.823100 -5.4244444 4.852891 2.62764 -9.591318 -3.729722 -3.140000 -3.15770 -1.91972 4.2666667 -3.831 -3.150000 -4.33333333 -3.3740 -8.902762 10.933175 -1.116975	-52.5540 -36.33 -69.992 -60.446333 -56.296620 -76.1419 -65.3249 -68.7331 -64.0914 -61.4453 -61.298600 -60.715 -52.352092 -52.54195 -65.049181 -50.21750 -51.74222 -54.85950 -59.41611 -54.7333333 -73.373 -54.833333 -58.1333333 -59.8684 -74.277887 -84.300110 -76.949831
	KDQF01001444 KU495122 MT524168 DQ502141 KDQF01003148 DQ502231 GU017437 GU017468 JF689950 JF689981 JF690012 JN690962 KDQF01000361 KDQF01001284 KDQF01002725 KF310916 KF310947 KF310978 KF311009 AF124114 JN690925 MT524147 DQ502132 KDQF01003229 JQ966839 MT524151 DQ502166 DQ502026	AF3635 CFBHT01886 MHNC8329 OMNH37410 MTR07509 OMNH36073 MutE2252 Acre12944 MorriD2147 Democ12620 Arip12539 MTR06376 AF0999 AF3171 LÇ25 APL14111 APL14442 APL12300 APL15683 ???? BPN1056 NMP6V711403 MPEG11923 MTR13041 APL12632 MTKD47771 1147 USNM546405	Allobates_granti Allobates_olfersioides Allobates_trilineatus Allobates_caeruleodactylus Allobates_crombiei Allobates_aff.hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_cf.flaviventris Allobates_granti Allobates_sp. Allobates_magnussoni Allobates_masniger Allobates_paleovarzensis Allobates_granti Allobates_talamancae Allobates_zaparo	Allobates_aff.granti Allobates_aff.olfersioides_1 Allobates_aff.trilineatus_1 Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_flaviventris Allobates_granti Allobates_insperatus/juami Allobates_magnussoni Allobates_masniger/nidicola Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae Allobates_zaparo	16 42 22 31 33 4 4 4 1 1 1 1 1 1 1 1 1 1 1 7 17 11 8 10 24 25 5 3	Group[34] Group[46] Group[49] Group[35] Group[22] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[18] Group[34] Group[31] Group[19] Group[20] Group[6] Group[9] Group[38] Group[33]	Species9 Species41 Species13 Species25 Species27 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species36 Species10 Species 3 Species34 Species31 Species17 Species18 Species32 Species 1	17 30 47 6 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 16 20 23 26 34 40 41 50	Itoupe Passo de Camarajibe Nva. Arequipa. Dto. Inambari. Prov. Tambopata Castanho, ca. 40 km S Manaus Guarai Porto Walter Mutum-Parana Brasileia Morrinhos direita madeira Democracia Novo Aripuana Igarape Camaipi, Rio Maraca Saul, Galbao layon acces Mitan D Lourenco, Calcoene Assentamento Raio de Sol, east of Pacaja Near ferry harbor across the Xingu River, Altamira Fazenda Treviso, Belterra Vila de Balbina, Presidente Figueiredo Cobija Lely Mountain Arboreto de la Universidad de Iquitos Rio Curua-Una Pacamiri, Rio Abacaxis Careiro Sao Joao de Baliza, BR 210 Coclé: El Copé	France Brazil Peru Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil France Brazil Brazil Brazil Brazil Brazil Brazil Bolivia Suriname Peru Brazil Brazil Brazil Brazil Panama Ecuador	French_Guiana AL Madre_de_Dios AM TO AC RO AC RO AM AM AP French_Guiana AP AP PA PA PA AM Bolivia Suriname Loreto PA AM AM Roraima Panama Ecuador	3.02302 -9.235 -12.937 -3.619444 -8.83416666 -8.266389 -9.6414 -10.9653 -9.0158 -5.8058 -5.1503 -0.0241667 3.63592 2.62764 2.316750 -3.729722 -3.140000 -3.15770 -1.91972 -11.0333 4.2666667 -3.831 -3.150000 -4.33333333 -3.3740 0.953 8.690095 -1.116975	-53.0954 -35.483 -72.963 -60.446333 -48.5102777 -72.743611 -64.8859 -68.7331 -64.0914 -61.4453 -60.3467 -51.8972222 -53.272562 -52.54195 -51.628528 -50.21750 -51.74222 -54.85950 -59.41611 -68.75 -54.7333333 -73.373 -54.833333 -58.1333333 -59.8684 -59.929 -80.633854 -76.949831
	KDQF01001578 DQ502126 MT524169 DQ502186 KDQF01003152 DQ502246 GU017438 GU017469 JF689951 JF689982 JF690013 JN690963 KDQF01000371 KDQF01001312 KDQF01002852 KF310917 KF310948 KF310979 KF311010 DQ502184 JN690926 MT524149 DQ502133 KDQF01004432 JQ966840 MT524152 EU342509 DQ523059	AG393 MTR06031 MHNC8330 MPEG13808 MTR07689 UTAA56478 MutE2255 Acre12945 MorriE2301 Democ12621 Arip12540 CM427 AF1019 AF3224 MPEG29445 APL14112 APL14449 APL12301 APL15684 OMNH36959 BPN0778 NMP6V711405 OMNH34460 VOGT2032 APL954 MTR10259 TNHCFS4803 AfemNautv5a	Allobates_granti Allobates_alagoanus Allobates_trilineatus Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis Allobates_femoralis Allobates_hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_cf.flaviventris Allobates_granti Allobates_marchesianus Allobates_magnussoni Allobates_masniger Allobates_paleovarzensis Allobates_spumaponens Allobates_talamancae Allobates_zaparo	Allobates_aff.granti Allobates_aff.olfersioides_2 Allobates_aff.trilineatus_1 Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_flaviventris Allobates_granti Allobates_insperatus/juami Allobates_magnussoni Allobates_masniger/nidicola Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae Allobates_zaparo	16 41 22 31 33 1 4 4 1 1 1 1 1 1 1 1 1 1 1 7 17 11 8 10 24 25 5 3	Group[34] Group[44] Group[49] Group[35] Group[22] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[18] Group[34] Group[31] Group[19] Group[20] Group[6] Group[9] Group[64] Group[33]	Species9 Species42 Species13 Species25 Species27 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species36 Species10 Species 3 Species34 Species31 Species17 Species18 Species32 Species 1	17 31 47 6 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 16 20 23 26 34 40 41 50	Dekou dekou Sao Jose da Vitoria, Fazenda Unacau Nva. Arequipa. Dto. Inambari. Prov. Tambopata Castanho, ca. 40 km S Manaus Babaculandia Kayser airstrip Mutum-Parana Brasileia Morrinhos esquerda Madeira Democracia Novo Aripuana Molokoi Saul, Galbao layon acces Trois paletuviers Trombetas, FLOTA Assentamento Raio de Sol, east of Pacaja Near ferry harbor across the Xingu River, Altamira Fazenda Treviso, Belterra Vila de Balbina, Presidente Figueiredo Parque Estadual Guajara-Mirim Ralleighvallen Puerto Almendras Rio Curua-Una Aripuana Janauaca Mabura Hill Forest Reserve, Guyana Panama Lago Bayano Iquitos	France Brazil Peru Brazil Brazil Suriname Brazil Brazil Brazil Brazil Brazil France France France Brazil Brazil Brazil Brazil Brazil Brazil Suriname Peru Brazil Brazil Brazil Guyana Panama Peru	French_Guiana BA Madre_de_Dios AM TO Suriname RO AC RO AM AM French_Guiana French_Guiana French_Guiana PA PA PA PA AM RO Suriname Loreto PA AM AM Guyana Panama Loreto	4.696879 -15.069267 -12.937 -3.619444 -7.20472222 3.048297 -9.6414 -10.9653 -9.0199 -5.8058 -5.1503 4.566667 3.63592 4.054582 -0.9627694 -3.729722 -3.140000 -3.15770 -1.91972 -10.321389 4.7166667 -3.817 -3.150000 -5.56611111 -3.4792 5.155 9.163211 -4.226749	-53.959808 -39.360852 -72.96 -60.446333 -47.7569444 -56.578653 -64.8859 -68.7331 -64.2172 -61.4453 -60.3467 -52.466667 -53.272562 -51.677007 -55.5223 -50.21750 -51.74222 -54.85950 -59.41611 -64.563333 -56.2166667 -73.367 -54.833333 -60.4138888 -60.2031 -58.7 -78.756707 -73.474988
	KDQF01001631 KDQF01003344 MT524170 DQ502212 KDQF01003153 DQ523023 GU017439 GU017470 JF689952 JF689983 JF690014 JQ436705 KDQF01000389 KDQF01001325 KDQF01003047 KF310918 KF310949 KF310980 KF311011 DQ502214 KDQF01000062 MT524165 DQ502189 KDQF01004444 JQ966841 MT524176 EU342510 DQ523062	AG467 MTR16435 MHNC8339 OMNH37411 MTR07690 AfemRSucv4a MutE2256 CSul12946 MorriE2302 Democ12622 Arip12542 APL2014 AF1077 AF3251 MTD47883 APL14113 APL14450 APL12303 APL15685 OMNH36993 AF0154 AJC2589 MPEG11921 VOGT2110 INPAH29185 AMNH20211 TNHCFS4993 AfemItaya2iii7d	Allobates_granti Allobates_olfersioides Allobates_trilineatus Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis Allobates_femoralis Allobates_aff.hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_cf.flaviventris Allobates_granti Allobates_marchesianus Allobates_magnussoni Allobates_nidicola Allobates_paleovarzensis Colostethus_sp. Allobates_talamancae Allobates_zaparo	Allobates_aff.granti Allobates_aff.olfersioides_2 Allobates_aff.trilineatus_1 Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_flaviventris Allobates_granti Allobates_insperatus/juami Allobates_magnussoni Allobates_masniger/nidicola Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae Allobates_zaparo	16 41 22 31 33 1 4 4 1 1 1 4 1 1 1 1 1 1 1 7 17 11 8 9 24 25 5 3	Group[34] Group[44] Group[49] Group[35] Group[22] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[18] Group[34] Group[31] Group[19] Group[20] Group[6] Group[9] Group[66] Group[33]	Species9 Species42 Species13 Species25 Species27 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species36 Species10 Species 3 Species34 Species31 Species17 Species18 Species32 Species 1	17 31 47 6 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 16 20 23 26 34 40 41 50	Alikene Itacare, Fazenda das Tres Barras Nva. Arequipa. Dto. Inambari. Prov. Tambopata Castanho, ca. 40 km S Manaus Babaculandia Madre de Dios Mutum-Parana Cruzeiro do Sul Morrinhos esquerda Madeira Democracia Novo Aripuana Cachoeira do Jirau Oiapoque, layon inselberg Trois paletuviers Iwokrama Assentamento Raio de Sol, east of Pacaja Near ferry harbor across the Xingu River, Altamira Fazenda Treviso, Belterra Vila de Balbina, Presidente Figueiredo Parque Estadual Guajara-Mirim Brownsberg Puerto Almendras Rio Curua-Una Cachoeirinha, Margem Esquerda Rio Madeira Janauaca Igarape-Araras Choco Quibdo, La Troje Iquitos	France Brazil Peru Brazil Brazil Peru Brazil Brazil Brazil Brazil Brazil Brazil Brazil France Guyana Brazil Brazil Brazil Brazil Brazil Suriname Peru Brazil Brazil Brazil Brazil Colombia Peru	French_Guiana BA Madre_de_Dios AM TO Madre_de_Dios RO AC RO AM AM RO AP French_Guiana Guyana PA PA PA AM RO Suriname Loreto PA AM AM AM Colombia Loreto	3.220044 -14.365513 -12.937 -3.619444 -7.20472222 -12.833333 -9.6414 -7.720152 -9.0199 -5.8058 -5.1503 -9.3347 3.862713 4.054582 4.6713889 -3.729722 -3.140000 -3.15770 -1.91972 -10.321389 4.936527 -3.817 -3.150000 -5.4944444 -3.4792 -3.014 5.728 -4.226749	-52.37855 -39.108696 -72.822 -60.446333 -47.7569444 -69.283333 -64.8859 -72.636981 -64.2172 -61.4453 -60.3467 -64.7375 -51.772056 -51.677007 -58.685 -50.21750 -51.74222 -54.85950 -59.41611 -64.563333 -55.194826 -73.367 -54.833333 -60.8230556 -60.2031 -60.398 -76.591 -73.474988
	GB	Voucher	ID_initial	ID_post_delimitation	GMYC	ABGD	mPTP	Consensus	Locality	Country	State	Latitude	Longitude
	KDQF01001634 KDQF01003547 MT524171 EU342541 KF250508 DQ523025 GU017440 GU017471 JF689953 JF689984 JF690015 JQ436706 KDQF01000406 KDQF01001326 KDQF01003234 KF310919 KF310950 KF310981 KJ130696 KDQF01002619 KDQF01000064 MT524167 DQ502196 KDQF01004445 JQ966842 DQ502115 EU342511 EU342530	AG476 MTR22221 MHNC8349 LSUMZ16929 APL13458 AfemTahuiv1b MutE2258 CSul12950 MorriE2308 Careiro12701 Arip12543 APL13419 AF1104 AF3252 MTR13137 APL14119 APL14451 APL12623 AJC3598 H4582 AF0158 GGU1040 OMNH34472 VOGT2111 INPAH29145 MJH3988 QCAZ16551 QCAZ25212	Allobates_granti Allobates_olfersioides Allobates_trilineatus Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis Allobates_femoralis Allobates_aff.hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_flaviventris Allobates_granti Allobates_marchesianus Allobates_magnussoni Allobates_nidicola Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae Allobates_zaparo	Allobates_aff.granti Allobates_aff.olfersioides_2 Allobates_aff.trilineatus_1 Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_flaviventris Allobates_granti Allobates_insperatus/juami Allobates_magnussoni Allobates_masniger/nidicola Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae Allobates_zaparo	16 41 22 31 33 1 4 4 1 1 1 4 1 1 1 1 1 1 1 7 17 11 8 9 24 25 5 3	Group[34] Group[45] Group[49] Group[35] Group[22] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[18] Group[34] Group[31] Group[19] Group[20] Group[6] Group[9] Group[63] Group[33]	Species9 Species42 Species13 Species25 Species27 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species36 Species10 Species 3 Species34 Species31 Species17 Species18 Species32 Species 1	17 31 47 6 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 16 20 23 26 34 40 41 50	Saul, Monts Belvedere Estacao Ecologica Estadual Wenceslau Guimaraes Nva. Arequipa. Dto. Inambari. Prov. Tambopata Castanho, ca. 40 km S Manaus Cachoeira do Espelho, Altamira, Iquitos Mutum-Parana Cruzeiro do Sul Morrinhos esquerda Madeira Careiro Novo Aripuana Cachoeira do Jirau Mana , Ilet Lezard rive G Trois paletuviers Pacamiri, Rio Abacaxis Assentamento Raio de Sol, east of Pacaja Near ferry harbor across the Xingu River, Altamira Nova Olinda do Norte Reserva Tanimboca UHE Jirau, Mutum Brownsberg Puerto Almendras Rio Curua-Una Cachoeirinha, Margem Esquerda Rio Madeira Codajas Manaus Pichincha Rio Sabalo Pastaza Kapawi Lodge	France Brazil Peru Brazil Brazil Peru Brazil Brazil Brazil Brazil Brazil Brazil France France Brazil Brazil Brazil Brazil Colombia Brazil Suriname Peru Brazil Brazil Brazil Brazil Ecuador Ecuador	French_Guiana BA Madre_de_Dios AM PA Loreto RO AC RO AM AM RO French_Guiana French_Guiana AM PA PA AM Colombia RO Suriname Loreto PA AM AM AM Ecuador Ecuador	3.719347 -13.5952778 -12.937 -3.619444 -3.650000 -4.226749 -9.6414 -7.720152 -9.0199 -3.3708 -5.1503 -9.3347 5.054481 4.054582 -4.33333333 -3.729722 -3.140000 -3.87440 -4.11667 -9.59131808 4.936527 -3.817 -3.150000 -5.4944444 -3.7715 -2.980833 0.081266 -2.522762	-53.412809 -39.7194444 -72.833 -60.446333 -52.375833 -73.474988 -64.8859 -72.636981 -64.2172 -59.8683 -60.3467 -64.7375 -53.8007 -51.677007 -58.1333333 -50.21750 -51.74222 -59.04610 -69.95 -65.0491813 -55.194826 -73.367 -54.833333 -60.8230556 -62.0205 -59.921111 -79.049281 -76.760895
	AY263233	RBE495	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Saul	France	French_Guiana	3.625556	-53.207222
	KDQF01001639 KDQF01002701 MT524172 EU342542 KF250509 DQ523040 GU017441 GU017472 JF689954 JF689985 JF690016 JQ436707 KDQF01000411 KDQF01001327 KDQF01003381 KF310920 KF310951 KF310982 KR811190 KDQF01002642 KDQF01000783 AF128583 DQ502203 KDQF01004446 JQ966843 KC520682 EU342512 EU342531	AG482 JFT959 MHNC8364 LSUMZ16926 APL13462 AfemRManv12 MutE2259 CSul12951 MorriE2310 Careiro12704 Arip12544 APL13260 AF1110 AF3253 MTR18652 APL14120 APL14452 APL12624 AF0557 HJ348 AF1998 ???? OMNH34468 VOGT2112 INPAH29184 MTD47771 LSUMZ15094 QCAZ16602	Allobates_granti Allobates_capixaba Allobates_conspicuus Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis Allobates_femoralis Allobates_aff.hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_flaviventris Allobates_granti Allobates_aff.insperatus Allobates_magnussoni Allobates_nidicola Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae Allobates_zaparo	Allobates_aff.granti Allobates_aff.olfersioides_3 Allobates_aff.trilineatus_1 Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_flaviventris Allobates_granti Allobates_insperatus/juami Allobates_magnussoni Allobates_masniger/nidicola Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae Allobates_zaparo	16 51 22 31 33 1 4 4 1 1 1 4 1 1 1 1 1 1 1 7 17 11 8 9 24 25 5 3	Group[34] Group[48] Group[49] Group[35] Group[22] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[18] Group[34] Group[31] Group[19] Group[20] Group[6] Group[9] Group[38] Group[33]	Species9 Species43 Species13 Species25 Species27 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species36 Species11 Species 3 Species34 Species31 Species17 Species18 Species32 Species 1	17 32 47 6 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 16 20 23 26 34 40 41 50	Saul, Monts Belvedere Serra Nva. Arequipa. Dto. Inambari. Prov. Tambopata Castanho, ca. 40 km S Manaus Cachoeira do Espelho, Altamira, Iquitos Mutum-Parana Cruzeiro do Sul Morrinhos esquerda Madeira Careiro Novo Aripuana Cachoeira do Jirau Mana, vers projet barrage rive G Trois paletuviers Rio Purus, Rio Purus Assentamento Raio de Sol, east of Pacaja Near ferry harbor across the Xingu River, Altamira Nova Olinda do Norte Savane virginie, layon UHE Jirau, Mutum Sipaliwini, Apalagadi Pompeya Rio Curua-Una Cachoeirinha, Margem Esquerda Rio Madeira Janauaca Mabura hill forest reserve Rio San Juan Isla de Diamante Morona Santiago Rio Yaupi, Jemkepeket	France Brazil Peru Brazil Brazil Peru Brazil Brazil Brazil Brazil Brazil Brazil France France Brazil Brazil Brazil Brazil France Brazil Suriname Ecuador Brazil Brazil Brazil Guyana Nicaragua Ecuador	French_Guiana ES Madre_de_Dios AM PA Loreto RO AC RO AM AM RO French_Guiana French_Guiana AM PA PA AM French_Guiana RO Suriname Ecuador PA AM AM Guyana Nicaragua Ecuador	3.719347 -20.156990 -12.937 -3.619444 -3.650000 -4.226749 -9.6414 -7.720152 -9.0199 -3.3708 -5.1503 -9.3347 5.099493 4.054582 -4.18314 -3.729722 -3.140000 -3.87440 4.1959 -9.591318 2.1728 -0.491 -3.150000 -5.4944444 -3.4792 5.1552778 11.015954 -3.60	-53.412809 -40.315675 -72.833 -60.446333 -52.375833 -73.474988 -64.8859 -72.636981 -64.2172 -59.8683 -60.3467 -64.7375 -53.800571 -51.677007 -61.48801 -50.21750 -51.74222 -59.04610 -52.149 -65.049181 -56.0969 -76.559 -54.833333 -60.8230556 -60.2031 -58.6997222 -84.381730 -78.54
	EU201066	CM185	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Trijonction	France	French_Guiana	2.333333	-54.6
	KDQF01001652 JN690935 MT524173 KDQF01003183 KJ747342 DQ523072 GU017442 GU017473 JF689955 JF689986 JF690017 JQ436708 KDQF01000443 KDQF01001409 KDQF01003398 KF310921 KF310952 KF310983 KR811191 KDQF01002649 KDQF01000792 AY364557 DQ502205 KJ747332 JQ966844 KDQF01000875 EU342513 HQ291003	AG506 MTR10084 MHNC8367 MTR10227 APL14115 AfemShucv3a MutE2261 CSul12947 MorriE2311 Careiro12706 Arip12546 APL13410 AF1200 AF3418 MTR18799 APL14123 APL14478 APL12627 AF0753 HJ391 AF2015 QCAZ16533 OMNH34461 APL12960 INPAH29117 AF2212 LSUMZ15095 ????	Allobates_granti Allobates_aff.tapajos3 Allobates_trilineatus Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis Allobates_femoralis Allobates_aff.hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_flaviventris Allobates_granti Allobates_insperatus Allobates_magnussoni Allobates_masniger Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae Allobates_zaparo	Allobates_aff.granti Allobates_aff.tapajos_1 Allobates_aff.trilineatus_1 Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_flaviventris Allobates_granti Allobates_insperatus/juami Allobates_magnussoni Allobates_masniger/nidicola Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae Allobates_zaparo	16 29 22 31 33 1 4 4 1 1 1 4 1 1 1 1 1 1 1 7 17 12 8 10 24 25 5 3	Group[34] Group[61] Group[49] Group[35] Group[22] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[18] Group[34] Group[31] Group[19] Group[20] Group[6] Group[9] Group[38] Group[33]	Species9 Species21 Species13 Species24 Species27 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species36 Species11 Species 3 Species34 Species31 Species17 Species18 Species32 Species 1	17 43 47 6 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 16 20 23 26 34 40 41 50	Saul, Monts Belvedere Santa Maria, Margem esquerda Rio Aripuana Nva. Arequipa. Dto. Inambari. Prov. Tambopata Igarape Acu, Rio Abacaxis Fazenda Raio de Sol Yurimaguas Mutum-Parana Cruzeiro do Sul Morrinhos esquerda Madeira Careiro Novo Aripuana Cachoeira do Jirau Trinite, aya, camp Bakhuis, near camp Rio Purus Assentamento Raio de Sol, east of Pacaja Volta Grande area, Altamira Nova Olinda do Norte St Georges, Saut Maripa UHE Jirau, Mutum Sipaliwini, Apalagadi Estacion Cientifica Yasuni, km 1 Rio Curua-Una Itaituba, Parque Nacional da Amazonia Manaquiri Sipaliwini Rio San Juan Isla de Diamante Napo: Jatun Sacha, via Ahuano	France Brazil Peru Brazil Brazil Peru Brazil Brazil Brazil Brazil Brazil Brazil France Suriname Brazil Brazil Brazil Brazil France Brazil Suriname Ecuador Brazil Brazil Brazil Suriname Nicaragua Ecuador	French_Guiana AM Madre_de_Dios AM PA Loreto RO AC RO AM AM RO French_Guiana Suriname AM PA PA AM French_Guiana RO Suriname Ecuador PA PA AM Suriname Nicaragua Ecuador	3.719347 -5.7977778 -12.937 -4.34416666 -3.729722 -5.908000 -9.6414 -7.720152 -9.0199 -3.3708 -5.1503 -9.3347 4.6025 4.72462 -4.18314 -3.729722 -3.127500 -3.87440 3.811159 -9.591318 2.1728 -0.68931 -3.150000 -4.169335 -3.4328 2.03253 11.015954 -1.085882	-53.412809 -60.2652778 -72.833 -58.635 -50.217500 -76.115685 -64.8859 -72.636981 -64.2172 -59.8683 -60.3467 -64.7375 -53.4143 -56.7638 -61.48801 -50.21750 -51.70972 -59.04610 -51.889644 -65.049181 -56.0969 -76.42903 -54.833333 -55.981064 -60.6122 -56.11449 -84.381730 -77.616249
	EU201067	RB38	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	St Eugene	France	French_Guiana	4.85	-53.0666667
	KDQF01001869 AY263232 MT524174 KDQF01003184 KJ747343 DQ523082 GU017443 GU017474 JF689956 JF689987 JN690937 JQ436709 KDQF01000485 KDQF01001609 KDQF01003417 KF310922 KF310953 KF310984 KR811192 KDQF01002663 KDQF01000793 AY364561 DQ502206 MT524124 JQ966845 KDQF01000876 EU342514	BOAM025 RBE492 MHNC8371 MTR10228 APL14116 AfemSapoiv10a JirE2006 Trev12266 MorriE2312 Careiro13119 CM370 APL13407 AF1291 AG440 MTR19082 APL14121 APL14479 APL12628 MTR24184 HJ545 AF2016 QCAZ16609 OMNH34471 RCV2243 INPAH29114 AF2213 KS16	Allobates_granti Allobates_aff.tapajos4 Allobates_sp. Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_flaviventris Allobates_granti Allobates_aff.insperatus Allobates_magnussoni Allobates_nidicola Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae	Allobates_aff.granti Allobates_aff.tapajos_2 Allobates_aff.trilineatus_1 Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_flaviventris Allobates_granti Allobates_insperatus/juami Allobates_magnussoni Allobates_masniger/nidicola Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae	16 27 22 31 33 1 4 1 1 1 1 4 1 1 1 1 1 1 1 7 17 11 8 9 24 25 5	Group[34] Group[27] Group[49] Group[35] Group[22] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[18] Group[34] Group[31] Group[19] Group[20] Group[6] Group[9] Group[38]	Species9 Species22 Species13 Species24 Species27 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species36 Species11 Species 3 Species34 Species31 Species17 Species18 Species32	17 44 47 6 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 16 20 23 26 34 40 41	Borne 4 Saul Yavari Miri Igarape Acu, Rio Abacaxis Fazenda Raio de Sol upstream of Cachoeira do Jirau Belterra, Fazenda Treviso Morrinhos esquerda Madeira Careiro Oiapoque Cachoeira do Jirau Nouragues, Parare G entre 2 camps Alikene Rio Purus, Moiobamba, margem direita, Rio Purus Assentamento Raio de Sol, east of Pacaja Volta Grande area, Altamira Nova Olinda do Norte Oiapoque UHE Jirau, Mutum Sipaliwini, Apalagadi Pastaza Puyo-Macas Rio Curua-Una Itapinima, Margem Direita Rio Madeira Manaquiri Sipaliwini San Blas Nusagandi	France France Peru Brazil Brazil Peru Brazil Brazil Brazil Brazil France Brazil France France Brazil Brazil Brazil Brazil Brazil Brazil Suriname Ecuador Brazil Brazil Brazil Suriname Panama	French_Guiana French_Guiana Loreto AM PA Loreto RO PA RO AM French_Guiana RO French_Guiana French_Guiana AM PA PA AM AP RO Suriname Ecuador PA AM AM Suriname Panama	2.3709 3.625556 -4.259917 -4.34416666 -3.729722 -6.77 -9.3347 -3.15770 -9.0199 -3.3708 3.816667 -9.3347 4.04218 3.220044 -4.720095 -3.729722 -3.127500 -3.87440 3.7960278 -9.591318 2.1728 -1.49 -3.150000 -5.4244444 -3.4328 2.03253 9.2994	-53.7728 -53.207222 -70.141172 -58.635 -50.217500 -76.94 -64.7375 -54.8595 -64.2172 -59.8683 -51.833333 -64.7375 -52.67664 -52.37855 -62.133036 -50.21750 -51.70972 -59.04610 -51.8629167 -65.049181 -56.0969 -77.98 -54.833333 -60.715 -60.6122 -56.11449 -78.987
	JN690927	CM231	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Saul	France	French_Guiana	3.625556	-53.207222
	KDQF01001872 EU201069 MT524175 KDQF01004182 KJ747344 EU201064 GU017444 GU017475 JF689957 JF689988 JN690938 JQ436710 KDQF01000490 KDQF01001633 KDQF01003424 KF310923 KF310954 KF310985 KX044265 KDQF01003624 KDQF01000968 DQ502239 DQ502211 MT524148 JQ966846 KDQF01000877 EU342515	BOAM029 AG092 MHNC8372 RCV2252 APL14117 AF0056 JirEA2023 Trev12270 MorriE2316 Careiro13122 MTR13726 APL2292 AF1323 AG471 MTR19170 APL14122 APL12277 APL12548 Careiro12702 MTR25599 AF2476 LSUMZ12948 OMNH34474 NMP6V711404 INPAH29124 AF2215 TNHCFS4802	Allobates_granti Allobates_aff.tapajos4 Allobates_sp. Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_flaviventris Allobates_granti Allobates_aff.insperatus Allobates_magnussoni Allobates_marchesianus Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae	Allobates_aff.granti Allobates_aff.tapajos_2 Allobates_aff.trilineatus_1 Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_flaviventris Allobates_granti Allobates_insperatus/juami Allobates_magnussoni Allobates_melanolaemus Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae	16 27 22 31 33 1 4 1 1 1 1 4 1 1 1 1 1 1 1 7 17 11 8 49 24 25 5	Group[34] Group[27] Group[50] Group[35] Group[22] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[18] Group[34] Group[31] Group[19] Group[7] Group[6] Group[9] Group[38]	Species9 Species22 Species13 Species24 Species27 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species36 Species10 Species 3 Species34 Species19 Species17 Species18 Species32	17 44 47 6 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 16 20 23 28 34 40 41	Borne 4 Saul, Crique Limonade Yavari Miri Itapinima, Margem Direita Rio Madeira Fazenda Raio de Sol Petit Saut upstream of Cachoeira do Jirau Belterra, Fazenda Treviso Morrinhos esquerda Madeira Careiro Serra do Navio Cachoeira do Jirau Saut Taconet Saul, Monts Belvedere Rio Purus, Moiobamba, margem direita, Rio Purus Assentamento Raio de Sol, east of Pacaja Itaituba Manicore Careiro Pacaas Novos, PARNA Nassau, trail camp Cuyabeno Rio Curua-Una Puerto Almendras Manaquiri Sipaliwini Cocle Cerro Gaital (CTF)	France France Peru Brazil Brazil France Brazil Brazil Brazil Brazil Brazil Brazil France France Brazil Brazil Brazil Brazil Brazil Brazil Suriname Ecuador Brazil Peru Brazil Suriname Panama	French_Guiana French_Guiana Loreto AM PA French_Guiana RO PA RO AM AP RO French_Guiana French_Guiana AM PA PA AM AM RO Suriname Ecuador PA Loreto AM Suriname Panama	2.3709 3.566667 -4.259917 -5.4244444 -3.729722 5.066667 -9.3347 -3.15770 -9.0199 -3.3708 0.9180556 -9.3347 4.03249 3.719347 -4.720095 -3.729722 -4.46220 -5.82310 -3.3708 -10.7906220 4.80262 -0.0886 -3.150000 -3.817 -3.4328 2.03253 8.667	-53.7728 -53.216667 -70.141172 -60.715 -50.217500 -53.05 -64.7375 -54.8595 -64.2172 -59.8683 -52.0027778 -64.7375 -52.526188 -53.412809 -62.133036 -50.21750 -56.28470 -61.29860 -59.8683 -63.6309949 -54.61502 -76.1419 -54.833333 -73.367 -60.6122 -56.11449 -80.592
	JN690928	PG301	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Haut Marwini	France	French_Guiana	2.6067	-54.0257
	KDQF01001888 KDQF01000622 DQ501986 KDQF01004429 KJ747345 EU201065 GU017445 GU017476 JF689958 JF689989 JN690939 JQ436711 KDQF01000573 KDQF01001657 KDQF01003427 KF310924 KF310955 KF310986 KX044266 KJ747336 KDQF01000971 HQ290959 DQ502216 DQ502272 JQ966847 KDQF01002090 EU342516	BOAM052 AF1658 74 VOGT2017 APL14118 CM303 JirEA2025 Trev12302 MorriE2320 Careiro13124 MTR11096 APL12865 AF1523 AG513 MTR19207 APL14126 APL12271 APL12549 Careiro12703 APL14523 AF2479 ???? OMNH34470 MUJ3520 APL12681 BPN3708 CH4998	Allobates_granti Allobates_tapajos Allobates_trilineatus Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_cf.flaviventris Allobates_granti Allobates_insperatus Allobates_magnussoni Allobates_niputidea Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae	Allobates_aff.granti Allobates_aff.tapajos_2 Allobates_aff.trilineatus_1 Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_flaviventris Allobates_granti Allobates_insperatus/juami Allobates_magnussoni Allobates_niputidea Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae	16 27 22 31 33 1 4 1 1 1 1 4 1 1 1 1 1 1 1 7 17 12 8 6 24 25 5	Group[34] Group[27] Group[49] Group[35] Group[22] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[18] Group[34] Group[31] Group[19] Group[39] Group[6] Group[9] Group[38]	Species9 Species22 Species13 Species24 Species27 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species36 Species10 Species 3 Species34 Species33 Species17 Species18 Species32	17 44 47 6 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 16 20 23 29 34 40 41	Borne 4 Saul, Limonade Lago Acai, Margem Esquerda Rio Aripuana Fazenda Raio de Sol Toponowini upstream of Cachoeira do Jirau Belterra, Fazenda Treviso Morrinhos esquerda Madeira Careiro Tapajos, FLONA Cachoeira do Jirau Montagne tortue grande Pic Coudreau du Sud Rio Purus Transcameta road, north of Novo Repartimento Itaituba Manicore Careiro Cachoeira do Jirau Nassau Rio Curua-Una Caldas, La Dorada, San Roque Manacapuru Parabara, Village Panama Rio Indio Arriba	France France Peru Brazil Brazil France Brazil Brazil Brazil Brazil Brazil Brazil France France Brazil Brazil Brazil Brazil Brazil Brazil Suriname Ecuador Brazil Colombia Brazil Guyana Panama	French_Guiana French_Guiana Madre_de_Dios AM PA French_Guiana RO PA RO AM PA RO French_Guiana French_Guiana AM PA PA AM AM RO Suriname Ecuador PA Colombia AM Guyana Panama	2.3709 3.56311 -12.61 -5.56611111 -3.729722 3.0521 -9.3347 -3.15770 -9.0199 -3.3708 -3.5 -9.3347 4.292663 2.2534 -4.702148 -3.541389 -4.46220 -5.82310 -3.3708 -9.3206 4.80262 -0.671316 -3.150000 5.666667 -3.1980 2.094500 8.762523	-53.7728 -53.19083 -68.99 -60.4138888 -50.217500 -52.705 -64.7375 -54.8595 -64.2172 -59.8683 -55.0666667 -64.7375 -52.349539 -54.3534 -62.309074 -49.74083 -56.28470 -61.29860 -59.8683 -64.7225 -54.61502 -76.400500 -54.833333 -74.766667 -60.4425 -59.240183 -80.149443
	JN690929	PG233	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Haute Wanapi	France	French_Guiana	2.5134	-53.8211
	KDQF01001989 KDQF01000741 DQ501998 KDQF01004430 KJ747346 EU342532 GU017446 GU017477 JF689959 JF689990 JN690940 JQ436712 KDQF01000575 KDQF01001662 KDQF01003566 KF310925 KF310956 KF310987 KX044267 KJ747337 KDQF01001414 MF624185 DQ502238 KP149362 JQ966848 KDQF01002954 EU342517	BPN1586 AF1906 KU215175 VOGT2019 APL14124 LSUMZ13667 BJirE2263 Trai12284 StAntE2154 Careiro13125 MTR13959 APL12879 AF1525 AGBM097 MTR24054 APL14127 APL12272 APL12550 Careiro12705 APL15137 AF3428 MZ010 OMNH34469 AJC3897 INPAH29135 MPEG30947 TNHCFS4801	Allobates_granti Allobates_tapajos Allobates_trilineatus Allobates_caeruleodactylus Allobates_crombiei Allobates_aff.hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_cf.flaviventris Allobates_granti Allobates_sp.AltoMazan Allobates_magnussoni Allobates_niputidea Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae	Allobates_aff.granti Allobates_aff.tapajos_2 Allobates_aff.trilineatus_1 Allobates_caeruleodactylus Allobates_crombiei Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_flaviventris Allobates_granti Allobates_insperatus/juami Allobates_magnussoni Allobates_niputidea Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae	16 27 22 31 33 4 1 1 1 1 1 4 1 1 1 1 1 1 1 7 17 12 8 6 24 25 5	Group[34] Group[27] Group[49] Group[35] Group[22] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[18] Group[34] Group[31] Group[19] Group[39] Group[6] Group[9] Group[38]	Species9 Species22 Species13 Species24 Species27 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species36 Species10 Species 3 Species34 Species33 Species17 Species18 Species32	17 44 47 6 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 16 20 23 29 34 40 41	Saul Flat de la Waki Cusco Amazonico, 15 km E Puerto Maldonado Lago Acai, Margem Esquerda Rio Aripuana Fazenda Raio de Sol Porto Walter downstream of Cachoeira do Jirau Trairao Santo Antonio Careiro Laranjal do Jari Cachoeira do Jirau Montagne tortue grande Monts Bakra Oiapoque Transcameta road, north of Novo Repartimento Itaituba Manicore Careiro Jaci-Parana Bakhuis, Slope 5k Alto Mazan Rio Curua-Una, 101 km S and 15 km E Santarem Novo Airao Serra do Acari. N ESEC Grao Para bocas del toro	France France Peru Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil France France Brazil Brazil Brazil Brazil Brazil Brazil Suriname Peru Brazil Colombia Brazil Brazil Panama	French_Guiana French_Guiana Madre_de_Dios AM PA AC RO PA RO AM AP RO French_Guiana French_Guiana AP PA PA AM AM RO Suriname Loreto PA Colombia AM PA Panama	3.625556 3.0835 -12.583 -5.56611111 -3.729722 -8.266389 -9.3114 -4.611843 -8.8309 -3.3708 -0.7166667 -9.3347 4.292663 3.3005 3.8355278 -3.541389 -4.46220 -5.82310 -3.3708 -9.1694 4.70646 -2.586111 -3.150000 7.3496 -3.1015 1.2854194 9.373852	-53.207222 -53.41257 -69.083 -60.4138888 -50.217500 -72.743611 -64.7172 -55.480257 -64.0206 -59.8683 -52.3833333 -64.7375 -52.349539 -52.9493 -51.8333333 -49.74083 -56.28470 -61.29860 -59.8683 -64.4289 -56.77943 -74.492222 -54.833333 -73.4981 -60.7878 -58.6959 -82.275711
	JN690930	CM302	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Toponowini	France	French_Guiana	3.0521	-52.705
	KDQF01001990 KDQF01000821 DQ502118 KDQF01001830 MT524110 EU342533 GU017447 GU017478 JF689960 JF689991 JN690941 JQ436713 KDQF01000576 KDQF01001665 KDQF01003664 KF310926 KF310957 KF310988 KX044268 KJ747338 KDQF01001415 MG243348 EU342522 KP149439 JQ966849 KDQF01002955 FJ784370 DQ502098	BPN1612 AF2072 527 BM163 AF4482 LSUMZ13633 BJirE2265 Trai12285 StAntE2155 Manaq13492 AF0246 APL2105 AF1527 AGBM106 MTR28151 APL14128 APL12274 APL12556 Careiro12707 APL15136 AF3429 MCP13290 LSUMZ15208 AJC3900 INPAH29171 MPEG30948 KRL796 MPEG12978	Allobates_granti Allobates_tapajos Allobates_trilineatus Allobates_crombiei Allobates_pongoensis Allobates_aff.hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_cf.flaviventris Allobates_granti Allobates_juami Allobates_magnussoni Allobates_niputidea Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae Allobates_sp.purus	Allobates_aff.granti Allobates_aff.tapajos_2 Allobates_aff.trilineatus_1 Allobates_carajas Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_flaviventris Allobates_granti Allobates_insperatus/juami Allobates_magnussoni Allobates_niputidea Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae Allobates_tinae	16 27 22 34 1 4 1 1 1 1 1 1 1 1 4 1 1 1 1 7 17 12 8 6 24 25 5 20	Group[34] Group[27] Group[49] Group[24] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[18] Group[34] Group[31] Group[19] Group[39] Group[6] Group[9] Group[38] Group[16]	Species9 Species22 Species13 Species27 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species36 Species10 Species 3 Species34 Species33 Species17 Species18 Species32 Species5	17 44 47 39 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 16 20 23 29 34 40 41 46	Saul Sipaliwini, Apalagadi Panguana UHE Belo Monte, Vitoria do Xingu, Rio Xingu Pongo-Barranquita Porto Walter downstream of Cachoeira do Jirau Trairao Santo Antonio Manacapuru Nouragues, Camp Inselberg Cachoeira de St. Antonio Savane Virginie Trinite, Crique Grand Leblond Serra do Divisor West of Novo Repartimento Itaituba Manicore Careiro Jaci-Parana Bakhuis, Slope 5k Japura, Estacao Ecologica Juami-Japura Rio Curua-Una, 101 km S and 15 km E Santarem Anama Serra do Acari. N ESEC Grao Para Parque Nacional G. D. Omar Torrijos Rio Ituxi at the Madeira Scheffer	France Suriname Peru Brazil Peru Brazil Brazil Brazil Brazil Brazil France Brazil France France Brazil Brazil Brazil Brazil Brazil Brazil Suriname Brazil Brazil Colombia Brazil Brazil Panama Brazil	French_Guiana Suriname Huanuco PA San_Martin AC RO PA RO AM French_Guiana RO French_Guiana French_Guiana AC PA PA AM AM RO Suriname AM PA Colombia AM PA Panama AM	3.625556 2.17816 -9.603932 -2.950584 -6.28639 -8.266389 -9.3114 -4.611843 -8.8309 -3.4272 4.083333 -8.6550 4.1959 4.6709 -7.44054897 -4.251111 -4.46220 -5.82310 -3.3708 -9.1694 4.70646 -1.96455 -3.150000 7.3496 -3.5145 1.2854194 8.667 -8.479444	-53.207222 -56.08521 -74.935799 -51.936607 -76.23164 -72.743611 -64.7172 -55.480257 -64.0206 -60.6150 -52.683333 -64.0195 -52.149 -53.2843 -73.6580170 -49.94500 -56.28470 -61.29860 -59.8683 -64.4289 -56.77943 -67.93579 -54.833333 -73.4981 -61.4539 -58.6959 -80.592 -65.716389
	JN690931	PG125	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Toponowini	France	French_Guiana	3.0521	-52.705
	KDQF01001993 KDQF01001115 DQ502198 MF624180 MT524115 EU342534 GU017448 GU017479 JF689961 JF689992 JN690942 JQ436714 KDQF01000627 KDQF01001759 KDQF01003685 KF310927 KF310958 KF310989 KX044269 KJ747339 KDQF01002947 MG243349 EU342523 KDQF01003001 JQ966850 KDQF01003186 FJ784428 DQ502136	BPN1687 AF2828 OMNH36026 TG3264 AF4635 QCAZ19167 BJirE2273 Trai12288 StAntE2156 Manaq13494 AF0015 APL2114 AF1670 AP016 MTR28559 APL14129 APL12276 APL12553 Careiro12708 APL15138 MPEG30888 MCP13287 LSUMZ15187 MPEG36777 INPAH29193 MTR10259 KRL905 LSUMZ17601	Allobates_granti Allobates_tapajos Allobates_trilineatus Allobates_sp.Carajas Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_cf.flaviventris Allobates_granti Allobates_juami Allobates_magnussoni Allobates_sp."Para" Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae Allobates_sp.purus	Allobates_aff.granti Allobates_aff.tapajos_2 Allobates_aff.trilineatus_1 Allobates_carajas Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_flaviventris Allobates_granti Allobates_insperatus/juami Allobates_magnussoni Allobates_nunciatus Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae Allobates_tinae	16 27 22 34 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 7 17 12 8 44 24 25 5 20	Group[34] Group[27] Group[49] Group[24] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[18] Group[34] Group[31] Group[19] Group[21] Group[6] Group[9] Group[38] Group[55]	Species9 Species22 Species13 Species27 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species36 Species11 Species 3 Species34 Species37 Species17 Species18 Species32 Species5	17 44 47 39 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 16 20 23 37 34 40 41 46	Saul Mitaraka, layon B Porto Walter Paraupebas, Floresta Nacional de Carajas Pucalpa Cuyabeno downstream of Cachoeira do Jirau Trairao Santo Antonio Manacapuru Kaw3, PK 25environ Cachoeira de St. Antonio Saul, Limonade Plote PPBIO, Porto Grande Boca do Acre, ramal Cruzeirinho West of Novo Repartimento Itaituba Manicore Careiro Jaci-Parana ESEC Grao Para, centro Japura, Estacao Ecologica Juami-Japura Rio Curua-Una, 101 km S and 15 km E Santarem MINA PALITO, Itaituba-PA Anori Igarape-Araras Parque Nacional G. D. Omar Torrijos Parque Estadual Guajara-Mirim	France France Brazil Brazil Peru Ecuador Brazil Brazil Brazil Brazil France Brazil France Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil Panama Brazil	French_Guiana French_Guiana AC Para San_Martin Ecuador RO PA RO AM French_Guiana RO French_Guiana AP AM PA PA AM AM RO PA AM PA PA AM AM Panama RO	3.625556 2.23577 -8.266389 -6.065481 -6.47275 -0.0886 -9.3114 -4.611843 -8.8309 -3.4272 4.548056 -8.6550 3.57317 0.979331 -8.75964302 -4.251111 -4.46220 -5.82310 -3.3708 -9.1694 0.6302806 -1.96455 -3.150000 -6.31731705 -3.6609 -3.0088889 8.667 -10.321389	-53.207222 -54.44928 -72.743611 -50.05638 -75.83630 -76.1419 -64.7172 -55.480257 -64.0206 -60.6150 -52.151944 -64.0195 -53.19851 -51.614895 -67.3083920 -49.94500 -56.28470 -61.29860 -59.8683 -64.4289 -55.7285 -67.93579 -54.833333 -55.7986444 -61.6628 -60.3969444 -80.592 -64.563333
	JN690932	PG175	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Armontabo	France	French_Guiana	3.804444	-52.288056
	KDQF01002254 KDQF01001311 EU342560 KC520676 AF124106 EU342535 GU017449 GU017480 JF689962 JF689993 JN690943 JQ436715 KDQF01000669 KDQF01001800 KDQF01003690 KF310928 KF310959 KF310990 KX044270 KJ747340 KDQF01003782 MG243350 KDQF01001831 MF624178 JQ966851 KDQF01004241 HQ290974 DQ502139	CM206 AF3221 KU215174 Alca1 ???? QCAZ16485 BJirE2278 Trai12295 StAntE2343 Manaq13498 CM383 APL2115 AF1772 BM009 MTR33509 APL14130 APL12275 APL12554 Careiro13117 APL15144 NZCS69 MCP13288 BM168 MNRJ79897 INPAH29188 SMNS12513 ???? MPEG13386	Allobates_granti Allobates_tapajos Allobates_trilineatus Allobates_chalcopis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_cf.flaviventris Allobates_granti Allobates_juami Allobates_magnussoni Allobates_olfersioides Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae Allobates_sp.purus	Allobates_aff.granti Allobates_aff.tapajos_2 Allobates_aff.trilineatus_1 Allobates_chalcopis Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_flaviventris Allobates_granti Allobates_insperatus/juami Allobates_magnussoni Allobates_olfersioides Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae Allobates_tinae	16 27 22 18 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 7 17 12 8 43 24 25 5 20	Group[34] Group[27] Group[49] Group[14] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[18] Group[34] Group[31] Group[19] Group[47] Group[6] Group[9] Group[63] Group[53]	Species9 Species22 Species13 Species8 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species36 Species10 Species 3 Species34 Species44 Species17 Species18 Species32 Species5	17 44 47 8 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 16 20 23 1 34 40 41 46	Saul Mitan G Cuzco Amazonico montagne pelee NA Yasuni, Estacion Cientifica Yasuni downstream of Cachoeira do Jirau Trairao Santo Antonio Manacapuru Regina Cachoeira de St. Antonio Apatou, Layon A Diadema UHE Belo Monte, Vitoria do Xingu, Rio Xingu Maraa West of Novo Repartimento Itaituba Manicore Careiro Jaci-Parana rosebel, rosebel gold mines, Mindrineti area Japura, Estacao Ecologica Juami-Japura UHE Belo Monte, Vitoria do Xingu, Rio Xingu Rio de Janeiro, Ponta Negra, Marica Anori Mabura hill forest reserve Pichincha: Río Sábalo Parque Estadual Guajara-Mirim	France France Peru France Brazil Ecuador Brazil Brazil Brazil Brazil France Brazil France Brazil Brazil Brazil Brazil Brazil Brazil Brazil Suriname Brazil Brazil Brazil Brazil Guyana Ecuador Brazil	French_Guiana French_Guiana Madre_de_Dios Martinique Brazil Ecuador RO PA RO AM French_Guiana RO French_Guiana PA AM PA PA AM AM RO Suriname AM PA RJ AM Guyana Ecuador RO	3.625556 2.6284 -12.583 14.81 -0.6781 -9.3114 -4.611843 -8.8309 -3.4272 4.3 -8.6550 5.25175 -2.950584 -1.74285507 -4.251111 -4.46220 -5.82310 -3.3708 -9.1694 5.124392 -1.96455 -2.950584 -22.95 -3.6609 5.1552778 0.080755 -10.321389	-53.207222 -52.55404 -69.083 -61.16 -76.3967 -64.7172 -55.480257 -64.0206 -60.6150 -52.116667 -64.0195 -54.20757 -51.936607 -65.7495581 -49.94500 -56.28470 -61.29860 -59.8683 -64.4289 -55.249254 -67.93579 -51.936607 -42.7 -61.6628 -58.6997222 -79.056538 -64.563333
	KDQF01000105	AF0275	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Nouragues, Camp Inselberg	France	French_Guiana	4.083333	-52.683333
	KDQF01002327 KDQF01002022 EU342561 KC520678 AF128572 EU342536 GU017450 HQ290951 JF689963 JF689994 JN690944 JQ436716 KDQF01000763 KDQF01001825 KDQF01003702 KF310929 KF310960 KF310991 KX044271 KJ747341 KDQF01003783 MG243351 KJ747328 MF624179 JQ966852 KF250494 MT524119 DQ502240	CM405 BPN2892 LSUMZ13691 Alca2 ???? TNHCFS4963 JacE2049 QCAZ16484 JaciD2076 Manaq13500 AF0071 APL2119 AF1957 BM086 MTR33628 APL14131 APL12278 APL12557 Careiro13120 APL15179 NZCS70 MCP13291 APL12989 MNRJ79899 INPAH29194 AMNH20030 QCAZ35236 MPEG13827	Allobates_granti Allobates_tapajos Allobates_trilineatus Allobates_chalcopis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_cf.flaviventris Allobates_granti Allobates_juami Allobates_magnussoni Allobates_olfersioides Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae Allobates_sp.purus	Allobates_aff.granti Allobates_aff.tapajos_2 Allobates_aff.trilineatus_1 Allobates_chalcopis Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_flaviventris Allobates_granti Allobates_insperatus/juami Allobates_magnussoni Allobates_olfersioides Allobates_paleovarzensis Allobates_sumtuosus Allobates_talamancae Allobates_tinae	16 27 22 18 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 7 17 12 8 43 24 25 5 20	Group[34] Group[27] Group[49] Group[14] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[18] Group[34] Group[31] Group[19] Group[47] Group[6] Group[9] Group[63] Group[53]	Species9 Species22 Species13 Species8 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species36 Species10 Species 3 Species34 Species44 Species17 Species18 Species32 Species5	17 44 47 8 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 16 20 23 1 34 40 41 46	Lucifer RAP Basecamp 1, Waterfall trail Porto Walter montagne pelee Porto Water Amazonas Leticia, Cerca Viva Jaci-Parana Estacion Cientifica Yasuni, km 7.5 Jaci-Parana Manacapuru St Georges Cachoeira de St. Antonio Sipaliwini UHE Belo Monte, Vitoria do Xingu, Rio Xingu Japura, Trilha Canoa Virada Near Tucurui Itaituba Manicore Careiro Jaci-Parana rosebel, rosebel gold mines, Mindrineti area Japura, Estacao Ecologica Juami-Japura Itaituba, Parque Nacional da Amazonia Rio de Janeiro, Ponta Negra, Marica Anori Sitio Tamaga, BR 174, km 80, Castanho, ca. 40 km S Manaus	France Suriname Brazil France Brazil Colombia Brazil Ecuador Brazil Brazil France Brazil Suriname Brazil Brazil Brazil Brazil Brazil Brazil Brazil Suriname Brazil Brazil Brazil Brazil Brazil Brazil	French_Guiana Suriname AC Martinique AC Colombia RO Ecuador RO AM French_Guiana RO Suriname PA AM PA PA AM AM RO Suriname AM PA RJ AM AM AM	4.7774 2.46554 -8.266389 14.81 -8.266389 -4.124167 -9.1694 -0.678194 -9.2045 -3.4272 3.888232 -8.6550 2.02439 -2.950584 -1.76130121 -3.534167 -4.46220 -5.82310 -3.3708 -9.1694 5.124392 -1.96455 -4.169335 -22.95 -3.6609 -2.223056 -3.619444	-53.9475 -55.62955 -72.743611 -61.16 -72.743611 -69.941389 -64.4289 -76.3965555 -64.3620 -60.6150 -51.807804 -64.0195 -56.12508 -51.936607 -66.3582666 -49.84583 -56.28470 -61.29860 -59.8683 -64.4289 -55.249254 -67.93579 -55.981064 -42.7 -61.6628 -60.065278 -60.446333
	KDQF01000106	AF0276	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Nouragues, Camp Inselberg	France	French_Guiana	4.083333	-52.683333
	KDQF01002328 KDQF01002914 EU342562 MT524113 AY326026 EU342537 GU017451 JF689933 JF689964 JF689995 JN690945 JQ436717 KDQF01000771 KDQF01001857 KDQF01003704 KF310930 KF310961 KF310992 KX044272 MF624172 KDQF01003219 MT524120 KJ747329 MT524105 JQ966853 KF250495 DQ502110 EU342565	CM406 MPEG30384 LSUMZ13690 AF4608 WED55470 LSUMZ17436 JacE2050 BJirE2275 JaciD2080 Manaq13502 AF0072 APL2124 AF1971 BOAM008 MTR33633 APL14132 APL12273 APL12559 Careiro13121 QCAZ54377 MTR12749 QCAZ44577 APL12987 AF4393 INPAH29195 AMNH20210 MJH3973 LSUMZ15435	Allobates_granti Allobates_tapajos Allobates_trilineatus Allobates_conspicuus Allobates_aff.hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_fratisenescus Allobates_grillisimilis Allobates_insperatus Allobates_magnussoni Allobates_ornatus Allobates_paleovarzensis Allobates_sumtuosus Allobates_tapajos Allobates_sp.purus	Allobates_aff.granti Allobates_aff.tapajos_2 Allobates_aff.trilineatus_1 Allobates_conspicuus/subfolionidificans Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_fratisenescus Allobates_grillisimilis Allobates_insperatus/juami Allobates_magnussoni Allobates_ornatus Allobates_paleovarzensis Allobates_sumtuosus Allobates_tapajos Allobates_tinae	16 27 22 14 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 50 32 12 8 13 24 25 29 20	Group[34] Group[27] Group[49] Group[26] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[2] Group[37] Group[31] Group[19] Group[3] Group[6] Group[9] Group[28] Group[16]	Species9 Species22 Species13 Species 2 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species30 Species26 Species 3 Species34 Species 4 Species17 Species18 Species20 Species5	17 44 47 9 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 13 18 20 23 33 34 40 42 46	Lucifer REBIO Maicuru Porto Walter Pucalpa Cuzco Amazonico Rio Formoso, Parque Estadual Guajara-Mirim Jaci-Parana downstream of Cachoeira do Jirau Jaci-Parana Manacapuru St Georges Cachoeira de St. Antonio Sipaliwini Borne 4 Japura, Trilha Canoa Virada Near Tucurui Itaituba Manicore Careiro Morona Santiago, Comunidad Jempekat, riachuelo Sao Sebastiao, Rio Abacaxis Itaituba, Parque Nacional da Amazonia URKU Anori Sao Joao da Baliza Rio Curua-Una Rio Ituxi at the Madeira Scheffer	France Brazil Brazil Peru Peru Brazil Brazil Brazil Brazil Brazil France Brazil Suriname France Brazil Brazil Brazil Brazil Brazil Ecuador Brazil Brazil Peru Brazil Brazil Brazil Brazil	French_Guiana PA AC Loreto Madre_de_Dios RO RO RO RO AM French_Guiana RO Suriname French_Guiana AM PA PA AM AM Ecuador AM PA San_Martin AM RR PA AM	4.7774 0.8286194 -8.266389 -6.47278 -12.583 -10.321 -9.1694 -9.3114 -9.2045 -3.4272 3.888232 -8.6550 2.02439 2.3709 -1.76130121 -3.534167 -4.46220 -5.82310 -3.3708 -2.782923 -4.308889 -4.169335 -6.46487 -3.6609 0.952778 -3.150000 -8.479444	-53.9475 -53.9312000 -72.743611 -75.84929 -69.083 -64.563306 -64.4289 -64.7172 -64.3620 -60.6150 -51.807804 -64.0195 -56.12508 -53.7728 -66.3582666 -49.84583 -56.28470 -61.29860 -59.8683 -78.180989 -58.636389 -55.981064 -76.35185 -61.6628 -59.928611 -54.833333 -65.716389
	KDQF01000125	AF0519	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	CisameG	France	French_Guiana	4.195265	-52.372028
	KDQF01003835 KDQF01002915 MT524134 MT524114 AY326027 EU342538 GU017452 JF689934 JF689965 JF689996 JN690946 JQ436718 KDQF01000782 KDQF01001866 KDQF01003837 KF310931 KF310962 KF310993 KX044273 DQ502052 KF250504 MT524121 KJ747330 MT524106 JQ966854 KF250496 EU342545 KDQF01002623	PG166 MPEG30385 JMP2313 AF4609 WED55560 LSUMZ15222 JacE2052 BJirE12848 JaciD2083 Manaq13516 AF0019 APL2156 AF1997 BOAM021 PG170 APL14133 APL12279 APL12560 Trairao12290 MPEG13003 APL12747 QCAZ52435 APL12988 AF4402 INPAH29146 AMNH20211 LSUMZ15296 HJ005	Allobates_granti Allobates_tapajos Allobates_sp. Allobates_conspicuus Allobates_aff.hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_aff.gasconi1 Allobates_grillisimilis Allobates_insperatus Allobates_magnussoni Allobates_ornatus Allobates_paleovarzensis Allobates_sumtuosus Allobates_tapajos Allobates_sp.purus	Allobates_aff.granti Allobates_aff.tapajos_2 Allobates_aff.trilineatus_2 Allobates_conspicuus/subfolionidificans Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_gasconi Allobates_grillisimilis Allobates_insperatus/juami Allobates_magnussoni Allobates_ornatus Allobates_paleovarzensis Allobates_sumtuosus Allobates_tapajos Allobates_tinae	16 27 47 14 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 30 32 11 8 13 24 25 29 20	Group[34] Group[27] Group[5] Group[26] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[29] Group[37] Group[31] Group[19] Group[3] Group[6] Group[9] Group[28] Group[55]	Species9 Species22 Species14 Species 2 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species23 Species26 Species 3 Species34 Species 4 Species17 Species18 Species20 Species5	17 44 48 9 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 14 18 20 23 33 34 40 42 46	Armontabo REBIO Maicuru Bosque al Este de Requena Pucalpa Cuzco Amazonico Rio Curua-Una, 101 km S and 15 km E Santarem Jaci-Parana downstream of Cachoeira do Jirau Jaci-Parana Manacapuru Saul Cachoeira de St. Antonio Sipaliwini, Apalagadi Borne 4 Armontabo Near Tucurui Itaituba Manicore Trairao Rio Ituxi at the Madeira Scheffer Borba Cuyabeno Itaituba, Parque Nacional da Amazonia URKU Codajas Sao Joao da Baliza Rio Curua-Una, 101 km S and 15 km E Santarem UHE Jirau, Porto Velho, Hidreletrica Jirau	France Brazil Peru Peru Peru Brazil Brazil Brazil Brazil Brazil France Brazil Suriname France France Brazil Brazil Brazil Brazil Brazil Brazil Ecuador Brazil Peru Brazil Brazil Brazil Brazil	French_Guiana PA Loreto Loreto Madre_de_Dios PA RO RO RO AM French_Guiana RO Suriname French_Guiana French_Guiana PA PA AM PA AM AM Ecuador PA San_Martin AM RR PA RO	3.804444 0.8286194 -5.12 -6.47278 -12.583 -3.150000 -9.1694 -9.3114 -9.2045 -3.4272 3.625556 -8.8309 2.16977 2.3709 3.961111 -3.534167 -4.46220 -5.82310 -4.6831 -8.479444 -4.4342 -0.0886 -4.169335 -6.46487 -3.7715 0.952778 -3.150000 -8.748483	-52.288056 -53.9312000 -73.8 -75.84929 -69.083 -54.833333 -64.4289 -64.7172 -64.3620 -60.6150 -53.207222 -64.0206 -56.08083 -53.7728 -52.428611 -49.84583 -56.28470 -61.29860 -56.0225 -65.716389 -59.6236 -76.1419 -55.981064 -76.35185 -62.0205 -59.928611 -54.833333 -63.903465
	KDQF01000130	AF0528	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Nouragues, Camp Inselberg	France	French_Guiana	4.08501	-52.68097
	KDQF01003851 KDQF01002948 DQ283044 MT524138 DQ283045 EU342539 GU017453 JF689935 JF689966 JF689997 JN690947 JQ436719 KDQF01000967 KDQF01001873 KDQF01003839 KF310932 KF310963 KF310994 KX044274 DQ502053 KF250505 MT524122 KJ747331 MT524107 JQ966855 KF250497 EU342546 KDQF01002639	PG299 MPEG30890 AMNHA159139 GGU895 LSUMZ17552 LSUMZ15213 JacE2055 BJirE12853 JaciD2084 HumaiE12900 PG451 APL2341 AF2473 BOAM030 PG179 APL14134 APL12280 APL12562 Treviso12268 MPEG12993 APL12788 QCAZ53540 APL13003 AF4403 INPAH29155 APL16720 LSUMZ15291 HJ298	Allobates_granti Allobates_tapajos Allobates_undulatus Allobates_sp. Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_aff.gasconi1 Allobates_grillisimilis Allobates_insperatus Allobates_magnussoni Allobates_ornatus Allobates_paleovarzensis Allobates_sumtuosus Allobates_tapajos Allobates_sp.purus	Allobates_aff.granti Allobates_aff.tapajos_2 Allobates_aff.undulatus Allobates_conspicuus/subfolionidificans Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_gasconi Allobates_grillisimilis Allobates_insperatus/juami Allobates_magnussoni Allobates_ornatus Allobates_paleovarzensis Allobates_sumtuosus Allobates_tapajos Allobates_tinae	16 27 38 14 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 30 32 11 8 13 24 25 29 20	Group[34] Group[27] Group[41] Group[26] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[29] Group[37] Group[31] Group[19] Group[3] Group[6] Group[9] Group[28] Group[16]	Species9 Species22 Species39 Species 2 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species23 Species26 Species 3 Species34 Species 4 Species17 Species18 Species20 Species5	17 44 49 9 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 14 18 20 23 33 34 40 42 46	Haut Marwini ESEC Grao Para, centro Estacion Biologica Panguana Parque Estadual Guajara-Mirim Rio Curua-Una, 101 km S and 15 km E Santarem Jaci-Parana downstream of Cachoeira do Jirau Jaci-Parana Humaita Mont Kotika Cachoeira de St. Antonio Nassau, trail camp Borne 4 Aratai Near Tucurui Itaituba Manicore Treviso Rio Ituxi at the Madeira Scheffer Borba Itaituba, Parque Nacional da Amazonia URKU Codajas REBio Trombetas Rio Curua-Una, 101 km S and 15 km E Santarem UHE Jirau, Porto Velho, Hidreletrica Jirau	France Brazil Venezuela Peru Brazil Brazil Brazil Brazil Brazil Brazil France Brazil Suriname France France Brazil Brazil Brazil Brazil Brazil Brazil Brazil Peru Brazil Brazil Brazil Brazil	French_Guiana PA Venezuela Huanuco RO PA RO RO RO AM French_Guiana RO Suriname French_Guiana French_Guiana PA PA AM PA AM AM PA San_Martin AM PA PA RO	2.6067 0.6302806 5.768102 -9.617 -10.321389 -3.150000 -9.1694 -9.3114 -9.2045 -7.5488 3.934722 -8.8309 4.80262 2.3709 3.9909 -3.534167 -4.46220 -5.82310 -3.15770 -8.479444 -4.4342 -4.169335 -6.46487 -3.7715 -1.370000 -3.150000 -8.748483	-54.0257 -55.7285 -66.130847 -74.933 -64.563333 -54.833333 -64.4289 -64.7172 -64.3620 -62.8772 -54.204722 -64.0206 -54.61502 -53.7728 -52.5902 -49.84583 -56.28470 -61.29860 -54.8595 -65.716389 -59.6236 -55.981064 -76.35185 -62.0205 -56.852222 -54.833333 -63.903465
	KDQF01000352	AF0976	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Saul, Galbao layon acces	France	French_Guiana	3.63592	-53.272562
	KDQF01003852 KDQF01003825 DQ502028 MT524139 DQ501990 GU017423 GU017454 JF689936 JF689967 JF689998 JN690948 JQ436720 KDQF01001035 KDQF01001875 KDQF01003876 KF310933 KF310964 KF310995 KX044275 DQ502202 KF250506 DQ502271 MT524159 MT524108 JQ966856 KF250498 KDQF01001798 KDQF01002647	PG300 PG100 AMNHA159141 GGU946 KU215179 AbuE2189 JacE2056 BJirE12839 JaciD2086 HumaiE12901 AF0057 APL12894 AF2644 BOAM032 PG472 APL14135 APL12281 APL12563 Treviso12269 OMNH36637 APL12674 ARA2394 AJC2416 AF4404 INPAH29079 APL17101 BM004 HJ384	Allobates_granti Allobates_tapajos Allobates_undulatus Allobates_sp. Allobates_aff.hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_aff.gasconi1 Allobates_grillisimilis Allobates_juanii Allobates_trilineatus Allobates_ornatus Allobates_paleovarzensis Allobates_sumtuosus Allobates_tapajos Allobates_sp.purus	Allobates_aff.granti Allobates_aff.tapajos_2 Allobates_aff.undulatus Allobates_conspicuus/subfolionidificans Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_gasconi Allobates_grillisimilis Allobates_juanii/ranoides Allobates_marchesianus Allobates_ornatus Allobates_paleovarzensis Allobates_sumtuosus Allobates_tapajos Allobates_tinae	16 27 38 14 4 4 1 1 1 1 1 1 1 1 1 1 1 1 1 30 32 36 21 13 24 25 29 20	Group[34] Group[27] Group[41] Group[26] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[29] Group[37] Group[40] Group[13] Group[3] Group[6] Group[9] Group[28] Group[16]	Species9 Species22 Species39 Species 2 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species23 Species26 Species28 Species6 Species 4 Species17 Species18 Species20 Species5	17 44 49 9 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 14 18 21 25 33 34 40 42 46	Haut Marwini Saul Rio Buncuya. Provincia Requena Cusco Amazonico, 15 km E Puerto Maldonado Abuna Jaci-Parana downstream of Cachoeira do Jirau Jaci-Parana Humaita Petit Saut Cachoeira do Jirau Atachi Bakka, camp1 Borne 4 Kotika, Mont Near Tucurui Itaituba Manicore Treviso Rio Ituxi at the Madeira Scheffer Nova Olinda do Norte Meta, Villavicencio, Pozo Azu Comunidad Puerto Vaupes. Rio Vaupes URKU Barcelos REBio Trombetas UHE Belo Monte, Vitoria do Xingu, Rio Xingu UHE Jirau, Mutum	France France Venezuela Peru Peru Brazil Brazil Brazil Brazil Brazil France Brazil France France France Brazil Brazil Brazil Brazil Brazil Brazil Colombia Colombia Peru Brazil Brazil Brazil Brazil	French_Guiana French_Guiana Venezuela Loreto Madre_de_Dios RO RO RO RO AM French_Guiana RO French_Guiana French_Guiana French_Guiana PA PA AM PA AM AM Colombia Colombia San_Martin AM PA PA RO	2.6067 3.566667 5.765854 -6.23333 -12.583 -9.516 -9.1694 -9.3114 -9.2045 -7.5488 5.066667 -9.3206 3.5671 2.3709 3.934722 -3.534167 -4.46220 -5.82310 -3.15770 -8.479444 -3.8744 4.065441 1.198 -6.46487 -0.9160 -1.370000 -2.950584 -9.591318	-54.0257 -53.216667 -66.133624 -74.4 -69.083 -65.3249 -64.4289 -64.7172 -64.3620 -62.8772 -53.05 -64.7225 -53.96164 -53.7728 -54.204722 -49.84583 -56.28470 -61.29860 -54.8595 -65.716389 -59.0461 -73.591382 -70.281 -76.35185 -62.9706 -56.852222 -51.936607 -65.049181
	KDQF01000370	AF1018	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Saul, Galbao layon acces	France	French_Guiana	3.625727	-53.280523
	KDQF01003868 KDQF01003826 DQ502029 MT524140 DQ502014 GU017424 GU017455 JF689937 JF689968 JF689999 JN690949 KDQF01000015 KDQF01001040 KDQF01001919 KDQF01003943 KF310934 KF310965 KF310996 KX044276 DQ502209 KF250507 EU342540 MT524160 MT524109 JQ966857 KF250499 KDQF01001805 KDQF01002668	PG406 PG101 AMNHA159140 GGU970 KU215177 AbuE2190 JacE2058 BJirE12854 JaciD2088 HumaiE12902 CM237 AF0006 AF2656 BPN0798 PG718 APL14136 APL12282 APL17130 Treviso12296 OMNH36636 APL12675 TNHCFS4944 AJC2443 AF4406 INPAH29091 APL17104 BM032 HJ622	Allobates_granti Allobates_tapajos Allobates_undulatus Allobates_sp. Allobates_aff.hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_aff.gasconi1 Allobates_grillisimilis Allobates_juanii Allobates_trilineatus Allobates_ornatus Allobates_paleovarzensis Allobates_sumtuosus Allobates_tapajos Allobates_sp.purus	Allobates_aff.granti Allobates_aff.tapajos_2 Allobates_aff.undulatus Allobates_conspicuus/subfolionidificans Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_gasconi Allobates_grillisimilis Allobates_juanii/ranoides Allobates_marchesianus Allobates_ornatus Allobates_paleovarzensis Allobates_sumtuosus Allobates_tapajos Allobates_tinae	16 27 38 14 4 4 1 1 1 1 1 1 1 1 1 1 1 1 1 30 32 36 21 13 24 25 29 20	Group[34] Group[27] Group[41] Group[26] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[29] Group[37] Group[40] Group[13] Group[3] Group[6] Group[9] Group[28] Group[55]	Species9 Species22 Species39 Species 2 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species23 Species26 Species28 Species6 Species 4 Species17 Species18 Species20 Species5	17 44 49 9 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 14 18 21 25 33 34 40 42 46	Kotika, Mont Saul Rio Buncuya. Provincia Requena Cusco Amazonico, 15 km E Puerto Maldonado Abuna Jaci-Parana downstream of Cachoeira do Jirau Jaci-Parana Humaita St elie, pist PK~8 Comte, Terrain michel Alikene, camp 1 Ralleighvallen St Georges, Saut Maripa Near Tucurui Itaituba Serra Azul, Monte Alegre Treviso Rio Ituxi at the Madeira Scheffer Nova Olinda do Norte Meta Villavicencio, Villavicencio-Restrepo road Comunidad Puerto Vaupes. Rio Vaupes URKU Unini REBio Trombetas UHE Belo Monte, Vitoria do Xingu, Rio Xingu Caicara, Porto Velho	France France Venezuela Peru Peru Brazil Brazil Brazil Brazil Brazil France France France Suriname France Brazil Brazil Brazil Brazil Brazil Brazil Colombia Colombia Peru Brazil Brazil Brazil Brazil	French_Guiana French_Guiana Venezuela Loreto Madre_de_Dios RO RO RO RO AM French_Guiana French_Guiana French_Guiana Suriname French_Guiana PA PA PA PA AM AM Colombia Colombia San_Martin AM PA PA RO	3.934722 3.566667 5.765854 -6.23333 -12.583 -9.516 -9.1694 -9.3114 -9.2045 -7.5488 5.283611 4.679471 3.20906 4.7166667 3.806111 -3.534167 -4.46220 -1.28222 -3.15770 -8.479444 -3.8744 4.065441 1.198 -6.46487 -1.6879 -1.370000 -2.950584 -8.696257	-54.204722 -53.216667 -66.133624 -74.4 -69.083 -65.3249 -64.4289 -64.7172 -64.3620 -62.8772 -53.053889 -52.338531 -52.402 -56.2166667 -51.893389 -49.84583 -56.28470 -54.12972 -54.8595 -65.716389 -59.0461 -73.591382 -70.281 -76.35185 -61.8319 -56.852222 -51.936607 -63.912477
	KDQF01000383	AF1050	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Saul, gros arbres	France	French_Guiana	3.615576	-53.227093
	KDQF01003874 KDQF01004176 HQ290950 MT524144 DQ502015 GU017425 GU017456 JF689938 JF689969 JF690000 JN690950 KDQF01000017 KDQF01001102 KDQF01001927 KDQF01003959 KF310935 KF310966 KF310997 KX044277 EU342543 AJ430673 HQ290960 MT524161 MT524112 JQ966858 KF250500 KR047027 KDQF01003431	PG467 RBE494 TNHCFS5551 FGZC3279 KU215180 AbuE2193 JacE2060 JaciE2062 JaciD2093 HumaiE12903 SMNS12002 AF0018 AF2781 BPN0826 PG760 APL14137 APL12283 APL17367 Treviso12300 LSUMZ15393 ???? ???? AJC2444 AF4526 INPAH29098 APL17127 APL12965 MTR19227	Allobates_granti Allobates_tapajos Allobates_algorei Allobates_sp. Allobates_aff.hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_aff.gasconi2 Allobates_humilis Allobates_juanii Allobates_trilineatus Allobates_cf.ornatus Allobates_paleovarzensis Allobates_sumtuosus Allobates_tapajos Allobates_sp.purus	Allobates_aff.granti Allobates_aff.tapajos_2 Allobates_algorei Allobates_conspicuus/subfolionidificans Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_gasconi Allobates_humilis/pittieri Allobates_juanii/ranoides Allobates_marchesianus Allobates_ornatus Allobates_paleovarzensis Allobates_sumtuosus Allobates_tapajos Allobates_tinae	16 27 48 14 4 4 1 1 1 1 1 1 1 1 1 1 1 1 1 30 15 36 21 13 24 25 29 20	Group[34] Group[27] Group[36] Group[26] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[29] Group[25] Group[40] Group[13] Group[3] Group[6] Group[9] Group[28] Group[54]	Species9 Species22 Species16 Species 2 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species23 Species12 Species28 Species6 Species 4 Species17 Species18 Species20 Species5	17 44 3 9 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 14 19 21 25 33 34 40 42 46	Kotika, Mont Saul Tachira Rio Buncuya. Provincia Requena Cusco Amazonico, 15 km E Puerto Maldonado Abuna Jaci-Parana Jaci-Parana Jaci-Parana Humaita Mabura hill forest reserve Saul Mitaraka, layon C Kayser St Georges, Saut Maripa Near Tucurui Itaituba Serra Azul, Monte Alegre Treviso Rio Ituxi at the Madeira Scheffer Laguna Los Cedros to the Paramo de Guaramacal Comunidad Puerto Vaupes. Rio Vaupes Biodiversidad Unini REBio Trombetas Parque Nacional da Amazonia, Itaituba Rio Purus	France France Venezuela Peru Peru Brazil Brazil Brazil Brazil Brazil Guyana France France Suriname France Brazil Brazil Brazil Brazil Brazil Venezuela Colombia Colombia Peru Brazil Brazil Brazil Brazil	French_Guiana French_Guiana Venezuela Loreto Madre_de_Dios RO RO RO RO AM Guyana French_Guiana French_Guiana Suriname French_Guiana PA PA PA PA AM Venezuela Colombia Colombia San_Martin AM PA PA AM	3.934722 3.625556 7.598630 -6.23333 -12.583 -9.516 -9.1694 -9.1694 -9.2045 -7.5488 5.1552778 3.625556 2.23577 3.048297 3.806111 -3.534167 -4.46220 -1.28222 -3.15770 -8.479444 9.253333 4.065441 1.198 -6.46971 -1.6879 -1.370000 -4.47027 -4.720095	-54.204722 -53.207222 -72.135138 -74.4 -69.083 -65.3249 -64.4289 -64.4289 -64.3620 -62.8772 -58.6997222 -53.207222 -54.44928 -56.578653 -51.893389 -49.84583 -56.28470 -54.12972 -54.8595 -65.716389 -70.218611 -73.591382 -70.281 -76.28102 -61.8319 -56.852222 -56.28444 -62.133036
	KDQF01000403	AF1102	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Ilet Lezard rive D	France	French_Guiana	5.054438	-53.797746
	KDQF01003875 KDQF01004394 MT524118 MT524156 DQ502088 GU017426 GU017457 JF689939 JF689970 JF690001 JN690951 KDQF01000019 KDQF01001154 KDQF01001996 KDQF01004013 KF310936 KF310967 KF310998 KX044278 EU342544 EU380801 KJ130697 MT524162 EU342549 JQ966859 KF250501 KR047028 KDQF01003435	PG468 UHE144 PK3798 PEP530 MPEG13415 AbuE2204 JacE2300 JaciE2299 JaciD2094 HumaiE12905 SMNS12003 AF0024 AF2949 BPN1876 PK3529 APL13484 APL12541 APL17368 Abuna2192 LSUMZ15392 EBRG3420 AJC3383 AJC2461 MHNSM22646 INPAH29106 APL18255 APL12968 MTR19290	Allobates_granti Allobates_tapajos Allobates_amissibilis Allobates_sp. Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_hodli Allobates_aff.gasconi1 Allobates_pittieri Allobates_ranoides Allobates_trilineatus Allobates_ornatus Allobates_paleovarzensis Allobates_sumtuosus Allobates_tapajos Allobates_sp.purus	Allobates_aff.granti Allobates_aff.tapajos_2 Allobates_amissibilis Allobates_conspicuus/subfolionidificans Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_gasconi Allobates_humilis/pittieri Allobates_juanii/ranoides Allobates_marchesianus Allobates_ornatus Allobates_paleovarzensis Allobates_sumtuosus Allobates_tapajos Allobates_tinae	16 27 23 14 1 4 1 1 1 1 1 1 1 1 1 1 1 1 4 30 15 36 21 13 24 25 29 20	Group[34] Group[27] Group[17] Group[26] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[29] Group[58] Group[65] Group[13] Group[3] Group[6] Group[9] Group[28] Group[54]	Species9 Species22 Species15 Species 2 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species23 Species12 Species28 Species6 Species 4 Species17 Species18 Species20 Species5	17 44 4 9 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 14 19 21 25 33 34 40 42 46	Kotika, Mont UHE SANTO ANTONIO AMAPA Yavari Miri Parque Estadual Guajara-Mirim Abuna Jaci-Parana Jaci-Parana Jaci-Parana Humaita Mabura hill forest reserve Saul, roche bateau Memora G Nouragues, Camp InselbergInselberg (trail XIII) Turtle camp, Iwokrama Near the airport in Altamira Novo Aripuana Serra Azul, Monte Alegre Abuna Rio Ituxi at the Madeira Scheffer La Trilla, Estado Aragua Casanare, Sabanalarga Comunidad Puerto Vaupes. Rio Vaupes San Martin 17km W from Tarapoto to Moyobamba Unini Manaus, Reserva Ducke Parque Nacional da Amazonia, Itaituba Rio Purus	France Brazil Peru Brazil Brazil Brazil Brazil Brazil Brazil Guyana France France France Guyana Brazil Brazil Brazil Brazil Brazil Venezuela Colombia Colombia Peru Brazil Brazil Brazil Brazil	French_Guiana AP Loreto RO RO RO RO RO AM Guyana French_Guiana French_Guiana French_Guiana Guyana PA AM PA RO AM Venezuela Colombia Colombia San_Martin AM AM PA AM	3.934722 -0.645593 -4.259917 -10.321389 -9.516 -9.1694 -9.1694 -9.2045 -7.5488 5.1552778 3.625556 3.31774 4.08479 4.6718 -3.243200 -5.15030 -1.28222 -9.5160 -8.479444 10.157929 4.773 1.198 -6.47889 -1.6879 -2.980833 -4.47027 -4.720095	-54.204722 -52.500599 -70.141172 -64.563333 -65.3249 -64.4289 -64.4289 -64.3620 -62.8772 -58.6997222 -53.207222 -52.19329 -52.68063 -58.6847 -52.24400 -60.34670 -54.12972 -65.3249 -65.716389 -68.244040 -73.037 -70.281 -76.5125 -61.8319 -59.921111 -56.28444 -62.133036
	KDQF01000419	AF1135	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Nouragues, ParareG	France	French_Guiana	4.04203	-52.68065
	KDQF01003901 KDQF01004395 KC520680 MT524177 DQ502089 GU017427 GU017458 JF689940 JF689971 JF690002 JN690952 KDQF01000020 KDQF01001156 KDQF01002064 KDQF01004015 KF310937 KF310968 KF310999 KX044279 KDQF01002640 EU380802 KJ130698 MT524163 EU342550 JQ966860 KF250502 KX524470 MT524111	PG591 UHE145 MTD47884 SCF637 OMNH34568 AbuE2205 MutD2158 JaciE2300 Borba12525 HumaiE12909 SMNS12004 AF0025 AF2950 BPN3580 PK3551 APL13486 APL12545 APL17369 Abuna2194 HJ299 EBRG3421 AJC3385 AJC2462 MHNSM22863 INPAH29097 APL18254 INPAH036565 AF4493	Allobates_granti Allobates_tapajos Allobates_amissibilis Allobates_conspicuus Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_hodli Allobates_gasconi Allobates_pittieri Allobates_ranoides Allobates_trilineatus Allobates_ornatus Allobates_paleovarzensis Allobates_sumtuosus Allobates_tapajos Allobates_femoralis	Allobates_aff.granti Allobates_aff.tapajos_2 Allobates_amissibilis Allobates_conspicuus/subfolionidificans Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_gasconi Allobates_humilis/pittieri Allobates_juanii/ranoides Allobates_marchesianus Allobates_ornatus Allobates_paleovarzensis Allobates_sumtuosus Allobates_tapajos Allobates_trilineatus	16 27 23 14 1 4 1 1 1 1 1 1 1 1 1 1 1 1 4 30 15 36 21 13 24 25 29 46	Group[34] Group[27] Group[17] Group[26] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[62] Group[58] Group[65] Group[13] Group[3] Group[6] Group[9] Group[28] Group[4]	Species9 Species22 Species15 Species 2 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species23 Species12 Species28 Species6 Species 4 Species17 Species18 Species20 Species13	17 44 4 9 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 14 19 21 25 33 34 40 42 7	Mitaraka, Borne 1/ Camp de base 1 UHE SANTO ANTONIO AMAPA Iwokrama Puesto de control y vigilancia La Novia Rio Curua-Una, 101 km S and 15 km E Santarem Abuna Mutum-Parana Jaci-Parana Borba Humaita Mabura hill forest reserve Saul, roche bateau Memora G Kabocalli, Potaro-Siparuni Turtle camp, Iwokrama Near the airport in Altamira Novo Aripuana Serra Azul, Monte Alegre Abuna UHE Jirau, Porto Velho, Hidreletrica Jirau La Trilla, Estado Aragua Casanare, Sabanalarga Comunidad Puerto Vaupes. Rio Vaupes San Martin 17km W from Tarapoto to Moyobamba Unini Manaus, Reserva Ducke Itatuiba Pongo-Barranquita	France Brazil Guyana Peru Brazil Brazil Brazil Brazil Brazil Brazil Guyana France France Guyana Guyana Brazil Brazil Brazil Brazil Brazil Venezuela Colombia Colombia Peru Brazil Brazil Brazil Peru	French_Guiana AP Guyana Ucayali PA RO RO RO AM AM Guyana French_Guiana French_Guiana Guyana Guyana PA AM PA RO RO Venezuela Colombia Colombia San_Martin AM AM PA San_Martin	2.2911 -0.645593 4.6713889 -9.988 -3.150000 -9.516 -9.6414 -9.1694 -4.4342 -7.5488 5.1552778 3.625556 3.31774 4.287840713 4.6718 -3.243200 -5.15030 -1.28222 -9.5160 -8.748483 10.157929 4.773 1.198 -6.47889 -1.6879 -2.980833 -4.171477 -6.28639	-54.5342 -52.500599 -58.685 -70.708 -54.833333 -65.3249 -64.8859 -64.4289 -59.6236 -62.8772 -58.6997222 -53.207222 -52.19329 -58.5084834 -58.6847 -52.24400 -60.34670 -54.12972 -65.3249 -63.903465 -68.244040 -73.037 -70.281 -76.5125 -61.8319 -59.921111 -56.015500 -76.23164
	KDQF01000424	AF1160	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Trinite, aya, 400m plateau	France	French_Guiana	4.6025	-53.4143
	KDQF01004321 KDQF01004468 MT524150 MT524178 DQ502090 GU017428 GU017459 JF689941 JF689972 JF690003 JN690953 KDQF01000023 KDQF01001157 KDQF01002091 KDQF01004111 KF310938 KF310969 KF311000 KX044280 KDQF01002660 EU380803 MT524123 MT524164 MT524129 JQ966861 KF250503 KX524471 MT524125	T3041 WAAM017 MTKD47884 SCF638 OMNH34572 AbuE2206 MutD2160 MutumD2163 Borba12514 HumaiD13421 SMNS12006 AF0035 AF2951 BPN3711 QM433 APL13488 APL12290 APL17370 Abuna2195 HJ480 MIZA339 QCAZA68477 AJC2498 JMP2132 INPAH29086 APL18253 INPAH036533 AJC3032	Allobates_granti Allobates_tapajos Anomaloglossus_sp. Allobates_conspicuus Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_hodli Allobates_gasconi Allobates_pittieri Allobates_fratisenescus Allobates_trilineatus Allobates_trilineatus Allobates_paleovarzensis Allobates_sumtuosus Allobates_tapajos Allobates_marchesianus	Allobates_aff.granti Allobates_aff.tapajos_2 Allobates_amissiblis Allobates_conspicuus/subfolionidificans Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_gasconi Allobates_humilis/pittieri Allobates_kingsburyi Allobates_marchesianus Allobates_paleovarzensis Allobates_paleovarzensis Allobates_sumtuosus Allobates_tapajos Allobates_undulatus	16 27 23 14 1 4 1 1 1 1 1 1 1 1 1 1 1 1 4 30 15 37 21 24 24 25 29 39	Group[34] Group[27] Group[17] Group[26] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[62] Group[58] Group[1] Group[13] Group[6] Group[6] Group[9] Group[28] Group[43]	Species9 Species22 Species15 Species 2 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species23 Species12 Species29 Species6 Species17 Species17 Species18 Species20 Species38	17 44 4 9 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 14 19 22 25 34 34 40 42 2	Mitaraka Flat de la waki Guyana, Iwokrama Forest Puesto de control y vigilancia La Novia Rio Curua-Una, 101 km S and 15 km E Santarem Abuna Mutum-Parana Mutum-Parana Borba Humaita Mabura hill forest reserve Saul, galbao Memora G Parabara, Village Montagne de fer Near the airport in Altamira Trairao Serra Azul, Monte Alegre Abuna UHE Jirau, Abuna La Trilla, Estado Aragua Zamora Chinchipe Laguna Pavon, cano Naquen Leticia, Km 10 carretera Barcelos Manaus, Reserva Ducke Itatuiba Pica en bosque entre rio Cuao y Tobogan del Cuao	France France Guyana Peru Brazil Brazil Brazil Brazil Brazil Brazil Guyana France France Guyana France Brazil Brazil Brazil Brazil Brazil Venezuela Ecuador Colombia Colombia Brazil Brazil Brazil Venezuela	French_Guiana French_Guiana Guyana Ucayali PA RO RO RO AM AM Guyana French_Guiana French_Guiana Guyana French_Guiana PA PA PA RO RO Venezuela Ecuador Colombia Colombia AM AM PA Venezuela	2.2666667 3.18341 4.411 -9.988 -3.150000 -9.516 -9.6414 -9.6414 -4.4342 -7.5488 5.1552778 3.6 3.31774 2.094500 5.4074 -3.243200 -4.68310 -1.28222 -9.5160 -9.69941 10.157929 -4.247309 2.271 -4.124 -0.9160 -2.980833 -4.171477 5.104	-54.5166667 -53.4775 -58.788 -70.708 -54.833333 -65.3249 -64.8859 -64.8859 -59.6236 -62.8772 -58.6997222 -53.283333 -52.19329 -59.240183 -53.5548 -52.24400 -56.02250 -54.12972 -65.3249 -65.358875 -68.244040 -78.65353 -68.265 -69.941 -62.9706 -59.921111 -56.015500 -67.508
	KDQF01000610	AF1628	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Saul, Limonade	France	French_Guiana	3.57317	-53.1985
	KDQF01004461 DQ502241 KU195698 DQ502134 DQ502091 GU017429 GU017460 JF689942 JF689973 JF690004 JN690954 KDQF01000083 KDQF01001162 KDQF01002137 KDQF01004174 KF310939 KF310970 KF311001 KX044281 KJ747333 EU380804 AY364549 MT524153 MT524130 JQ966862 AF124113 KX524472 MT524126	WAAM010 MPEG13383 INPAH35406 MPEG12321 OMNH36066 AbuE2213 MutD2165 MutumD2173 Borba12516 HumaiD13422 SMNS12005 AF0195 AF2962 CAAM25 RB32 APL14106 APL12286 APL17371 Abuna2196 APL14410 MIZA315 QCAZ16523 MTR10147 JMP2196 INPAH29082 ???? INPAH036519 AJC3033	Allobates_granti Allobates_aff.tapajos2 Allobates_sp. Allobates_conspicuus Allobates_aff.hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_hodli Allobates_gasconi Allobates_pittieri Allobates_kingsburyi Colostethus_sp. Allobates_trilineatus Allobates_paleovarzensis Allobates_talamancae Allobates_tapajos Allobates_marchesianus	Allobates_aff.granti Allobates_aff.tapajos_3 Allobates_bacurau Allobates_conspicuus/subfolionidificans Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_gasconi Allobates_humilis/pittieri Allobates_kingsburyi Allobates_masniger/nidicola Allobates_paleovarzensis Allobates_paleovarzensis Allobates_talamancae Allobates_tapajos Allobates_undulatus	16 28 25 14 4 4 1 1 1 1 1 1 1 1 1 1 1 1 4 30 15 37 10 24 24 5 29 39	Group[34] Group[59] Group[10] Group[26] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[30] Group[58] Group[1] Group[20] Group[6] Group[6] Group[38] Group[28] Group[43]	Species9 Species22 Species18 Species 2 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species23 Species12 Species29 Species31 Species17 Species17 Species32 Species20 Species38	17 45 5 9 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 14 19 22 26 34 34 41 42 2	Flat de la waki Parque Estadual Guajara-Mirim Estrada do Miriti, Manicore Porto Walter Porto Walter Abuna Mutum-Parana Mutum-Parana Borba Humaita Mabura hill forest reserve Montagne des singes Memora G Montagne cacao St Eugene North of Altamira, Near Vitoria do Xingu Trairao Serra Azul, Monte Alegre Abuna Eirunepe, Rio Jurua Ojo de Agua, Curimagua, Estado vFalcon Zamora Chinchipe Chicana River Cachoeira das Pombas Leticia, Varzea del del rio Ta Kana, km 10 carretera Barcelos San Blas Itatuiba Pica en bosque entre rio Cuao y Tobogan del Cuao	France Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil Guyana France France France France Brazil Brazil Brazil Brazil Brazil Venezuela Ecuador Brazil Colombia Brazil Panama Brazil Venezuela	French_Guiana RO AM AC AC RO RO RO AM AM Guyana French_Guiana French_Guiana French_Guiana French_Guiana PA PA PA RO AM Venezuela Ecuador AM Colombia AM Panama PA Venezuela	3.18341 -10.321389 -5.86777 -8.266389 -8.266389 -9.516 -9.6414 -9.6414 -4.4342 -7.5488 5.1552778 5.066667 3.31774 2.349128 4.85 -2.804160 -4.68310 -1.28222 -9.5160 -6.763889 11.187761 -4.06695 -6.417609 -4.106 -0.9160 9.2994 -4.171477 5.104	-53.4775 -64.563333 -61.28694 -72.743611 -72.743611 -65.3249 -64.8859 -64.8859 -59.6236 -62.8772 -58.6997222 -52.716667 -52.19329 -53.215985 -53.0666667 -52.07972 -56.02250 -54.12972 -65.3249 -70.163611 -69.651731 -78.950817 -60.358057 -69.25 -62.9706 -78.987 -56.015500 -67.508
	KDQF01000740	AF1904	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Flat de la Waki	France	French_Guiana	3.08466	-53.4101
	KDQF01003760 KDQF01002638 KU195699 DQ502135 DQ502092 GU017430 GU017461 JF689943 JF689974 JF690005 JN690955 KDQF01000091 KDQF01001179 KDQF01002288 KDQF01004184 KF310940 KF310971 KF311002 KX686022 KJ747334 EU380805 AY364550 DQ502101 MT524131 JQ966863 AF128586 KX524473 MT524127	MTR977126 HJ285 INPAH35408 OMNH35997 OMNH36070 AbuE2214 MutD2166 StAntD2117 Borba12519 HumaiD13424 SMNS12007 AF0226 AF2992 CM314 RCV2274 APL19824 APL12291 APL17129 APL14419 APL14411 MIZA313 QCAZ16613 MPEG13821 JMP2197 INPAH29083 ???? INPAH036577 AJC3034	Allobates_magnussoni Allobates_tapajos Allobates_sp. Allobates_conspicuus Allobates_aff.hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_gasconi Allobates_pittieri Allobates_kingsburyi Allobates_nidicola Allobates_trilineatus Allobates_paleovarzensis Allobates_talamancae Allobates_tapajos Allobates_marchesianus	Allobates_aff.magnussoni Allobates_aff.tapajos_3 Allobates_bacurau Allobates_conspicuus/subfolionidificans Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_gasconi Allobates_humilis/pittieri Allobates_kingsburyi Allobates_masniger/nidicola Allobates_paleovarzensis Allobates_paleovarzensis Allobates_talamancae Allobates_tapajos Allobates_undulatus	8 28 25 14 4 4 1 1 1 1 1 1 1 1 1 1 1 1 1 30 15 37 9 24 24 5 29 39	Group[56] Group[60] Group[10] Group[26] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[30] Group[58] Group[1] Group[20] Group[6] Group[6] Group[38] Group[28] Group[43]	Species35 Species22 Species18 Species 2 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species23 Species12 Species29 Species31 Species17 Species17 Species32 Species20 Species38	24 45 5 9 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 14 19 22 26 34 34 41 42 2	Juruena UHE Jirau, Porto Velho, Hidreletrica Jirau Estrada do Miriti, Manicore Porto Walter Porto Walter Abuna Mutum-Parana Cachoeira de St. Antonio Borba Humaita Mabura hill forest reserve Mana, Angouleme Memora D crique Sparouine Itapinima, Margem Direita Rio Madeira North of Altamira, Near Vitoria do Xingu Trairao Serra Azul, Monte Alegre Comunidade Aracazal, Eirunepe Eirunepe, Rio Jurua Ojo de Agua, Curimagua, Estado vFalcon Zamora Chinchipe Tundayme Castanho, ca. 40 km S Manaus Leticia, Varzea del del rio Ta Kana, km 10 carretera Barcelos Nusagandi Itatuiba Pica en bosque entre rio Cuao y Tobogan del Cuao	Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil Guyana France Brazil France Brazil Brazil Brazil Brazil Brazil Brazil Venezuela Ecuador Brazil Colombia Brazil Panama Brazil Venezuela	MT RO AM AC AC RO RO RO AM AM Guyana French_Guiana AP French_Guiana AM PA PA PA AM AM Venezuela Ecuador AM Colombia AM Panama PA Venezuela	-10.3236111 -8.748483 -5.86777 -8.266389 -8.266389 -9.516 -9.6414 -8.6550 -4.4342 -7.5488 5.1552778 5.383333 3.31294 5.266667 -5.4244444 -2.804160 -4.68310 -1.28222 -6.735762 -6.763889 11.187761 -4.06695 -3.619444 -4.106 -0.9160 9.3489 -4.171477 5.104	-58.4927778 -63.903465 -61.28694 -72.743611 -72.743611 -65.3249 -64.8859 -64.0195 -59.6236 -62.8772 -58.6997222 -53.65 -52.18036 -54.266667 -60.715 -52.07972 -56.02250 -54.12972 -70.194858 -70.163611 -69.651731 -78.950817 -60.446333 -69.25 -62.9706 -78.966 -56.015500 -67.508
	KDQF01001034	AF2642	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Atachi Bakka	France	French_Guiana	3.5671	-53.9614
	KDQF01003761 MT524132 KU195700 KF250492 DQ502093 GU017431 GU017462 JF689944 JF689975 JF690006 JN690956 KDQF01000095 KDQF01001190 KDQF01002317 KDQF01004323 KF310941 KF310972 KF311003 KX686023 KJ747335 EU380806 EU342526 DQ502142 MT524155 MF624182 AY364562 KX524474 MT524128	MTR977138 IDLAR5271 INPAH35402 APL1691 OMNH34102 AbuE2215 MutD2167 StAntD2118 Borba12520 HumaiD13425 SMNS12008 AF0230 AF3015 CM386 T4468 APL19825 APL12292 APL17131 APL14422 APL14416 MIZA314 QCAZ16525 MPEG13820 PEP529 MTR36338 QCAZ15562 INPAH036528 AJC3036	Allobates_magnussoni Allobates_sp. Allobates_sp. Allobates_subfolionidificans Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_gasconi Allobates_pittieri Allobates_kingsburyi Allobates_nidicola Allobates_melanolaemus Allobates_sp.Ica Allobates_talamancae Allobates_tapajos Allobates_marchesianus	Allobates_aff.magnussoni Allobates_aff.trilineatus_1 Allobates_bacurau Allobates_conspicuus/subfolionidificans Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_gasconi Allobates_humilis/pittieri Allobates_kingsburyi Allobates_masniger/nidicola Allobates_paleovarzensis Allobates_paleovarzensis Allobates_talamancae Allobates_tapajos Allobates_undulatus	8 22 25 14 2 4 1 1 1 1 1 1 1 1 1 1 1 1 1 30 15 37 9 24 24 5 29 39	Group[56] Group[49] Group[51] Group[26] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[30] Group[58] Group[1] Group[20] Group[6] Group[6] Group[63] Group[28] Group[43]	Species35 Species13 Species18 Species 2 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species23 Species12 Species29 Species31 Species17 Species17 Species32 Species20 Species38	24 47 5 9 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 14 19 22 26 34 34 41 42 2	Juruena Estacion Biologica Panguana Estrada do Miriti, Manicore Parque Zoobotanico, UFAC, Rio Branco Cuyabeno Abuna Mutum-Parana Cachoeira de St. Antonio Borba Humaita Mabura hill forest reserve Mana, Angouleme Memora D Apatou Mana, Angouleme North of Altamira, Near Vitoria do Xingu Trairao Serra Azul, Monte Alegre Comunidade Aracazal, Eirunepe Eirunepe, Rio Jurua Ojo de Agua, Curimagua, Estado vFalcon Zamora Chinchipe near Panguitza Castanho, ca. 40 km S Manaus Yavari Miri Sao Pedro, Rio Ica EsmeraldasDurango Itaituba, Parana-Mirim Pica en bosque entre rio Cuao y Tobogan del Cuao	Brazil Peru Brazil Brazil Ecuador Brazil Brazil Brazil Brazil Brazil Guyana France Brazil France France Brazil Brazil Brazil Brazil Brazil Venezuela Ecuador Brazil Peru Brazil Ecuador Brazil Venezuela	MT Huanuco AM AC Ecuador RO RO RO AM AM Guyana French_Guiana AP French_Guiana French_Guiana PA PA PA AM AM Venezuela Ecuador AM Loreto AM Ecuador PA Venezuela	-10.3236111 -9.617 -5.86777 -9.9500 -0.0886 -9.516 -9.6414 -8.6550 -4.4342 -7.5488 5.1552778 5.383333 3.31294 5.166667 5.3833333 -2.804160 -4.68310 -1.28222 -6.735761 -6.763889 11.187761 -4.06695 -3.619444 -4.259917 -9.461081 1.0858 -4.5825 5.104	-58.4927778 -74.933 -61.28694 -67.9500 -76.1419 -65.3249 -64.8859 -64.0195 -59.6236 -62.8772 -58.6997222 -53.65 -52.18036 -54.333333 -53.65 -52.07972 -56.02250 -54.12972 -70.194857 -70.163611 -69.651731 -78.9508166 -60.446333 -70.141172 -67.417928 -78.6956 -56.6617 -67.508
	KDQF01001055	AF2682	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Alikene, crique	France	French_Guiana	3.21515	-52.4019
	KDQF01003644 MT524141 KU195701 KF250493 DQ502094 GU017432 GU017463 JF689945 JF689976 JF690007 JN690957 KDQF01000096 KDQF01001207 KDQF01002324 KDQF01004455 KF310942 KF310973 KF311004 KX686024 KDQF01004209 KJ940454 EU342527 DQ502210 DQ502109 MT524143 AY843577 KX524475 MT524116	MTR28013 FGZC3247 INPAH35401 APL1692 OMNH34104 AbuE2217 Acre12931 StAntD2119 NOlin12625 Manic12547 MTR13800 AF0231 AF3049 CM399 WAAM002 APL19826 APL12294 APL15678 APL14427 SAMA8574 CVULA5690 QCAZ16524 MPEG13819 MJH3909 FGZC3276 USNMFS52055 INPAH036570 AJC3040	Allobates_sp.divisor Allobates_sp. Allobates_sp. Allobates_subfolionidificans Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_goianus Allobates_humilis Allobates_kingsburyi Allobates_nidicola Allobates_paleovarzensis Allobates_sp. Allobates_talamancae Allobates_tapajos Allobates_aff.undulatus	Allobates_aff.melanolaemus Allobates_aff.trilineatus_1 Allobates_bacurau Allobates_conspicuus/subfolionidificans Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_goianus Allobates_humilis/pittieri Allobates_kingsburyi Allobates_masniger/nidicola Allobates_paleovarzensis Allobates_sp."Huanuco" Allobates_talamancae Allobates_tapajos Allobates_undulatus	26 22 25 14 2 4 4 1 1 1 1 1 1 1 1 1 1 1 1 35 15 37 9 24 19 5 29 39	Group[8] Group[49] Group[10] Group[26] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[23] Group[25] Group[1] Group[20] Group[6] Group[12] Group[38] Group[28] Group[43]	Species19 Species13 Species18 Species 2 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species27 Species12 Species29 Species31 Species17 Species7 Species32 Species20 Species38	27 47 5 9 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 15 19 22 26 34 35 41 42 2	Serra do Divisor Estacion Biologica Panguana Estrada do Miriti, Manicore Parque Zoobotanico, UFAC, Rio Branco Cuyabeno Abuna Brasileia Cachoeira de St. Antonio Nova Olinda Manicore Serra do Navio Mana, Angouleme Mitan G Lucifer Flat de la waki North of Altamira, Near Vitoria do Xingu Trairao Vila de Balbina, Presidente Figueiredo Comunidade Aracazal, Eirunepe Minacu Zamora Chinchipe near Panguitza Castanho, ca. 40 km S Manaus Fazenda Sao Francisco Estacion Biologica Panguana bocas del toro Itaituba, Parana-Mirim Pica en bosque entre rio Cuao y Tobogan del Cuao	Brazil Peru Brazil Brazil Ecuador Brazil Brazil Brazil Brazil Brazil Brazil France France France France Brazil Brazil Brazil Brazil Brazil Venezuela Ecuador Brazil Brazil Peru Panama Brazil Venezuela	AC Huanuco AM AC Ecuador RO AC RO PA AM AP French_Guiana French_Guiana French_Guiana French_Guiana PA PA AM AM GO Venezuela Ecuador AM AM Huanuco Panama PA Venezuela	-7.43802200 -9.617 -5.86777 -9.9500 -0.0886 -9.516 -10.9653 -8.6550 -3.8744 -5.823100 0.9180556 5.383333 2.6284 4.7774 3.18341 -2.804160 -4.68310 -1.91972 -6.735760 -13.5408333 8.8678 -4.06695 -3.619444 -3.205241 -9.617 9.373852 -4.5825 5.104	-73.6587350 -74.933 -61.28694 -67.9500 -76.1419 -65.3249 -68.7331 -64.0195 -59.0461 -61.298600 -52.0027778 -53.65 -52.55404 -53.9475 -53.4775 -52.07972 -56.02250 -59.41611 -70.194856 -48.0902777 -70.4861 -78.9508166 -60.446333 -60.442132 -74.933 -82.275711 -56.6617 -67.508
	KDQF01001078	AF2728	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Mitaraka, layon B	France	French_Guiana	2.23577	-54.4492
	MF624181 MT524142 KU195702 KDQF01000007 DQ502113 GU017433 GU017464 JF689946 JF689977 JF690008 JN690958 KDQF01000099 KDQF01001208 KDQF01002325 KF310912 KF310943 KF310974 KF311005 KX686025 KDQF01004210 MT524133 EU342528 EU342518 JQ966835 MT524145 DQ502025 KX524476 AY263227	MCP10188 FGZC3272 INPAH35404 AAGARDA854 MJH3976 AbuE2218 Acre12932 StAntD2124 Democ12611 Manic12551 MTR13802 AF0235 AF3050 CM401 APL14107 APL14421 APL12293 APL15679 APL14435 SAMA8575 JMP1703 QCAZ16614 LSUMZ16941 INPAH29176 FGZC3348 326 INPAH036559 ???	Allobates_sp.Cruzeiro_do_Sul Allobates_sp. Allobates_sp. Allobates_crombiei Allobates_femoralis Allobates_hodli Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_goianus Allobates_trilineatus Allobates_kingsburyi Allobates_nidicola Allobates_paleovarzensis Allobates_sp. Allobates_talamancae Allobates_tapajos Allobates_zaparo	Allobates_aff.melanolaemus Allobates_aff.trilineatus_1 Allobates_bacurau Allobates_crombiei Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_goianus Allobates_insperatus/juami Allobates_kingsburyi Allobates_masniger/nidicola Allobates_paleovarzensis Allobates_sp."Huanuco" Allobates_talamancae Allobates_tapajos Allobates_zaparo	26 22 25 33 1 4 4 1 1 1 1 1 1 1 1 1 1 1 1 35 12 37 9 24 19 5 29 3	Group[11] Group[49] Group[52] Group[22] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[23] Group[31] Group[1] Group[20] Group[6] Group[12] Group[38] Group[28] Group[33]	Species19 Species13 Species18 Species27 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species27 Species 3 Species29 Species31 Species17 Species7 Species32 Species20 Species 1	27 47 5 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 15 20 22 26 34 35 41 42 50	Cruzeiro do Sul, Mata da Infraero Cashiboya. Contamana Estrada do Miriti, Manicore Tailandia, Reserva CPA Oeste Manaus Abuna Brasileia Cachoeira de St. Antonio Democracia Manicore Serra do Navio Montagne des singes Mitan G Lucifer BR-230 road, west of Pacaja North of Altamira, Near Vitoria do Xingu Trairao Vila de Balbina, Presidente Figueiredo Comunidade Aracazal, Eirunepe Minacu rio Takana, km 9 carretera Leticia Zamora Chinchipe near Arenas Castanho, ca. 40 km S Manaus Janauaca Estacion Biologica Panguana Bocas del toro, S end of Isla Popa Itaituba, Parana-Mirim NA	Brazil Peru Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil France France France Brazil Brazil Brazil Brazil Brazil Brazil Colombia Ecuador Brazil Brazil Peru Panama Brazil Ecuador	AC Ucayali AM PA AM RO AC RO AM AM AP French_Guiana French_Guiana French_Guiana PA PA PA AM AM GO Colombia Ecuador AM AM Huanuco Panama PA Ecuador	-7.6141 -7.657483 -5.86777 -2.920212 -2.980833 -9.516 -10.9653 -8.6550 -5.8058 -5.823100 0.9180556 5.066667 2.6284 4.7774 -3.577222 -2.804160 -4.68310 -1.91972 -6.735759 -13.5408333 -4.136 -4.06695 -3.619444 -3.4792 -9.617 9.134973 -4.5825	-72.776917 -74.932776 -61.28694 -48.899459 -59.921111 -65.3249 -68.7331 -64.0195 -61.4453 -61.298600 -52.0027778 -52.716667 -52.55404 -53.9475 -51.04583 -52.07972 -56.02250 -59.41611 -70.194855 -48.0902777 -69.924 -78.950817 -60.446333 -60.2031 -74.933 -82.105754 -56.6617
	KDQF01001134	AF2914	Allobates_granti	Allobates_aff.granti	16	Group[34]	Species9	17	Memora G	France	French_Guiana	3.31774	-52.1932
	KDQF01003353 MT524157 KU195703 KDQF01000393 DQ502117 GU017434 GU017465 JF689947 JF689978 JF690009 JN690959 KDQF01000206 KDQF01001227 KDQF01002556 KF310913 KF310944 KF310975 KF311006 KX686026 EU201068 MT524135 EU342529 EU342519 JQ966836 DQ502074 DQ502056 KX524477 AY364578	MTR17821 PEP531 INPAH35399 AF1089 MJH7354 AbuE2219 Acre12937 MorriD2134 Democ12613 Manic12552 MTR13936 AF0693 AF3078 FTA66 APL14108 APL14438 APL12268 APL15680 APL14441 AF0148 IDLAR8198 QCAZ16612 LSUMZ16956 APL12625 379 361 INPAH036526 QCAZ16601	Allobates_olfersioides Allobates_trilineatus Allobates_sp. Allobates_crombiei Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_femoralis Allobates_granti Allobates_sp. Allobates_kingsburyi Allobates_nidicola Allobates_paleovarzensis Allobates_sp.Neblina Allobates_talamancae Allobates_tapajos Allobates_zaparo	Allobates_aff.olfersioides_1 Allobates_aff.trilineatus_1 Allobates_bacurau Allobates_crombiei Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_femoralis/hodli Allobates_granti Allobates_insperatus/juami Allobates_kingsburyi Allobates_masniger/nidicola Allobates_paleovarzensis Allobates_sp."Neblina" Allobates_talamancae Allobates_tapajos Allobates_zaparo	42 22 25 33 1 4 4 1 1 1 1 1 1 1 1 1 1 1 1 17 11 37 9 24 40 5 29 3	Group[46] Group[49] Group[10] Group[22] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[32] Group[34] Group[31] Group[1] Group[20] Group[6] Group[42] Group[38] Group[28] Group[33]	Species41 Species13 Species18 Species27 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species 1 Species10 Species 3 Species29 Species31 Species17 Species40 Species32 Species20 Species 1	30 47 5 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 16 20 22 26 34 36 41 42 50	Mata do Catole, Maceio Heath River Wildlife Centre Estrada do Miriti, Manicore Babaculandia Panguana Abuna Brasileia Morrinhos direita madeira Democracia Manicore Lourenco Paracou Mitan G Rio Vila Nova, Mazagao, FLOTA II BR-230 road, west of Pacaja Near ferry harbor across the Xingu River, Altamira Fazenda Treviso, Belterra Vila de Balbina, Presidente Figueiredo Comunidade Aracazal, Eirunepe Brownsberg Rio Curaray. Maynas Morona Santiago 10 km S Gualaquiza Castanho, ca. 40 km S Manaus Careiro Neblina Base Camp on Rio Mawarinuma Itaituba, Parana-Mirim Morona SantiagoSantiago	Brazil Bolivia Brazil Brazil Peru Brazil Brazil Brazil Brazil Brazil Brazil France France Brazil Brazil Brazil Brazil Brazil Brazil Suriname Peru Ecuador Brazil Brazil Venezuela Nicaragua Brazil Ecuador	AL La_Paz AM TO Huanuco RO AC RO AM AM AP French_Guiana French_Guiana AP PA PA PA AM AM Suriname Loreto Ecuador AM AM Venezuela Nicaragua PA Ecuador	-9.51154 -12.68 -5.86777 -7.20472222 -9.603932 -9.516 -10.9653 -9.0158 -5.8058 -5.823100 2.3236111 5.2754 2.6284 0.449335 -3.577222 -3.140000 -3.15770 -1.91972 -6.735758 4.936527 -1.577639 -3.60 -3.619444 -3.3740 0.830556 10.933175 -4.5825 -3.60	-35.730143 -68.712 -61.28694 -47.7569444 -74.935799 -65.3249 -68.7331 -64.0914 -61.4453 -61.298600 -51.6452778 -52.9236 -52.55404 -52.022137 -51.04583 -51.74222 -54.85950 -59.41611 -70.194854 -55.194826 -75.590382 -78.54 -60.446333 -59.8684 -66.161111 -84.300110 -56.6617 -78.54
													105 109 119 121 123 125

Table II .

 II S3: Matrix of sequences used in the Operational Taxonomic Units (OTUs) phylogenetic reconstruction of the genus Allobates. The species column lists the different OTUs and outgroups included in the reconstruction. The Voucher column indicates the specimen number used as representative of the species/OTU. The 12S, 16S, ND1, COI and CytB columns are filled with accession number of the sequenced used for each species/OTU for each locus. Complete phylogenetic reconstruction including outgroups is in

	Allobates_carajas Allobates_trilineatus	BM163 AF4493	MT524111	MT627183
	Allobates_chalcopis Allobates_undulatus	Alca1 AJC3040		MT627182 MT627181
	Allobates_conspicuus/subfolionidificans Allobates_zaparo	FGZC3279 USNM546405	DQ502026	MT627186	DQ502752 DQ502455
	Allobates_crombiei Ameerega_hahneli	AF1097 AF2673		MT627174 MT627178
	Allobates_femoralis/hodli Anomaloglossus_baeobatrachus	AF3224 AF2590	MT627179 NC_030054
	Allobates_flaviventris Aromobates_saltuensis/nocturnus	HJ545 TNHCFS5541/AMNHA130042	HQ290970	MT627192	DQ502860 DQ502592
	Allobates_fratisenescus Colostethus_fugax	QCAZ54377 QCAZ16513	MF624172 HQ290958		MF614174 HQ290538
	Allobates_gasconi Dendrobates_auratus	HJ480 MVZHerp149723		MT627191 JX564862
	Allobates_goianus Epipedobates_boulengeri	SAMA8574 UMMZ227952/QCAZ16574	HQ290997	MT627207	DQ502742 DQ502447
	Allobates_granti Mannophryne_collaris	AF1998 FS5523		MT627176 MT627188
	Allobates_grillisimilis Phyllobates_terribilis	MTR12749 TNHC64420/AMNHA118566	HQ291006	MT627200	DQ502861 DQ502593
	Allobates_humilis/pittieri Rheobates_palmatus	CVULA5690 RHEOPALM	KJ940454	MT627206
	Allobates_juanii/ranoides Figure S2 Allobates_insperatus/juami Silverstoneia_nubicola/erasmios	ARA2394 JMP1703 TNHCFS4942/MAR336	DQ502271 HQ290966	MT627193	DQ502933 DQ502702 MF614333 MF614237
	species Allobates_kingsburyi Allobates_aff.granti Allobates_magnussoni Allobates_aff.magnussoni Allobates_marchesianus Allobates_aff.melanolaemus Allobates_masniger/nidicola Allobates_aff.olfersioides_1 Allobates_melanolaemus Allobates_aff.olfersioides_2 Allobates_niputidea Allobates_aff.olfersioides_3 Allobates_nunciatus Allobates_aff.tapajos_1 Allobates_olfersioides Allobates_aff.tapajos_2 Allobates_ornatus Allobates_aff.tapajos_3 Allobates_paleovarzensis Allobates_aff.trilineatus_1 Allobates_sp."Huanuco" Allobates_aff.trilineatus_2 Allobates_sp."Neblina" Allobates_aff.undulatus Allobates_sp."Ucuyali" Allobates_algorei Allobates_sumtuosus Allobates_amissibilis Allobates_talamancae Allobates_bacurau Allobates_tapajos Allobates_caeruleodactylus Allobates_tinae	voucher QCAZ16523 125PG BM168 977126 AJC2498 MTR28013 MTR10155 MTR17821 NMP6V711404 MTR16435 MUJ3520 JFT959 MPEG36777 MTR10084 MNRJ79897 AF1906 MHNSM22863 HJ285 JMP2196 FGZC3247 FGZC3348 JMP2313 MTR15537 AMNHA159139 GGU684 TNHCFS5551 AF2212 PK3798 QCAZ35236 INPAH35401 MJH3973 MTR10227 HJ298	12S AY364549 JN690205 DQ502272 16S HQ290963 JN690931 MT524148 KDQF01003353 KDQF01002701 MF624178 EU342550 DQ283044 MT524137 HQ290950 DQ502110 KU195701	ND1 MT627184 MT627180 MT627173 MT627198 MT627203 MT627202 MT627196 MT627197 MT627175 MT627194 MT627189 MT627187 MT627185 MT627201 MT627195 MT627177 MT627205 MT627204 MT627199 MT627190	COI DQ502934 DQ502703 HQ290541 Cytb MF614175 DQ502756 DQ502459 HQ290530 DQ502820 DQ502542
					135 136

Table II .

 II S5: BioGeoBEARS model estimates for the eight Neotropical biogeographic areas analysis for the genus Allobates. The model column lists the different model compared with BioGeoBEARS. Log likelihood for each model is in the Lnl column. Best fit of the anagenetic dispersal, extinction and founder event parameters are found respectively in columns d, e and j. Akaike Information Criterion is given in the AIC column.

	model	LnL	numparams	d	e	j	AIC
	DEC+J	-89.411	3	0.002	1.00E-12	0.028	184.8
	DIVA+J	-90.557	3	0.003	1.00E-12	0.028	187.1
	BAYAREA+J	-93.371	3	0.002	1.00E-12	0.033	192.7
	DEC	-104.260	2	0.005	1.00E-12	0	212.5
	DIVA	-112.226	2	0.004	1.00E-12	0	228.5
	BAYAREA	-125.675	2	0.004	0.055	0	255.4

Table II .

 II S6: BioGeoBEARS model estimates for the 14 Neotropical biogeographic areas analysis for the genus Allobates.The model column lists the different model compared with BioGeoBEARS. Log likelihood for each model is in the Lnl column. Best fit of the anagenetic dispersal, extinction and founder event parameters are found respectively in columns d, e and j. Akaike Information Criterion is given in the AIC column.

	model	LnL	numparams	d	e	j	AIC
	DEC+J	-156.530	3	0.003	1.00E-12	0.042	319.1
	DIVA+J	-156.830	3	0.004	1.00E-12	0.036	319.7
	BAYAREA+J	-160.345	3	0.003	1.00E-12	0.043	326.7
	DEC	-185.418	2	0.007	0.010	0	374.8
	DIVA	-196.245	2	0.004	0.009	0	396.5
	BAYAREA	-204.061	2	0.008	0.093	0	412.1

Table II .

 II S7: Summary of dispersal events inferred by the Biogeographic Stochastic Mappings (BSMs) in the fourteen Neotropical biogeographic areas analysis with DEC+J model for the genus Allobates. Counts of founder and anagenetic (Ana.) dispersal events are the average of all 50 BSMs. As we are mainly interested in Amazonian history, we depicted only Amazonian areas in this table. Areas are shown in the map of Fig.1d.

	Areas	Ana received	Ana emitted	Founder emitted	Founder received	Total
	Inambari	4.52	3.92	6.62	2.58	17.64
	Rondônia	2.28	3.16	4.92	2.88	13.24
	Guiana	0.14	0.2	3.38	3.8	7.52
	Tapajós	2.42	1.14	1.3	2.3	7.16
	Huallaga	1.5	1.96	1.84	1.72	7.02
	Napo	0.3	1.28	0.92	2.88	5.38
	Xingu	0.7	1.96	0.94	1.4	5
	Imeri	0.96	0.1	1.58	2.02	4.66
	Belém	0.96	0.18	0	0.04	1.18

Table II .

 II S8: Results of the laser diversification model comparison realized on the Maximum clade credibility tree of the genus Allobates obtained withBEAST 2.5.

	model	LH	r1	r2	r3	r4	st	st2	st3	a	AIC
	yule3r	-3.815	0.187	0.058	0.005	NA	9.802	3.95	NA	NA	17.631
	yule4r	-2.735	0.170	0.428	0.061	0.005	10.355	9.98	3.95	NA	19.47
	DDL	-8.314	0.279	NA	NA	NA	NA	NA	NA	NA	20.627
	yule2r	-9.33	0.115	0.009	NA	NA	4.311	NA	NA	NA	24.659
	DDX	-18.927	0.528	NA	NA	NA	NA	NA	NA	NA	41.854
	yule	-25.731	0.078	NA	NA	NA	NA	NA	NA	NA	53.462
	bd	-25.731	0.078	NA	NA	NA	NA	NA	NA	0	55.462

LH: Likelihood; r1: first speciation rate; r2: second speciation rate; r3: third speciation rate; r4: fourth speciation rate; st: first rate shift; st2: second rate shift; st3: third rate shift; a: extinction rate; AIC: Akaike Information Criterion.

  P. fenestratus; P. conspicillatus clades). The P. ramagii clade is moderately supported (pp=0.78) and encompasses 20 OTUs distributed in Amazonia and in the Atlantic Forest, its crown age is estimated at 20.. Most species of this clade belong to the P. vilarsi and P. ramagii subclades but also includes three deeply diverging lineages corresponding to OTUs from the northern part of the Atlantic Forest (P. sp. "Camacan") and the breijos de altitud in Nordeste (P. sp. "Pacoti") as well as an early diverging lineage from the Guiana Shield and Tobago (P. gutturalis and P. charlottevillensis). The P. ramagii subclade is circumscribed to the Atlantic forest, with eight OTUs that diversified over the last 9.1 My (CI 95%: 7 -11.9). Its sister clade, the P. vilarsi subclade display a crown age estimated at 13.2 Mya (CI 95%: 10.4 -16.4) and also encompasses eight mostly distributed in the Guiana Shield but includes one OTU in the upper Madeira. The sister group of the P.

ramagii clade consists of the clade that dates back 19.6 Mya (CI 95%: 17.

1 -22.1)

formed by the two other major clades (P. conspicillatus and P. fenestratus clades). The P. fenestratus clade is distributed mostly in the Brazilian Shield but two OTUs occur in the Guiana Shield while it only marginally extends into Western Amazonia (P. khoeleri); it encompasses sixteen OTUs and started its diversification some 14.5 Mya (CI 95%: 12.5 -16.6). The P. conspicillatus clade, that encompasses 21 OTUs, extensively diversified in Western Amazonia over the last 16.9 Mya (CI 95%: 14.5 -19.1). Additionally, few species of this clade occur in the trans-Andean forests: P. achatinus and a subclade composed of three OTUs related to P. gaigei; suggesting two distinct trans-Andean dispersals around 15 Mya.

  gaigei clade / P. peruvianus clade; P. achatinus / other species of the P. conspicillatus clade, except P. lymani), bothMarín et al., 2017). Given the low dispersal abilities and narrow bioclimatic niches of the Pristimantis conspicillatus group, we hypothesize that the Andean uplift induced two concomitant vicariant speciation events within the group. The Andes acted as a barrier to dispersal 15 Mya onward for the P.

	dated around 15 Mya (Fig. III.1b). These time estimates seem concomitant with a similar trans-Andean
	event that occurred in the forest litter dweller genus Allobates (Réjaud et al., 2020). Conversely, in
	amphibian taxa with higher dispersal abilities, such as glassfrogs or toads, some species have been
	isolated by the Andes (Bessa-Silva et al., 2020; Castroviejo-Fisher et al., 2014), while other species
	even display a cross-Andean distribution (e.g. Boana boans, Atractus occipitoalbus; Köhler, 2011;

conspicillatus group, possibly since the intense orogeny phase that occurred 12 Mya

[START_REF] Mora | Tectonic history of the Andes and Subandean zones: Implications for the development of the Amazon drainage basin Tectonic history of the Andes and sub-Andean zones: implications for the development of the Amazon drainage basin[END_REF]

.

Table III .1: Results of the TreePar diversification through time analysis, on a random sample of 100 trees from the BEAST 2 iterations.

 III First column lists the different models. The second and third column respectively display the log likelihood and AICc scores of each model. The first diversification rates (from the tips to the first time shift) are stored in the fourth column, while the first time shifts and second diversification rates (between the first and second time shift) are displayed in the fifth and sixth

	column respectively.
	Model	logL	AICc	DivRate1 ShiftTime1 DivRate2
	NoShiftTim e	-184.934	374.079	0.126	NA	NA
	1ShiftTime	-175.757	362.625	0.005	1.638	0.147
	2ShiftTime s	-173.358	365.540	0.003	1.612	0.127
	3ShiftTime s	-171.334	370.167	0.002	1.585	0.117
	4ShiftTime s	-169.439	376.211	0.001	1.573	0.116

Table III .2: Estimates of trans-riverine dispersal events obtained from summaries of the 50 biogeographical stochastic mappings (BSMs) for the 14 Neotropical biogeographic areas. The first

 III column corresponds to the major Amazonian rivers. The second and third column are respectively the sums of trans-riverine anagenetic dispersals and founder events for a given river, while the fourth column is the sum of the second and third columns.

	Japura	0	0	0
	Tocantins	0	0	0
		Ana	Founders	All disp.
	Upper Amazon	2.1	2.52	4.62
	Madeira	2.6	0.96	3.56
	Tapajos	1.54	0.92	2.46
	Lower Amazon	1.1	1.02	2.12
	Negro	2	0.1	2.1
	Xingu	1.12	0.28	1.4
	Huallagua	0.42	0.76	1.18

Table III .

 III S2: 16S DNA sequence information and delimitation results for the Pristimantis conspicillatus group. GeneBank accession numbers are in the column labelled GB, new sequences are indicated with an accession starting with >XXXXXXXX or >ZONI (accession will be added pending manuscript acceptance); sequence starting with >ZONI are under embargo with concealed gps coordinates. Specimen numbers are indicated in the Voucher column. Specimen identification prior and posterior to delimitation analyses are indicated in the ID_initial and ID_post_delimitation columns respectively. Species assignation of each sequence per delimitation method are indicated in columns ABGD, mPTP and GMYC. The consensus of the three delimitation methods is indicated in the consensus column. Locality, country and state are indicated in said columns. Spatial coordinates corresponding to localities are found in the latitude and longitude columns.

	>KDQF01002571 >XXXXXXXXMTR40394 >JN691265 >KDQF01001352 >ZONIA65512 >KDQF01001606 >KX242532 >KDQF01002879 >KX925981 >KDQF01001812 >ZONIK23715 >JN691222 >KDQF01002012	GN213 MTR40394 MTR13722 AF3286 AJC2785 AG437 SILVES41 MPEG30066 LZATM739 BM047 SCF384 PGZ60 BPN2822	Pristimantis_fenestratus Pristimantis_vilarsi Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_sp. Pristimantis_gutturalis Pristimantis_sp. Pristimantis_koehleri Pristimantis _latro Pristimantis_fenestratus Pristimantis_skydmainos Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Pristimantis_aff.fenestratus_5 Pristimantis_aff.vilarsi_3 Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_citriogaster Pristimantis_gutturalis Pristimantis_koehleri Pristimantis_koehleri Pristimantis_latro Pristimantis_latro Pristimantis_skydmainos Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Group[36] Species17 12 Group[17] Species24 38 Group[33] Species20 2 Group[33] Species20 1 Group[6] Species10 20 Group[32] Species29 43 Group[38] Species13 6 Group[38] Species13 7 Group[37] Species18 10 Group[37] Species18 10 Group[43] Species21 26 Group[29] Species26 33 Group[29] Species26 33	24 36 29 29 6 44 17 17 25 25 30 39 39	Guaranta do Norte Maturacá Serra do Navio Trois paletuviers Pongo de Rentema, carretera Bagua-Sara Merisa Alikene Silves Serra do Acari. N ESEC Grao Para Anapu UHE Belo Monte, Vitoria do Xingu, Rio Xingu Senda a 2.5 km rio arriba del Puesto de control y vigilancia Cocama Mitaraka RAP Basecamp 1, nr camp	MT AM AP French Guiana Amazonas French Guiana AM PA PA PA Ucayali French Guiana Suriname	Brazil Brazil Brazil France Peru France Brazil Brazil Brazil Brazil Peru France Suriname	-9.7875 0.621452 0.9180556 4.054582 -5,301 3.220044 -2.7296 1.2854194 -3.470440 -2.950584 -10,388 2.2666667 2.477	-54.91 -66.120803 -52.0027778 -51.677007 -78,396 -52.37855 -58.192792 -58.6959 -51.198919 -51.936607 -71,183 -54.5166667 -55.62941
	>KDQF01004380 >XXXXXXXXMTR41360 >JN691266 >KDQF01001628 >XXXXXXXXQCAZ41868 >KDQF01001608 >KX242533 >KDQF01003018 >KX925983 >KDQF01001816 >JX267387 >JN691223 >KDQF01002029	TM512 MTR41360 MTR13740 AG463 QCAZ41868 AG439 EA213 MSH10387 LZATM744 BM058 MTR13615 PGZ62 BPN2956	Pristimantis_fenestratus Pristimantis_vilarsi Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_citriogaster Pristimantis_gutturalis Pristimantis_sp. Pristimantis_malkini Pristimantis _latro Pristimantis_fenestratus Pristimantis_sp.Camacan Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Pristimantis_aff.fenestratus_5 Pristimantis_aff.vilarsi_3 Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_citriogaster Pristimantis_gutturalis Pristimantis_koehleri Pristimantis_koehleri Pristimantis_latro Pristimantis_latro Pristimantis_sp.Camacan Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Group[36] Species17 12 Group[17] Species24 38 Group[33] Species20 2 Group[33] Species20 1 Group[6] Species10 20 Group[32] Species29 43 Group[38] Species13 6 Group[38] Species13 6 Group[37] Species18 10 Group[37] Species18 10 Group[16] Species27 59 Group[29] Species26 33 Group[29] Species26 33	24 36 29 29 6 44 17 17 25 25 40 39 39	Guaranta do Norte Rio Inhambú Serra do Navio Alikene Morona Santiago: General Leonidas Plaza Gutiérrez, Limón Alikene Manaus E.E. Anavilhanas, Terra firme 1 Anapu UHE Belo Monte, Vitoria do Xingu, Rio Xingu Camacan Mitaraka RAP Basecamp 2,	MT AM AP French Guiana French Guiana AM AM PA PA BA French Guiana Suriname	Brazil Brazil Brazil France Ecuador France Brazil Brazil Brazil Brazil Brazil France Suriname	-9.7875 -0.231949 0.9180556 3.220044 -2.972291 3.220044 -3.081709 -2.5344444 -3.470440 -2.950584 -15.432842 2.2666667 2.47881	-54.91 -65.416147 -52.0027778 -52.37855 -78.440068 -52.37855 -60.003571 -60.8366667 -51.198919 -51.936607 -39.496528 -54.5166667 -55.63161
	>JX267540 >KDQF01004281 >JN691267 >XXXXXXXXAM016 >EF493701 >KDQF01001868 >KX242534 >ZONIK07015 >KX925984 >KDQF01001833 >JX267386 >JN691224 >KDQF01002034	MZUSP139440 SMS915 MTR13765 AM016 KU217857 BOAM024 SISJAT78 SCF165 LZATM742 BM199 MTR04514 PGZ63 BPN2984	Pristimantis_cf.fenestratus Pristimantis_vilarsi Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_condor Pristimantis_gutturalis Pristimantis_sp. Pristimantis_fenestratus Pristimantis _latro Pristimantis_fenestratus Pristimantis_sp.Pacoti Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Pristimantis_aff.fenestratus_6 Pristimantis_aff.vilarsi_3 Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_condor Pristimantis_gutturalis Pristimantis_koehleri Pristimantis_koehleri Pristimantis_latro Pristimantis_latro Pristimantis_sp.Pacoti Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Group[34] Species19 13 Group[17] Species24 38 Group[33] Species20 2 Group[33] Species20 1 Group[5] Species10 48 Group[32] Species29 43 Group[38] Species13 6 Group[38] Species13 4 Group[37] Species18 10 Group[37] Species18 10 Group[52] Species30 58 Group[29] Species26 33 Group[29] Species26 33	26 36 29 29 12 44 17 17 25 25 50 39 39	Tapirape, Maraba Apiau Serra do Navio Inini Tolenga Borne 4 Silves Puesto de control y vigilancia Cocama Ucayali PA RR AP French Guiana French Guiana AM Sen. José Porfírio PA UHE Belo Monte, Vitoria do Xingu, Rio Xingu PA Pacoti CE Mitaraka French Guiana RAP Basecamp 2, Kasikasima trail Suriname	Brazil Brazil Brazil France Ecuador France Brazil Peru Brazil Brazil Brazil France Suriname	-5.401372 2.4334167 0.9180556 3.663159 -3.353876 2.3709 -2.7296 -10,399 -2.589873 -2.950584 -4.225 2.2666667 2.97705	-49.047279 -61.4138333 -52.0027778 -53.928308 -78.577930 -53.7728 -58.192792 -71,165 -51.949139 -51.936607 -38.9233333 -54.5166667 -55.40802
	>KX242515 >KDQF01004282 >JN691268 >XXXXXXXXAP05 >EF493529 >KDQF01002145 >KX242535 >ZONIK07115 >KX925985 >KDQF01001846 >KDQF01002494 >JN691225 >KDQF01002122	MPEG49 SMS917 MTR13792 AP05 QCAZ28448 CAAM34 SISJAT6 SCF166 LZATM748 BM351 FJ05 PGZ65 CAAM07	Pristimantis_sp.A Pristimantis_vilarsi Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_conspicillatus Pristimantis_gutturalis Pristimantis_sp. Pristimantis_fenestratus Pristimantis _latro Pristimantis_fenestratus Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Pristimantis_aff.fenestratus_6 Pristimantis_aff.vilarsi_3 Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_conspicillatus Pristimantis_gutturalis Pristimantis_koehleri Pristimantis_koehleri Pristimantis_latro Pristimantis_latro Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Group[34] Species19 13 Group[17] Species24 38 Group[33] Species20 2 Group[33] Species20 2 Group[53] Species10 23 Group[32] Species29 43 Group[38] Species13 6 Group[38] Species13 4 Group[37] Species18 10 Group[37] Species18 10 Group[22] Species23 41 Group[29] Species26 33 Group[29] Species26 33	26 36 29 29 9 44 17 17 25 25 32 39 39	Maraba Apiau Serra do Navio Plote PPBIO Sucumbios, Monte Tour Montagne cacao Puesto de control y vigilancia Cocama Ucayali PA RR AP Porto Grande French Guiana Sen. José Porfírio PA UHE Belo Monte, Vitoria do Xingu, Rio Xingu PA Jamari, FLONA RO Mitaraka French Guiana Montagne cacao French Guiana	Brazil Brazil Brazil AP Ecuador France Brazil Peru Brazil Brazil Brazil France France	-5.19818805 2.4334167 0.9180556 0.979331 0.168932 2.349128 -10,399 -2.589873 -2.950584 -9.113842 2.2666667 2.349128	-49.2835842 -61.4138333 -52.0027778 -51.614895 -77.547211 -53.215985 -71,165 -51.949139 -51.936607 -62.960111 -54.5166667 -53.215985
	>KX242516 >AMTMP35715 >JN691269 >XXXXXXXXAP099 >MH516169 >KDQF01002211 >KX242536 >ZONIK07215 >KX925986 >KDQF01002842 >KDQF01002627 >JN691226 >KDQF01002233	MPEG17745 MHNLS17560 MTR13847 AP099 QCAZ18007 CM063 MPEG21875 SCF167 LZATM751 MPEG24685 HJ039 PGZ66 CM129	Pristimantis_sp.A Pristimantis_sp. Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_conspicillatus Pristimantis_gutturalis Pristimantis_sp. Pristimantis_fenestratus Pristimantis _latro Pristimantis_fenestratus Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Pristimantis_aff.fenestratus_6 Pristimantis_aff.vilarsi_4 Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_conspicillatus Pristimantis_gutturalis Pristimantis_koehleri Pristimantis_koehleri Pristimantis_latro Pristimantis_latro Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Group[34] Species19 13 Group[18] Species24 40 Group[33] Species20 2 Group[33] Species20 2 Group[53] Species10 23 Group[32] Species29 43 Group[38] Species13 6 Group[38] Species13 4 Group[37] Species18 10 Group[37] Species18 10 Group[22] Species23 41 Group[29] Species26 33 Group[29] Species26 33	26 37 29 29 9 44 17 17 25 25 32 39 39	Tucuruí Lourenco Plote PPBIO Monts Bakra Puesto de control y vigilancia Cocama Ucayali PA Bolivar AP Porto Grande French Guiana Sen. José Porfírio PA Caxiuana, FLONA , Plote PPBIO PA UHE Jirau, Porto Velho, Hidreletrica Jirau RO Mitaraka French Guiana Tibourou French Guiana	Brazil Venezuela Brazil AP Ecuador France Brazil Peru Brazil Brazil Brazil France France	-3.772766 5,299 2.3236111 0.979331 -1.065517 3.3005 -10,399 -2.589873 -1.7923056 -8.748483 2.2666667 4.416667	-49.688417 -63,359 -51.6452778 -51.614895 -77.617650 -52.9493 -71,165 -51.949139 -51.4340278 -63.903465 -54.5166667 -52.3
	GB >AMTMP36215 >KX242517 >JN691270 >KDQF01001863 >MH516193 >KDQF01002212 >KX242537 >ZONIK12415 >KX925987 >KDQF01003008 >KDQF01002645 >JN691227 >KDQF01002520	Voucher MPEG221 MHNLS17604 MTR13848 BOAM016 QCAZ25392 CM064 MPEG30070 SCF228 LZATM386 MPEG36815 HJ377 PGZ61 FTA101	ID_initial Pristimantis_sp.A Pristimantis_sp. Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_sp. Pristimantis_gutturalis Pristimantis_sp. Pristimantis_fenestratus Pristimantis _latro Pristimantis_fenestratus Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus	ID_post_delimitation Pristimantis_aff.fenestratus_6 Pristimantis_aff.vilarsi_4 Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_conspicillatus Pristimantis_gutturalis Pristimantis_koehleri Pristimantis_koehleri Pristimantis_latro Pristimantis_latro Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus	ABGD Group[34] Species19 13 mPTP GMYC Consensus 26 Group[18] Species24 40 37 Group[33] Species20 2 29 Group[33] Species20 1 29 Group[53] Species10 23 9 Group[32] Species29 43 44 Group[38] Species13 7 17 Group[38] Species13 4 17 Group[37] Species18 10 25 Group[37] Species18 10 25 Group[22] Species23 41 32 Group[29] Species26 33 39 Group[29] Species26 33 39	Locality Senda a 2.5 km rio arriba del Puesto Maraba Lourenco Borne 4 Monts Bakra de control y vigilancia Cocama Altamira MINA PALITO, Itaituba-PA UHE Jirau, Mutum Mitaraka Rio Vila Nova, Mazagao, FLOTA II	State Bolivar PA AP French Guiana French Guiana Ucayali PA PA RO French Guiana AP	Country Brazil Venezuela Brazil France Ecuador France Brazil Peru Brazil Brazil Brazil France Brazil	Latitude -5.19818805 5,065 2.3236111 2.3709 -0.706944 3.3005 -10,388 -3.217433 -6.31731705 -9.591318 2.2666667 0.449335	Longitude 49.2835842 --63,688 -51.6452778 -53.7728 -76.738611 -52.9493 -71,183 -52.227434 -55.7986444 -65.049181 -54.5166667 -52.022137
	>JN104676 >KDQF01001155 >ZONIF18912 >JN691271 >KDQF01002018 >KY712677 >KDQF01002248 >KX926014 >ZONIK12515 >KX925988 >EF493392 >KDQF01002666 >JN691228 >KDQF01002540	UVC15867 AF295 AJC2477 MTR13849 BPN2855 APL20807 CM180 INPAH34577 SCF229 BIOTA1218 KU218019 HJ612 PGZ64 FTA216	Pristimantis_achatinus Pristimantis_fenestratus Pristimantis_vilarsi Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_sp. Pristimantis_gutturalis Pristimantis _fenestratus Pristimantis_fenestratus Pristimantis _latro Pristimantis_lymani Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Pristimantis_achatinus Pristimantis_aff.fenestratus_6 Pristimantis_aff.vilarsi_4 Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_fenestratus Pristimantis_gutturalis Pristimantis_koehleri Pristimantis_koehleri Pristimantis_latro Pristimantis_lymani Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Group[1] Group[34] Species19 63 Species9 51 Group[18] Species24 71 Group[33] Species20 2 Group[33] Species20 1 Group[41] Species15 15 Group[32] Species29 43 Group[38] Species13 4 Group[38] Species13 4 Group[37] Species18 10 Group[11] Species11 47 Group[22] Species23 41 Group[29] Species26 33 Group[29] Species26 33	59 26 37 29 29 20 44 17 17 25 15 32 39 39	Vila Rica Laguna Pavon, cano Naquen Lourenco RAP Basecamp 1, nr helipad km 620 of BR-319 Highway Trijonction Borba 1 Senda a 2.5 km rio arriba del Puesto de control y vigilancia Cocama Altamira 3.9 km E Loja, ca Loja-Zamora rd Caicara, Porto Velho Mitaraka Rio Amapa Grande, Calcoene, FLOTA III-	MT Guainia AP Suriname AM French Guiana AM Ucayali PA RO French Guiana AP	Colombia Brazil Colombia Brazil Suriname Brazil France Brazil Peru Brazil Ecuador Brazil France Brazil	4.7867 -9.9241667 2,271 2.3236111 2.46606 -7.220011 2.333333 -4.380000 -10,388 -3.217433 -3.98 -8.696257 2.2666667 2.127111	-76.2319 -51.2383333 -68,265 -51.6452778 -55.62973 -63.080075 -54.6 -59.580000 -71,183 -52.227434 -79.16 -63.912477 -54.5166667 -51.191333
	>ZONIA58212 >KT221610 >ZONIA02012 >JN691272 >XXXXXXXXCAAM24 >KY712678 >KDQF01002550 >KX926015 >ZONIK12615 >KX925989 >ZONID15212 >KDQF01002669 >JN691229 >XXXXXXXXHT1613 >XXXXXXXXAJC2704 >KX242518 >ZONIA05012 >JN691273 >KDQF01002269 >KDQF01001037 >KDQF01002554 >KX926016 >ZONIK12715 >KX925990 >ZONID15312 >KDQF01002670 >JN691230 >KDQF01002849 >ZONIA58412 >KDQF01002485 >ZONIA10912 >JN691274 >KDQF01002305 >KDQF01003173 >KDQF01003139 >KX926017 >ZONIK23215 >KX925991 >JX267297 >KDQF01003614 >JN691231 >KDQF01002865 >DQ195453 >KDQF01002486 >ZONIA11212 >JN691275 >KDQF01002312 >KDQF01003177 >KDQF01003265 >KY712668 >ZONIK23315 >KX925992 >JX267299 >KDQF01003617 >JN691232 >KDQF01002866	AJC2702 ??? AJC2886 MTR13855 CAAM24 APL20316 FTA29 INPAH34562 SCF230 BIOTA1111 JMP2315 HJ636 MTR13827 HT1613 AJC2704 CTGA114 AJC2941 MTR13960 CM265 AF265 FTA45 INPAH34578 SCF231 LZATM279 JMP2316 HJ637 MTR13976 MPEG29437 AJC2704 ESTR2177 AJC3052 MTR13965 CM350 MTR10145 MTR06276 INPAH34575 SCF379 LZATM281 MNRJ46880 MTR25532 AF0248 MPEG29685 MC10866 ESTR2178 AJC3061 MTR13966 CM371 MTR10160 MTR13803 ZUEC13327 SCF380 LZATM230 MNRJ46788 MTR25550 AF0253 MPEG29688	Pristimantis_peruvianus Pristimantis_fenestratus Pristimantis_vilarsi Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_sp. Pristimantis_gutturalis Pristimantis _fenestratus Pristimantis_fenestratus Pristimantis _latro Pristimantis_malkini Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_bipunctatus Pristimantis_sp.A Pristimantis_vilarsi Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_fenestratus Pristimantis_gutturalis Pristimantis _fenestratus Pristimantis_fenestratus Pristimantis _latro Pristimantis_malkini Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_peruvianus Pristimantis_fenestratus Pristimantis_vilarsi Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_fenestratus Pristimantis_gutturalis Pristimantis _fenestratus Pristimantis_fenestratus Pristimantis _latro Pristimantis_cf.paulodutrai Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_carranguerorum Pristimantis_fenestratus Pristimantis_vilarsi Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_fenestratus Pristimantis_gutturalis Pristimantis_aff.fenestratus Pristimantis_fenestratus Pristimantis _latro Pristimantis_cf.ramagii Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Pristimantis_aff.bipunctatus Pristimantis_aff.fenestratus_7 Pristimantis_aff.vilarsi_4 Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_fenestratus Pristimantis_gutturalis Pristimantis_koehleri Pristimantis_koehleri Pristimantis_latro Pristimantis_malkini Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_aff.bipunctatus Pristimantis_aff.fenestratus_7 Pristimantis_aff.vilarsi_4 Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_fenestratus Pristimantis_gutturalis Pristimantis_koehleri Pristimantis_koehleri Pristimantis_latro Pristimantis_malkini Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_aff.bipunctatus Pristimantis_aff.fenestratus_7 Pristimantis_aff.vilarsi_4 Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_fenestratus Pristimantis_gutturalis Pristimantis_koehleri Pristimantis_koehleri Pristimantis_latro Pristimantis_paulodutrai Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_aff.carranguerorum_1 Pristimantis_aff.fenestratus_7 Pristimantis_aff.vilarsi_4 Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_fenestratus Pristimantis_gutturalis Pristimantis_koehleri Pristimantis_koehleri Pristimantis_latro Pristimantis_paulodutrai Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Group[12] Species1 Group[35] Species19 14 32 Group[18] Species24 39 Group[33] Species20 2 Group[33] Species20 1 Group[41] Species15 15 Group[32] Species29 44 Group[38] Species13 4 Group[38] Species13 4 Group[37] Species18 10 Group[9] Species10 21 Group[22] Species23 41 Group[29] Species26 33 Group[29] Species26 33 Group[12] Species1 32 Group[35] Species19 14 Group[18] Species24 39 Group[33] Species20 3 Group[33] Species20 1 Group[41] Species15 16 Group[32] Species29 44 Group[38] Species13 4 Group[38] Species13 4 Group[37] Species18 10 Group[9] Species10 21 Group[22] Species23 41 Group[29] Species26 33 Group[29] Species26 33 Group[12] Species1 32 Group[35] Species19 14 Group[18] Species24 39 Group[33] Species20 2 Group[33] Species20 1 Group[41] Species15 16 Group[32] Species29 44 Group[38] Species13 4 Group[38] Species13 4 Group[37] Species18 10 Group[23] Species5 27 Group[22] Species23 41 Group[29] Species26 33 Group[29] Species26 33 Group[47] Species30 46 Group[35] Species19 14 Group[18] Species24 39 Group[33] Species20 2 Group[33] Species20 1 Group[41] Species15 16 Group[32] Species29 43 Group[38] Species13 6 Group[38] Species13 4 Group[37] Species18 10 Group[23] Species5 27 Group[22] Species23 41 Group[29] Species26 33 Group[29] Species26 33	2 27 37 29 29 20 44 17 17 25 7 32 39 39 2 27 37 29 29 20 44 17 17 25 7 32 39 39 2 27 37 29 29 20 44 17 17 25 54 32 39 39 48 27 37 29 29 20 44 17 17 25 54 32 39 39	Cordillera Azul Trocha Santa Rosa-Carolina Fundo Copaiba, alrededores de Puerto Ayacucho Lourenco Montagne cacao km 620 of BR-319 Highway Rio Vila Nova, Mazagao, FLOTA II Borba 1 Senda a 2.5 km rio arriba del Puesto de control y vigilancia Cocama Altamira Bosque al Este de Requena, a unos 5-8 km del pueblo Caicara, Porto Velho Serra do Navio UHE Sto Antônio do Jari (Limeira) Catarata de Santa Rosa Cordillera azul, Santa rosa Araguaína Cano alrededores cerro Las Pavas, carretera Puerto Ayacucho-Gavilan Laranjal do Jari RN2, DZ5 Aripuana Rio Vila Nova, Mazagao, FLOTA II Borba 1 Senda a 2.5 km rio arriba del Puesto de control y vigilancia Cocama Altamira Bosque al Este de Requena, a unos 5-8 km del pueblo Caicara, Porto Velho Laranjal do Jari Trombetas, FLOTA Cordillera Azul Trocha Santa Rosa-Catarata de Santa Rosa Estreito 3 km al N de Puerto Ayacucho, carretera Puerto Ayacucho-Puerto Laranjal do Jari Lucifer Cachoeira das Pombas, Margem direita Rio Aripuana Igarape Camaipi, Rio Maraca Borba 1 Senda a 2.5 km rio arriba del Puesto de control y vigilancia Cocama Altamira Pacaas Novos, PARNA Nouragues, Camp Inselberg ESEC Grao Para Estreito Paez Carretera Puerto Ayacucho-Gavilan Laranjal do Jari Regina Sao Jose das Pombas, Margem Esquerda Rio Aripuana Serra do Navio Ducke Reserve, Manaus Senda a 2.5 km rio arriba del Puesto de control y vigilancia Cocama Medicilândia NA Pacaas Novos, PARNA Nouragues, Camp Inselberg ESEC Grao Para	Ucayali MA Amazonas AP French Guiana AM AP AM Ucayali PA Loreto RO AP AP Ucuyali TO Amazonas AP French Guiana MT AP AM Ucayali PA Loreto RO AP PA Ucayali MA Amazonas AP French Guiana AM AP AM Ucayali PA BA RO French Guiana PA MA Amazonas AP French Guiana AM AP AM Ucayali PA SE RO French Guiana PA	Peru Venezuela Brazil France Brazil Brazil Brazil Peru Brazil Peru Brazil Brazil Brazil Peru Brazil Venezuela Brazil France Brazil Brazil Brazil Peru Brazil Peru Brazil Brazil Brazil Peru Brazil Venezuela Brazil France Brazil Brazil Brazil Peru Brazil Brazil Brazil France Brazil NA Brazil Venezuela Brazil France Brazil Brazil Brazil Peru Brazil Brazil Brazil France Brazil	-7.792483 -7.343964 5,691 2.3236111 2.349128 -7.220011 0.449335 -4.380000 -10,388 -3.217433 -5,12 -8.696257 0.9180556 -0.645593 -8.636873 -7.205832 5,582 -0.7166667 4.05 -10.0530555 0.449335 -4.380000 -10,388 -3.217433 -5,12 -8.696257 -0.7166667 -0.9627694 -7.792483 -6.56055555 5,701 -0.7166667 4.7774 -6.417609 -0.0241667 -4.380000 -10,388 -3.217433 -14.3667 -10.5691100 4.083333 0.6302806 -6.56055555 5,579 -0.7166667 4.3 -6.410325 0.9180556 -2.950000 -10,388 -3.447790 -10.7860769 4.083333 0.6302806	-75.904287 -47.462859 -67,609 -51.6452778 -53.215985 -63.080075 -52.022137 -59.580000 -71,183 -52.227434 -73,8 -63.912477 --52.500599 52.0027778 -48.255136 -67,494 -52.3833333 -52.016667 -59.4938888 -52.022137 -59.580000 -71,183 -52.227434 -73,8 -63.912477 -52.3833333 -55.5223 -75.746644 -47.4511111 -67,589 -52.3833333 -53.9475 -60.358057 -51.8972222 -59.580000 -71,183 -52.227434 -40.1667 -63.6191390 -52.683333 -55.7285 -75.904287 -47.4511111 -67,546 -52.3833333 -52.116667 -60.375052 -52.0027778 -59.920000 -71,183 -52.890238 -63.6272100 -52.683333 -55.7285
	>KP149401 >XXXXXXXXQCAZ51916 >JN991447 >JN991449 >KDQF01003487 >JN691276 >XXXXXXXXFTA33 >XXXXXXXXMTR25511 >KDQF01003270 >KY712669 >ZONIK25815 >KX925993 >JX267300 >KDQF01004261 >JN691233 >KDQF01002887 >JX267492 >JN691277 >XXXXXXXXFTA44 >KDQF01003757 >KDQF01003278 >KY712670 >ZONIK26015 >KX925994 >JX267484 >KDQF01004262 >JN691234 >KDQF01002888	AJC3490 QCAZ51916 AJC1339 nrps9 MTR20659 MTR13967 FTA33 MTR25511 MTR13823 ZUEC13328 SCF417 LZATM243 MNRJ49744 SMS258 AF0280 MPEG30120 AFlab0445 MTR13983 FTA44 MTR976868 MTR13858 ZUEC13332 SCF419 LZATM255 MNRJ49745 SMS259 AF0281 MPEG30123	Pristimantis_lutitus Pristimantis_conspicillatus Pristimantis_gaigei Pristimantis_gaigei Pristimantis_vilarsi Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_sp. Pristimantis_gutturalis Pristimantis_aff.fenestratus Pristimantis_fenestratus Pristimantis _latro Pristimantis_cf.ramagii Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_cf.vinhai Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_fenestratus Pristimantis_gutturalis Pristimantis_aff.fenestratus Pristimantis_fenestratus Pristimantis _latro Pristimantis_cf.ramagii Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Pristimantis_aff.carranguerorum_2 Pristimantis_aff.condor_1 Pristimantis_aff.gaigei_1 Pristimantis_aff.gaigei_2 Pristimantis_aff.vilarsi_4 Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_fenestratus Pristimantis_gutturalis Pristimantis_koehleri Pristimantis_koehleri Pristimantis_latro Pristimantis_paulodutrai Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_aff.vinhai_1 Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_fenestratus Pristimantis_gutturalis Pristimantis_koehleri Pristimantis_koehleri Pristimantis_latro Pristimantis_paulodutrai Pristimantis_sp.Rondonia Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Group[48] Species30 62 Group[7] Species10 25 Group[56] Species28 52 Group[55] Species28 53 Group[18] Species24 40 Group[33] Species20 2 Group[33] Species20 3 Group[41] Species15 15 Group[32] Species29 44 Group[38] Species13 6 Group[38] Species13 4 Group[37] Species18 10 Group[23] Species5 27 Group[22] Species23 41 Group[29] Species26 33 Group[29] Species26 33 Group[28] Species2 60 Group[33] Species20 3 Group[33] Species20 3 Group[41] Species15 16 Group[32] Species29 44 Group[38] Species13 6 Group[38] Species13 4 Group[37] Species18 10 Group[23] Species5 27 Group[22] Species23 41 Group[29] Species26 33 Group[29] Species26 33	52 11 42 43 37 29 29 20 44 17 17 25 54 32 39 39 28 29 29 20 44 17 17 25 54 32 39 39	Tunguragua, Río Verde Pacaraima Laranjal do Jari Rio Vila Nova Pacaás Novos Serra do Navio Ducke Reserve, Manaus a 12 km al norte del Puesto de control Medicilândia Porto Velho Nouragues, Camp Inselberg Serra do Acari. N ESEC Grao Para y vigilancia Cocama maraca Rio Vila Nova Juruena Lourenco Ducke Reserve, Manaus a 12 km al norte del Puesto de control y vigilancia Cocama Medicilândia Porto Velho Nouragues, Camp Inselberg Serra do Acari. N ESEC Grao Para	RR AP AP PARNA AP AM Ucayali PA SE RO French Guiana PA BA AP AP MT AP AM Ucayali PA SE RO French Guiana PA	Colombia Ecuador Colombia Colombia Brazil Brazil Brazil RO Brazil Brazil Peru Brazil Brazil Brazil France Brazil Brazil Brazil Brazil Brazil Brazil Brazil Peru Brazil Brazil Brazil France Brazil	7.0416 -1.40 6.55 6.82 4.473034897 -0.7166667 0.449335 -10.7864380 0.9180556 -2.950000 -10,435 -3.447790 -11.52 -8.748483 4.083333 1.2854194 -15.272817 -0.2 0.449335 -10.3236111 2.3236111 -2.950000 -10,435 -3.447790 -11.52 -8.748483 4.083333 1.2854194	-72.9588 -78.30 -74.78 -75.15 --52.3833333 -52.022137 -63.6279410 -52.0027778 -59.920000 -71,269 -52.890238 -37.51 -63.903465 -52.683333 -58.6959 61.1352092 -39.070487 -51.9 -52.022137 -58.4927778 -51.6452778 -59.920000 -71,269 -52.890238 -37.51 -63.903465 -52.683333 -58.6959
	>XXXXXXXXQCAZ63391 >XXXXXXXXMTR35972 >XXXXXXXXQCAZ57584 >XXXXXXXXAF4494 >XXXXXXXXAF4505 >XXXXXXXXAF4506 >XXXXXXXXAF4516 >JN991461 >EU192256 >EU192273 >EU192274 >EU192275 >EU192276 >EU192277 >EF493393 >KP149278 >KP149333 >KP149334 >KP149384 >JX267362 >JN691278 >XXXXXXXXHT0656 >KDQF01004263 >KDQF01003599 >KY712671 >ZONIK27215 >KX925995 >XXXXXXXXAF4655 >ZONIF10112 >JN691235 >KDQF01002901 >JX267363 >JN691279 >KDQF01002903 >KDQF01004411 >KDQF01003899 >KY712672 >ZONIK27315 >KX925996 >XXXXXXXXAF4656 >ZONIF10412 >JN691236 >KDQF01002902 >JX267365 >JN691280 >KDQF01002904 >KDQF01004439 >KDQF01004459 >KY712673 >ZONIK27415 >KX925997 >XXXXXXXXAF4657 >ZONIF13612 >JN691237 >KDQF01002949 >XXXXXXXXHT1769 >JN691281 >KDQF01001141 >KDQF01002905 >FJ784385 >XXXXXXXXSCF606 >KY712674 >ZONIK32215 >KX925998 >XXXXXXXXAF4659 >ZONIF15412 >JN691238 >KDQF01002950 >KDQF01002833 >JN691282 >KDQF01001165 >KDQF01002906 >FJ784412 >ZONIK40915 >KY712675 >ZONIK32315 >KX925999 >ZONID06912 >ZONIF15512 >JN691239 >KDQF01002951 >EF493702 >JN691283 >KDQF01001176 >KDQF01003276 >FJ784461 >ZONIK41015 >KY712676 >ZONIK32415 >KX926000 >ZONID07012 >ZONIA05712 >JN691240 >KDQF01003031 >EF493356 >JN691284 >KDQF01001180 >KDQF01003572 >FJ784487 >ZONIK42115 >MG806047 >ZONIK32515 >KX926001 >JX267318 >ZONIA05812 >JN691241 >KDQF01003590	QCAZ63391 MTR35972 QCAZ57584 AF4494 AF4505 AF4506 AF4516 AJC2025 MNCNDNA4109 MNKA6629 MNKA6630 MNKA6631 MNCN43031 MHNC3130 ??? AJC3944 AJC3957 AJC3450 AJC3946 MTR13417 MTR00937 HT0656 SMS263 MTR24259 INPAH20929 SCF438 CTGA1168 AF4655 AJC2268 CM425 MPEG30287 MTR13504 MNHN20010800 MPEG30300 uniban1926 PG557 INPAH20930 SCF439 SISTAP1235 AF4656 AJC2271 PG084 MPEG30288 MTR13631 PG351 MPEG30307 VOGT2047 WAAM008 INPAH20931 SCF440 SISTAP1240 AF4657 AJC2409 AF0070 MPEG30892 HT1769 AF0247 AF2925 MPEG30309 KRL825 SCF606 APL14716 SCF510 SISTAP1244 AF4659 AJC2434 AF0043 MPEG30893 MPEG23245 AF0255 AF2965 MPEG30310 KRL864 SCF606 APL14921 SCF511 SISTAP1246 JMP2575 AJC2435 CM347 MPEG30894 KU291638 AF0256 AF2986 MTR13846 KRL1079 SCF607 APL16371 SCF512 SISTAP1253 JMP2576 AJC2960 CM356 MTD47746 ??? AF0283 AF2993 MTR24099 KRL1196 SCF624 CTGA_N_5682 SCF513 SISTAP1256 MNRJ36751 AJC2961 PG118 MTR24198	Pristimantis_peruvianus Pristimantis_peruvianus Pristimantis_condor Pristimantis_sp.gr.conspicillatus Pristimantis_aff.conspicillatus Pristimantis_aff.condor_1 Pristimantis_aff.condor_2 Pristimantis_aff.condor_3 Pristimantis_citriogaster Pristimantis_aff.conspicillatus Pristimantis_citriogaster Pristimantis_aff.conspicillatus Pristimantis_citriogaster Pristimantis_aff.conspicillatus Pristimantis_peruvianus Pristimantis_aff.iiap Pristimantis_fenestratus Pristimantis_aff.koehleri Pristimantis_fenestratus Pristimantis_aff.koehleri Pristimantis_fenestratus Pristimantis_aff.koehleri Pristimantis_fenestratus Pristimantis_aff.koehleri Pristimantis_fenestratus Pristimantis_aff.koehleri Pristimantis_fenestratus Pristimantis_aff.koehleri Pristimantis_skydmainos Pristimantis_aff.skydmainos Pristimantis_vilarsi Pristimantis_aff.vilarsi_1 Pristimantis_vilarsi Pristimantis_aff.vilarsi_1 Pristimantis_vilarsi Pristimantis_aff.vilarsi_1 Pristimantis_vilarsi Pristimantis_aff.vilarsi_1 Pristimantis_cf.vinhai Pristimantis_aff.vinhai_2 Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_fenestratus Pristimantis_fenestratus Pristimantis_gutturalis Pristimantis_gutturalis Pristimantis_fenestratus Pristimantis_koehleri Pristimantis_fenestratus Pristimantis_koehleri Pristimantis _latro Pristimantis_latro Pristimantis_iiap Pristimantis_peruvianus Pristimantis_vilarsi Pristimantis_vilarsi Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_cf.vinhai Pristimantis_aff.vinhai_2 Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_fenestratus Pristimantis_fenestratus Pristimantis_gutturalis Pristimantis_gutturalis Pristimantis_fenestratus Pristimantis_koehleri Pristimantis_fenestratus Pristimantis_koehleri Pristimantis _latro Pristimantis_latro Pristimantis_iiap Pristimantis_peruvianus Pristimantis_vilarsi Pristimantis_vilarsi Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_cf.vinhai Pristimantis_aff.vinhai_3 Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_fenestratus Pristimantis_fenestratus Pristimantis_gutturalis Pristimantis_gutturalis Pristimantis_fenestratus Pristimantis_koehleri Pristimantis_fenestratus Pristimantis_koehleri Pristimantis _latro Pristimantis_latro Pristimantis_iiap Pristimantis_peruvianus Pristimantis_vilarsi Pristimantis_vilarsi Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_aff.zeuctotylus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_gaigei Pristimantis_gaigei Pristimantis_peruvianus Pristimantis_iiap Pristimantis_fenestratus Pristimantis_koehleri Pristimantis_fenestratus Pristimantis_koehleri Pristimantis _latro Pristimantis_latro Pristimantis_iiap Pristimantis_peruvianus Pristimantis_vilarsi Pristimantis_vilarsi Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_aff.zeuctotylus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_gaigei Pristimantis_gaigei Pristimantis_reichlei Pristimantis_iiap Pristimantis_fenestratus Pristimantis_koehleri Pristimantis_fenestratus Pristimantis_koehleri Pristimantis _latro Pristimantis_latro Pristimantis_peruvianus Pristimantis_peruvianus Pristimantis_vilarsi Pristimantis_vilarsi Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_bipunctatus Pristimantis_bipunctatus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_gaigei Pristimantis_gaigei Pristimantis_reichlei Pristimantis_iiap Pristimantis_fenestratus Pristimantis_koehleri Pristimantis_fenestratus Pristimantis_koehleri Pristimantis _latro Pristimantis_latro Pristimantis_peruvianus Pristimantis_peruvianus Pristimantis_vilarsi Pristimantis_vilarsi Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_buccinator Pristimantis_buccinator Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_gaigei Pristimantis_gaigei Pristimantis_reichlei Pristimantis_iiap Pristimantis_fenestratus Pristimantis_koehleri Pristimantis_fenestratus Pristimantis_koehleri Pristimantis _latro Pristimantis_latro Pristimantis_ramagii Pristimantis_ramagii Pristimantis_vilarsi Pristimantis_vilarsi Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Group[7] Group[10] Species10 67 Species10 25 Group[4] Species10 68 Group[2] Species10 22 Group[2] Species10 22 Group[2] Species10 22 Group[2] Species10 22 Group[51] Species30 54 Group[39] Species13 8 Group[39] Species13 8 Group[39] Species13 8 Group[39] Species13 8 Group[39] Species13 8 Group[39] Species13 8 Group[57] Species22 26 Group[20] Species24 34 Group[20] Species24 34 Group[20] Species24 34 Group[20] Species24 34 Group[27] Species8 30 Group[33] Species20 3 Group[33] Species20 3 Group[41] Species15 15 Group[32] Species29 43 Group[38] Species13 4 Group[38] Species13 4 Group[37] Species18 11 Group[13] Species1 31 Group[19] Species24 35 Group[29] Species26 33 Group[29] Species26 33 Group[27] Species8 30 Group[33] Species20 1 Group[33] Species20 1 Group[41] Species15 15 Group[32] Species29 43 Group[38] Species13 4 Group[38] Species13 4 Group[37] Species18 11 Group[13] Species1 31 Group[19] Species24 35 Group[29] Species26 33 Group[29] Species26 33 Group[26] Species8 57 Group[33] Species20 1 Group[33] Species20 1 Group[41] Species15 16 Group[32] Species29 43 Group[38] Species13 4 Group[38] Species13 4 Group[37] Species18 11 Group[13] Species1 31 Group[19] Species24 35 Group[29] Species26 33 Group[29] Species26 33 Group[30] Species25 64 Group[33] Species20 1 Group[33] Species20 1 Group[33] Species20 1 Group[15] Species28 42 Group[50] Species30 45 Group[38] Species13 4 Group[38] Species13 4 Group[37] Species18 11 Group[13] Species1 31 Group[19] Species24 35 Group[29] Species26 33 Group[29] Species26 33 Group[30] Species25 65 Group[33] Species20 1 Group[33] Species20 2 Group[33] Species20 1 Group[15] Species28 42 Group[50] Species30 45 Group[38] Species13 4 Group[38] Species13 4 Group[37] Species18 11 Group[13] Species1 31 Group[19] Species24 35 Group[29] Species26 33 Group[29] Species26 33 Group[14] Species1 49 Group[33] Species20 1 Group[33] Species20 2 Group[33] Species20 2 Group[15] Species28 42 Group[50] Species30 45 Group[38] Species13 4 Group[38] Species13 4 Group[37] Species18 11 Group[13] Species1 31 Group[19] Species24 36 Group[29] Species26 33 Group[29] Species26 33 Group[54] Species10 24 Group[33] Species20 1 Group[33] Species20 3 Group[33] Species20 3 Group[15] Species28 42 Group[50] Species30 45 Group[38] Species13 6 Group[38] Species13 4 Group[37] Species18 11 Group[24] Species4 28 Group[19] Species24 36 Group[29] Species26 33 Group[29] Species26 33	11 13 14 8 8 8 8 49 18 18 18 18 18 18 31 33 33 33 33 57 29 29 20 44 17 17 25 1 34 39 39 57 29 29 20 44 17 17 25 1 34 39 39 58 29 29 20 44 17 17 25 1 34 39 39 38 29 29 29 41 47 17 17 25 1 34 39 39 38 29 29 29 41 47 17 17 25 1 34 39 39 3 29 29 29 41 47 17 17 25 1 34 39 39 10 29 29 29 41 47 17 17 25 53 34 39 39	Provincia: Tungurahua. Canton: Baños. Parroquia: Río Negro. Comunidade Cachoeirinha, Rio Içá Zamora Chinchipe: Reserva Numbami, 18 km vía Zamora-Romerillos PongoBarranquita Biodiversidad Biodiversidad Biodiversidad Puerto Inca, Panguana, Rio Yuyapichis (AKA Rio Llullapiches) Cochabamba roadLos Gua´charos -ElPalmar La Paz Chalalan La Paz Sadiri, Arroyo Yariapo Cochabamba Los Guacharos Cochabamba Los Guacharos Madre de Dios Cocha Camungo Caserio Miraflores, Vereda La Balastrera, Finca Los Almendros Caserio Miraflores, Vereda La Balastrera, Finca Los Almendros Caserio Miraflores, Vereda La Balastrera, Finca Los Almendros Caserio Miraflores, Vereda La Balastrera, Finca Los Almendros Igarape Camaipi, Rio Maraca UHE Sto Antônio do Jari (Harroto) Porto Velho Lourenco Borba a 12 km al norte del Puesto de control y vigilancia Cocama Flona Tapajós Biodiversidad Cano Kuduyari. Comunidad Pirasemo Molokoi REBIO Maicuru REBIO Maicuru Divisa dos Municipios de Montenegro e Cacaulandia Armontabo Borba a 12 km al norte del Puesto de control y vigilancia Cocama Flona Tapajós Biodiversidad Cano Kuduyari. Comunidad Pirasemo Mont Saint Marcel REBIO Maicuru Haute Wanapi REBIO Maicuru Boca do Juma, Margem Direita Rio Aripuana Flat de la waki Borba a 12 km al norte del Puesto de control y vigilancia Cocama Flona Tapajós Biodiversidad Comunidad Puerto Vaupes. Rio Vaupes Petit Saut ESEC Grao Para, centro Mineração Taboca, Rio Pitinga Nouragues, Camp Inselberg Memora G REBIO Maicuru Parque Nacional G. D. Omar Torrijos Puesto de control La Novia Porto Velho Puesto de control y vigilancia La Novia Ucayali AM San Martin San Martin San Martin San Martin BA AP AP RO AP AM Ucayali PA San Martin Vaupes French Guiana PA BA French Guiana PA RO French Guiana AM Ucayali PA San Martin Vaupes French Guiana PA BA French Guiana PA AM French Guiana AM Ucayali PA San Martin Vaupes French Guiana PA AM French Guiana French Guiana PA Ucayali RO Flona Tapajós PA Biodiversidad San Martin Comunidad Puerto Vaupes. Rio Vaupes Vaupes Petit Saut French Guiana ESEC Grao Para, centro PA Faro, FLOTA PA Nouragues, Camp Inselberg French Guiana Memora D, roche mon pere AP REBIO Maicuru PA Parque Nacional G. D. Omar Torrijos Puesto de control y vigilancia La Novia Ucayali Porto Velho RO Puesto de control y vigilancia La Novia Ucayali Flona Tapajós PA Cataratas Auashiyacu, carretera Tarapoto-Yurimaguas San Martin Comunidad Puerto Vaupes. Rio Vaupes Lucifer French Guiana ESEC Grao Para, centro PA Vaupes Oxapampa Nouragues, Camp Inselberg French Guiana Memora D, roche mon pere AP Lourenco AP Parque Nacional G. D. Omar Torrijos Puesto de control y vigilancia La Novia Ucayali Porto Velho RO Puesto de control y vigilancia La Novia Ucayali Flona Tapajós PA Cataratas Auashiyacu, carretera Tarapoto-Yurimaguas San Martin Pica en bosque tras el campamento turistico de Raudal de Danto Amazonas Lucifer French Guiana Kabo forestry consession Suriname NA Kaw2 French Guiana Memora D AP Oiapoque AP Parque Nacional G. D. Omar Torrijos Puesto de control y vigilancia La Novia Ucayali UFAM campus AM Puesto de control y vigilancia La Novia Ucayali Flona Tapajós PA AL Pica en bosque tras el campamento turistico de Raudal de Danto Amazonas Mont Saint Marcel French Guiana Lourenco AP	Ecuador Brazil Ecuador Peru Peru Peru Peru Peru Bolivia Bolivia Bolivia Bolivia Bolivia Peru Peru Colombia Colombia Colombia Colombia Brazil Brazil Brazil Brazil Brazil Brazil Peru Brazil Peru Colombia France Brazil Brazil France Brazil Brazil France Brazil Peru Brazil Peru Colombia France Brazil Brazil France Brazil Brazil France Brazil Peru Brazil Peru Colombia France Brazil Brazil France France Brazil Panama Peru Brazil Peru Brazil Peru Colombia France Brazil Brazil France Brazil Brazil Panama Peru Brazil Peru Brazil Peru Colombia France Brazil Peru France Brazil Brazil Panama Peru Brazil Peru Brazil Peru Venezuela France Suriname Peru France Brazil Brazil Panama Peru Brazil Peru Brazil Brazil Venezuela France Brazil	-1.411920 -2.8714 -4.136269 -6.28639 -6.46971 -6.46971 -6.46971 -9.614 -16.702327 -14.191353 -14.169059 -16.702327 -16.702327 -12.432 -9.09 3.228 3.228 3.228 3.228 -16.583141 -0.0241667 -0.645593 -8.748483 2.3215556 -4.380000 -10,435 -4.102547 -6.46971 1,317 4.566667 0.8286194 -16.583141 2.2666667 0.8286194 -10.33 3.804444 -4.380000 -10,435 -4.102547 -6.46971 1,317 2.3887 0.8286194 -15.432842 2.5134 0.8286194 -6.01472222 3.18341 -4.380000 -10,435 -4.102547 -6.46971 1,198 5.066667 0.6302806 -0.791830 4.083333 3.31774 0.8286194 8.667 -9,988 -9.349958 -9,988 -4.102547 -6.46971 1,198 5.066667 0.6302806 -1.7140111 4.083333 3.28215 0.8286194 8.667 -9,988 -9.100000 -9,988 -4.102547 -6,455 1,198 4.7774 0.6302806 -10.594242 4.083333 3.28215 2.3236111 8.667 -9,988 -9.349958 -9,988 -4.102547 -6,455 5,044 4.7774 5.378132 4.516105 3.31294 3.86 8.667 -9,988 -3.076111 -9,988 -4.102547 -9.598844 5,044 2.3887 2.3526389	-78.21526 -68.3470 -78.939206 -76.23164 -76.28102 -76.28102 -76.28102 -74.936 -65.647774 -68.335330 -67.941691 -65.647774 -65.647774 -70.6692 -75.16 -73.852 -73.852 -73.852 -73.852 -51.8972222 -52.500599 -63.903465 -51.6108611 -59.580000 -71,269 -54.910231 -76.28102 -70,389 -52.466667 -53.9312000 -39.115033 -54.5166667 -53.9312000 -63.0222222 -52.288056 -59.580000 -71,269 -54.910231 -76.28102 -70,389 -53.0144 -53.9312000 -39.115033 -53.8211 -53.9312000 -60.1791666 -53.4775 -59.580000 -71,269 -54.910231 -76.28102 -70,281 -53.05 -55.7285 -39.496528 -52.683333 -52.19329 -53.9312000 -80.592 -70,708 -64.680043 -70,708 -54.910231 -76.28102 -70,281 -53.05 -55.7285 -60.138753 --52.683333 -52.20263 -53.9312000 -80.592 -70,708 -64.500000 -70,708 -54.910231 -76,308 -70,281 -53.9475 -55.7285 57.2133000 -52.683333 -52.20263 -51.6452778 -80.592 -70,708 -64.680043 -70,708 -54.910231 -76,308 -67,561 -53.9475 -55.621491 -75.314705 -52.10053 -52.18036 -51.7660278 -80.592 -70,708 -59.963889 -70,708 -54.910231 -35.957397 -67,561 -53.0144 -51.6152222
	>XXXXXXXXAF4521 >EU712632 >KP149391 >EU712630 >JN691285 >KDQF01001183 >KDQF01003808 >FJ784490 >EF493703 >MG806048 >ZONIK32615 >KX926002 >JX267319 >ZONIA05912 >JN691242 >KDQF01003788	AF4521 MNCNDNA544 AJC2113 MNCNDNA9504 CM428 AF2998 PG022 KRL1202 ??? CTGA_N_5651 SCF514 SISTAP1257 MNRJ50249 AJC2962 PG119 NZCS81	Pristimantis_citriogaster Pristimantis_fenestratus Pristimantis_vilarsi Pristimantis_buccinator Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_gaigei Pristimantis_aff.koehleri Pristimantis_fenestratus Pristimantis_fenestratus Pristimantis _latro Pristimantis_ramagii Pristimantis_vilarsi Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Pristimantis_aff.conspicillatus Pristimantis_aff.koehleri Pristimantis_aff.vilarsi_1 Pristimantis_buccinator Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_gaigei Pristimantis_koehleri Pristimantis_koehleri Pristimantis_koehleri Pristimantis_latro Pristimantis_ramagii Pristimantis_vilarsi Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Group[2] Group[39] Species13 8 Species10 22 Group[20] Species24 34 Group[54] Species10 24 Group[33] Species20 1 Group[33] Species20 2 Group[33] Species20 1 Group[15] Species28 42 Group[38] Species13 4 Group[38] Species13 6 Group[38] Species13 4 Group[37] Species18 11 Group[24] Species4 28 Group[19] Species24 36 Group[29] Species26 33 Group[29] Species26 33	8 18 33 10 29 29 29 41 17 17 17 25 53 34 39 39	Biodiversidad La Paz Road from Charazani to polo Caserio Miraflores, Vereda La Balastrera, Finca Los Almendros Cusco Blanquillo Molokoi Memora D Mitaraka Parque Nacional G. D. Omar Torrijos NA UFAM campus Puesto de control y vigilancia La Novia Ucayali San Martin French Guiana AP French Guiana AM Flona Tapajós PA AL Pica en bosque tras el campamento turistico de Raudal de Danto Amazonas Mont Saint Marcel French Guiana Brownsberg, mazaroni weg, Brownsberg Suriname	Peru Bolivia Colombia Peru France Brazil France Panama Peru Brazil Peru Brazil Brazil Venezuela France Suriname	-6.46971 -15.157391 3.228 -12.427074 4.566667 3.31294 2.2911 8.667 -3.076111 -9,988 -4.102547 -9.312315 5,044 2.3887 4.936527	-76.28102 -68.496399 -73.852 -70.705701 -52.466667 -52.18036 -54.5342 -80.592 -59.963889 -70,708 -54.910231 -35.942911 -67,561 -53.0144 -55.194826
	>XXXXXXXXAF4523 >ZONIG24013 >KP149438 >EU712631 >JN691286 >KDQF01001222 >KDQF01003958 >JN991448 >EU192278 >ZONIG31013 >ZONIK41215 >KX926003 >JX267380 >ZONIA06012 >JN691243 >KDQF01003795	AF4523 JMP084 AJC3945 MNCNDNA9506 CM112 AF3072 PG759 CH6471 MNCN42990 AMNH20032 SCF610 SISTAP1259 AFlab426 AJC2963 BPN1593 NZCSA1029	Pristimantis_sp.levreblanche Pristimantis_fenestratus Pristimantis_vilarsi Pristimantis_buccinator Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_gaigei Pristimantis_koehleri Pristimantis_malkini Pristimantis_fenestratus Pristimantis _latro Pristimantis_ramagii Pristimantis_vilarsi Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Pristimantis_aff.conspicillatus Pristimantis_aff.koehleri Pristimantis_aff.vilarsi_1 Pristimantis_buccinator Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_gaigei Pristimantis_koehleri Pristimantis_koehleri Pristimantis_koehleri Pristimantis_latro Pristimantis_ramagii Pristimantis_vilarsi Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Group[2] Group[39] Species13 8 Species10 22 Group[20] Species24 34 Group[54] Species10 24 Group[33] Species20 1 Group[33] Species20 1 Group[33] Species20 1 Group[15] Species28 42 Group[38] Species13 5 Group[38] Species13 6 Group[38] Species13 4 Group[37] Species18 11 Group[24] Species4 28 Group[19] Species24 36 Group[29] Species26 33 Group[29] Species26 33	8 18 33 10 29 29 29 41 17 17 17 25 53 34 39 39	Biodiversidad Chalalan. Franz Tamayo Caserio Miraflores, Vereda La Balastrera, Finca Los Almendros Cusco Cocha Camungo Tibourou Mitan G St Georges, Saut Maripa Santa Cruz Km 6 Angostura-Samaipata Tamaga, CF 5 Km 7, BR 174 approx. Km 85 Puesto de control y vigilancia La Novia Ucayali San Martin La Paz French Guiana French Guiana French Guiana Amazonas Flona Tapajós PA PE Pica en bosque tras el campamento turistico de Raudal de Danto Amazonas Saul French Guiana Tafelberg, Bush northern Rudi Kappel airstrip Suriname	Peru Bolivia Colombia Peru France France France Panama Bolivia Brazil Peru Brazil Brazil Venezuela France Suriname	-6.46971 -14.429279 3.228 -12.903931 4.416667 2.6284 3.806111 9.13 -18.18 -2,223 -9,988 -4.102547 -7.848874 5,044 3.625556 3.804888	-76.28102 -67.921401 -73.852 -71.709522 -52.3 -52.55404 -51.893389 -82.5 -63.72 -60,066 -70,708 -54.910231 -34.909684 -67,561 -53.207222 -56.153905
	>XXXXXXXXAF4561 >XXXXXXXXAF4580 >KDQF01004207 >KX242528 >KX926012 >KX926013 >KDQF01003203 >KDQF01004442 >KDQF01002570 >ZONIF27912 >XXXXXXXXMTR39991 >EU201061 >JN691295 >KDQF01001336 >ZONIA65412 >XXXXXXXXAF4778 >KX242531 >KDQF01002878 >KX925980 >KDQF01001797 >ZONIK23615 >EU201059 >JN691252 >ZONIG27113 >XXXXXXXXMTR39974 >EU186650 >JN691294 >KDQF01001335 >ZONIA65312 >KDQF01001281 >KX242530 >KDQF01002877 >KX242525 >KX926011 >ZONIK23515 >EF376083 >JN691251 >ZONIC01112 >XXXXXXXXMTR39945 >KP149324 >JN691293 >KDQF01001334 >ZONIA63712 >KDQF01001240 >KX242529 >XXXXXXXXJMP153 >KX242524 >KX926010 >ZONIK23415 >JX267491 >JN691250 >ZONIB04212 >ZONIB05412 >AMTMP36015 >KDQF01003551 >XXXXXXXXSCF377 >ZONIK23015 >JN691292 >KDQF01001330 >ZONIA63612 >XXXXXXXXAF2806 >KU496876 >XXXXXXXXHT7354 >KX242523 >KX926009 >XXXXXXXXSCF381 >JX267364 >JN691249 >JN691291 >KDQF01001329 >EF493700 >KDQF01000917 >EU712633 >XXXXXXXXHT6792 >KX242522 >KX926008 >XXXXXXXXJMP443 >JX267343 >JN691248 >ZONIB61412 >ZONIB62012 >ZONIB62212 >ZONIB04912 >ZONIF09412 >ZONIF09512 >ZONIF12212 >AMTMP35915 >KY652650 >JN691287 >KDQF01001294 >KDQF01004298 >JN991450 >EU192279 >XXXXXXXXBPN3719 >ZONIK45915 >KX926004 >EU192289 >ZONIA06112 >JN691244 >KDQF01003956 >MG820139 >JN691288 >KDQF01001299 >KDQF01004384 >JN691313 >EU192280 >KDQF01002635 >KX242519 >KX926005 >EU192290 >ZONIA08012 >JN691245 >KDQF01003957 >MG820140 >JN691289 >KDQF01001300 >KDQF01004399 >KDQF01000254 >EU192281 >XXXXXXXXHT5896 >KX242520 >KX926006 >EU192291 >ZONIA08112 >JN691246 >KDQF01004098 >JN691290 >KDQF01001307 >KDQF01004456 >KDQF01000481 >EU192282 >XXXXXXXXHT6725 >KX242521 >KX926007 >EU192292 >ZONIA09312 >JN691247 >KDQF01004224	AF4561 AF4580 RRT46 INPAH34565 INPAH34580 INPAH34579 MTR12665 VOGT2099 GN212 AJC2595 MTR39991 AF0162 PG120 AF3265 AJC2784 AF4778 EA210 MPEG30062 LZATM743 BM001 SCF383 AF0100 SMNS11999 JMP949 MTR39974 ??? PG112 AF3264 AJC2783 AF3168 INPAH34571 MPEG30061 SISTAP1145 MPEG177 SCF382 CM095 SMNS11998 JMP944 MTR39945 LSB385 CM366 AF3263 AJC2761 AF3102 INPAH34573 JMP153/MNCN43013 SISTAP1239 MPEG165 SCF381 MNRJ53765 SMNS11997 JMP703 JMP728 MHNLS17582 MTR23072 SCF377 SCF377 AF0030 AF3256 AJC2760 AF2806 KST0568 HT7354 LZATM213 MPEG160 SCF381 MTR13607 BPN0987 AF0033 AF3255 KU212278 AF2356 MNCNDNA9505 HT6792 LZATM277 MPEG109 JMP443 AFlab0923 BPN0986 JMP299 JMP318 JMP320 JMP598 AJC2260 AJC2261 AJC2391 MHNLS17581 MUSM33269 AG133 AF3187 ST295 KRL8880 MNKA6627 BPN3719 SCF679 SISTAP1260 MNKA6626 AJC2964 BPN1594 PG756 EB2113 AG202 AF3198 UHE132 PG577 MNCN42983 HJ134 LZATM467 SISTAP1261 MNCN42988 AJC3000 BPN1681 PG757 EB2213 AF0032 AF3200 UHE166 AF0818 MNCN42986 HT5896 EH1416 SISTAP1275 MNCN42989 AJC3001 CM227 QM392 AF0034 AF3209 WAAM003 AF1278 MNCN43013 HT6725 LZATM742 MPEG095 MNCN42987 AJC3024 BPN0985 SMNS12001	Pristimantis_sp.levreblanche Pristimantis_sp.gr.conspicillatus Pristimantis_aff.conspicillatus Pristimantis_aff.conspicillatus Pristimantis_fenestratus Pristimantis_aff.fenestratus_1 Pristimantis _fenestratus Pristimantis_aff.fenestratus_2 Pristimantis _fenestratus Pristimantis_aff.fenestratus_2 Pristimantis _fenestratus Pristimantis_aff.fenestratus_2 Pristimantis_fenestratus Pristimantis_aff.fenestratus_3 Pristimantis_fenestratus Pristimantis_aff.fenestratus_4 Pristimantis_fenestratus Pristimantis_aff.fenestratus_5 Pristimantis_malkini Pristimantis_aff.malkini_1 Pristimantis_vilarsi Pristimantis_aff.vilarsi_3 Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_sp. Pristimantis_citriogaster Pristimantis_gutturalis Pristimantis_gutturalis Pristimantis_sp. Pristimantis_koehleri Pristimantis_koehleri Pristimantis_koehleri Pristimantis _latro Pristimantis_latro Pristimantis_fenestratus Pristimantis_latro Pristimantis_skydmainos Pristimantis_skydmainos Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_fenestratus Pristimantis_aff.koehleri Pristimantis_vilarsi Pristimantis_aff.vilarsi_3 Pristimantis_terraebolivaris Pristimantis_charlottevillensis Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_sp. Pristimantis_citriogaster Pristimantis_gutturalis Pristimantis_gutturalis Pristimantis _fenestratus Pristimantis_koehleri Pristimantis_koehleri Pristimantis_koehleri Pristimantis_latro Pristimantis_latro Pristimantis _latro Pristimantis_latro Pristimantis_skydmainos Pristimantis_skydmainos Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_fenestratus Pristimantis_aff.koehleri Pristimantis_vilarsi Pristimantis_aff.vilarsi_3 Pristimantis_carranguerorum Pristimantis_carranguerorum Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_sp. Pristimantis_citriogaster Pristimantis_gutturalis Pristimantis_gutturalis Pristimantis _fenestratus Pristimantis_koehleri Pristimantis_koehleri Pristimantis_koehleri Pristimantis _latro Pristimantis_latro Pristimantis _latro Pristimantis_latro Pristimantis_skydmainos Pristimantis_skydmainos Pristimantis_cf.vinhai Pristimantis_vinhai Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_fenestratus Pristimantis_aff.koehleri Pristimantis_fenestratus Pristimantis_aff.koehleri Pristimantis_sp. Pristimantis_aff.vilarsi_3 Pristimantis_vilarsi Pristimantis_aff.vilarsi_3 Pristimantis_buccinator Pristimantis_buccinator Pristimantis_buccinator Pristimantis_buccinator Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_sp. Pristimantis_citriogaster Pristimantis_gutturalis Pristimantis_gutturalis Pristimantis_fenestratus Pristimantis_koehleri Pristimantis_fenestratus Pristimantis_koehleri Pristimantis _latro Pristimantis_latro Pristimantis _latro Pristimantis_latro Pristimantis_skydmainos Pristimantis_skydmainos Pristimantis_cf.vinhai Pristimantis_vinhai Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_citriogaster Pristimantis_citriogaster Pristimantis_gutturalis Pristimantis_gutturalis Pristimantis_aff.koehleri Pristimantis_koehleri Pristimantis_fenestratus Pristimantis_koehleri Pristimantis_latro Pristimantis_latro Pristimantis _latro Pristimantis_latro Pristimantis_samaipatae Pristimantis_samaipatae Pristimantis_cf.vinhai Pristimantis_vinhai Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_fenestratus Pristimantis_aff.koehleri Pristimantis_fenestratus Pristimantis_aff.koehleri Pristimantis_fenestratus Pristimantis_aff.koehleri Pristimantis_fenestratus Pristimantis_aff.koehleri Pristimantis_vilarsi Pristimantis_aff.vilarsi_2 Pristimantis_vilarsi Pristimantis_aff.vilarsi_2 Pristimantis_vilarsi Pristimantis_aff.vilarsi_2 Pristimantis_sp. Pristimantis_aff.vilarsi_3 Pristimantis_buccinator Pristimantis_buccinator Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_gaigei Pristimantis_gaigei Pristimantis_koehleri Pristimantis_koehleri Pristimantis_chiastonotus Pristimantis_koehleri Pristimantis_fenestratus Pristimantis_koehleri Pristimantis _latro Pristimantis_latro Pristimantis_samaipatae Pristimantis_samaipatae Pristimantis_vilarsi Pristimantis_vilarsi Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_buccinator Pristimantis_buccinator Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_gutturalis Pristimantis_gutturalis Pristimantis_koehleri Pristimantis_koehleri Pristimantis_koehleri Pristimantis_koehleri Pristimantis _latro Pristimantis_latro Pristimantis _latro Pristimantis_latro Pristimantis_samaipatae Pristimantis_samaipatae Pristimantis_vilarsi Pristimantis_vilarsi Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_buccinator Pristimantis_buccinator Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_gutturalis Pristimantis_gutturalis Pristimantis_koehleri Pristimantis_koehleri Pristimantis_fenestratus Pristimantis_koehleri Pristimantis_latro Pristimantis_latro Pristimantis _latro Pristimantis_latro Pristimantis_samaipatae Pristimantis_samaipatae Pristimantis_vilarsi Pristimantis_vilarsi Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_chiastonotus Pristimantis_gutturalis Pristimantis_gutturalis Pristimantis_koehleri Pristimantis_koehleri Pristimantis_fenestratus Pristimantis_koehleri Pristimantis_latro Pristimantis_latro Pristimantis _latro Pristimantis_latro Pristimantis_samaipatae Pristimantis_samaipatae Pristimantis_vilarsi Pristimantis_vilarsi Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus Pristimantis_zeuctotylus	Group[2] Group[2] Group[42] Species14 69 Species10 22 Species10 22 Group[44] Species16 17 Group[44] Species16 17 Group[44] Species16 17 Group[45] Species16 66 Group[46] Species16 70 Group[36] Species17 12 Group[8] Species10 18 Group[17] Species24 38 Group[33] Species20 1 Group[33] Species20 1 Group[33] Species20 3 Group[6] Species10 20 Group[32] Species29 43 Group[38] Species13 6 Group[38] Species13 7 Group[37] Species18 10 Group[37] Species18 10 Group[43] Species21 26 Group[29] Species26 33 Group[29] Species26 33 Group[39] Species13 8 Group[17] Species24 38 Group[31] Species29 50 Group[33] Species20 1 Group[33] Species20 3 Group[6] Species10 20 Group[32] Species29 43 Group[38] Species13 4 Group[38] Species13 7 Group[37] Species18 11 Group[37] Species18 11 Group[43] Species21 26 Group[29] Species26 33 Group[29] Species26 33 Group[39] Species13 8 Group[17] Species24 38 Group[49] Species30 61 Group[33] Species20 1 Group[33] Species20 3 Group[6] Species10 20 Group[32] Species29 43 Group[38] Species13 4 Group[38] Species13 5 Group[37] Species18 11 Group[37] Species18 11 Group[43] Species21 26 Group[25] Species7 29 Group[29] Species26 33 Group[39] Species13 8 Group[39] Species13 8 Group[17] Species24 38 Group[17] Species24 38 Group[54] Species10 24 Group[54] Species10 24 Group[33] Species20 1 Group[33] Species20 1 Group[6] Species10 20 Group[32] Species29 43 Group[38] Species13 4 Group[38] Species13 6 Group[37] Species18 10 Group[37] Species18 11 Group[43] Species21 26 Group[25] Species7 29 Group[29] Species26 33 Group[33] Species20 1 Group[33] Species20 1 Group[6] Species10 20 Group[32] Species29 43 Group[38] Species13 4 Group[38] Species13 6 Group[37] Species18 10 Group[37] Species18 11 Group[40] Species12 9 Group[25] Species7 29 Group[29] Species26 33 Group[39] Species13 8 Group[39] Species13 8 Group[39] Species13 8 Group[39] Species13 8 Group[21] Species24 37 Group[21] Species24 37 Group[21] Species24 37 Group[17] Species24 38 Group[54] Species10 24 Group[33] Species20 1 Group[33] Species20 2 Group[33] Species20 1 Group[15] Species28 42 Group[38] Species13 5 Group[38] Species13 7 Group[38] Species13 4 Group[37] Species18 11 Group[40] Species12 9 Group[19] Species24 36 Group[29] Species26 33 Group[29] Species26 33 Group[54] Species10 24 Group[33] Species20 1 Group[33] Species20 1 Group[33] Species20 3 Group[32] Species29 43 Group[38] Species13 5 Group[38] Species13 4 Group[37] Species18 10 Group[37] Species18 11 Group[40] Species12 9 Group[19] Species24 36 Group[29] Species26 33 Group[29] Species26 33 Group[54] Species10 24 Group[33] Species20 1 Group[33] Species20 2 Group[33] Species20 1 Group[32] Species29 43 Group[38] Species13 5 Group[38] Species13 6 Group[37] Species18 10 Group[37] Species18 11 Group[40] Species12 9 Group[19] Species24 36 Group[29] Species26 33 Group[29] Species26 33 Group[33] Species20 1 Group[33] Species20 1 Group[33] Species20 1 Group[32] Species29 43 Group[38] Species13 5 Group[38] Species13 6 Group[37] Species18 10 Group[37] Species18 11 Group[40] Species12 9 Group[19] Species24 36 Group[29] Species26 33 Group[29] Species26 33	8 8 19 21 21 21 22 23 24 4 36 29 29 29 6 44 17 17 25 25 30 39 39 18 36 45 29 29 6 44 17 17 25 25 30 39 39 18 36 51 29 29 6 44 17 17 25 25 30 56 39 18 18 36 36 10 10 29 29 6 44 17 17 25 25 30 56 39 29 29 6 44 17 17 25 25 16 56 39 18 18 18 18 35 35 35 36 10 29 29 29 41 17 17 17 25 16 34 39 39 10 29 29 29 44 17 17 25 25 16 34 39 39 10 29 29 29 44 17 17 25 25 16 34 39 39 29 29 29 44 17 17 25 25 16 34 39 39	Biodiversidad Pongo rea de influencia diretado da barragem, PCH Rondonopolis, Ro Borba 2 Borba 2 Borba 2 Igarape Jacunda, Rio Abacaxis Lago Acai, Margem Esquerda Rio Aripuana Guaranta do Norte rio Takana, km 9 carretera Leticia Maturacá Brownsberg Mont Saint Marcel Taparabo Pongo de Rentema, carretera Bagua-Sara Merisa Galbao Manaus Serra do Acari. N ESEC Grao Para Anapu UHE Belo Monte, Vitoria do Xingu, Rio Xingu Senda a 2.5 km rio arriba del Puesto de control y vigilancia Cocama Road to Apura, Goliathberg PK7 avant piste Mabura hill forest reserve Serrania Bella Vista. Larecaja Maturacá ca King's Bay Reservoir Mont Saint Marcel Taparabo Pongo de Rentema, carretera Bagua-Sara Merisa Mitan D Borba 1 Serra do Acari. N ESEC Grao Para Flona Tapajós Flona Tapajós Senda a 2.5 km rio arriba del Puesto de control y vigilancia Cocama Tibourou Mabura hill forest reserve Valle de Zongo Maturacá Lucifer Taparabo Pongo de Rentema, carretera Bagua-Sara Merisa, charca Mitan G Borba 1 Santa Cruz: La Chonta Flona Tapajós Flona Tapajós Senda a 2.5 km rio arriba del Puesto de control y vigilancia Cocama Mabura hill forest reserve Camino a San Onofre. Carrasco Camino a San Onofre. Carrasco Serra da maroquinha puesto de control Cocama de control y vigilancia Cocama Saul, roche bateau Trois paletuviers Pongo de Rentema, carretera Bagua-Sara Merisa, charca Mitaraka Madre de Dios, Puerto Maldonado, Albergue Inkaterra Reserva Ducke, Base principal Altamira Flona Tapajós puesto de control Cocama Sipaliwini Senda a 2.5 km rio arriba del Puesto Saul, galbao Trois paletuviers Cataratas Ahnashiyacu, 14 km NE Tarapoto Mapaou Madre de Dios Cocha Camungo Reserva Ducke, Base principal Altamira Flona Tapajós Camino a Bella Vista. Florida. Santa Cruz Sipaliwini Campamento los Guacharos. Parque Nacional Carrasco. Chapare Camino los Guacharos -El Palmar. A 8 Km de los Guacharos Camino los Guacharos -El Palmar. A 8 Km de los Guacharos San Jose de Uchupiamonas. Iturralde Mitu, Plaza Principal Cano Kuduyari. Comunidad Pirasemo Comunidad Trubon. Rio Vaupes Los Amigos Biological Station, Provincia Manu, Region Madre de Dios Saul Mitan D Parna Tumucumaque, Serra Macacoara Parque Nacional G. D. Omar Torrijos Santa Cruz Km 6 Angostura-Samaipata Parabara Puesto de control y vigilancia La Novia Ucayali San Martin San Martin MT AM AM AM AM AM MT Amazonas AM French Guiana AP Amazonas French Guiana AM PA PA PA Ucayali La Paz AM French Guiana AP Amazonas AP AM PA PA PA Ucayali French Guiana La Paz AM French Guiana AP Amazonas French Guiana AM PA PA Ucayali BA Cochabamba Cochabamba Bolivar RR Ucayali Ucayali French Guiana French Guiana Amazonas French Guiana AM PA PA Ucayali BA French Guiana French Guiana San Martin French Guiana AM PA PA BA Cochabamba Cochabamba Cochabamba La Paz Vaupes Vaupes Vaupes Bolivar French Guiana AP AP Village Flona Tapajós PA Santa Cruz Km 6 Angostura-Samaipata Pica en bosque tras el campamento Amazonas Saul French Guiana St Georges, Saut Maripa French Guiana turistico de Raudal de Danto Trinite, Crique Grand Leblond French Guiana Mitan G French Guiana UHE SANTO ANTONIO AMAPA AP Mitaraka, Borne 1/ Camp de base 1 French Guiana Santa Cruz La Chonta UHE Jirau, Porto Velho, Hidreletrica Jirau RO Anapu PA Flona Tapajós PA Santa Cruz Km 6 Angostura-Samaipata Tercer cano de la pica entre rio Cuao y Amazonas Saul French Guiana St Georges, Saut Maripa French Guiana Tobogan del Cuao (cano Bejuco) Cusco, Paucartambo, Kosnipata, Estacion Biologica Villa Carmen Saul, galbao French Guiana Mitan D AP UHE SANTO ANTONIO PARA PA Savane virginie, layon French Guiana Santa Cruz La Chonta Rio Jatapu, São José do Jabote, Igarapé do Tabocal AM Anapu PA Flona Tapajós PA Santa Cruz Km 6 Angostura-Samaipata Tercer cano de la pica entre rio Cuao y Amazonas Saul French Guiana Montagne de fer French Guiana Tobogan del Cuao (cano Bejuco) Cusco, Paucartambo, Kosnipata, Estacion Biologica Villa Carmen Saul, galbao French Guiana Mitan G French Guiana Flat de la waki French Guiana Nouragues, Camp InselbergT6 litiere 12H French Guiana Santa Cruz La Chonta Reserva Ducke, Base principal AM Flona Tapajós PA Santa Cruz Km 6 Angostura-Samaipata Tercer cano de la pica entre rio Cuao y Tobogan del Cuao (cano Bejuco) Amazonas Sipaliwini Mabura hill forest reserve	Peru Peru Brazil Brazil Brazil Brazil Brazil Brazil Brazil Colombia Brazil Suriname France Brazil Peru France Brazil Brazil Brazil Brazil Peru Suriname Guyana Bolivia Brazil Trinidad_Tobago 11.267518 -6.46971 -6.37223 -15.5405555 -56.19 -76.28102 -76.28374 -4.391401 -4.391401 -59.589173 -4.391401 -4.174831 -5.56611111 --9.7875 -54.91 -4,136 -69,924 0.621452 -66.120803 4.936527 -55.194826 2.3887 -53.0144 4.016066 -51.693802 -5,301 -78,396 4.488 -52.0445327 -3.081709 -60.003571 1.2854194 -58.6959 -3.470440 -51.198919 -2.950584 -51.936607 -10,388 -71,183 5.183333 -55.616667 58.6997222 5.1552778 -60.4138888 -15.596906 -68.135178 0.621452 -66.120803 France 2.3887 -53.0144 Brazil 4.016066 -51.693802 Peru -5,301 -78,396 Brazil 2.62764 -52.54195 Brazil -4.380000 -59.580000 Brazil 1.2854194 -58.6959 Brazil -4.102547 -54.910231 Brazil -4.102547 -54.910231 Peru -10,388 -71,183 France 4.416667 -52.3 Guyana 58.6997222 5.1552778 --60.547134 -58.703241 Bolivia -16.106642 -68.063279 Brazil 0.621452 -66.120803 Colombia 5.4108 France 4.7774 -53.9475 Brazil 4.016066 -51.693802 Peru -5,295 -78,406 France 2.6284 -52.55404 Brazil -4.380000 -59.580000 Bolivia -17.693839 -63.187822 Brazil -4.102547 -54.910231 Brazil -4.102547 -54.910231 Peru -10,388 -71,183 Brazil -13.735755 -39.156102 Guyana 58.6997222 5.1552778 --72.6710 -59.589173 Bolivia -17.415183 -64.935607 Bolivia -17.415183 -64.935607 Venezuela 5,063 -63,686 Brazil 2.380344 -61.357627 Peru -10.405842 Peru -10,388 -71,183 France 3.625556 -53.207222 France 4.054582 -51.677007 Peru -5,295 -78,406 France 2.23577 -54.44928 Peru -12.537 -69.05 Brazil -2.932222 -59.973333 Brazil -3.217433 -52.227434 Brazil -4.102547 -54.910231 Peru -10.405842 -71.149334 Brazil -15.432842 -39.496528 Suriname 56.1166667 2.0333333 -France 3.6 -53.283333 France 4.054582 -51.677007 Peru -6,455 -76,308 France 4.189842 -52.314725 Peru -12.432 -70.6692 Brazil -2.932222 -59.973333 Brazil -3.217433 -52.227434 Brazil -4.102547 -54.910231 Bolivia -17.769689 -63.109759 Brazil -15.190772 -39.506521 Suriname 56.1166667 2.0333333 --71.149334 -59.589173 Bolivia -17.400934 -64.975053 Bolivia -17.365620 -66.047321 Bolivia -17.365620 -66.047321 Bolivia -13.112908 -67.930885 Colombia 1,258 -70,237 Colombia 1,317 -70,389 Colombia 1,21 -70,62 Venezuela 4,229 -63,283 Peru -12.56861 France 3.625556 -53.207222 Brazil 2.62764 -52.54195 Brazil 0.909122 -53.228479 Panama 8.667 -80.592 Bolivia -18.18 -63.72 Guyana 2.094500 -59.240183 Peru -9,988 -70,708 Brazil -4.102547 -54.910231 Bolivia -18.18 -63.72 Venezuela 5,044 -67,561 France 3.625556 -53.207222 France 3.806111 -51.893389 -70.09917 France 4.6709 -53.2843 France 2.6284 -52.55404 Brazil -0.645593 -52.500599 France 2.2911 -54.5342 Bolivia -17.693839 -63.187822 Brazil -8.748483 -63.903465 Brazil -3.470440 -51.198919 Brazil -4.102547 -54.910231 Bolivia -18.18 -63.72 Venezuela 5,099 -67,498 France 3.625556 -53.207222 France 3.806111 -51.893389 Peru -12.88817 -71.40729 France 3.6 -53.283333 Brazil 2.62764 -52.54195 Brazil -0.657141 -52.553611 France 4.1959 -52.149 Bolivia -17.693839 -63.187822 Brazil -1.883333 -58.233333 Brazil -3.470440 -51.198919 Brazil -4.102547 -54.910231 Bolivia -18.18 -63.72 Venezuela 5,099 -67,498 France 3.625556 -53.207222 France 5.4074 -53.5548 Peru -12.88817 -71.40729 France 3.6 -53.283333 France 2.6284 -52.55404 France 3.18341 -53.4775 France 4.08479 -52.68063 Bolivia -17.693839 -63.187822 Brazil -2.932222 -59.973333 Brazil Brazil -4.102547 -54.910231 Bolivia -18.18 -63.72 Venezuela 5,099 -67,498 Suriname 2.0333333 -56.1166667 Guyana -5.1552778 58.6997222
											200

Table III .

 III S3: Matrix of sequences used in the Operational Taxonomic Units (OTUs) phylogenetic reconstruction of the Pristimantis conspicillatus group. The species column lists the different OTUs and outgroups included in the reconstruction. The Voucher column indicates the specimen number used as representative of the species/OTU. The 12S, 16S, ND1, COI, CytB, TYR and RAG1 columns are filled with accession number of the sequenced used for each species/OTU for each locus. Complete phylogenetic reconstruction including outgroups is in FigureS2.

	Species Pristimantis_aff.vilarsi_1 Pristimantis_lasalleorum	Voucher AJC3944 ICN55758/MHUAA8711	12S NA KY494221 16S KP149278	ND1 NA NA	ND2 NA NA	COI KP149083 KY627812	CytB NA NA	Tyr NA NA	Rag1 NA NA
	Craugastor_augusti Pristimantis_aff.vilarsi_2 Pristimantis_latro Craugastor_fitzingeri Pristimantis_aff.vilarsi_3 Pristimantis_lymani Pristimantis_aff.vilarsi_4 Pristimantis_malkini	TNHC-GDC AJC2261 BM351/BIOTA1111 12606/CarenGoldberg MTR23072 KU218019 DMH86-112/FMNH257745/USNM563033 MTR20659 JMP2315	NA NA	AJC2261 EF493392 AY326001 JMP2315	JX564870 NA hiseq hiseq NA GQ345245 AY273117 DQ350195 DQ350238 NA AJC2261 NA NA NA NA hiseq NA NA NA NA	DQ282963 KX208741 NA NA KX259193 NA NA NA NA NA NA NA NA NA NA NA
	Holoaden_luderwaldti Pristimantis_aff.vinhai_1 Pristimantis_moa	AF1600/MZUSP131872 AFlab0445 ESTR2177	JX267412 JX267492	NA	hiseq hiseq	NA	NA	NA	EU186768 EU186747 JX267726 JX267593 NA NA
	Ischnocnema_sp. Pristimantis_aff.vinhai_2 Pristimantis_paulodutrai	CFBH27554/CFBH41809 MTR13417 MNRJ46880		JX267362 JX267297	MH492733 NA NA NA NA	NA NA	NA NA	MH537785 MH537778 JX267720 JX267587 JX267706 JX267572
	Pristimantis_achatinus Pristimantis_aff.vinhai_3 Pristimantis_peruvianus	UVC15867/AJC0573 MTR13631 AF4657	NA	JN104676	NA	hiseq hiseq	NA	JN371121	NA	JN991552 JX267724 JX267591 NA NA NA
	Pristimantis_actites Pristimantis_aff.zeuctotylus Pristimantis_pictus	QCAZA40430/KU217830 MPEG23245 ABAM2146	NA NA	MPEG23245 MK992524	NA NA	hiseq	NA NA	NA MN010669	NA NA	EF493494 EF493432 NA NA NA NA
	Pristimantis_aff.bipunctatus Pristimantis_bipunctatus Pristimantis_ramagii	AJC2704 KU291638 MNRJ36751	EF493702 JX267318	NA NA	hiseq	NA NA	NA NA	NA NA	NA EF493492 EF493430 NA JX267797 JX267658
	Pristimantis_aff.carranguerorum_1 MC10866 Pristimantis_buccinator SCF377/MUSM33269 Pristimantis_samaipatae JMP443	NA	DQ195453	NA	hiseq hiseq	NA	NA	NA	NA KY681071 KY672966 NA NA NA
	Pristimantis_aff.carranguerorum_2 AJC3490 Pristimantis_carranguerorum LSB385 Pristimantis_skydmainos SCF381	NA NA	KP149401 KP149324	NA NA	hiseq	NA NA	KP149196 KP149128	NA NA	NA NA NA	NA NA NA
	Pristimantis_aff.condor_1 Pristimantis_chalceus Pristimantis_sp."Camacan"	QCAZ51916 KU177638 MTR13615		EF493675	NA	hiseq hiseq	NA	NA	NA	NA NA JX267819 JX267677 NA NA
	Pristimantis_aff.condor_2 Pristimantis_charlottevillensis Pristimantis_sp."Juru/Pari"	MTR35972 ??? MAP22	NA	EU186650 MK992620	NA NA	hiseq	NA NA	NA MN010742	NA NA	NA NA NA	NA NA NA
	Pristimantis_aff.condor_3 Pristimantis_chiastonotus Pristimantis_sp."Pacoti"	QCAZ57584 AF2459/BPN1620 MTR04514/MRT4514	JX267461 JX267386	NA	hiseq hiseq	NA	NA	NA	NA JN691986 JX267818 JX267676 NA NA
	Pristimantis_aff.conspicillatus Pristimantis_citriogaster Pristimantis_sp."TerradoMeio"	AF4506 QCAZ41868 SA37	NA	MK992628	NA	hiseq hiseq	NA	MN010743	NA	NA NA NA	NA NA NA
	Pristimantis_aff.fenestratus_1 Pristimantis_condor Pristimantis_sp."Rondonia"	RRT46 KU217857 SMS259	NA	EF493701 SMS259	NA NA	hiseq	NA NA	NA SMS259	NA NA	NA EF493504 EF493443 NA NA NA
	Pristimantis_aff.fenestratus_2 Pristimantis_conspicillatus Pristimantis_thymelensis	INPAH34579 QCAZ28448 TNHCGDC14370/QCAZ62803	NA	KX926013 EF493529	NA NA JX564889	NA NA	NA NA	NA NA	NA EF493499 EF493437 NA NA MK881392
	Pristimantis_aff.fenestratus_3 Pristimantis_curtipes Pristimantis_vilarsi	MTR12665 QCAZ35330/QCAZ48309 AJC2435	NA	AJC2435	NA	hiseq hiseq	NA	AJC2435	NA	NA NA NA	NA KX525470 NA
	Pristimantis_aff.fenestratus_4 Pristimantis_aff.fenestratus_5 Pristimantis_danae Pristimantis_vinhai Pristimantis_aff.gaigei_1 Pristimantis_fenestratus/pluvian Pristimantis_w-nigrum Pristimantis_aff.gaigei_2 Pristimantis_gaigei Pristimantis_zeuctotylus Pristimantis_aff.iiap Pristimantis_galdi Pristimantis_zimmermanae Pristimantis_aff.koehleri Pristimantis_aff.malkini_1 Pristimantis_aff.malkini_2 Pristimantis_aff.paulodutrai Pristimantis_illotus Pristimantis_iiap Pristimantis_gutturalis Pristimantis_giorgii Strabomantis_biporcatus	VOGT2099 TM512 JOG876/MVZ:272358 AFlab0923/MNRJ53765 AJC1339 UNIBAN1926 QCAZA31779/UVC15923 nrps9 KRL8880/CH6471 AF2794/BPN1069 AJC2025 QCAZA65032/QCAZ32368 AF2607 MNKA6629 AJC2838 QCAZ51154 MNRJ40299 QCAZ56760 SCF606 AF2356/PG557 AF295/MPEG17745 CVULA7073/CVULA7073	JX267343 JN991511 JN991447 JN991513 JN991449 JN991514 JN991450 NA JN991461 FJ438809 EU192273 JX267399 JX267360 NA SCF606 EU186691	hiseq hiseq hiseq hiseq hiseq hiseq hiseq hiseq hiseq hiseq hiseq hiseq hiseq GQ345249 NA NA NA NA NA NA NA NA	NA NA NA NA NA NA NA NA NA	NA JN991376 JN991378 NA JN991392 NA NA NA NA	NA NA JX267725 JX267592 NA KY681073 KY672968 NA NA NA KT898370 KT898311 NA NA NA JN691953 NA JN991570 JQ025184 JQ025186 NA NA NA EU186767 EU186746 NA NA NA NA JX267707 JX267573 NA NA NA NA NA NA JN692012 NA NA KX259184 NA GQ345204 EU186775 EU186754 NA NA NA NA NA NA NA
	Pristimantis_aff.paulodutrai Pristimantis_koehleri	MNRJ35338 SCF513/MPEG30070		JX267296	NA	hiseq	NA	NA	NA	JX267705 JX267571 KX259214 NA
	Pristimantis_aff.skydmainos Pristimantis_lanthanites	??? QCAZA19209		EF493393	NA	hiseq	NA	NA	NA	NA NA	NA NA
											205 206

Table III -

 III S4: BioGeoBEARS model estimates for the eight Neotropical biogeographic areas analysis for the Pristimantis conspicillatus group. The model column lists the different model compared with BioGeoBEARS. Log likelihood for each model is in the Lnl column. Best fit of the anagenetic dispersal, extinction and founder event parameters are found respectively in columns d, e and j. Akaike Information Criterion is given in the AIC column.

	Models	LnL	d	e	j	AIC
	DECJ	-78.928	0.004	1.00e-12	0.0146	163.9
	DEC	-93.82	0.008	0.006	0	191.6
	DIVAJ	-79.433	0.005	1.00e-12	0.014	164.9
	DIVA	-89.322	0.005	0.001	0	182.6
	BAYAREAJ	-80.745	0.003	1.00e-12	0.021	167.5
	BAYAREA	-115.555	0.007	0.055	0	235.1

Table III -

 III S5: BioGeoBEARS model estimates for the 14 Neotropical biogeographic areas analysis for the Pristimantis conspicillatus group. The model column lists the different model compared with BioGeoBEARS. Log likelihood for each model is in the Lnl column. Best fit of the anagenetic dispersal, extinction and founder event parameters are found respectively in columns d, e and j. Akaike Information Criterion is given in the AIC column.

	Models	LnL	d	e	j	AIC
	DECJ	-127.146	0.004	1.00e-12	0.015	260.3
	DIVAJ	-127.429	0.006	1.00e-12	0.012	260.9
	BAYAREAJ	-131.52	0.003	1.00e-12	0.021	269
	DEC	-144.03	0.006	1.91e-11	0	292.1
	DIVA	-148.544	0.004	0.001	0	301.1
	BAYAREA	-180.529	0.008	0.086	0	365.1

Table IV .1: Diversity of the species groups chosen for the comparative biogeography of Amazonian amphibians.

 IV The Family to which belong each species groups is indicated in the second column. The known reproductive modes (sensuHaddad & Prado, 2005) are listed for each species groups in the third column. The habitat type is depicted in the fourth column, while the microhabitat type can be found in the fifth column. References are listed in the sixth column.

	Species groups	Family	Reproductive mode	Main habitat type	Microhabitat type	References
			Mainly eggs hatching into exotrophic tadpoles that
			are carried to water by adult (mode 20). Eggs and
			exotrophic tadpoles in water in tree holes or aerial			Caldwell & Lima, 2003;
	Allobates	Aromobatidae	plants (mode 6; A. bromelicola). Eggs hatching into	Terra firme forest	Terrestrial	Lötters & Mutschmann, 2007;
			endotrophic tadpoles that complete their			Test, 1956.
			development in the nest (mode 21; A. masniger and
			A. nidicola, A. chalcopis)
	Amazophrynella	Bufonidae	Eggs and exotrophic tadpoles in lentic water (mode 1)	Terra firme forest	Terrestrial	Menin et al., 2020.
	Pristimantis gr. conspicillatus	Craugastoridae	Direct development of terrestrial eggs or eggs hatching into froglets (modes 23 & 27).	Terra firme forest	Scansorial	Jiménez-Robles et al., 2017.
	Boana gr. albopunctata	Hylidae	Eggs and exotrophic tadpoles in lentic or lotic water (Modes 1 & 2)	Flooded and open habitats Terra-firme forest,	Arboreal	de Sá et al., 2014.
	Osteocephalus / Dryaderces / Tepuihyla	Hylidae	Eggs and exotrophic tadpoles in lentic or lotic water, as well as water in tree holes or aerial plants (Modes 1, 2 & 6)	Flooded and Terra-firme forest	Arboreal	Jungfer et al., 2013.
	Leptodactylus gr. podicipinus	Leptodactylidae	Foam nest floating on water accumulated in constructed basins or on ponds (modes 11 & 13)	Flooded and Terra-firme forest, open habitats	Terrestrial	Jiménez-Robles et al., 2017; Prado et al., 2002.

Table IV .

 IV S1: 16S DNA sequence information and delimitation results for the Leptodactylus podicipinus group. GeneBank accession numbers are in the column labelled GB, new sequences are indicated with an accession starting with >XXXXXXXX or >ZONI (accession will be added pending manuscript acceptance), sequence names starting by >ZONI are under embargo and gps coordinates are concealed. Specimen numbers are indicated in the Voucher column. Specimen identification prior and posterior to delimitation analyses are indicated in the ID_initial and ID_post_delimitation columns respectively. Species assignation of each sequence per delimitation method are indicated in columns ABGD, mPTP and GMYC. The consensus of the three delimitation methods is indicated in the consensus column. Locality, country and state are indicated in said columns. Spatial coordinates corresponding to localities are found in the latitude and longitude columns.

	XXXXXXXXXXXAJC2527 JF789902 ZONIC73712 KDQF01002467 JF789917 KU495349 JN691187 KDQF01000722 KDQF01001726 KDQF01002912 KDQF01003706 XXXXXXXXXXXHT482 ZONIF27012 KDQF01000588 KDQF01002845	AJC2527 MJ1276 JMP1756 ESTR0786 MJ1303 TG061 AG251 AF1860 AMS374 MPEG30376 MTR33653 HT482 AJC2580 AF1550 MPEG29422	Leptodactylus_diedrus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_cf.podicipinus Leptodactylus_podicipinus Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_wagneri Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	Leptodactylus_diedrus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	50 22 22 31 40 39 8 8 11 3 2 2 49 19 19	52 25 20 34 41 38 15 16 2 1 5 5 6 28 30	5 16 16 15 12 13 25 25 36 37 35 35 34 20 19	6 27 27 22 13 14 38 38 49 50 48 48 47 31 30	Guiana nuflo de Chavez, San Sebastian Iquitos, Moronacocha, Barrio Versalles Faz Florence Estreito Sara, Buenavista Lencois Paulista Kaw, Canal de Flat de la Waki Kukrit Stump, West of Rupununi R Swamp REBIO Maicuru Japura, Trilha de Santa Fe Av. Torres/Mata Goiabinha rio Takana, km 9 carretera Leticia Matoury , crique manceliere, crique manceliere Trombetas, FLOTA	Loreto MA SP French Guiana French Guiana PA AM AM French Guiana PA	Colombia Bolivia Peru Brazil Bolivia Brazil France France Guyana Brazil Brazil Brazil Colombia France Brazil	1.21 -16.359633 -3.729 -6.56055555 -17.481683 -22.603 4.516105 3.08364 3.215567 0.8286194 -1.75339473 -4.136 4.85708 -0.9627694	-70.619167 -62.000050 -73.27 -47.4511111 -63.684867 -48.792 -52.10053 -53.41185 -59.413283 -53.9312000 -66.3382190 -69.924 -52.339175 -55.5223
	ZONIC56612 JF789903 ZONIC73812 KDQF01002468 JF789918 KU495350 JN691188 KDQF01000743 KDQF01001804 KDQF01002913 KDQF01003734 XXXXXXXXXXXHT497 ZONIK00815 KDQF01000602 KDQF01003576	JMP1460 MJ1282 JMP1757 ESTR0787 MJ1304 TG007 AF0008 AF191 BM031 MPEG30377 MTR33863 HT497 SCF101 AF1609 MTR24131	Leptodactylus_diedrus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_cf.podicipinus Leptodactylus_podicipinus Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	Leptodactylus_diedrus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	44 22 22 31 40 39 8 8 4 3 4 2 6 19 19	73 25 20 34 41 39 15 18 7 1 7 5 13 27 28	4 16 16 15 12 13 25 23 22 37 22 35 27 20 20	5 27 27 22 13 14 38 34 33 50 33 48 40 31 31	Umarital, Rio Ampiyacu nuflo de Chavez, San Sebastian Iquitos, Moronacocha, Barrio Versalles Faz Florence Estreito Sara, Buenavista Estreito Kaw3, Asarco Apiacas UHE Belo Monte, Vitoria do Xingu, Rio Xingu REBIO Maicuru Peixe-Boi Av. Torres/Mata Goiabinha Paraiso Saul, Limonade Oiapoque	Loreto Loreto MA MA French Guiana MT PA PA PA AM Ucayali French Guiana AP	Peru Bolivia Peru Brazil Bolivia Brazil France Brazil Brazil Brazil Brazil Brazil Peru France Brazil	-3.267 -16.359633 -3.729 -6.56055555 -17.481683 -6.548 4.568211 -9.650846 -2.950584 0.8286194 -1.143941 -10.09 3.57317 3.8794167	-72.267 -62.000050 -73.27 -47.4511111 -63.684867 -47.445 -52.217774 -57.393665 -51.936607 -53.9312000 -47.288139 -71.007 -53.19851 -51.7709722
	ZONIF10312 JF789904 ZONIC73912 KDQF01002482 JF789919 KU495351 JN691189 KDQF01000756 KDQF01001818 KDQF01002919 KDQF01003755 XXXXXXXXXXXHT520 ZONIK12115 KDQF01000638 KDQF01003579	AJC2270 MJ1305 JMP1758 ESTR2026 MJ1306 TG093 AF0010 AF1945 BM063 MPEG30506 MTR976856 HT520 SCF222 AF1697 MTR24141	Leptodactylus_diedrus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_cf.podicipinus Leptodactylus_podicipinus Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	Leptodactylus_diedrus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	50 22 22 31 40 39 8 8 4 3 8 1 6 19 19	52 24 20 32 41 38 15 16 7 1 18 9 13 27 28	5 16 16 15 12 13 25 25 22 37 23 32 27 20 20	6 27 27 22 13 14 38 38 33 50 34 45 40 31 31	Cano Kuduyari. Comunidad Pirasemo Sara, Buenavista Iquitos, Moronacocha, Barrio Versalles Estreito Sara, Buenavista Borborema Kaw3, arlesienne Flat de la Waki UHE Belo Monte, Vitoria do Xingu, Rio Xingu Paru, FLOTA Lado direito do rio Paru de Leste Juruena BR-319 Puesto de control y vigilancia Cocama Saul, Limonade Oiapoque	Loreto MA SP French Guiana French Guiana PA PA MT RO Ucayali French Guiana AP	Colombia Bolivia Peru Brazil Bolivia Brazil France France Brazil Brazil Brazil Brazil Peru France Brazil	1.317 -17.481683 -3.729 -6.56055555 -17.481683 -21.627 4.548404 3.08361 -2.950584 -0.9439694 -10.3236111 -8.053554 -10.399 3.56258 3.8794167	-70.389 -63.684867 -73.27 -47.4511111 -63.684867 -49.12 -52.152064 -53.4121 -51.936607 -53.2363000 -58.4927778 -63.518423 -71.165 -53.21046 -51.7709722
	AY943239 JN691219 ZONID04012 KDQF01003144 JF789920 KU495352 JN691190 KDQF01000794 KDQF01001824 KDQF01002920 KDQF01003756 XXXXXXXXXXXHT7431 ZONIK33715 KDQF01000651 KDQF01003937	QCAZ16788 VOGT2172 JMP1403 MTR07465 MJ1324 CFBHT09465 AF938 AF2018 BM084 MPEG30507 MTR976857 HT7431 SCF525 AF1733 PG689	Leptodactylus_discodactylus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_cf.podicipinus Leptodactylus_podicipinus Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	Leptodactylus_discodactylus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	36 22 22 31 40 39 8 3 4 3 8 16 6 19 19	45 24 20 32 41 38 16 1 7 1 18 10 13 28 28	14 16 16 15 12 13 25 37 22 37 23 33 27 20 20	19 27 27 22 13 14 38 50 33 50 34 46 40 31 31	NA Cachoeirinha, Margem Esquerda Rio Madeira Arboretum de la UNAM Guarai Yucuma, Los Lagos Corumba Igarape Camaipi, Rio Maraca Sipaliwini, Apalagadi UHE Belo Monte, Vitoria do Xingu, Rio Xingu Paru, FLOTA Lado direito do rio Paru de Leste Juruena Parque Nacional do Jaú Igarapé preto Puesto de control y vigilancia La Novia Chutes Voltaire, chemin crique Voltaire Nouragues , ParareG	AM Loreto TO MG AP PA PA MT AM Ucayali French Guiana French Guiana	Ecuador Brazil Peru Brazil Bolivia Brazil Brazil Suriname Brazil Brazil Brazil Brazil Peru France France	-5.4944444 -3.746075 -8.83416666 -12.771967 -19.03 -0.0241667 2.1728 -2.950584 -0.9439694 -10.3236111 -1.915278 -9.988 5.03255 4.08585	-60.8230556 -73.245614 -48.5102777 -65.810933 -57.545 -51.8972222 -56.0969 -51.936607 -53.2363000 -58.4927778 -61.413333 -70.708 -54.08689 -52.68132
	DQ283433 KC477247 ZONID05512 KDQF01003159 JF789921 KU495353 JN691191 KDQF01000853 KDQF01001982 KDQF01002921 KDQF01003770 XXXXXXXXXXXHT7432 XXXXXXXXXXXMTR39719 KDQF01000757 KDQF01003963	RdeSa AAGUFU4199 JMP2521 MTR08221 AS0464 CFBHT09485 MTR13775 AF2126 BPN1385 MPEG30511 NZCS42 HT7432 MTR39719 AF1946 PG784	Leptodactylus_discodactylus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_cf.podicipinus Leptodactylus_podicipinus Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_sp.maturaca Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	Leptodactylus_discodactylus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB_Maturaca Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	36 22 22 31 40 39 8 3 8 3 3 16 15 19 19	45 22 20 33 41 38 16 1 15 1 1 10 14 27 28	14 16 16 15 12 13 25 37 25 37 37 33 26 20 20	19 27 27 22 13 14 38 50 38 50 50 46 39 31 31	Rio Branco, UFAM Requena Petrolina Yucuma, Los Lagos Teodoro Sampaio Serra do Navio Sipaliwini, camp1 to Apalagadi Kaw2 Paru, FLOTA Lado direito do rio Paru de Leste maripaston, caught on transect 1 Parque Nacional do Jaú Igarapé preto Maturacá Flat de la Waki St Georges, Savane 14 juillet	AC Loreto GO SP AP French Guiana PA AM AM French Guiana French Guiana	Ecuador Brazil Peru Brazil Bolivia Brazil Brazil Suriname France Brazil Suriname Brazil Brazil France France	-9.944835 -5.057 -16.144938 -12.772 -22.511 0.9180556 2.09753 4.516105 -0.9439694 5.268728 -1.915278 0.621452 3.08361 3.967639	-67.800339 -73.854 -49.316454 -65.8109 -52.317 -52.0027778 -56.1472 -52.10053 -53.2363000 -55.435886 -61.413333 -66.120803 -53.4121 -51.87225
	GB KDQF01003727 EU342502 ZONID05612 KDQF01003195 JF789922 KU495354 JN691192 KDQF01000855 KDQF01002011 KDQF01002922 KDQF01003789 XXXXXXXXXXXLZUMSH15456 LZUMSH15456 Voucher QCAZ16625 MTR33844 JMP2522 MTR12009 MJ1365 CFBHT11491 BPN0890 AF2133 BPN2817 MPEG30515 NZCS82 XXXXXXXXXXXMTR40007 MTR40007 KDQF01000769 AF1966 KDQF01004097 QM390	ID_initial Leptodactylus_discodactylus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_cf.podicipinus Leptodactylus_podicipinus Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_spgrpodicipinusB Leptodactylus_podicipinus Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	ID_post_delimitation Leptodactylus_discodactylus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB_Maturaca Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	ABGD 36 22 22 34 40 39 8 3 3 3 8 6 15 20 19	GMYC mPTP consensus 45 14 19 20 16 27 20 16 27 37 15 25 41 12 13 39 13 14 16 25 38 1 37 50 1 37 50 1 37 50 16 25 38 13 27 40 14 26 39 31 18 29 28 20 31	locality Parque Nacional Yasuni Tefe, Estrada do Abial Requena Linhares, Reserva da Companhia Vale do Rio Doce Yucuma, Los Lagos Sucupira Brownsberg Sipaliwini RAP Basecamp 1, nr camp Paru, FLOTA Lado direito do rio Paru de Leste Brownsberg, mazaroni weg, Brownsberg Maturacá Sipaliwini Montagne de fer	state Loreto AM ES TO PA AM AM Suriname French Guiana	country Ecuador Brazil Peru Brazil Bolivia Brazil Suriname Suriname Suriname Brazil Suriname Brazil Brazil Suriname France	latitude -0.67716666 -3.45944009 -5.057 -19.278091 -12.771967 -11.979 4.936527 2.02682 2.477 -0.9439694 4.936527 0.625169 2.02439 5.4074	longitude -76.4011666 -64.7509004 -73.854 -40.169563 -65.810933 -48.913 -55.194826 -56.12564 -55.62941 -53.2363000 -55.194826 -66.117435 -56.12508 -53.5548
	XXXXXXXXXXXHT552 XXXXXXXXXXXMTR36335 KDQF01003742 ZONID05712 KDQF01003198 JN691171 XXXXXXXXXXXAF2585 JN691193 KDQF01000903 KDQF01002075 KDQF01002984 KDQF01004019 XXXXXXXXXXXLZUMSH17425 LZUMSH17425 HT552 MTR36335 MTR36303 JMP2523 MTR12197 MTR13985 AF2585 BPN1684 AF2336 BPN3613 MPEG36705 PK3688 EU201129 CM215 KDQF01000770 AF1967 KDQF01004104 QM405	Leptodactylus_petersii Leptodactylus_discodactylus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_cf.podicipinus Leptodactylus_sp.gr.podicipinus Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_spgrpodicipinusB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	Leptodactylus_aff.discodactylus Leptodactylus_discodactylus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	38 37 22 22 34 40 40 8 8 11 13 3 8 19 20 19	66 44 21 20 37 41 41 16 15 2 17 1 18 28 31 28	14 14 16 16 15 12 12 25 25 36 24 37 23 20 18 20	21 20 27 27 25 13 13 38 38 49 37 50 34 31 29 31	BR-319 Serra do Kukoinhokren, Rio Riozinho Requena Linhares, Reserva da Companhia Vale do Rio Doce Macapa SANTANA Saul Machicou Sandstone, Potaro-Siparuni MINA PALITO, Itaituba-PA Close to Iwokrama field station Apatou Sipaliwini Montagne de fer	RO AM PA Loreto ES AP AP French Guiana French Guiana PA RO French Guiana Suriname French Guiana	Brazil Brazil Brazil Peru Brazil Brazil Brazil France France Guyana Brazil Guyana Brazil France Suriname France	-8.053554 -2.89187 -7.8333333 -5.057 -19.278091 0.0458333 -0.040816 3.625556 3.890031 4.413793471 -6.31731705 4.672190 -10.776 5.166667 2.02439 5.4074	-63.518423 -68.35518 -51.9166667 -73.854 -40.169563 -51.05 -51.186018 -53.207222 -52.573535 -58.9216016 -55.7986444 -58.684733 -65.326 -54.333333 -56.12508 -53.5548
	AMTMP30315 XXXXXXXXXXXMTR36562 KDQF01004327 ZONID05812 KDQF01003243 JN691172 JN691194 KDQF01000915 KDQF01002094 KDQF01003006 KDQF01004051 XXXXXXXXXXXMTR25740 EU201130 KDQF01000817 KR811150	MHNLS16797 MTR36562 TG2659 JMP2524 MTR13683 MTR13986 SMNS12040 AF2353 BPN3717 MPEG36808 PV2293 MTR25740 AG056 AF2058 AF0810	Leptodactylus_sp. Leptodactylus_discodactylus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_cf.podicipinus Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_sp.gr.podicipinus Leptodactylus_spgrpodicipinusB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	Leptodactylus_colombiensis Leptodactylus_discodactylus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	30 37 22 22 34 40 3 8 11 13 4 8 19 20 19	57 44 22 20 37 41 1 15 2 17 7 18 27 31 28	8 14 16 16 15 12 37 25 36 24 22 23 20 18 20	9 20 27 27 25 13 50 38 49 37 33 34 31 29 31	Cruzeiro do Sul Requena Linhares, Reserva da Companhia Vale do Rio Doce Macapa Mabura hill forest reserve Mapaou Parabara, Village MINA PALITO, Itaituba-PA Buriti do Tocantins Pacaás Novos Trinite, Mont Tabulaire Sipaliwini, Apalagadi Savane virginie, layon	AM AC Loreto ES AP French Guiana PA TO RO French Guiana Suriname French Guiana	Venezuela Brazil Brazil Peru Brazil Brazil Guyana France Guyana Brazil Brazil Brazil France Suriname France	10.263938 -3.03741 -7.712039 -5.057 -19.278091 0.0458333 5.1552778 4.189842 2.094500 -6.31731705 -5.21631520 -10.7996140 4.6061 2.17965 4.1959	-66.487167 -68.88163 -72.003719 -73.854 -40.169563 -51.05 -58.6997222 -52.314725 -59.240183 -55.7986444 -48.3877819 -63.6271889 -53.3527 -56.09078 -52.149
	AMTMP33315 XXXXXXXXXXXMTR36588 KF723177 ZONID05912 KDQF01003317 JN691173 JN691195 KDQF01000985 KDQF01002131 KDQF01003044 KDQF01004052 XXXXXXXXXXXMTR25799 EU201131 KDQF01000820 KR811151	MHNLS17373 MTR36588 MNKA9505 JMP2525 MTR14331 CMC SMNS12041 AF2561 CAAM18 MTD47781 PV2313 MTR25799 AF0183 AF2069 PG783	Leptodactylus_sp. Leptodactylus_discodactylus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_cf.podicipinus Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_spgrpodicipinusB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	Leptodactylus_colombiensis Leptodactylus_discodactylus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	29 37 22 22 31 40 3 8 8 3 4 8 19 20 19	58 44 24 20 32 41 1 15 16 1 7 18 30 31 28	7 14 16 16 15 12 37 25 25 37 22 23 19 18 20	8 20 27 27 22 13 50 38 38 50 33 34 30 29 31	San Sebastian Requena Estacao Ecologica Serra Geral do Tocantins Kaw1 Mabura hill forest reserve RN2 PK65 mare Montagne cacao Mabura hill forest reserve Maraba Pacaás Novos Road to Apura, Goliathberg piste PK7 Sipaliwini, Apalagadi St Georges, Savane 14 juillet	AM Loreto TO French Guiana French Guiana French Guiana PA RO Suriname French Guiana	Venezuela Brazil Bolivia Peru Brazil France Guyana France France Guyana Brazil Brazil Suriname Suriname France	6.257713 -3.03728 -16.3596 -5.057 -11.2208333 4.516105 5.1552778 4.487368 2.349128 5.1552778 -4.834147 -10.7869789 5.183333 2.17816 3.967639	-63.190089 -68.88165 -62.0001 -73.854 -46.8855555 -52.10053 -58.6997222 -52.347811 -53.215985 -58.6997222 -48.96534 -63.6273050 -55.616667 -56.08521 -51.87225
	AY943237 ZONIC58112 KF723178 ZONID15912 KDQF01003763 JN691174 JN691196 KDQF01001003 KDQF01002389 KDQF01003065 KDQF01004060 XXXXXXXXXXXMTR33862 JN691205 KDQF01000882 KR811152 EF632052 EU368907 KF723179 ZONID16012 KDQF01004208 JN691175 JN691197 KDQF01001108 KDQF01002390 KDQF01003140 KDQF01004065 XXXXXXXXXXXMTR35690 JN691206 KDQF01000905 XXXXXXXXXXXAF2407	USNM535964 JMP1477 SMF94137 JMP2322 MTR978164 CMD SMNS12042 AF2586 DAN001 MTD47963 QM020 MTR33862 MTR13809 AF2224 AF0735 LSUNZH12885l MNCN43541 MNKA9729 JMP2323 SAMA8573 CME SMNS12043 AF2795 DAN005 MTR06305 QM070 MTR35690 AF0040 AF2338 AF2407	Leptodactylus_melanonotus Leptodactylus_petersii Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_cf.podicipinus Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_sp.gr.podicipinus Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_colombiensis Leptodactylus_griseigularis Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_cf.podicipinus Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_spgrpodicipinusB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_spgrpodicipinusC	Leptodactylus_colombiensis Leptodactylus_discodactylus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_colombiensis Leptodactylus_griseigularis Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	27 37 22 22 35 40 3 8 8 11 8 4 19 20 19 27 26 22 22 31 40 3 3 8 8 8 2 19 19 19	59 44 24 20 36 41 1 16 15 2 15 7 27 31 28 49 60 24 20 33 41 1 1 15 16 15 5 27 27 29	6 14 16 16 15 12 37 25 25 36 25 22 20 18 20 6 14 16 16 15 12 37 37 25 25 25 35 20 20 19	7 20 27 27 26 13 50 38 38 49 38 33 31 29 31 7 18 27 27 22 13 50 50 38 38 38 48 31 31 30	CAYObetween San Jacinto and Spanish Umarital, Rio Ampiyacu San Sebastian Bosque al Este de Requena Vila Rica Kaw1 Mabura hill forest reserve PEDRABRANCA St Georges, Station d'epuration du bourg de Iwokrama Matiti Peixe-Boi Serra do Navio Sipaliwini St Georges, Saut Maripa Lookout road onWebster Highway Cuyabeno La Paz, Serrania Bellavista San Sebastian Bosque al Este de Requena Minacu Kaw1 Mabura hill forest reserve Mitaraka, Layon A St Georges, Station d'epuration du bourg de Igarape Camaipi, Rio Maraca Guatemala Saul, galbao Ekini Nassau	Loreto Loreto MT French Guiana AP French Guiana French Guiana PA AP Suriname French Guiana Loreto GO French Guiana French Guiana French Guiana AP French Guiana AM French Guiana French Guiana Suriname	Belize Peru Bolivia Peru Brazil France Guyana Brazil France Guyana France Brazil Brazil Suriname France Ecuador Bolivia Bolivia Peru Brazil France Guyana France France Brazil France Brazil France France	16.871723 -3.267 -16.3596 -5.12 -9.9241667 4.516105 5.1552778 0.772972 3.894655 4.6713889 5.050249 -1.143941 0.9180556 2.03253 3.811159 -0.0886 -16.212074 -16.3596 -5.12 -13.5408333 4.516105 5.1552778 2.23577 3.894655 -0.0241667 5.15 -3.21026 3.6 4.05 4.81701	-88.942876 -72.267 -62.0001 -73.8 -51.2383333 -52.10053 -58.6997222 -51.94571 -51.799943 -58.685 -52.571622 -52.0027778 -56.11449 -51.889644 -47.288139 -76.1419 -67.912572 -62.0001 -73.8 -48.0902777 -52.10053 -58.6997222 -54.44928 -51.799943 -51.8972222 -52.633333 -64.8053 -53.283333 -52.4667 -54.60373
	EF632053 KM091588 KF723180 ZONIF27812 KDQF01004486 JN691217 JN691198 KDQF01001130 KDQF01002391 KDQF01003199 KDQF01004330 XXXXXXXXXXXMTR36069 JN691207 KDQF01000961 XXXXXXXXXXXAF3810 EF632054 XXXXXXXXXXXAJC2617 KF723181 ZONIK09515 KM091602 JN691218 JN691199 KDQF01001203 KDQF01002392 KDQF01003217 KDQF01004367 XXXXXXXXXXXMTR36546 JN691208 KDQF01000969 XXXXXXXXXXXAM002 KM091579 KP149302 KP149318 KP149403 KP149444 KP149459 KP149468 MG030714 XXXXXXXXXXXLSUH12415 XXXXXXXXXXXMTR28117 XXXXXXXXXXXMTR33747 ZONIA36512 ZONIA37912 ZONIA51512 ZONIA51712 ZONIA53712 ZONIA53912 AY943236 JF789893 JF789894 JF789895 JF789896 JF789897 ZONIB15212 JN691215 EU201132 KDQF01003971 KDQF01000493 KDQF01001583 KDQF01002685 KDQF01003457 XXXXXXXXXXXBPN1746 ZONIC61012 KDQF01000347 KDQF01001598 ZONIB15112 AY948760 EF632051 KDQF01003119 KDQF01000369 KDQF01001576 KDQF01002674 KDQF01003456 XXXXXXXXXXXAP098 ZONIC58212 KDQF01000344 KDQF01001597 ZONIB15012 AY364380 EF632050 KDQF01002720 KDQF01000279 KDQF01001572 KDQF01002671 KDQF01003440 XXXXXXXXXXXAP049 ZONIA38212 KDQF01000343 KDQF01001516 XXXXXXXXXXXMJ1235 XXXXXXXXXXXMTR36845 XXXXXXXXXXXMTR37043 XXXXXXXXXXXTG2658 ZONIA70012 ZONIB14812 ZONIB14912 ZONIK46515 EF632049 KDQF01002662 KDQF01000278 KDQF01001552 KDQF01002658 KDQF01003437 XXXXXXXXXXXAG442 ZONIA38012 KDQF01000320 KDQF01001461 ZONIK24915 ZONIK33815 ZONIK33915 ZONIK38615 ZONIK46315 ZONIK46415 AMTMP36615 KDQF01001482 KDQF01001483 KDQF01002451 KDQF01002458 KDQF01002479 JN691204 JN691220 KDQF01000011 KDQF01000243 KDQF01000244 KDQF01001430 KDQF01001432 KDQF01001433 KDQF01001548 KDQF01001551 KDQF01002430 KDQF01002552 KDQF01002621 KDQF01002630 KDQF01002631 KDQF01003268 KDQF01003391 KDQF01003411 KDQF01003413 KDQF01003429 XXXXXXXXXXXAF3754 XXXXXXXXXXXAF3829 XXXXXXXXXXXAF3924 XXXXXXXXXXXAF743 XXXXXXXXXXXAG013 XXXXXXXXXXXMTR41361 XXXXXXXXXXXPK1516 XXXXXXXXXXXTJC1262 ZONIA37012 ZONIA37112 KDQF01000146 KDQF01000211 KDQF01000225 KDQF01000296 KDQF01000304 KDQF01001081 KDQF01001110 KDQF01001296 KDQF01001390 KDQF01001459 Leptodactylus_sp. EF632048 LSUMZH13653 Leptodactylus_colombiensis SBH171130 Leptodactylus_griseigularis SMF94193 Leptodactylus_leptodactyloides AJC2594 Leptodactylus_sp. ZUFRJ230 Leptodactylus_natalensis MTR05426 Leptodactylus_podicipinus SMNS12044 Leptodactylus_petersii AF2892 Leptodactylus_petersii DAN006 Leptodactylus_petersii MTR12652 Leptodactylus_petersii TG2844 Leptodactylus_petersii MTR36069 Leptodactylus_spgrpodicipinusB AG263 Leptodactylus_spgrpodiC AF2446 Leptodactylus_spgrpodiC AF3810 Leptodactylus_spgrpodicipinusC USNM320988 Leptodactylus_wagneri AJC2617 Leptodactylus_griseigularis MNKA10357 Leptodactylus_leptodactyloides SCF195 Leptodactylus_leptodactyloides USNM533998 Leptodactylus_natalensis MTR05492 Leptodactylus_podicipinus SMNS12045 Leptodactylus_petersii AF3044 Leptodactylus_petersii DAN007 Leptodactylus_petersii MTR12746 Leptodactylus_petersii TJC1276 Leptodactylus_petersii MTR36546 Leptodactylus_spgrpodicipinusB AG014 Leptodactylus_spgrpodiC AF2477 Leptodactylus_spgrpodiC AM002 Leptodactylus_spgrpodiC MVJ1618 Leptodactylus_colombiensis AJC4000 Leptodactylus_colombiensis AJC3848 Leptodactylus_colombiensis AJC3981 Leptodactylus_colombiensis AJC3986 Leptodactylus_colombiensis LSB378 Leptodactylus_colombiensis AJC3978 Leptodactylus_colombiensis AJC5035 Leptodactylus_colombiensis LSUH12415 Leptodactylus_colombiensis MTR28117 Leptodactylus_griseigularis MTR33747 Leptodactylus_colombiensis JMP2185 Leptodactylus_leptodactyloides JMP2055 Leptodactylus_wagneri AJC2616 Leptodactylus_sp. AJC2618 Leptodactylus_sp. AJC2644 Leptodactylus_sp. AJC2646 Leptodactylus_sp. ??? Leptodactylus_leptodactyloides AS0017 Leptodactylus_leptodactyloides AS0185 Leptodactylus_leptodactyloides AS0186 Leptodactylus_leptodactyloides AS0240 Leptodactylus_leptodactyloides AS0547 Leptodactylus_leptodactyloides JMP170 Leptodactylus_leptodactyloides MTR978129 Leptodactylus_natalensis CM155 Leptodactylus_cf.podicipinus PHV2553 Leptodactylus_podicipinus AF1327 Leptodactylus_petersii AG403 Leptodactylus_petersii INPA152 Leptodactylus_petersii MTR20419 Leptodactylus_petersii BPN1746 Leptodactylus_spgrpodicipinusB JMP1522 Leptodactylus_petersii AF0954 Leptodactylus_spgrpodiC AG427 Leptodactylus_spgrpodiC JMP151 Leptodactylus_leptodactyloides MVZ207294 Leptodactylus_melanonotus USNM053124 Leptodactylus_podicipinus MTJ0369 Leptodactylus_podicipinus AF1017 Leptodactylus_petersii AG390 Leptodactylus_petersii HJ681 Leptodactylus_petersii MTR20418 Leptodactylus_petersii AP098 Leptodactylus_sp.gr.podicipinus JMP1478 Leptodactylus_petersii AF0948 Leptodactylus_spgrpodiC AG426 Leptodactylus_spgrpodiC JMP149 Leptodactylus_leptodactyloides ??? Leptodactylus_melanonotus UC278 Leptodactylus_podicipinus LAJ483 Leptodactylus_podicipinus AF0850 Leptodactylus_petersii AG382 Leptodactylus_petersii HJ645 Leptodactylus_petersii MTR19378 Leptodactylus_petersii AP049 Leptodactylus_sp.gr.podicipinus JMP2209 Leptodactylus_wagneri AF0947 Leptodactylus_spgrpodiC AG206 Leptodactylus_spgrpodiC MJ1235 Leptodactylus_leptodactyloides MTR36845 Leptodactylus_leptodactyloides MTR37043 Leptodactylus_leptodactyloides TG2658 Leptodactylus_leptodactyloides AJC2842 Leptodactylus_sp. JMP147 Leptodactylus_leptodactyloides JMP148 Leptodactylus_leptodactyloides SCF685 Leptodactylus_leptodactyloides UC244 Leptodactylus_podicipinus HJ522 Leptodactylus_cf.podicipinus AF0849 Leptodactylus_petersii AG340 Leptodactylus_petersii HJ465 Leptodactylus_petersii MTR19322 Leptodactylus_petersii AG442 Leptodactylus_petersii JMP2056 Leptodactylus_wagneri AF0914 Leptodactylus_spgrpodiC AF3709 Leptodactylus_spgrpodiC SCF406 Leptodactylus_leptodactyloides SCF526 Leptodactylus_leptodactyloides SCF527 Leptodactylus_leptodactyloides SCF582 Leptodactylus_leptodactyloides SCF683 Leptodactylus_leptodactyloides SCF684 Leptodactylus_leptodactyloides MHNLS17713 UC243 Leptodactylus_podicipinus AF749 Leptodactylus_podicipinus AF750 Leptodactylus_podicipinus ESTR0223 Leptodactylus_podicipinus ESTR0419 Leptodactylus_podicipinus ESTR1625 Leptodactylus_podicipinus PK1664 Leptodactylus_petersii MSH10374 Leptodactylus_petersii AAGARDA895 Leptodactylus_petersii AF0789 Leptodactylus_petersii AF0790 Leptodactylus_petersii AF3464 Leptodactylus_petersii AF3467 Leptodactylus_petersii AF3482 Leptodactylus_petersii AG333 Leptodactylus_petersii AG339 Leptodactylus_petersii DSM138 Leptodactylus_petersii FTA293 Leptodactylus_petersii HJ003 Leptodactylus_petersii HJ056 Leptodactylus_petersii HJ057 Leptodactylus_petersii MTR13812 Leptodactylus_petersii MTR18764 Leptodactylus_petersii MTR18955 Leptodactylus_petersii MTR19027 Leptodactylus_petersii MTR19221 Leptodactylus_petersii AF3754 Leptodactylus_spgrpodicipinusB AF3829 Leptodactylus_spgrpodicipinusB AF3924 Leptodactylus_spgrpodicipinusB AF0743 Leptodactylus_petersii AG013 Leptodactylus_petersii MTR41361 Leptodactylus_cf.leptodactylodes Leptodactylus_spgrpodiB Leptodactylus_colombiensis Leptodactylus_griseigularis Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_colombiensis Leptodactylus_griseigularis Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_colombiensis Leptodactylus_colombiensis Leptodactylus_colombiensis Leptodactylus_colombiensis Leptodactylus_colombiensis Leptodactylus_colombiensis Leptodactylus_colombiensis Leptodactylus_colombiensis Leptodactylus_colombiensis Leptodactylus_colombiensis Leptodactylus_colombiensis Leptodactylus_colombiensis Leptodactylus_colombiensis Leptodactylus_griseigularis Leptodactylus_griseigularis Leptodactylus_griseigularis Leptodactylus_griseigularis Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_leptodactyloides Leptodactylus_melanonotus Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_leptodactyloides Leptodactylus_melanonotus Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB PK1516 Leptodactylus_spgrpodicipinusB Leptodactylus_spgrpodiB TJC1262 Leptodactylus_spgrpodicipinusB Leptodactylus_spgrpodiB JMP2087 Leptodactylus_petersii Leptodactylus_spgrpodiB JMP2186 Leptodactylus_petersii Leptodactylus_spgrpodiB AF0570 Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC AF0702 Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC AF0724 Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC AF0885 Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC AF0898 Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC AF2738 Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC AF2802 Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC AF3190 Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC AF3369 Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC AF3702 Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC KDQF01001474 AF481 Leptodactylus_podicipinus Leptodactylus_podicipinus JN691203 PK1506 Leptodactylus_petersii Leptodactylus_spgrpodiB KDQF01001422 AF3446 Leptodactylus_petersii Leptodactylus_spgrpodiB KDQF01002429 DSM136 Leptodactylus_petersii Leptodactylus_spgrpodiB KDQF01003267 MTR13811 Leptodactylus_petersii Leptodactylus_spgrpodiB XXXXXXXXXXXAF3745 AF3745 Leptodactylus_spgrpodicipinusB Leptodactylus_spgrpodiB XXXXXXXXXXXMTR40972 MTR40972 Leptodactylus_cf. petersii Leptodactylus_spgrpodiB KDQF01000126 AF0520 Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC KDQF01001064 AF2696 Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC ZONIA51312 AJC2614 Leptodactylus_sp. Leptodactylus_griseigularis ZONIA51412 AJC2615 Leptodactylus_sp. Leptodactylus_griseigularis KM091608 KU218194 Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides ZONIK24615 SCF399 Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides KU495347 MTR_ALCX101P28 Leptodactylus_natalensis Leptodactylus_natalensis KDQF01000476 AF1264 Leptodactylus_podicipinus Leptodactylus_podicipinus JN691202 SMNS12047 Leptodactylus_petersii Leptodactylus_spgrpodiB KDQF01001379 AF3346 Leptodactylus_petersii Leptodactylus_spgrpodiB KDQF01002401 DAN035 Leptodactylus_petersii Leptodactylus_spgrpodiB KDQF01003266 MTR13810 Leptodactylus_petersii Leptodactylus_spgrpodiB MG806060 CTGA_N_5437 Leptodactylus_cf.petersii Leptodactylus_spgrpodiB XXXXXXXXXXXMTR40622 MTR40622 Leptodactylus_cf. petersii Leptodactylus_spgrpodiB JN691211 AF0179 Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC KDQF01001022 AF2618 Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC KF723183 AS483 Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides ZONIK24515 SCF398 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Leptodactylus_podicipinus Leptodactylus_podicipinus JN691200 SMNS12079 Leptodactylus_petersii Leptodactylus_spgrpodiB KDQF01001349 AF3283 Leptodactylus_petersii Leptodactylus_spgrpodiB KDQF01002396 DAN017 Leptodactylus_petersii Leptodactylus_spgrpodiB KDQF01003238 MTR13214 Leptodactylus_petersii Leptodactylus_spgrpodiB KDQF01004469 WAAM018 Leptodactylus_petersii Leptodactylus_spgrpodiB XXXXXXXXXXXMTR36973 MTR36973 Leptodactylus_spgrpodicipinusB Leptodactylus_spgrpodiB JN691209 AG208 Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC KDQF01000970 AF2478 Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC XXXXXXXXXXXCAAM40 CAAM40 Leptodactylus_spgrpodicipinusC Leptodactylus_spgrpodiC	27 25 22 22 31 41 3 8 8 10 12 2 19 19 19 27 23 22 22 31 41 3 8 8 10 3 2 19 19 19 27 27 27 27 27 27 27 27 27 27 27 27 27 23 23 23 24 22 22 22 22 22 22 22 35 40 41 8 8 1 11 3 5 19 19 22 47 39 39 8 8 6 11 8 5 19 19 22 47 39 39 8 8 7 1 8 49 19 19 22 22 22 22 22 22 22 22 39 40 8 8 6 1 8 49 19 19 22 22 22 22 22 22 39 41 41 39 39 39 3 16 4 8 8 3 3 8 8 8 8 8 8 7 9 8 1 1 1 1 3 3 8 8 8 2 3 3 17 49 19 19 19 19 19 19 19 19 20 19 39 3 3 8 8 3 2 19 19 23 23 22 22 31 39 3 3 8 8 11 16 19 19 22 22 31 39 3 3 8 10 8 16 19 19 22 22 33 39 3 8 8 10 8 2 19 19 19	50 63 24 20 35 42 1 15 15 19 67 5 27 29 30 49 43 24 22 35 42 1 15 15 19 1 5 28 29 27 51 51 64 51 51 51 51 51 49 50 49 49 49 43 43 43 72 21 25 24 24 24 24 24 36 41 42 15 15 9 4 1 8 28 27 24 53 39 69 16 15 13 4 16 8 28 27 24 53 38 39 15 15 12 9 16 6 28 27 24 20 20 22 20 24 24 22 38 41 15 15 13 9 16 6 27 27 22 22 22 22 22 22 38 42 42 39 39 39 1 10 7 15 15 1 1 15 15 15 15 16 18 12 65 16 9 9 9 9 1 1 15 15 15 5 1 1 71 6 28 28 28 28 27 27 27 27 31 27 38 1 1 15 16 1 5 28 27 43 43 20 22 35 39 1 1 15 16 3 10 30 27 24 22 35 38 1 1 15 19 16 10 28 29 24 22 35 38 1 15 15 19 16 5 28 29 27	6 14 16 16 15 11 37 25 25 23 30 35 20 19 19 6 14 16 16 15 11 37 25 25 23 37 35 20 19 20 6 6 6 6 6 6 6 6 6 6 6 6 6 14 14 14 14 16 16 16 16 16 16 16 15 12 11 25 25 32 36 37 31 20 20 16 2 13 13 25 25 27 36 25 31 20 20 16 2 13 13 25 25 28 32 25 34 20 20 16 16 16 16 16 16 16 16 13 12 25 25 27 32 25 34 20 20 16 16 16 16 16 16 13 11 11 13 13 13 37 33 22 25 25 37 37 25 25 25 25 25 23 28 23 25 32 32 32 32 37 37 25 25 25 35 37 37 21 34 20 20 20 20 20 20 20 20 18 20 13 37 37 25 25 37 35 20 20 14 14 16 16 15 13 37 37 25 25 36 33 19 20 16 16 15 13 37 37 25 23 25 33 20 19 16 16 15 13 37 25 25 23 25 35 20 19 20	7 17 27 27 22 12 50 38 38 36 43 48 31 30 30 7 15 27 27 22 12 50 38 38 36 50 48 31 30 31 7 7 7 7 7 7 7 7 7 7 7 7 7 15 15 15 16 27 27 27 27 27 27 27 26 13 12 38 38 45 49 50 44 31 31 27 2 14 14 38 38 40 49 38 44 31 31 27 2 14 14 38 38 41 45 38 47 31 31 27 27 27 27 27 27 27 27 14 13 38 38 40 45 38 47 31 31 27 27 27 27 27 27 14 12 12 14 14 14 50 46 33 38 38 50 50 38 38 38 38 38 34 41 35 38 45 45 45 45 50 50 38 38 38 48 50 50 32 47 31 31 31 31 31 31 31 31 29 31 14 50 50 38 38 50 48 31 31 15 15 27 27 22 14 50 50 38 38 49 46 30 31 27 27 22 14 50 50 38 36 38 46 31 30 27 27 24 14 50 38 38 36 38 48 31 30 31	5km N Porto Walter, inland from the Rio Perú, Huanuco, Potanischer Garten in Tingo Maria, 641 m San Sebastian rio Takana, km 9 carretera Leticia Usina Serra grande, Ibateguara APM Manso Mabura hill forest reserve Roura St Georges, Station d'epuration du bourg de Igarape Jacunda, Rio Abacaxis Cruzeiro do Sul, Res. Extrativista Riozinho Liberdade Lucifer Nassau, trail 11 Voltzberg Jurua. Coca, 1km ENE of Tiguino San Martin San Sebastian Puesto de control y vigilancia Cocama APM Manso Mabura hill forest reserve Memora G St Georges, Station d'epuration du bourg de Igarape Acu, Rio Abacaxis NARIL Guyane Nassau, trail camp Inini Tolenga Santander; Charalá, Virolín Caserio Miraflores, Vereda La Balastrera Santander, Puente Nacional, Puente Costa Atlantica, Tubara Serra do Divisor Japura, Trilha da Anta Leticia, Varzea del del rio Ta Kana, km 10 carretera Leticia, Kilometro 13 m carretera arribade la quebrada principal Shucshuyacu charca bode carretera 50 m carretera arribade la quebrada principal Carretera Tarapoto-Tocachi Carretera Tarapoto-Tocachi Serra do Kukoinhokren nuflo de Chavez, San Sebastian nuflo de Chavez, San Sebastian nuflo de Chavez, San Sebastian Velasco, Caparu nuflo de Chavez, San Sebastian Natural Amboro Vila Rica Kaw1 Agua boa Saut Grand Machicou Chutes Voltaire BR-319 E.E. Maraca Saramacca River Bridge Confluencia de los Rios Supai y Sabalo RN2 Atachi Bakka Campamento La Chonta. Parque Campamento La Chonta. Parque Natural Amboro Entrance Santa Rosa Nat Fazenda Jatai Januaria Saul, Galbao crique Dekou dekou UHE Jirau, Abuna E.E. Maraca Plote PPBIO Umarital, Rio Ampiyacu Matoury , Lac des Americains Atachi Bakka Parque Nacional Cavernas do Peruacu, Campamento La Chonta. Parque Natural Amboro Chacara Renascer; Bauru UHE Lajeado Chutes Voltaire, camp St Pierre Caicara, Porto Velho Rio Purus Plote PPBIO Porto Grande Leticia Matoury , Lac des Americains Trinite, Crique Grand Leblond Rio Purus, Moiobamba, margem direita, Cruzeiro do Sul Isla de los Micos Campamento La Chonta. Parque Natural Amboro Natural Amboro Puesto de control y vigilancia La Novia Estancia Mimosa, Mun de Bonito UHE Jirau, Mutum Chutes Voltaire, camp Macouria, La Cesaree UHE Jirau, Abuna Rio Purus Alikene Leticia, Kilometro 13 Pic Coudreau du Sud Itoupe Campamento La Chonta. Parque Puesto de control y vigilancia Cocama Puesto de control y vigilancia La Novia Puesto de control y vigilancia La Novia Puesto de control y vigilancia La Novia Puesto de control y vigilancia La Novia Puesto de control y vigilancia La Novia Estancia Mimosa, Mun de Bonito APM Manso APM Manso Carolina Palmeirante Palmeirante Kaieteur NP E.E. Anavilhanas, Base 2 Tailandia, Reserva da Agropalma (Areial) Le Galion Le Galion Spari Creek Spari Creek St Georges, RN2 pk 185 Petit Laussat Macouria, La Cesaree Monsinery Rio Amapa Grande, Calcoene, FLOTA III-UHE Jirau, Porto Velho, Hidreletrica Jirau UHE Jirau, Mutum UHE Jirau, Mutum Serra do Navio Rio Purus Rio Purus, Lago Chaviana, Itapuru, margem direita, Rio Purus, Moiobamba, margem direita, Rio Purus Rio Purus Voltzberg Voltzberg Montagne de fer St Georges, savane Rio Inhambú Kaieteur NP NARIL Leticia, Los Lagos Leticia, Varzea del del rio Ta Kana, km 10 carretera Le Galion Paracou Paracou Matoury, Mirande Pic Coudreau du Sud Mitaraka Mitaraka Mitan G Bakhuis, Low creek Itoupe Rio Ivai Kaieteur NP Spari Creek Monsinery Serra do Navio Voltzberg Boa Vista CisameG Alikene, camp polissoir Nacional Shucshuyacu charca bode carretera 50 m carretera arribade la quebrada Shucshuyacu charca bode carretera 50 m carretera arribade la quebrada principal Shucshuyacu charca bode carretera 50 Puesto de control y vigilancia Cocama Maceio, Serra da Saudinha UHE Lajeado Mabura hill forest reserve Bakhuis, Creek 10k St Georges, piste Saut Maripa Serra do Navio Curirú piste Atachi Bakka, camp1 Road to Apura, Goliathberg PK7 avant Yucuma, Los Lagos Puesto de control y vigilancia Cocama Salvador Babaculandia Mabura hill forest reserve Bakhuis, near camp St Georges, RN 2 PK168 Palhalzinho, Rio Abacaxis Flat de la waki Santa Helena St Georges, armee Nassau principal San Sebastian Puesto de control y vigilancia Cocama Corumba Iwokrama Taparabo St Georges, route du pont Pacamiri, Rio Abacaxis Flat de la waki Trinite, Crique Grand Leblond Nassau Montagne cacao	AC AL MT French Guiana French Guiana AM AC AM French Guiana Suriname Suriname Peru Ucayali MT French Guiana French Guiana AM AM French Guiana Suriname AC AM San Martin San Martin San Martin San Martin PA MT French Guiana MT French Guiana French Guiana AM RR Loreto French Guiana French Guiana SP MG French Guiana French Guiana RO RR AP Loreto French Guiana French Guiana SP TO French Guiana French Guiana RO AM AP French Guiana French Guiana AM AM AC Ucayali MS RO French Guiana French Guiana RO AM French Guiana French Guiana French Guiana Ucayali Ucayali Ucayali Ucayali Ucayali Ucayali MS MT MT MA TO TO AM PA French Guiana French Guiana French Guiana French Guiana French Guiana French Guiana AP RO RO RO AP AM AM AM AM French Guiana French Guiana French Guiana AM French Guiana French Guiana French Guiana French Guiana French Guiana French Guiana French Guiana French Guiana Suriname French Guiana PR French Guiana AP AM French Guiana French Guiana San Martin Ucayali Alagoas TO French Guiana AP RR French Guiana Ucayali Bahia TO French Guiana AM French Guiana AM French Guiana Suriname San Martin Ucayali MS AP French Guiana AM French Guiana AM French Guiana Suriname French Guiana	Brazil Peru Bolivia Colombia Brazil Brazil Guyana France France Brazil Brazil Brazil France Suriname Ecuador Peru Bolivia Peru Brazil Guyana France France Brazil Guyana Brazil France Suriname French Guiana Colombia Colombia Colombia Colombia Colombia Colombia Colombia Brazil Brazil Colombia Colombia Peru Peru Peru Peru Brazil Bolivia Bolivia Bolivia Bolivia Bolivia Bolivia Brazil France Brazil France France Brazil Brazil Peru France France Bolivia Costa_Rica Brazil Brazil France France Brazil Brazil Brazil Peru France France Bolivia Brazil Brazil France France Brazil Brazil Brazil Colombia France France Brazil Brazil Brazil Colombia Bolivia Bolivia Peru Brazil Brazil France France Brazil Brazil France Colombia France France Peru Peru Peru Peru Peru Peru Brazil Brazil Brazil Brazil Brazil Brazil Guyana Brazil Brazil France France Suriname Suriname France France France France Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil Brazil France France France Brazil Guyana Guyana Colombia Colombia France France France France France France France France Suriname France Brazil Guyana Suriname France Brazil Brazil France France 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-62.309074 -52.37855 -69.961 -54.3534 -53.13286 -71.165 -70.708 -70.708 -70.708 -70.708 -70.708 -56.4819 -55.0666667 -55.0666667 -47.26147 -47.9258333 -47.9258333 -59.7686111 -60.8366667 -48.899459 -52.432684 -52.432684 -55.80462 -55.80462 -51.843853 -53.883405 -52.533717 -52.47466 -51.191333 -63.903465 -63.903465 -63.903465 -52.0027778 -61.48801 -61.48801 -62.133036 -62.309074 -56.18568 -56.21116 -53.554662 -51.788964 -65.416147 -59.500294 -59.10673 -69.95 -69.25 -52.432684 -52.9236 -52.9236 -52.349432 -54.3534 -54.44928 -54.44928 -52.55404 -56.77911 -53.13286 -52.682934 -59.7686111 -55.80462 -52.47466 -52.0027778 -56.18568 -65.408344 -52.372028 -52.39747 -76.615 -73.695 -71.165 -35.611 -48.3708333 -58.6997222 -56.78649 -51.863422 -52.0027778 -61.78059 -55.616667 -53.96164 -76.615 -65.8109 -71.165 -38.507 -47.7569444 -58.6997222 -56.7638 -51.931057 -58.1333333 -53.4775 -61.779996 -51.807804 -54.61502 -76.615 -62.0001 -71.165 -57.635078 -58.685 -51.693802 -51.807804 -58.1333333 -53.4775 -63.14366 -53.35 -54.61502 -53.215985
	ZONIA38112 ZONIC60912 ZONIC61112 JF789898 ZONIB15812 JN691216 JF789912 KDQF01004195 KDQF01000515 KDQF01001607 KDQF01002839 KDQF01003474 XXXXXXXXXXXBPN2958 ZONIC62912 KDQF01000387 KDQF01001630 JF789899 ZONIB16012 KDQF01001481 JF789913 KF723184 KDQF01000520 KDQF01001679 KDQF01002840 KDQF01003481 XXXXXXXXXXXH0056 ZONID16112 KDQF01000444 KDQF01001769 JF789900 ZONIB16212 KDQF01002447 JF789914 KF723185 KDQF01000604 KDQF01001680 KDQF01002841 KDQF01003588 XXXXXXXXXXXHT1787 ZONIF26712 KDQF01000482 KDQF01001894	JMP2120 JMP1520 JMP1523 AS0551 JMP520 MTR978163 AS0216 RGA5340 AF1391 AG438 MPEG23260 MTR20531 BPN2958 JMP1547 AF1072 AG465 AS0556 JMP1232 AF689 AS0275 SMF94320 AF1407 AL120414G1 MPEG23261 MTR20596 H0056 JMP2324 AF1201 AP079 MJ1263 JMP124 ESTR0077 AS0276 MNKA9638 AF1611 AL120414G2 MPEG23262 MTR24193 HT1787 AJC2577 AF1280 BOAM062	Leptodactylus_wagneri Leptodactylus_wagneri Leptodactylus_wagneri Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_spgrpodicipinusB Leptodactylus_petersii Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_sp.gr.podicipinus Leptodactylus_petersii Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_wagneri Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	Leptodactylus_colombiensis Leptodactylus_colombiensis Leptodactylus_colombiensis Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	27 27 27 22 22 32 39 42 8 8 3 11 3 5 19 19 22 22 31 39 39 8 8 3 11 7 18 19 19 22 22 31 39 39 8 8 3 8 11 49 19 19	49 49 49 25 23 61 40 70 15 16 1 2 1 8 28 27 24 22 34 40 40 15 15 1 2 12 74 27 28 25 23 32 40 40 16 15 1 16 3 6 27 27	6 6 6 16 16 15 13 10 25 25 37 36 37 31 20 20 16 16 15 13 13 25 25 37 36 28 21 20 20 16 16 15 13 13 25 25 37 25 36 34 20 20	7 7 7 27 27 23 14 11 38 38 50 49 50 44 31 31 27 27 22 14 14 38 38 50 49 41 32 31 31 27 27 22 14 14 38 38 50 38 49 47 31 31	Leticia, Km 10 carretera Umarital, Rio Ampiyacu Confluencia de los Rios Supai y Sabalo nuflo de Chavez, San Sebastian Kharahuasi. Carrasco Vila Rica Velasco, Caparu UHE Guapore Aratai Alikene Faro, FLOTA 1 RAP Basecamp 2 Confluencia de los Rios Supai y Sabalo Oiapoque, derriere inselberg Alikene nuflo de Chavez, San Sebastian San Sebastian. Estacion Biologica Tahuamanu Urucui-Una Velasco, Caparu Velasco, Caparu Aratai Roura Faro, FLOTA E.E. Maraca Rio Madeira esquerda Bosque al Este de Requena Trinite, aya, Plote PPBIO, Porto Grande nuflo de Chavez, San Sebastian Parque Nacional Madidi Carolina Velasco, Caparu Velasco, Caparu Saul, Limonade Roura Faro, FLOTA Lourenco Mineração Taboca, Rio Pitinga rio Takana, km 9 carretera Leticia Nouragues, Camp Inselbergbas fond crique nouragues 18H Borne 4	Loreto Loreto MT MT French Guiana French Guiana PA RR Loreto AP French Guiana PI French Guiana French Guiana PA RR RO Loreto French Guiana AP MA French Guiana French Guiana PA AP AM French Guiana French Guiana	Colombia Peru Peru Bolivia Bolivia Brazil Bolivia Brazil France France Brazil Brazil Peru Brazil France Bolivia Bolivia Brazil Bolivia Bolivia France France Brazil Brazil Brazil Peru France Brazil Bolivia Bolivia Brazil Bolivia Bolivia France France Brazil Brazil Brazil Colombia France France	-4.124 -3.267 -3.065 -16.359633 -17.410141 -9.9241667 -14.912117 -15.3333333 3.9909 3.220044 -1.7140111 3.361979685 2.47881 -3.065 3.859566 3.220044 -16.359633 -10.283646 -8.9225 -14.912117 -14.912117 3.9909 4.736741 -1.7140111 3.361979685 -9.350734 -5.12 4.6025 0.979331 -16.359633 -14.168909 -7.22941 -14.912117 -14.912117 3.57317 4.736741 -1.7140111 2.3526389 -0.791830 -4.136 4.08479 2.3709	-69.941 -72.267 -72.168 -64.934758 -51.2383333 -61.082467 -58.85 -52.5902 -52.37855 -57.2133000 -61.4343688 -55.63161 -51.759514 -52.37855 -72.168 -62.000050 -65.901339 -44.9744 -61.082467 -61.082467 -52.5902 -52.327724 -57.2133000 -61.4343688 -64.856815 -53.4143 -51.614895 -73.8 -62.000050 -62.000050 -67.917366 -47.26147 -61.082467 -61.082467 -53.19851 -52.327724 -57.2133000 -51.6152222 -60.138753 -69.924 -52.68063 -53.7728
	ZONIF28612 AY943230 JF789901 ZONIB18212 KDQF01002453 JF789915 KF723186 KDQF01000620 KDQF01001688 KDQF01002864 KDQF01003625 XXXXXXXXXXXHT2768 ZONIF26812 KDQF01000509 KDQF01001896 ZONIC68112 KDQF01002456 JF789916 KU495348 KDQF01000660 KDQF01001696 KDQF01002911 KDQF01003696 XXXXXXXXXXXHT4065 ZONIF26912 KDQF01000533 KDQF01002331	AJC2602 USNM30715 MJ1275 JMP123 ESTR0313 AS0277 AS286 AF1648 AM011 MPEG29675 MTR25620 HT2768 AJC2578 AF1374 BOAM064 JMP1655 ESTR0399 AS0278 TG009 AF1754 AM025 MPEG30373 MTR33583 HT4065 AJC2579 AF1432 CM415	Leptodactylus_sp. Leptodactylus_diedrus Leptodactylus_leptodactyloides Leptodactylus_petersii Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_cf.petersii Leptodactylus_wagneri Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_petersii Leptodactylus_gr.wagneri-podicipinus Leptodactylus_wagneri Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	Leptodactylus_colombiensis Leptodactylus_diedrus Leptodactylus_leptodactyloides Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC Leptodactylus_leptodactyloides Leptodactylus_natalensis Leptodactylus_podicipinus Leptodactylus_podicipinus Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiB Leptodactylus_spgrpodiC Leptodactylus_spgrpodiC	27 43 22 22 31 39 39 8 8 3 8 11 49 19 19 22 31 39 39 8 8 3 2 2 49 19 19	49 52 24 23 32 40 40 16 16 1 18 4 6 28 27 20 34 40 39 15 16 1 5 5 6 28 27	6 5 16 16 15 13 13 25 25 37 23 36 34 20 20 16 15 13 13 25 25 37 35 35 34 20 20	7 6 27 27 22 14 14 38 38 50 34 49 47 31 31 27 22 14 14 38 38 50 48 48 47 31 31	rio Takana, km 9 carretera Leticia near Neblina base camp on left bank of Rio Baria nuflo de Chavez, San Sebastian Parque Nacional Madidi Filadelfia Velasco, Caparu Velasco, Caparu Saul, Limonade Inini Tolenga ESEC Grao Para Pacaas Novos, PARNA Ilha de Maracá-Grade PPBio rio Takana, km 9 carretera Leticia Bagne des annamites Borne 4 Pucaurquillo, Rio Ampiyacu Estreito Velasco, Caparu Porto Franco Chutes Voltaire, camp Voltaire Inini Tolenga REBIO Maicuru Maraa, Trilha da Capoeira Purupuru-TN-2500 rio Takana, km 9 carretera Leticia Saut Taconet Lucifer	TO French Guiana French Guiana PA RO RR French Guiana French Guiana Loreto TO MA French Guiana French Guiana PA AM AM French Guiana French Guiana	Colombia Venezuela Bolivia Bolivia Brazil Bolivia Bolivia France France Brazil Brazil Brazil Colombia France France Peru Brazil Bolivia Brazil France France Brazil Brazil Brazil Colombia France France	-4.136 0.830556 -16.359633 -14.168909 -7.345868 -14.912117 -14.912117 3.56258 3.663159 0.6302806 -10.7926250 3.405556 -4.136 4.81821 2.3709 -3.331 -6.553769 -14.912117 -6.35 5.05226 3.663159 0.8286194 -1.75918780 -3.353333 -4.136 4.03249 4.7774	-69.924 -66.161111 -67.917366 -47.50627 -61.082467 -61.082467 -53.21046 -53.928308 -55.7285 -63.6297070 -61.488333 -52.522123 -53.7728 -69.924 -62.000050 -71.917 -47.466831 -61.082467 -47.406 -54.08909 -53.928308 -53.9312000 -65.7482692 -59.855556 -69.924 -52.526188 -53.9475
													252 264 265

Table IV .

 IV S2: Matrix of sequences used in the Operational Taxonomic Units (OTUs) phylogenetic reconstruction of the Leptodactylus podicipinus group. The species column lists the different OTUs and outgroups included in the reconstruction. The Voucher column indicates the specimen number used as representative of the species/OTU. The 12S, 16S, ND1, COI and Cyt, columns are filled with accession number of the sequenced used for each species/OTU for each locus.

	Leptodactylus_podicipinus_sp13 Leptodactylus_spgrpodiB_sp44	AF3930 JMP1522			AF3930 JMP1522
	Leptodactylus_podicipinus_sp14 Leptodactylus_spgrpodiB_sp45	LAJ483 MTR19221			LAJ483 MTR19221
	Leptodactylus_griseigularis_sp15 Leptodactylus_spgrpodiB_sp46	AJC2617 MTR40433		MTR40433	AJC2617 NA	NA	NA
	Species Leptodactylus_griseigularis_sp16 Leptodactylus_spgrpodiB_sp47	AJC2646 AJC2577	Voucher	H1 AJC2646	ND1 NA AJC2577	COI NA	Cytb NA
	Adenomera_andreae Leptodactylus_griseigularis_sp17 Leptodactylus_spgrpodiB_sp48	VOGT2010 SBH171130 MTR33653		KM091472/KM091588	VOGT2010 NA MTR33653	NA	NA
	Hydrolaetare_shmidti Leptodactylus_griseigularis_sp18 Leptodactylus_spgrpodiB_sp49	MTR33546 MNCN43541 MTR20419		EU368907	MTR33546 NA MTR20419	NA	EU368893
	Lithodytes_lineatus Leptodactylus_discodactylus_sp19 Leptodactylus_spgrpodiB_sp50	MTR18688 QCAZ16788 AF3829		AY943239	MTR18688 NA AF3829	NA	NA
	Crossodactylodes_izecksohni Leptodactylus_discodactylus_sp20	UFMG13747/RBF598 MTR36588	KP146086	NA MTR36588	KF534655 KF534664
	Paratelmatobius_sp Leptodactylus_aff.discodactylus_sp21	ITH938 HT552			ITH938 HT552
	Engystomops_petersi Leptodactylus_natalensis_sp22	AF1923 MTR07465			AF1923 MTR07465
	Physalaemus_cuvieri Leptodactylus_natalensis_sp23	MACN38640/isolate099 MTR978163	AY843729 JN690580/JN691216	JX204060 JQ627310 AY843975 NA NA NA
	Pleurodema_thaul Leptodactylus_natalensis_sp24	MVZ:Herp:164826 MTR976586	KM091497/KM091613	JX564888 NA	NA	NA
	Pseudopaliducola_boliviana Leptodactylus_natalensis_sp25	AF2146 MTR12197		MTR12197	AF2146 NA	NA	NA
	Leptodactylus_rhodomystax Leptodactylus_natalensis_sp26	AF2775 MTR978129		JN690579/JN691215	AF2775 NA	NA	NA
	Leptodactylus_stenodema Leptodactylus_leptodactyloides_sp27	AF2312 MTR37043			AF2312 MTR37043
	Leptodactylus_mystaceus Leptodactylus_nesiotus_sp28	USNM534008/MZUSP70371/SMNS12036 AF2895	AY948952	AY911286 KC603992 KC603979 AF2895
	Leptodactylus_notoaktites Leptodactylus_spgrpodiC_sp29	AF1101 AF3369			AF1101 AF3369
	Leptodactylus_chaquensis Leptodactylus_spgrpodiC_sp30	MTR33849 AF179		JN690564/JN691211	MTR33849 NA	NA	NA
	Leptodactylus_guianensis Leptodactylus_spgrpodiC_sp31	AF3817 AF2738			AF3817 AF2738
	Leptodactylus_riveroi_sp1 Leptodactylus_spgrpodiB_sp32	MTR39685 JMP2324			MTR39685 JMP2324
	Leptodactylus_melanonotus_sp2 Leptodactylus_spgrpodiB_sp33	MVZ207294 BM084			JX564873 BM084
	Leptodactylus_pustulatus_sp3 Leptodactylus_spgrpodiB_sp34	CHUNB11258 MTR25620		KC477259	NA MTR25620	NA	NA
	Leptodactylus_diedrus_sp4 Leptodactylus_spgrpodiB_sp35	MTR07484 HJ057		HJ057	MTR07484 NA	NA	NA
	Leptodactylus_diedrus_sp5 Leptodactylus_spgrpodiB_sp36	JMP1460 MTR12746		JMP1460	NA MTR12746	NA	NA
	Leptodactylus_colombiensis_sp6 Leptodactylus_spgrpodiB_sp37	AJC2527 MPEG36705			AJC2527 MPEG36705
	Leptodactylus_colombiensis_sp7 Leptodactylus_spgrpodiB_sp38	MTR33747 AF2336			MTR33747 AF2336
	Leptodactylus_colombiensis_sp8 Leptodactylus_spgrpodiB_Maturaca_sp39 MTR40007 MHNLS17373		MHNLS17373	NA MTR40007	NA	NA
	Leptodactylus_validus_sp9 Leptodactylus_spgrpodiB_sp40	MHNLS16797 HJ681		MHNLS16797 HJ681	NA NA	NA NA	NA NA
	Leptodactylus_podicipinus_sp10 Leptodactylus_spgrpodiB_sp41	BPN3666 HJ056			BPN3666 HJ056
	Leptodactylus_podicipinus_sp11 Leptodactylus_spgrpodiB_sp42	RGA5340 MJ1331		RGA5340 JF789909	NA NA	NA NA	NA NA
	Leptodactylus_podicipinus_sp12 Leptodactylus_spgrpodiB_sp43	AF750 TG2844			AF750 TG2844
							269 270

Table IV .

 IV S3: Mean distribution area size (km²) for each group. Group, mean species distribution area and ecotype are indicated in the referred columns.

	Group	Mean species distribution area (km²)	Ecotype
	Allobates	199849.261 Eco-conserved
	Amazophrynella	57379.231 Eco-conserved
	Pristimantis gr. conspicillatus	116221.527 Eco-conserved	282
	Boana gr. albopunctata	693850.228 Eco-diverse
	Leptodactylus gr. podicipinus	278343.649 Eco-diverse
	Osteocephalus	363710.685 Eco-diverse

Table IV .

 IV S4: Summary of the habitat shift analyses per group. Counts of OTUs per habitat are displayed in referred columns, the habitat hosting the most OTUs per group is in bold. The counts of OTUs in two or more habitats per group is indicated in the seventh column. The counts of environmental shifts are displayed according to the Biogeographical Stochastic Mappings (BSMs) under a DEC model. Ecotype correspond to the ecological category of a species group.

	Group	Terra-firme forest	Counts of OTUs per habitat type Flooded forest Open area Montainous Phytotelmata	Counts of OTUs in two or more habitats	Counts of environmental shifts	Ecotype
	Allobates	48	1	0	1	0	0	1.5 Eco-conserved
	Amazophrynella	39	0	0	0	0	0	0 Eco-conserved
	Pristimantis gr. conspicillatus	46	0	0	4	0	0	2.1 Eco-conserved
	Boana gr. albopunctata	13	19	9	0	0	18	18.3 Eco-diverse
	Leptodactylus gr. podicipinus	32	38	13	0	0	32	36.1 Eco-diverse
	Osteocephalus	11	29	0	4	17	11	15.3 Eco-diverse

ACKNOWLEDGEMENTS

We warmly thank P-O Antoine, J. Cornuault, F. Condamine, Amaia Iribar-Pelozuelo, Sophie Manzi J.

Murienne, and Uxue Suescun for their help or comments. J. Allen, E. Ávila, D. Baeta, C. Barrio-Amorós, C. Bedeau, M. Blanc, R. Boistel, S. Cally, J. Cassimiro, O. Chaline, M. Chouteau, J. Chretien, A. Cochard, E. Courtois, S. Couto, F. Dal Vechio, J. C. de Massary, S. M. de Souza, M. Dewynter, P. Dias, J. Dias Lima, M. A. Freitas, R. Gallais, P. Gaucher, R. Geiger, J. M. B. Ghellere, J.O. Gomes, M. Hölting, R. Jairam, A. C. Leonel, J. J. López Rojas, J. R. Martin, Q. Martínez, C. Marty, M. Medina, R. Mesa, G. Montingelli, L. J. C. L. Moraes, R. Mori-Pezo, A. Nogueira, B. P. Noonan, P. Nunes, J. M. Padial, A. Pasukonis, P. Peloso, A. Percequillo, A.K. Peres-Jr., J. Pérez-Marin, K. Pineau, I. Prates, V. Premel, J. Raysky, R. Recoder, I. Rey, B. Rojas, F. Rojas-Runjaic, V. Rufray, R. Salvatierra, D. J. Santana, M. A. Sena, A. Snyder, P. I. Simões, M. Teixeira-Jr, K. S. Ting, V. Trevine, J.P. Vacher, N. Vidal, B. Villette, kindly provided material or field assistance. MPEG museum shared samples under their care, Centro URKU and SERFOR granted sampling permits in Peru. Funding: This study benefited from an "Investissement d'Avenir" grant managed by the Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01; TULIP, ref. ANR-10-LABX-0041; ANAEE-France: ANR-11-INBS-0001). AF and FPW acknowledge the French/Brazilian GUYAMAZON program action (IRD, CNRS, CTG, CIRAD and Brazilian Fundação de Amparo à Pesquisa do Estado do Amazonas-FAPEAM 062.00962/2018) and co-coordinated by A. Fouquet and F. P. Werneck. MTR thanks Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação de Amparo à Pesquisa do Estado de São Paulo [FAPESP grant numbers: 2003/10335-8, 2011/50146-6, and NSF-FAPESP Dimensions of Biodiversity Program [grant numbers: BIOTA 2013/50297-0, NSF-DEB 1343578] and NASA. SRR acknowledges a grant from SENESCYT (Arca de Noé Initiative). FPW thanks CNPq (Productivity Fellowship), FAPEAM, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-CAPES (Visiting Professor Fellowship), and the L'Oréal-UNESCO For Women In Science Program. AFJ is supported by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior-Brasil (CAPES Finance Code: 88882.462010/2019-01). JM is supported by the Ministry of Culture of the Czech Republic (DKRVO 2019-20023/6.VI.b, National Museum Prague, 00023272). PJRK was supported by research grants from the Fonds voor Wetenschappelijk Onderzoek ACKNOWLEDGEMENTS We warmly thank P-O Antoine, J. Cornuault, F. Condamine, Amaia Iribar-Pelozuelo, Sophie Manzi J. Murienne, and Uxue Suescun for their help or comments. J. Allen, C. Barrio-Amoros, S. Castroviejo-Fisher, O. Chaline, J. Chretien, M. Chouteau, A. J. Crawford, F. Dal Vechio, V. T. De Carvalho, S. M. de Souza, M. Dewynter, G. Gagliardi, L. Groeneveld, M. Guerra Panijo, J. J. Lopez Rojas, J. R. Martin, R. Mesa, B. P. Noonan, J. M. Padial, P. Peloso, K. Pineau, I. Prates, I. Prates, V. Premel, R. Recoder, B. Rogel, F. Rojas-Runjaic, P. I. Solimoes, M. Teixeira Jr., E. Twomey, kindly provided material or field assistance. PUCE museum shared samples under their care, Centro URKU and SERFOR granted sampling permits in Peru.

ACKNOWLEDGEMENTS

We warmly thank J. Cornuault, F. Condamine, Amaia Iribar-Pelozuelo, Sophie Manzi J. Murienne, and Uxue Suescun for their help or comments. E. Avila, D. Baeta, C. Barrio-Amoros, M. Blanc, S.

Castroviejo-Fisher, O. Chaline, J. C. Chaparro, F. Dal Vechio, V. T. de Carvalho, S. M. de Souza, M. Dewynter, G. Gagliardi-Urrutia, T. Grant, R. Jairam, P. kok, J. J. Lopez Rojas, J. R. Martin, R. Mesa, B. P. Noonan, M. Odicio, J. M. Padial, J. Perez, J. Perez-Marin, I. Prates, J. Raysky, R. Recoder, M.

The research team consists of herpetologists who are interested in amphibian diversity and the evolutionary processes generating Amazonian diversification.

AUTHOR CONTRIBUTIONS

A.R., J.C. and A.F conceived the study. A.R., M.T.R., A.J.C., S.C.F., A.F.J., J.C.C., F.G., G.L.A.G-U., J.M., I.J.D.R., P.P., A.P.L., F.P.W., T.H., S.R.R., R.E., P.J.R.K., and A.F. conducted sampling. A.R, A.J.C, S.C.F., A.D and A.F. generated DNA sequence data. A.R. analyzed the data and wrote the paper; and all authors commented on the final draft.

ABSTRACT Aim:

We investigated the spatio-temporal context of the diversification of one of the major lineages of Pristimantis, a widespread genus of Neotropical frogs. This particular group has diversified throughout Amazonia but also in the Atlantic Forest. We tested whether diversity in these regions originated in situ or through repeated dispersals events. We also investigated whether this diversification was continuous through time or whether key periods could be identified and related to important landscape changes, notably the transition ten million years ago (Mya) from the Pebas system to the modern configuration of Amazonia.

Location: Neotropics.

Taxon: Pristimantis conspicillatus group (Anura: Craugastoridae).

Methods:

We gathered a spatially and taxonomically extensive sampling of mitochondrial DNA sequences from 577 Pristimantis conspicillatus group specimens to delimit Operational Taxonomic Units (OTUs). Complete mitogenomes of these OTUs were assembled to reconstruct a time-calibrated phylogeny used to infer the historical and spatial origin of the Pristimantis conspicillatus group lineages.

Results: Diversification was constant through time and the phylogenetic analyses along with the ancestral area reconstruction allowed to identify three major clades that seemingly diversified at constant rates from early Miocene onward. Two of these clades are mostly restricted to western Amazonia and the Brazilian Shield areas respectively (P. conspicillatus and P. chiastonotus clades)

and the third one to the Atlantic Forest and the Guiana Shield (the P. ramagii clade). Nevertheless, a few ancient (Miocene) dispersals from the Atlantic Forest to the Guiana Shield, from western Amazonia toward the Choco and between the Brazilian and the Guiana Shields are identified as well as recent ones (Pliocene) across the Madeira and Negro rivers.

Main Conclusions:

We recovered speciation events within all the Amazonian areas and the Atlantic forest, but most of the P. gr. conspicillatus diversity originated from Western Amazonia and particularly along the Andean foothills. The modern configuration of major Amazonian rivers has probably limited the connectivity between the Amazonian areas, shaping the distribution pattern of the species; it also possibly promoted recent allopatric speciation.

KEYWORDS

Anura, DNA-based species delimitation, Historical biogeography, Mitogenomics, Neogene, Neotropics, riverine barrier.

alignments were performed gene by gene using the MAFFT7 online server. For rRNA genes we chose the E-INS-i strategy, which is designed for sequences with multiple conserved domain and long gaps, while for the protein-coding genes, we chose the G-INS-i strategy, recommended for sequences with global homology (Katoh et al., 2017). Coding regions were realigned considering reading frame and concatenated in Geneious V9.1.8.

The best-fit partition scheme and model of evolution for each partition was chosen using PartitionFinder V2.1.1 (Lanfear et al., 2016), according to the Bayesian Information Criterion (BIC). We predefined four data blocks, one for rRNA genes (12S and 16S) and one for each codon position of all CDS regions.

The time-calibrated gene tree was reconstructed using a birth-death tree prior using BEAST 2.5 (Bouckaert et al., 2014), to account for extinction processes and incomplete sampling. We parametrized unlinked substitution models according to the estimates obtained in the PartitionFinder V2.1.1 analysis (Lanfear et al., 2016). Calibration was implemented using uncorrelated relaxed lognormal clock model of distribution of rate variation among branches for each partition (Drummond et al., 2006). There is no available Craugastoridae fossil evidence, therefore we choose to rely on secondary calibration. We favoured secondary calibration over more distant fossil calibration since it would have implied expand the matrix to distantly related lineages within Hyloidea which would have probably overestimated node ages (Molak & Ho, 2015;Papadopoulou et al., 2010). Instead we relied on three secondary calibration from Feng et al. (2017): the crown age for Craugastor (M = 30.2, SD = 2.9 Mya), the crown age of the clade formed by Holoaden and Pristimantis (M = 39.2, SD = 2.7 Mya)

as well as the crown age of the clade formed by Holoaden, Pristimantis and Strabomantis (M = 43.2, SD = 2.6 Mya). The phylogenetic reconstruction of Feng et al. (2017) was obtained using an extensive genomic dataset (88 kb) for Anurans and more recent larger genomic dataset (Hime et al., 2020) found similar ages. Notwithstanding, we acknowledge that our phylogenetic reconstruction is based only on mitochondrial DNA and can therefore overestimate speciation dates (McCormack et al., 2011;Near et al., 2012). Herein, we favored spatial and taxonomic completeness of our data over genomic completeness and future works using genomic approaches will certainly improve these time estimates that we consider with caution.

partitioning also matches early Neogene East/West and North/South patterns of diversification (Fouquet et al., 2014;Sheu et al., 2020). Boundaries for the three Amazonian areas correspond to three important modern riverine barriers the Madeira River, the Negro River and the lower course of the Amazon River.

The second Amazonian partitioning is based on the major Amazonian interfluves, recognized as the nine Amazonian areas of endemism (AAE) of Cracraft (1985). They have been largely used in other studies investigating the historical biogeography of taxa that diversified during late Neogene notably the riverine barrier hypothesis (e.g. Smith et al., 2014). We use this partitioning to identify patterns of diversification within the larger Amazonian areas of the 8A analysis. In this analysis, Brazilian Shield is subdivided into four areas (Rondônia, Tapajos, Xingu, Belém) as well as western Amazonia (Napo, Inambari, Huallagua and Imeri), for a total of 14 Neotropical areas (referred hereafter as the 14A analysis; see Appendix III.S3 for additional details on area boundaries).

2.4| Diversification through time

To investigate Pristimantis diversification through time, we used two complementary approaches. First, we tested if diversification was significantly different than a yule pure-birth model, using a Monte Carlo Constant Rate (MCCR) test implemented in the package APE V5.3 [START_REF] Paradis | Package 'ape.' Analyses of Phylogenetics and Evolution[END_REF]Pybus & Harvey, 2000). A lineage through-time (LTT) plot was computed on a random sample of 100 trees, from the BEAST2 iterations, to account for uncertainties in topology and calibration estimates, using the APE V5.3 package and compared to a null model of pure-birth [START_REF] Paradis | Package 'ape.' Analyses of Phylogenetics and Evolution[END_REF]. Then, we fitted five models of diversification through time on the same sample of 100 trees, from the BEAST2 iterations, using the Rscript provided in [START_REF] Condamine | Teasing apart mountain uplift, climate change and biotic drivers of species diversification[END_REF]. These models include a classic version of the birth-death model and four variants of the birth-death with n shift of diversification rate model (respectively with one, two, three and four shifts; [START_REF] Stadler | Mammalian phylogeny reveals recent diversification rate shifts[END_REF]. Adjustments of these models were compared with the Akaike Information Criterion (AIC).

3| RESULTS

3.1| Species delimitation

The results obtained from the three methods of delimitation were overall congruent, with most differences originating from the fact that mPTP was more conservative identifying 30 OTUs, while the two other methods, GMYC and ABGD, delimited 57 and 71 OTUs, respectively (Table III.S2; Fig.

III.S2

). The majority-rule consensus of the three methods led to the delimitation of 59 OTUs. At the exception of P. koehleri, all OTUs exhibited small distribution ranges that were not overlapping among the ones that are closely related (Fig. III.S1). The reevaluation of these distributions confirmed that species richness within the clade is heterogeneously distributed within Amazonian with a higher diversity in western Amazonia (25 OTUs) than in the Brazilian (12 OTUs) and the Guiana Shield ( 8OTUs; Table III.S1).

3.2| Time-calibrated species phylogeny

Our final matrix totalized 78 terminals (59 ingroup and 19 outgroups) and 15,814 aligned nucleotide sites per tips. Among these, 40 comprised at least 10000 nucleotides. The inferred phylogenetic tree was well-resolved with 60 nodes out of the 77 exhibiting posterior probabilities above 0.95. Haddad (2012) with discordance being limited to relationships with low supports.

3.3| Ancestral area reconstructions

Model comparisons identified DEC+J as the best model for the two geographic partitioning (Tables III

Molecular data acquisition

We extracted DNA from liver or muscle tissue (thigh or toe-clip) of the 134 samples using the Wizard Genomic extraction protocol (Promega; Madison, WI, USA). We targeted a c.a. 400bp fragment of the 16S rDNA. We used primers N16R and N16F (Salducci et al., 2005), to which we added NNN + 8nucleotide labels (hereafter designated as "tags") for sample identification as all resulting PCR products were mixed into single libraries: 32 tags for forward primer (N16R) and 36 tags for reverse primer (N16F). The final volume of PCRs was 20 µl, and contained 2 µl of DNA extract diluted 10 fold, 10 µl of Amplitaq Gold® 360 Master Mix (Life Technologies, Carlsbad, CA, USA), 5.84 µl of Nuclease Free Water Ambion (Thermo Fisher Scientific, Massachusetts, USA), 0.25 µM of each primer and 3.2 µg of Bovine Serum Albumin (Roche Diagnostic, Basel, Switzerland). We ran the PCR in duplicate for each sample, and we included blank PCR controls in the analysis using nuclease-free water as template. First, the mixture was denaturated at 95°C for 10 min, followed by 40 cycles of 30 sec at 95°C, 30 sec at 55°C and 1 min at 72°C; followed by a final step of 7 min at 72°C. Libraries of mixed PCR products were sequenced using 2 x 250 paired-end reads sequencing technology through MiSeq high throughput sequencing (Illumina) at the Génopole (Toulouse, France). The resulting outputs were analysed with the OBITOOLS software suite (Boyer et al., 2016a). Paired-end reads were assembled and merged, and we used the tag attached to the primer to assign each reads to its label. Then we removed low quality reads (alignment scores <50, containing Ns or shorter than 50 bp). The resulting batch of reads was dereplicated while keeping the coverage information (number of reads merged). All sequences <100 bp were discarded. Eventually, every sequences that we included in our dataset were all >380 bp long.

Species delimitation

The ABGD delimitation was performed with a prior of intraspecific divergence P between 0.001 and 0.1, a proxy for minimum relative gap width, X, of 1, and a number of steps n equal to 30. We kept the partition such that P = 0.0127, as it corresponds to the end of a plateau for group number and it is close to the 1% threshold of intraspecific divergence proposed in other vertebrates' delimitation studies using 16S barcodes (Puillandre et al., 2012). For mPTP delimitation we first reconstructed a ML tree with RAxML v. 8.2.4 (Stamatakis, 2014) using the GTR + G model. We used 16 outgroups including 11 representatives of other Pristimantis major lineages as well as one Holoadeninae, three

Craugastorinae and one Brachycephalidae (Table III.S2). The mPTP delimitation was undertaken on the rooted tree, with 5 million Markov chain Monte Carlo (MCMC) iterations, sampling every 10,000 th iteration and discarding initial 10% iterations as burn-in. For the GMYC delimitation, we reconstructed a time-calibrated phylogeny using BEAST 2.5 (Bouckaert et al., 2014). We used a birth and death population model to account for extinction processes and incomplete sampling. We included the same ten outgroups used in RAXML reconstruction. We used a single partition with a GTR + + I substitution model, with an uncorrelated relaxed lognormal clock model of rate variation among branches (Drummond et al., 2006). We used a secondary calibration that relies on estimates of three comprehensive dataset of nuclear loci for an extensive sampling of anurans and used many reliable fossils as calibration points. For the MCMC parameters, we used four independent chains of 100 million iterations, recording every 10,000 th iteration and discarding the first 10% iterations as burn-in.

We combined the log files of the independent runs using LogCombiner 2.5 (Bouckaert et al., 2014) and checked the convergence of our parameters, confirmed by all ESS being above 200. Then, we extracted the maximum clade credibility tree using Tree annotator 2.5 with a burn-in of 10% (Bouckaert et al., 2014). After retrieving outgroups, we performed a GMYC delimitation on the ultrametric tree using the GMYC function of the {splits} R package (Ezard et al., 2009), with a threshold interval between 0 and 10 Mya and by using the single threshold method. Operational Taxonomic Units (OTUs) were defined using a majority-rule consensus from the results of the three methods, i.e., a lineage is considered as being an OTU if supported by at least two of the three methods.

All but 22 occurrence records were georeferenced and used to create distribution maps with convex polygons under QGIS 2.14 (Team & others, 2015).

APPENDIX III.S2: Mitogenome sequencing, assembling and annotation

For complete mitochondrial sequencing we used 200 ng of genomic DNA for library construction.

These libraries were constructed using the Illumina TruSeq Nano DNA Sample Prep kit following supplier instructions at the Genotoul-Get-PlaGe core facility (Toulouse, France). Genomic DNA was fragmented by sonication, fragments were size-selected (50 -400 bp), were adenylated and ligated to indexed sequencing adapters. Eight cycles of Polymerase Chain Reaction (PCR) were applied to amplify libraries before library quantification and validation. 48 libraries were multiplexed to be sequenced on one lane on an Illumina Hiseq3000 flow cell (Illumina Inc., San Diego, CA). Read assembly was performed using the ORGanelle ASseMbler (Boyer et al., 2016b), a python program developed especially for organelle and ribosomal DNA reconstructions from a genome skimming dataset. The first step is to sort and index the reads with the oa index command. Then, with the oa buildgraph command the program use a seed (reference sequence) to assemble the reads and creates an assembling graph with all the different fragments assembled. Finally, the oa unfold command find an optimal path in the assembling graph and allows the sequence reconstruction (Boyer et al., 2016b).

For the oa index command, we used two options: (i) -estimate-length 0.9 that allows to estimate the reads' length with 90% of the reads; and (ii) -bypass-filtering when the read indexing was not successful due to a low read quality (too many sequencing ambiguities), because this option disables the reads' quality trimming. For the oa buildgraph command we used did not use options in most cases except if the assembling graph was empty due to errors in coverage estimation. If so, we used the -coverage option to give coverage values close to slightly lower than the estimate. For the oa unfold command we did not use options except if the program was unable to find the optimal assembling path. If so, we checked the assembling graph with yEd Graph Editor (organic layout, default parameters) and we selected the green fragments (that corresponds to seed similar fragments)

and extracted them with the --path --option on the oa unfold command. Then, the fragments were mapped on a seed with the Geneious (R9.1.7; Biomatters Ltd, Auckland, New Zealand) map to reference function with medium-low sensitivity and one iteration. The mapping consensus was extracted and considered as a mitochondrion.

For annotation, sequences were aligned on MAFFT7 online server under default parameters except the E-INS-i strategy which is designed for sequences with multiple conserved domain and long gaps (Katoh et al., 2017). Then, with the Geneious R9.1.7 transfer annotations functions we annotate all the alignments sequences from a reference sequence (Anomaloglossus baeobatrachus; NC_030054). Finally, we checked for all the sequences that each CDS began with a methionine (ATA or ATG codon for mitochondrial vertebrates' genomes) and ended with a STOP codon.

APPENDIX III.S3: Delimitation of the nine Neotropical biogeographical areas

For the 14 Neotropical biogeographic areas analysis (14A), we divided Amazonia in nine areas corresponding to the areas of endemism described by Cracraft (1985), they are the major Amazonian interfluvia. We chose to reproduce the limits displayed in Smith et al. (2014) with: Napo (Na) -that is west of the Japurá River and north of the Amazon river; Imerí (Im) -which is west of the Negro River to east of the Japurá River; Guiana (G) -delineated westward by the Negro River, it corresponds to the Eastern Amazonia area of the 8A analysis; Huallaga (Hu) -delineated northward by the Amazon river and eastward as well as southward by the Amazon river; Inambari (In) -the area east of the Huallaga River, south of the Amazon River, and west of the Madeira River; Rondônia (Ro)delineated westward by the Madeira River, eastward by the Tapajós River and northward by the Amazon river; Tapajós (Ta) -that is located at the east of the Tapajós River, at the west of the Xingú River and at the south of the Amazon river; Xingú (Xi) -the region east of the Xingu River, west of the Tocantins River and south of the Amazon River; and finally, Belém (Be) -the area east of the upper Tocantins River. These areas have been argued to match range boundaries of many vertebrates (e.g., Avila-Pires, 1995;de Castro Godinho & da Silva, 2018) and also largely correspond to Allobates species range boundaries.

The other non-Amazonian Neotropical regions that we considered are: The Atlantic Forest (historical range; Af); and (v) The lesser Antilles archipelago (lA). We delineate all these regions with QGIS using the world ecoregions retrieved from www.worldwildlife.org and we assigned subregions to species according to their geographical distributions (Team & others, 2015). We compared the historical biogeography of six Amazonian frog species groups, that can be divided in two ecotypes: those that diversified within terra firme habitats; and those that have adapted to a wider array of arboreal and terrestrial habitats. We tested the discordances and congruences across the biogeographical patterns for these two ecotypes.

APPENDIX III.S4: Supplementary tables

Location: Neotropics.

Taxon: Anura: Hyloidea.

Methods:

We gathered six time-calibrated phylogenetic reconstructions and performed ancestral area reconstruction analyses coupled to biogeographical stochastic mappings to estimate diversification rates for each Neotropical areas and dispersal rates between each pair of areas.

Results: Western Amazonia was identified as the principal source of diversification within Amazonia for all of the six groups included. However, we detected with temporal variation between ecotypes as Western Amazonia acted a source only between 10 and 5 Mya for species that are associated with terra firme habitats and after 10 Mya for the species that have adapted to a wider range of habitat.

Overall, species that diversified within terra firme habitats displayed lower diversification and dispersal rates than species that have adapted to a wider range of habitats. Furthermore, our results suggest that riverine barrier effect affected mostly species that diversified in terra firme habitats and was more important when the river courses were stable over time.

Main Conclusions: Our results suggest that differences in niche conservatism can strongly affect biogeographical patterns; possibly because niche adaptiveness might confer higher dispersal abilities and slower niche saturation; resulting in a longer diversification phase and higher diversification rates.

KEYWORDS

Amazonia, Anura, comparative biogeography, dispersal, diversification, historical biogeography, Neogene, Neotropics, riverine barrier.

2.2| Ancestral area reconstruction

Biogeographic inferences were conducted using the BioGeoBEARS R package (Matzke, 2013).

BioGeoBEARS infers the geographic distributions of ancestral species and explores for each node the relative probabilities of potential biogeographic events based on a maximum likelihood algorithm.

Following the advice of Ree & Sanmartín (2018), we chose not to compare all models and rather considered the DEC model as the more suitable for a mainly continental setting such as the Neotropics (Antonelli et al., 2018). We modified the list of geographic ranges in order to forbid state transitions between non-adjacent areas.

Our biogeographic partitioning differentiates major geological features within Amazonia:

Western Amazonia, the Brazilian Shield and the Guiana Shield. The Western Amazonia underwent dynamic hydrological and geological changes throughout Neogene, while the Brazilian and Guiana Shields remained more stable (Hoorn et al., 2010;Albert et al., 2018). Modern riverine barriers roughly delimit boundaries for these three Amazonian areas: the Madeira River, the Negro River and the lower course of the Amazon River and correspond to the large biogeographic regions known as Wallace's districts (Hoorn et al., 2010;Wallace, 1854). These districts were recently confirmed as major breaks of species composition in birds (Oliveira et al., 2017) and amphibians (Godinho & da Silva, 2018).

Along these three Amazonian areas, we included five non-Amazonian Neotropical areas, for a total of eight Neotropical areas: trans-andean forests (Tf), northern Andes (nA), dry diagonal (DD), Atlantic forest (Af) and Lesser Antilles (lA).

We extracted the nodes with an Amazonian dispersal or vicariance event according to the BioGeoBEARS outputs, i.e. a change of maximum likelihood state between the ancestral node and one or two of the descendant nodes. We chose not to include dispersal events that involved more than one change of states along a single branch, because it is not possible to clearly identify the areas involved in the dispersal event. A linear mixed effects regression was fitted using the lme4 V1. 1-23 R package (lmer;Bates et al., 2014), with the node age (Mya) as response variable, the interaction between the ecotype and the pair of areas involved as fixed effects, while the taxonomic group was set as a random effect.

We also ran the BioGeoBEARS biogeographical stochastic mapping option 500 times for each group, to determine biogeographical event counts for the DEC model (Dupin et al., 2017). We divided T. Rodrigues, B. Rogel, R. Rojas, F. Rojas-Runjaic, A. Snyder, S. Taricuarima and M. Teixeira Jr , kindly provided material or field assistance.

APPENDIX IV.S1: Species delimitation and phylogenetic reconstruction of the Leptodactylus gr. podicipinus.

1| MATERIAL AND METHODS

1.1| Species delimitation

Our first objective was to delimit all major mitochondrial DNA (mtDNA) lineages for each group independently. Our sampling included 66 Leptodactylus gr. podicipinus tissue samples, obtained through fieldwork and loans from collaborators. We sequenced a fragment of the 16S rDNA region, a classical marker for Neotropical amphibian taxonomy and systematics (Vences et al., 2005). We also retrieved homologous sequences from GenBank (537 accessions). In total, we obtained 603 16S mitochondrial sequences for this study (sequencing protocols can be found in Appendix S1 and database details are provided in Table S2). These samples span the entire range of distribution of the genus Allobates. DNA sequence alignment was conducted on the MAFFT online server under the E-INS-i option with default parameters, which is designed for sequences with multiple conserved domains and long gaps (Katoh et al., 2017).

16S sequencing and data acquisition

We extracted DNA from liver or muscle tissue (thigh or toe-clip) of the 66 samples using the Wizard Genomic extraction protocol (Promega; Madison, WI, USA). We targeted a c.a. 400bp fragment of the 16S rDNA. We used primers N16R and N16F (Salducci et al., 2005), to which we added NNN + 8nucleotide labels (hereafter designated as "tags") for sample identification as all resulting PCR products were mixed into single libraries: 32 tags for forward primer (N16R) and 36 tags for reverse primer (N16F). The final volume of PCRs was 20 µl, and contained 2 µl of DNA extract diluted 10-fold, 10 µl of Amplitaq Gold® 360 Master Mix (Life Technologies, Carlsbad, CA, USA), 5.84 µl of Nuclease Free Water Ambion (Thermo Fisher Scientific, Massachusetts, USA), 0.25 µM of each primer and 3.2 µg of Bovine Serum Albumin (Roche Diagnostic, Basel, Switzerland). We ran the PCR in duplicate for each sample, and we included blank PCR controls in the analysis using nuclease-free water as template. First, the mixture was denaturated at 95°C for 10 min, followed by 40 cycles of 30 sec at 95°C, 30 sec at 55°C and 1 min at 72°C; followed by a final step of 7 min at 72°C. Libraries of mixed PCR products were sequenced using 2 x 250 paired-end reads sequencing technology through MiSeq high throughput sequencing (Illumina) at the Génopole (Toulouse, France). The resulting outputs were analysed with the OBITOOLS software suite (Boyer et al., 2016a). Paired-end reads were assembled and merged, and we used the tag attached to the primer to assign each read to its label. Then we removed low quality reads (alignment scores <50, containing Ns or shorter than 50 bp). The resulting batch of reads was dereplicated while keeping the coverage information (number of reads merged). All sequences <100 bp were discarded. Eventually, every sequence that we included in our dataset were all >380 bp long.

Species delimitation analyses

We first clustered DNA sequences into taxonomic units, which may represent valid species. To this end, we applied three DNA-based single-locus species delimitation approaches: (a) a distancebased method, the Automated Barcode Gap Discovery (ABGD; (Puillandre et al., 2012); (b) a singlethreshold coalescent-based method, the Generalized Mixed Yule Coalescent approach (single threshold GMYC; Monaghan et al., 2009;Pons et al., 2006); and (c) a multi-rate coalescent based method, the multi-rate Poisson Tree Processes model approach (mPTP; Kapli et al., 2017).

Operational Taxonomic Units (OTUs) were defined using a majority-rule consensus from the results of the three methods, i.e., a lineage is considered as being an OTU if supported by at least two of the three methods.

The ABGD delimitation was performed with a prior of intraspecific divergence P between 0.001 and 0.1, a proxy for minimum relative gap width, X, of 1, and a number of steps n equal to 30.

We kept the partition such that P = 0.0127, as it corresponds to the end of a plateau for group number and it is close to the 1% threshold of intraspecific divergence proposed in other vertebrates' delimitation studies using 16S barcodes (Puillandre et al., 2012). For mPTP delimitation we first reconstructed a ML tree with RAxML v. 8.2.4 (Stamatakis, 2014) using the GTR + G model. We used 15 outgroups including six representants of other Leptodactylus species groups, a representant of each other genus of Leptodactylinae (Adenomera, Hydrolaetare and Lithodytes), as well as three representatives of Leiuperinae and two of Paratelmatobiinae (Table S2 in Appendix S4). The mPTP delimitation was undertaken on the rooted tree, with 5 million Markov chain Monte Carlo (MCMC) iterations, sampling every 10,000th iteration and discarding initial 10% iterations as burn-in. For the GMYC delimitation, we reconstructed a time-calibrated phylogeny using BEAST 2.5 (Bouckaert et al., 2014). We used a birth and death population model to account for extinction processes and incomplete sampling. We included the same 15 outgroups used in RAXML reconstruction. We used a single partition with a GTR + G + I substitution model, with an uncorrelated relaxed lognormal clock model of rate variation among branches (Drummond et al., 2006). The node used as secondary calibration point is the common ancestor of the Leptodactylinae subfamily, which was estimated based on the time-calibrated phylogeny of Feng et al. (2017) and was consequently constrained with a normal prior distribution (Mean = 36.2 Million years ago; Mya, SD = 3.29 Mya). We consider it the best prior because Feng et al. (2017) included a comprehensive dataset of nuclear loci for an extensive sampling of anurans and used many reliable fossils as calibration points. For the MCMC paramete rs, we used four independent chains of 100 million iterations, recording every 10,000th iteration and discarding the first 10% iterations as burn-in. We combined the log files of the independent runs using LogCombiner 2.5 (Bouckaert et al., 2014) and checked the convergence of our parameters, confirmed by all ESS being above 200. Then, we extracted the maximum clade credibility tree using Tree annotator 2.5 with a burn-in of 10% (Bouckaert et al., 2014). After retrieving outgroups, we performed a GMYC delimitation on the ultrametric tree using the GMYC function of the {splits} R package (Ezard et al., 2009), with a threshold interval between 0 and 10 Mya and by using the single threshold method. Operational Taxonomic Units (OTUs) were defined using a majority-rule consensus from the results of the three methods, i.e., a lineage is considered as being an OTU if supported by at least two of the three methods.

All but 20 occurrence records were georeferenced and used to create distribution maps with convex polygons under QGIS 2.14 (Team & others, 2015). OTUs were assigned to taxa based on the field/museum identification and sometimes corrected in accordance with type localities and known distribution (Frost, 2020;IUCN, 2020).

1.2| Time-calibrated species phylogeny

We selected one representative for each OTU (n=50, see Results) for complete mitogenome sequencing in order to infer the topology and obtain time calibrated relationships. These mitogenomic sequences were obtained through low-coverage shotgun sequencing. They were combined with available mtDNA sequences in GenBank for OTU for which we could not obtain tissue samples (see database details in Table S2).

Mitogenome sequencing

To reconstruct a time-calibrated phylogenetic tree, we recovered mitochondrial genome assemblies for 31 OTU representatives. For complete mitochondrial sequencing we used 200 ng of genomic DNA for library construction. These libraries were constructed using the Illumina TruSeq Nano DNA Sample

Prep kit following supplier instructions at the Genotoul-Get-PlaGe core facility (Toulouse, France).

Genomic DNA was fragmented by sonication, fragments were size-selected (50 -400 bp), were adenylated and ligated to indexed sequencing adapters. Eight cycles of Polymerase Chain Reaction (PCR) were applied to amplify libraries before library quantification and validation. 48 libraries were multiplexed to be sequenced on one lane on an Illumina Hiseq3000 flow cell (Illumina Inc., San Diego, CA). Read assembly was performed using the ORGanelle ASseMbler (Boyer et al., 2016), a python program developed especially for organelle and ribosomal DNA reconstructions from a genome skimming dataset. The first step is to sort and index the reads with the oa index command. Then, with the oa buildgraph command the program use a seed (reference sequence) to assemble the reads and creates an assembling graph with all the different fragments assembled. Finally, the oa unfold command finds an optimal path in the assembling graph and allows the sequence reconstruction (Boyer et al., 2016).

For the oa index command, we used two options: (i) -estimate-length 0.9, which estimates the reads' length using 90% of the reads; and (ii) -bypass-filtering when the read indexing failed due to an over-filtering that resulted in an empty index. For the oa buildgraph command we used default settings in most cases, except if the assembly graph was empty due to errors in coverage estimation. In these cases, we modified the -coverage value to be slightly lower than the estimate. For the oa unfold 2| RESULTS

2.1| Species delimitation

The three species delimitation methods yielded largely congruent results (Table S2; Fig. IV. S2). The GMYC method was found to be the less conservative, delimiting 74 candidate species while mPTP and ABGD delimited 37 and 50 candidate species, respectively. The majority-rule consensus of the three methods led to the delimitation of 50 OTUs in the Leptodactylus gr. podicipinus.

2.2| Phylogenetic reconstruction and historical biogeography of the Leptodactylus gr. podicipinus

Our

1.1| Distribution area

We calculated the distribution area for each OTU by using the coordinates of the sampling location of the specimens from which the 16S sequences were obtained. For OTUs with three or more occurrence points, we calculated the area of the convex hull using the areapl function from the splancs V2.01-40 Rpackage (Bivand et al., 2017). For OTUs with two occurrence points we calculated the geographic distance using the distGeo function of the geosphere V1.5-10 Rpackage [START_REF] Hijmans | Package 'geosphere[END_REF]; which was considered a corridor of one-kilometer radius between the two occurrence points in order to obtain an surface value. Finally, for species with a single occurrence point, we applied the area of a one-kilometer radius circle, i.e. 3.64 square kilometers. We insured that the minimum area value was set to be 3.64 square kilometers for all species. We performed a student test with Welch correction to test whether there is a significant difference between the mean species area between the two ecological categories. We performed a two-sample t-test with Welch correction to compare the mean species distribution area between the two ecotypes.

1.2| Ecological shifts

We considered habitat type as discrete character and used ancestral area reconstruction to model ecological shift within Amphibian clades, we conducted it with the BioGeoBEARS R package (Matzke, 2013). Following the advice of Ree & Sanmartín (2018), we chose not to compare all models and rather considered the DEC model as the more suitable for ecological shift modelling as it accounts for a wider range of cladogenetic events than its counterparts.

We quantified ecological shifts between five habitat types present in the Neotropics that host very distinct frog communities: (i) the unflooded forests (or terra firme forests), that cover most of Amazonia; the (ii) riverine habitats and seasonally flooded forests, that originate from the low relief of river basins and cover around 20% of Amazonia; (iii) the open areas (e.g. savannahs), which cover only a small portion of Amazonia but are dominant in adjacent Cerrado, Llanos; (iv) mountainous habitats (higher than 1400 meters above sea level) that are principally found in the Andes, but also in

CHAPTER V: Thesis conclusion

Investigating the extent and the origins of the Amazonian diversity remains a challenging research area, particularly because a large part of this diversity remains to be discovered and described (Funk et al., 2012;Mayer et al., 2019;Vacher et al., 2020). In this context, the present thesis brings new insights on the spatial and temporal patterns of Amazonian diversification for Amphibians, a major vertebrate clade that diversified intensively within Amazonia throughout Neogene.

We first used phylogenetic and ancestral area analyses to investigate the historical biogeography of the genus Allobates (Chapter II) and of the Pristimantis gr. conspicillatus (Chapter III), two Amazonian frog clades inhabiting terra firme forest habitats. Results suggested little connectivity among the different Amazonian areas during their diversification. Therefore, most of the diversity resulted from in situ diversification which mainly accumulated in Western Amazonia, particularly along the Andean foothills. We hypothesized that the complex topography of the Andean foothills which were isolated from the rest of Amazonia by the Pebas, contributed to the diversification of Allobates and of P. gr. conspicillatus by geographic isolation and/or diversification along the altitudinal gradient (Fjeldså et al., 2012). Similar pattern of low connectivity and ancient diversification were already observed in other Amphibian groups associated to terra firme habitats (Carvalho et al., 2020;Fouquet et al., 2012;Fouquet et al., 2014;Jaramillo et al., 2020). Instead, species groups that have adapted to various types of habitats, notably riverine/flooded forest (igapo, varzea), have generally undergo numerous dispersal events throughout Amazonia and surrounding areas during similar periods (Caminer & Ron, 2020;de Sá et al., 2014;Jungfer et al., 2013;Pirani et al., 2020).

From these observations, we developed a comparative biogeography approach across six clades of amphibians that diversified throughout Neogene in Amazonia aiming at testing spatial and temporal congruences and discordances among Amazonian frog clades depending on ecological specialization (Chapter IV). These groups can be categorized into two ecological categories: the species that mainly occur in terra-firme habitats; and those that have adapted to a wider range of habitats. We identified Western Amazonia as the principal source of diversification for both ecological categories of Amazonian amphibians; with high in-situ diversification events and high dispersal rates towards other Amazonian areas. However, we recovered important differences between the two ecological categories, as species that mainly occur in terra firme habitats have lower dispersal and diversification rates, older dispersal times and did not diversified extensively over the last five Mya;

when compared to species that have adapted to a wider range of habitats. Hence, differences in niche conservatism seem to strongly affect biogeographical patterns; possibly because niche adaptiveness might confer higher dispersal abilities and slower niche saturation; resulting in a longer diversification phase and higher diversification rates While these results are among the first to depict such variation of historical biogeography patterns for Amphibians in Amazonia, it is important to remind that they were obtained considering only a small fraction of the total diversity i.e. six taxonomic groups. That being said, similar variation in historical biogeography that could be associated with differences in clades' ecology as also been observed for other large vertebrate groups such as squamates (e.g. [START_REF] Hamdan | A complex biogeographic history of diversification in Neotropical lancehead pitvipers (Serpentes, Viperidae)[END_REF]Sheu et al., 2020) or birds [START_REF] Harvey | Habitat association predicts genetic diversity and population divergence in amazonian birds[END_REF][START_REF] Ribas | Biogeography and diversification of Rhegmatorhina (Aves: Thamnophilidae): Implications for the evolution of Amazonian landscapes during the Quaternary[END_REF]. The processes of diversification of Amazonian organisms are complex and this ecological variation should be accounted for to elaborate refined biogeographical hypotheses; as advocated for phylogeographical studies by [START_REF] Papadopoulou | Toward a paradigm shift in comparative phylogeography driven by trait-based hypotheses[END_REF]. So far, many comparative studies could not consider the evolution of such trait simply because of the lack of reliable ecological data at the species level in the tropics (Hortal et al., 2015) which is understandable when the species involved are not even described.