Anne Marie Geneviere

A-M Genevière

R Pennati

M P Miglietta

Y Matsumoto

S Mercurio

F Bonasoro

G Scarì

C Gissi

S Piraino

Organizer: A-M Genevière

HAL is a multi-disciplinary

Introduction to MARISTEM -stem cells of marine/aquatic

invertebrates: from basic research to innovative applications

L Ballarin Department of Biology, University of Padova

Marine/aquatic invertebrates constitute the largest biodiversity and the widest phylogenetic radiation on Earth, from morphologically simple organisms (e.g., sponges, cnidarians), to the more complex mollusks, crustaceans, echinoderms, and protochordates. Today, adult marine/aquatic invertebrate stem cell (MISC) biology is of prime research and medical interest. However, studies on stem cells from organisms outside the classical vertebrate (e.g., human, mouse, and zebrafish) and invertebrate (e.g., Drosophila, Caenorhabditis) models have not been pursued vigorously. These organisms contain a variety of MISC-types that allow the production of a large number of novel bioactive-molecules, many of which are of significant potential interest for human health. MISCs further participate in aging and regeneration phenomena, including whole-body regeneration.

For years, the European MISC-community has been highly fragmented and has established scarce ties with biomedical industries in an attempt to harness MISCs for human welfare. Thus, it is important to (i) consolidate the European community of researchers working on MISCs; (ii) promote and coordinate European research on MISC biology; (iii) stimulate young researchers to embark on research in MISC-biology; (iv) develop, validate, and share novel MISC tools and methodologies; (v) establish the MISC discipline as a forefront interest of biomedical disciplines, including nanobiomedicine; and (vi) establish collaborations with industries to exploit MISCs as sources of bioactive molecules. In order to fill the recognized gaps, the EC-COST Action 16203 "MARISTEM" has recently been launched.

At its initial stage, the consortium unites scientists from 24 EC countries, Cooperating countries, and Near Neighbor Countries.

General meeting (Speakers in alphabetical order)

Evo-devo of non-embryonic development in colonial ascidians

A Alié 1 , L Hiebert 2,3 , P Simion 4 , M Scelzo 1 , F Brown 2,3 , S Tiozzo 1 . 1 Ascidians of the Styelidae family regroup more than 550 species and comprise both solitary and colonial forms. Whereas solitary species can reproduce only sexually, colonial ones have the ability to also propagate asexually by different modes of non-embryonic development and often can regenerate the body completely after injury 1 . We recently generated a robust phylogeny of Styelidae based on 20 new transcriptomes, showing that asexual reproduction has been acquired twice in this family by convergence 2 . Notably, the species Polyandrocarpa zorritensis acquired asexual reproduction independently of all other species and displays a unique mode of bud formation that is a combination of epithelial morphogenesis and proliferation of putative blood stem cells 3 . In order to understand the molecular bases of convergent acquisition of budding across ascidian species, we are conducting a comparative transcriptomic study of budding tissues between distant colonial species (e.g. including Polyandrocarpa zorritensis and Botryllus schlosseri). In addition, we will generate transcriptomes of single blood cells in colonial and solitary species -with an emphasis on candidate totipotent stem cells -to provide molecular-level description of blood cells in ascidians, and pave the way to their functional characterization during budding. Taken together, this ongoing work will help to elucidate the plastic evolution of non-embryonic development in ascidians, in order to better understand why colonial species are able to propagate asexually, while solitary species are not. Some marine animals evolved long-term functional partnership with photosynthetic microalgae they reared inside the animal tissues. The biology and physiology of the green flatworm Symsagittifera roscoffensis show how a population of around 100.000 photosynthetically active green unicellular algae (Tetraselmis convolutae) is controlled beneath the epidermis of this animal. The non-photosymbiotic juvenile animal must ingest (not digest) micro-algae found in the surrounding environment or die (after 15 days in the lab) if no ingestion occurred. Once ingested, algae divide, confer the green color to the animal and supply energy, releasing in the tissues various photosynthates, the unique source of food for the animal.

Controlling the life cycle in captivity of this flatwormincluding the induction of photosymbiosis (i.e culture of the free living photosynthetic partner)allows having access to any developmental stages in order to explore the intimate trophic relationship and other features. In the animal tissues algae are fully dedicated to photosynthesis as suggested by the absence of typical structures expressed in the algae free-living state for which lot of energy is allocated, such as the synthesis of body-wall (complex polysaccharides) and the movement flagella. The in hospite algae take advantage of the animal nitrogen waste and recycle them. At the very beginning of the establishment of the symbiosis algae also recycle the uric acid crystals that accumulate in the non-photosymbiotic juveniles flatworm. Intertidal natural S. roscoffensis colonies submitted to submarine groundwater discharge, enriched in nitrogen also show that the flatworms are nitrate interceptors.

In the natural environment, animals are exposed, several hours each day, to sun rays and are adapted to overcome the excess of sun (including UVs) and high oxygen concentration (from photosynthesis) in their tissues.

Beside coping with putative detrimental physiological conditions (oxidative stress) S. roscoffensis also exhibits strong capacities of tissue regeneration including brain.

After more than a decade of functional exploration related to S. roscoffensis, -Tuning the techniques for completing life cycle in captivity -Investing huge efforts in genomics/transcriptomics, -Morphological (cell types) characterization advances, many conditions are met for starting a formal exploration of the molecular and cellular mechanisms underlying brain and peripheral nervous system regeneration.

Corals as sources of bioactive molecules

R Benchaouir Coraliotech, Monaco

The corals, organisms relatively under-studied compared to many other species over the world, must represent a very diverse source of active substances with potential applications in the fields of human well-being and health. CORALIOTECH, young Startup of Marine Biotechnology located in Monaco, proposes an ecological technology of production of active products (proteins and peptides) originating from corals. Based on genetic engineering and biotechnologies, CORALIOTECH uses the coral genetic information provided by its scientific partners (such as the Centre Scientifique de Monaco) to clone genes of interest before implementation of an artificial process of production and purification of the corresponding proteins. Our technology does not require the use or exploitation of the coral itself. In addition to guaranteeing the preservation of natural ecosystems, our production activity also has the advantage of being clean (waste of essentially biological nature), safe (fully automated processes) and scalable (possibility for a rapid rise towards industrial productions). After R&D proof of concept, the process is performed at pilot scale before initiation of product evaluation assays. Most of our innovative effects are valorized through patenting. Our B2B activity targets more specifically the pharmaceutical, biotech and cosmetic companies. Finally, CORALIOTECH aims to progressively enrich its product pipeline and contribute, through its scientific partnerships, to a better knowledge of coral biology. There are many reasons for that, but the lack of proper research models and biomarkers to identify physiological modes of toxic action of environmental pollutants is among the most important ones as pointed by G. Vogt (2011) in his paper entitled "Hidden Treasures in Stem Cells of Indeterminately Growing Bilaterian Invertebrates". However, it is also true that advance of a scientific discipline could generate new research models to better address scientific questions. This holds true for aquatic invertebrate stem cells and their potential applicability in environmental toxicology as proper research models. For example, a number of environmental toxicology studies have already been done using digestive gland cells, hepatopancreas of isopods (crustaceans) as a test model. In addition to existing endpoints, one could use also stem cells of hepatopancreas located at the blind ends of the digestive gland tubules (hepatopancreas) which are resembling the apical meristem of plants (Zimmer, 2002). Also, hematopoietic organs provide an environment where undifferentiated stem cells could be used to measure of response (endpoint) to variable conditions and agents. Another possible biomarker of cellular development and morphogenesis is the alteration of the cholinergic system, which is involved in embryonic development. Paraoanua et al (2007) report that locally produced acetylcholine might function as an intercellular signal, modulating the proliferation of stem cells. In our previous study, we have demonstrated altered activity of AChE to be related to altered sea urchin early development (Mesarič et al, 2015). Further, echinoderms represent a phylum with exceptional regenerative capabilities that can reconstruct both external appendages and internal organs (Reinardy et al., 2015) Sponges (Porifera) are thought to be the sister group of all other animals and the earliest branching multicellular lineage of extant animals and as such a key group for understanding of the evolutionary history of animal stem cells and their regulation. Sponges are known to possess remarkable reconstitutive and regenerative abilities and high cell dynamic.

Marine/aquatic invertebrate stem cells as promising models in environmental toxicology

There is a widespread opinion that all sponges cells are capable of transdifferentiation and under certain conditions exhibit properties of pluripotency. However, the experiments on the regeneration and reaggregation of dissociated cells, have shown that not all cells exhibit the properties of stem cells.

Sponges do not have well-established stem cell lineages. Furthermore, presumable stem cells differ between four sponge classes. The most consistent model of the stem cell system is elaborated for fresh-water Demospongiae. According to this model demosponges have two stem cell lineages: archaeocytes and choanocytes. Both express the ortholog of the stem cell marker Piwi and show the proliferation activity in the intact sponges (Funayama, 2018). During regeneration in demosponges these cell types play an important role: they give rise to the new exopinacoderm and participate in the restoration of the choanosome structures. Additionally, the archaeocytes and choanocytes have ability for (trans)differentiation to various cell types during the restoration processes after sponge tissue dissociation (Borisenko et al. 2015;Lavrov, Kosevich, 2016). Despite the importance of archaeocytes as stem cells of demosponges, there is still no ultrastructural characterization of this type of cell; moreover, there are contradictory and unclear interpretations of the morphology of this cell type.

However, both Calcarea and many Homoscleromorpha do not have mesohyl cells similar to demosponges archaeocytes. In these sponge clades choanocytes and pinacocytes exhibit properties of polypotentiality, as follow from gametogenesis, experiments on regeneration, and cell dissociation. These cells can directly transdifferentiate into other cell types without archaeocytes-like stage (Ereskovsky et al. 2015;Lavrov et al. 2018). Finally, it is necessary to emphasize the importance of models diversification: the comparison between different sponge taxa may help to shed light on the diversity of stem cells in Porifera and their properties.

Financial support by the Russian Science Foundation n° 17-14-01089 is gratefully acknowledged.

Coral skeletal proteins and their function in mineral formation

T Mass University of Haifa, Department of Marine Biology, The Leon H. Charney School of Marine Sciences, Mt. Carmel, Haifa 3498838, Israel

Coral biomineralization is important at the organismal, ecosystem, and global scales, yet the biological component has not been well understood. In particular, identities, roles, and environmental susceptibility of the proteins retained in coral skeleton were previously unknown. Understanding the cellular and molecular responses of stony corals to ocean acidification is key to predicting their ability to calcify under projected high CO2 conditions. Of specific interest are the links between biomineralization proteins and the precipitation of new calcium carbonate (CaCO3), which potentially can provide a better understanding of the biomineralization process. To address this, we developed a novel coral tissue cultures to investigate the biophysical mechanism of calcification in corals. Our goals were to establish an experimental system in which calcification is facilitated at the cellular level, while simultaneously allowing in vitro manipulations of the calcifying fluid, and to study the mineral initiation mechanism in corals.

Viable cell cultures of the hermatypic, zooxanthellate coral, Stylophora pistillata, have been maintained for 6 to 8 weeks. Using an enriched seawater medium with aragonite saturation state which mimiks open ocean surface waters ( arag ~4). We have shown that within 72 hr after isolation, cultures of separated coral cells aggregate into proto-polyps and form an extracellular organic matrix (ECM) and precipitate aragonite crystals at a rate comparable to the intact organism and with geochemical properties similar to parent skeleton.

Responses of molluscan cells to ultra-low temperature exposure N Odintsova, Y Kipryushina, M Maiorova, K Yakovlev, A Boroda National Scientific Center of Marine Biology, Far Eastern Branch of the RAS, Vladivostok, Russia

The study is focused on the alterations that occur in mussel larval cells both in standard culture conditions and in response to ultra-low temperature exposure. Development of this direction is important for understanding the mechanisms of cold susceptibility of marine organisms. The main pathways of molluscan cell death have been found to be associated with mechanical cellular disruption caused by the freezing-thawing processes themselves, as well as with apoptosis and necrosis. Apoptosis was not the main death pathway after a freeze-thaw cycle, but it was induced in a significant part of mussel cells immediately after thawing and depended mostly on the cryoprotectant used. Nothing is known about the causes of apoptosis in mollusks now, although this phenomenon is described in different classes of mollusks. We suggested that the use of the apoptotic inhibitors, known to mammalian cells, could provide a higher yield of viable cells after thawing. For comparison, we used primary mouse embryonic fibroblasts and human colon tumor cells. Additionally, the analysis of nuclear aberrations (such as few multipolar mitoses or the absence of a division spindle in mitotic cells) has been shown to be a useful tool for assessing cell disruption of molluscan cells regardless of the used cryoprotectant. The best cryoprotectant for bivalve cells was 5% dimethyl sulfoxide without any additives. Only staurosporine resulted in evident apoptosis in molluscan larval cells. Unfortunately, we did not reveal apoptotic inhibitors that could significantly reduce apoptosis in molluscan cells after freezing-thawing.

This study was partially supported by the COST.

The medusa of the Mediterranean jellyfish Turritopsis dohrnii can revert into the preceding polyp stage, completing a full morph rejuvenation, by a process known as reverse development (RD). RD is achieved through different stages ending with the formation of a ball-like cyst from which eventually a new stolon will form. Stem cell proliferation and differentiation are supposed to play key roles during RD. Hydrozoans possess a population of stem cells, called interstitial cells, characterized by a large nuclear to cytoplasmic ratios and prominent nucleoli. Cells with these characteristics are mainly present in the manubrium and in the canal system of T. dohrnii medusa, as revealed by TEM analysis. In cnidarians, orthologs of c-Myc and Sox2 have been found expressed in stem cell lines of Hydra polyps and in Clytia hemisphaerica medusae and planula larvae. We identified homologues of these genes in T. dohrnii and studied their expression pattern. We found that they are mainly expressed where i-cells are localized in the medusa. Their expression is turned off during the early stages of RD, whereas transcripts can be detected again during late RD stages. Moreover, by EdU incorporation assays, we demonstrated that proliferation is an active process in the medusa and during the late stage of RD. Our results suggest that stem cells proliferation and differentiation may play a role primarily during the cyst stage and the formation of the stolon.

Stem cells in marine invertebrates -an overview

B Rinkevich National Institute of Oceanography, Haifa, Israel

Stem cells are unspecialized cells in multicellular organisms that have the capabilities to differentiate into other types of cells and can also renew themselves to produce the same type of stem cells. Most interesting are the adult stem cells (ASCs; somatic stem cells) that are found throughout the body after development, and in the vertebrates are the cellular tools used particularly to replenish dying cells and to regenerate damaged tissues (as multipotent cell types). In these organisms they form all/many cell types of the organ from which they originate. In contrast to what is known from the vertebrates, many marine invertebrates reveal significant different characteristics for ASCs. These include high abundancy of ASCs in marine invertebrates (up to 30% of total cells), pluri-and even totipotency, very limited dependency on niches (also the unique appearance or transitory niches), the frequent dedifferentiation and transdifferentiation associated with ASCs, the expression of germ cells markers in somatic stem cells (e.g., specific markers for the germ line are equally expressed in somatic stem cells, such as Piwi; further highlighting that there are no boundaries between somatic and germ cells lineage in many marine invertebrates) and the consideration of ASCs as units of selections and units of regeneration. In a wide range of marine invertebrates, ASCs may emerge de novo and are contributors to dramatic changes in biological features, such as whole body regeneration, rejuvenilization, torpor phenomena (hibernation and aestivation) and more. ASCs in marine invertebrates may also reveal unique phenomena such as germ cell transformation to somatic stem cells and vice versa. ASCs of marine invertebrates differ structurally from the typical ASCs in the vertebrates and even between different marine phyla (such as the interstitial cells in hydrozoans, the neoblasts in flat worms, the archeocytes in sponges and the lymphocyte-like cells in tunicates), and there are unknown tumors of ASCs, rarely any neoplastic or age-related diseases. The above and other characteristics may point to new perspectives for the evolutionary forces that dictate the development of ASCs.

Tissue crosstalk is required to induce a potential stem cell based regenerative response in the anthozoan cnidarian Nematostella vectensis

A Amiel, S Ferreira, K Foucher, E Röttinger CNRS INSERM, University Côte d'Azur -Institute for Research on Cancer and Aging, Nice (IRCAN), France Whole body regeneration in anthozoan cnidarians is poorly understood but has recently been investigated using the sea anemone Nematostella vectensis. In this organism, while cell proliferation is required for regeneration, stem cells have yet to be identified.

In order to highlight the location of the potential stem cells involved in regeneration in N. vectensis, we focused our efforts on a very detailed characterization of the regenerative capacities of various body parts. In addition, to gain a better understanding of the cellular mechanisms involved in the regenerative response, we analyzed the cellular behavior during regeneration using pulse and chase as well as irradiations experiments.

Our analysis revealed that body parts depleted of a specific structure are unable to regenerate. Further, we have shown that this particular structure is essential for inducing proliferation at the amputation site. The analysis of cell behavior during regeneration suggests that two populations of potential stem cells, located in different structures of the animal body, are reactivated in response to the injury.

Taken together, our results strongly suggest that a tissue crosstalk is required to induce a potential stem cell based regenerative response in the anthozoan cnidarian N. vectensis. Commitment of stem or progenitor cells to undergo terminal divisions and differentiation is a critical step during development, tissue maintenance or regeneration. While the molecular processes that are involved in cell cycle control and cell growth are conserved among eukaryotes the processes that mediate the progressive commitment of stem cells and progenitors remains largely elusive. We have recently identified the zinc finger transcription factor Glass to play a critical role in neural progenitors to mediate commitment towards a defined photoreceptor cell fate in the fruit fly Drosophila melanogaster. In order to explore if this function of Glass is evolutionarily conserved, we have analyzed the expression of Glass in the marine annelid Platynereis dumerillii. Interestingly in Platynereis we could not detect expression of glass in photoreceptors. Similarly, analyzing published single cell RNAseq data of the flatworm Schmidtea mediterranea indicates that opsin genes are not coexpressed with glass. These findings suggest that in distinct animal clades different developmental mechanisms act to specify a similar neural fate. To further explore the diversity of neural stem cells and progenitors we have initiated a single-cell transcriptomic in cnidarians and acoels.

Insights in the evolution

Application of nano titanium dioxide treated surfaces for marine organisms growth inhibition

V Vrecko Cinkarna, Celje, Slovenija

As producer of nanomaterial titanium dioxide we have been asked to search for the solution of slippery access to the sea on the concrete surfaces at the beaches. The challenge is to find a solution that could be applied to existing surfaces and would sufficiently slow the growth of marine organisms on the treated objects. We have prepared several mixtures of the fast setting cement based material and applied them to the demonstration object. To be able to understand the mode of action of the photocatalytic surfaces on the growth of marine organisms, we approached Biotechnical Faculty of University of Ljubljana to help us in characterization and understanding of the desired and eventual unintentional effects of our materials. We plan to observe the dynamics of the growth of organisms in one year period.

Tissues regeneration and in hospite microalgae proliferation in the photosymbiotic marine flatworm Symsagittifera roscoffensis (Xenacoelomorpha, Acoela) X Bailly

  1 Tiozzo S. et al. (2008) Regeneration and stem cells in ascidians. In: Bosch TCG (ed) Stem Cells. Springer, Dordrecht, pp. 95-112. 2 Alié A. et al. (2018) Convergent acquisition of nonembryonic development in styelid ascidians. Mol Biol Evol. 35: 1728.

	Sorbonne Université, CNRS, Station Biologique de
	Roscoff, Place Georges Teissier, 29680 Roscoff,
	France

3 Scelzo et al. In prep.

: future prospects and research needs

	D Drobne
	Research	group	for	nanobiology	and
	nanotoxicology, Department of biology, Biotechnical
	Faculty,	University	of	Ljubljana,	Slovenia,
	http://www.bionanoteam.com
	Most of ecotoxicity studies have been carried
	out in whole organisms at various developmental
	stages. Selected cell lines have been used less
	frequently to elucidate the mechanisms of toxicity.

  Aquatic Toxicology 163: 158-166, 2015. Paraoanu LE, et al. Expression and possible functions of the cholinergic system in a murine embryonic stem cell line. Life Sciences 30: 80, 2375-2379, 2007. Reinardy HC, et al. Tissue Regeneration and Biomineralization in Sea Urchins: Role of Notch Signaling and Presence of Stem Cell Markers. PLoS ONE 10: e0133860, 2015.

	. Understanding how regenerative processes respond to changing environmental conditions (contamination of ecosystem and variation in normal weather patterns) is paramount to predict the future vulnerability or success of these keystone marine animals. It is a privilege of MARISTEM project to transform the tremendous potential of research outcomes on marine invertebrate stem cells into guidelines for animal (humans included) health and environmental protection. Mesaric T, et al. Sperm exposure to carbon-based nanomaterials causes abnormalitiesin early development of purple sea urchin (Paracentrotus lividus). Zimmer M. Nutrition in terrestrial isopods (Isopoda: Oniscidea): an evolutionary-ecological approach. Biol Rev. 77: 455-493, 2002. Vogt G. Hidden treasures in stem cells of indeterminately growing bilaterian invertebrates. Stem Cell Rev. 8(2): 305-17, 2012. Stem cells cell in sponges (Porifera): an update A Ereskovsky 1,2 1 Institut Méditerranéen de Biodiversité et d'Ecologie Marine et Continentale (IMBE), Aix Marseille University, CNRS, IRD, Avignon University, Marseille, France 2 Department of Embryology, Faculty of Biology, Saint-Petersburg State University, Saint-Petersburg, Russia


Leandro Aristide
email: leandroaristi@gmail.com

Hélène Morlon
email: morlon@biologie.ens.fr

UNDERSTANDING THE EFFECT OF COMPETITION DURING EVOLUTIONARY RADIATIONS: AN INTEGRATED MODEL OF PHENOTYPIC AND SPECIES DIVERSIFICATION

Keywords: phylogenetics, macroevolution, disparity, diversity-dependence, comparative methods, morphospace, protracted speciation, adaptive radiation adaptive radiation, phylogenetics, macroevolution, disparity, diversitydependence, comparative methods, morphospace, protracted speciation

published or not. The documents may come

Understanding the effect of competition during evolutionary radiations: an integrated model of phenotypic and species diversification

Leandro Aristide, Hélène

INTRODUCTION

A long-standing goal in ecology and evolutionary biology is to understand how competition among species modulates the origination and extinction of lineages, and the evolution of phenotypic differences among them [START_REF] Sepkoski | Competition in macroevolution: the double wedge revisited[END_REF][START_REF] Doebeli | Evolutionary Branching and Sympatric Speciation Caused by Different Types of Ecological Interactions[END_REF][START_REF] Rundle | Ecological speciation[END_REF][START_REF] Pfennig | Character displacement: ecological and reproductive responses to a common evolutionary problem[END_REF][START_REF] Rabosky | Diversity-Dependence, ecological speciation, and the role of competition in macroevolution[END_REF]Vermeij 2013;[START_REF] Liow | Ecological interactions on macroevolutionary time scales: Clams and brachiopods are more than ships that pass in the night[END_REF][START_REF] Silvestro | The role of clade competition in the diversification of North American canids[END_REF]. Competition is in particular thought to play a key role during adaptive radiations -monophyletic lineages diversifying into several adaptive forms, where each occupies a portion of available ecological space through changes in relevant phenotypic traits [START_REF] Simpson | The Majors Features of Evolution[END_REF][START_REF] Futuyma | Evolutionary Biology[END_REF][START_REF] Schluter | The ecology of adaptive radiation[END_REF]. Adaptive radiation theories have postulated that a lineage can diversify rapidly driven by competition and natural selection in face of an ecological opportunity, and that species and trait diversification rates should slowdown progressively as the available ecological space becomes more densely occupied [START_REF] Gavrilets | Adaptive Radiation: Contrasting Theory with Data[END_REF][START_REF] Harmon | Early bursts of body size and shape evolution are rare in comparative data[END_REF][START_REF] Rabosky | Diversity-Dependence, ecological speciation, and the role of competition in macroevolution[END_REF].

A large number of recent studies have looked for adaptive radiation signatures in comparative datasets (i.e. a phylogeny and associated trait data; [START_REF] Soulebeau | The hypothesis of adaptive radiation in evolutionary biology: hard facts about a hazy concept[END_REF].

As phylogenetic methods that explicitly encapsulate ecological mechanisms are generally lacking [START_REF] Pennell | An integrative view of phylogenetic comparative methods: Connections to population genetics, community ecology, and paleobiology[END_REF]; but see [START_REF] Drury | Estimating the effect of competition on trait evolution using maximum likelihood inference[END_REF][START_REF] Clarke | Trait Evolution in Adaptive Radiations: Modeling and Measuring Interspecific Competition on Phylogenies[END_REF][START_REF] Manceau | A Unifying Comparative Phylogenetic Framework Including Traits Coevolving Across Interacting Lineages[END_REF], most researchers resort to pattern-based tests (e.g. g statistic for trees, [START_REF] Pybus | Testing macro-evolutionary models using incomplete molecular phylogenies[END_REF][START_REF] Phillimore | Density-dependent cladogenesis in birds[END_REF]; K statistics for traits, [START_REF] Blomberg | Testing for phylogenetic signal in comparative data: behavioral traits are more labile[END_REF] or phenomenological models (e.g. "early-burst" for traits, [START_REF] Harmon | Early bursts of body size and shape evolution are rare in comparative data[END_REF] built upon the general prediction for adaptive radiations of declining macroevolutionary rates. The most mechanistic studies have considered diversitydependent models built upon the prediction that rates decline as species accumulate [START_REF] Rabosky | Density-dependent diversification in North American wood warblers[END_REF][START_REF] Mahler | Ecological opportunity and the rate of morphological evolution in the diversification of greater Antillean anoles[END_REF]Etienne et al. 2012;Weir & Mursleen 2013), or competition models where species phenotypes tend to evolve in unoccupied parts of phenotypic space [START_REF] Drury | Estimating the effect of competition on trait evolution using maximum likelihood inference[END_REF][START_REF] Clarke | Trait Evolution in Adaptive Radiations: Modeling and Measuring Interspecific Competition on Phylogenies[END_REF][START_REF] Manceau | A Unifying Comparative Phylogenetic Framework Including Traits Coevolving Across Interacting Lineages[END_REF]. Using these tools, success or failure in finding the expected signatures is interpreted in terms of the mechanisms generating diversity in the studied clade. Despite a substantial research effort, the role and the effect of competition on diversification during evolutionary radiations remain controversial. Several studies indicate that competition within species can promote lineage splitting through disruptive selection on traits (i.e. ecological speciation ;[START_REF] Dieckmann | On the origin of species by sympatric speciation[END_REF][START_REF] Schluter | Ecology and the origin of species[END_REF]), but competition can also prevent diversification by reducing ecological opportunity and by driving species to extinction [START_REF] Bailey | Competition both drives and impedes diversification in a model adaptive radiation[END_REF][START_REF] Rabosky | Diversity-Dependence, ecological speciation, and the role of competition in macroevolution[END_REF].

How these contrasting effects of competition interact to shape traits and lineages macroevolutionary dynamics and the patterns observed in comparative datasets remains unclear [START_REF] Meyer | The effects of competition and predation on diversification in a model adaptive radiation[END_REF][START_REF] Mcpeek | The Ecological Dynamics of Clade Diversification and Community Assembly[END_REF][START_REF] Bailey | Competition both drives and impedes diversification in a model adaptive radiation[END_REF][START_REF] Clarke | Trait Evolution in Adaptive Radiations: Modeling and Measuring Interspecific Competition on Phylogenies[END_REF]. Intriguingly, while competition mechanistically links phenotypic evolution with speciation (via ecological character displacement, [START_REF] Doebeli | Evolutionary Branching and Sympatric Speciation Caused by Different Types of Ecological Interactions[END_REF][START_REF] Schluter | Ecology and the origin of species[END_REF][START_REF] Rundle | Ecological speciation[END_REF] and extinction (via competitive exclusion of phenotypically similar species, Sepkoski 1978), these processes have not been integrated in a unified macroevolutionary model (Weber et al. 2017).

In order to clarify the effect of competition on diversification dynamics and the patterns left on comparative data, we begin by developing an integrated lineage-based model, the Matching Competition Birth-Death model (MCBD), where traits evolve according to a matching competition process [START_REF] Nuismer | Predicting rates of interspecific interaction from phylogenetic trees[END_REF][START_REF] Drury | Estimating the effect of competition on trait evolution using maximum likelihood inference[END_REF]) and influence speciation-extinction dynamics in a protracted birth-death process [START_REF] Etienne | Prolonging the past counteracts the pull of the present: Protracted speciation can explain observed slowdowns in diversification[END_REF]. Next, we examine the effect of competition by simulating the MCBD model. Particularly, we investigate the conditions under which competition spurs species and phenotypic diversity, and generates the evolutionary rate declines expected from verbal adaptive radiation theory. Finally, we investigate the signatures left on comparative data and discuss our results.

METHODS

The Model

We model lineage diversification using a protracted speciation-extinction process (PBD, [START_REF] Etienne | Prolonging the past counteracts the pull of the present: Protracted speciation can explain observed slowdowns in diversification[END_REF], an extension of the classical birth-death [START_REF] Nee | The reconstructed evolutionary process[END_REF]) that accounts for the fact that speciation takes time to complete [START_REF] Etienne | Prolonging the past counteracts the pull of the present: Protracted speciation can explain observed slowdowns in diversification[END_REF]. Under the PBD, species produce "incipient species" at a rate λ1 (speciation initiation) that become "good species" at a rate λ2 (speciation completion). This modeling approach provides a convenient intermediate between simpler lineage-based BD models that cannot incorporate intra-specific processes such as competition-driven population divergence, and more mechanistic models from populations genetics (e.g. [START_REF] Slatkin | Ecological character displacement[END_REF] or adaptive dynamics (e.g. [START_REF] Doebeli | Diversity and coevolutionary dynamics in highdimensional phenotype spaces[END_REF], which have the advantage of being based on first principles of drift, fitness, and response to selection, but generally do not make predictions that can be confronted to comparative phylogenetic data (but see [START_REF] Aguilée | Clade diversification dynamics and the biotic and abiotic controls of speciation and extinction rates[END_REF].

F o r R e v i e w O n l y process (BD;
We consider two versions of the model: one with "asymmetric" speciation initiation (main text), where the ancestral good species persists as a good species and produces one daughter incipient species, and one with "symmetric" speciation initiation (Supplement Information), where the ancestral good species gives rise to two incipient lineages. Incipient lineages inherit trait values from their parent species, and do not give rise to new incipient species. Speciation completion is contingent upon the accumulation of phenotypic differences (Fig. 1a): the speciation completion rate of incipient species i at time t increases with the phenotypic distance with its parent good species (or sister incipient species) j at this time:

𝜆 "# (𝑡) = 𝜏 ) 𝑒 b+, -(.)/, 0 (.)1 2 (1)
where τ0 is the basal speciation completion rate, 𝑥 # (𝑡) and 𝑥 5 (𝑡) are the trait values of lineages i and j at time t, and b expresses the extent of the effect of trait differences on the speciation completion rate.

Lineages are driven to extinction if they fail to become sufficiently different from other competing lineages, according to the principles of limiting similarity and competitive exclusion (Fig. 1b). The extinction rate µi of a lineage i (incipient or good) is a decaying function of the phenotypic distance with all other lineages at time t:

𝜇 # = 𝛼 8 𝜇 ) 𝑒 /9 : (∑ (, -(.)/, 0 (.)) 2 0<- ) + 𝜇 >? (2) F o r R e v i e w O n l y
where 𝜇 >? is a background extinction rate, µ0 modulates the relative contribution of competition-related extinction with respect to 𝜇 >? , and α1 modulates the effect of trait differences on the extinction rate. 𝜇 >? and µ0 can take distinct values for good and incipient species.

We model trait evolution using a modified version of the "matching competition" (MC) process described in [START_REF] Drury | An assessment of phylogenetic tools for analyzing the interplay between interspecific interactions and phenotypic evolution[END_REF], which is based on a model derived by [START_REF] Nuismer | Predicting rates of interspecific interaction from phylogenetic trees[END_REF] from quantitative genetics first principles. The MC model represents character displacement, with competition between co-existing lineages driving phenotypic divergence (Fig. 1c). Competition-driven trait divergence facilitates speciation completion following Eq. 1, and reduces extinction following Eq.

2. We express the change of trait value x on lineage i after an infinitesimally small timestep dt as:

𝑥 # (𝑡 + 𝑑𝑡) = 𝑥 # (𝑡) + 𝑚𝛼 " BC 𝑠𝑖𝑔𝑛 H 5I# J𝑥 # (𝑡) -𝑥 5 (𝑡)L × 𝑒 /N 2 J, -(.)/, 0 (.)L 2 O 𝑑𝑡 + 𝛿 (3)
where δ, the Brownian Motion (BM), is a normally distributed random variable with mean 0 and variance σ 2 dt, and m is a scaling parameter modulating the contribution of the deterministic competition component with respect to the BM. The "sign" term determines the direction of the repulsion in trait space (i.e. species i evolves towards higher trait values if 𝑥 # > 𝑥 5 , and lower trait values if 𝑥 # < 𝑥 5 ), and n is the number of lineages at time t. Finally, α2 modulates the effect of trait differences on trait evolution.

When α2=0, trait evolution is purely neutral (i.e. following BM). Eq. 3 is equivalent to Eq. 1 from [START_REF] Drury | An assessment of phylogenetic tools for analyzing the interplay between interspecific interactions and phenotypic evolution[END_REF], without the Ornstein-Uhlenbeck attraction to an optimum, and the 𝑚 parameter here equals

T U N 2
where 𝑚 V is the 𝑚 parameter in Eq. 1 from [START_REF] Drury | An assessment of phylogenetic tools for analyzing the interplay between interspecific interactions and phenotypic evolution[END_REF]. This alternative parametrization allows us to evaluate the effect of competition on trait evolution by varying only the parameter α2. We consider two versions of the model: one with an unbounded trait space, and one with hard limits on trait space beyond which lineages cannot evolve [START_REF] Clarke | Trait Evolution in Adaptive Radiations: Modeling and Measuring Interspecific Competition on Phylogenies[END_REF]. Such hard limits can arise from physiological or genetic constraints. They also represent the

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idea of "ecological limits", that is the existence of a limited breadth of resources that puts a hard limit on the number of species a system can sustain [START_REF] Rabosky | Ecological limits and diversification rate: alternative paradigms to explain the variation in species richness among clades and regions[END_REF]. Note that hard ecological limits are not required to produce ecological constraints: as soon as clades diversify and species start occupying niches, this imposes constraints on the trait values that emerging species can take.

Eqs. 2-3 implement a Gaussian functional form, a convenient and realistic approximation to model the effect of phenotypic distance in trait-driven models of competitive interactions [START_REF] Macarthur | The limiting similarity, convergence, and divergence of coexisting species[END_REF]Dieckman & Doebeli 1999). We kept a similar functional form (i.e. exponential) in Eq. 1 for consistency, but with the speciation completion rate increasing with phenotypic distance, ensuring that speciation occurs when large phenotypic differences are attained.

Finally, we allow all lineages to interact with one another, implying that they evolve in the same geographic area, a simplifying assumption but an arguably common feature of adaptive radiations [START_REF] Stroud | Ecological opportunity and adaptive radiation[END_REF]. We consider the case of a univariate trait, although it would be straightforward to extend Eqs. 1-3 to consider a multidimensional trait. The univariate case is the simplest, and could be a common scenario if radiations unfold along single, highly integrated phenotypic axes (e.g. lines of least evolutionary resistance; Schluter 1996).

Simulations

We implemented a time-discretization of the model in which we sequentially computed trait values and lineage speciation-extinction over many small time-steps.

In order to control the crown age of the process, we started each simulation with one good and one incipient species (attributed equal trait values). We ran the simulations for 50 Ma, with time steps of 0.01 Ma. Each simulation produced a diversifying trajectory of lineages and traits through time (Fig. 1d,e). We followed these radiations by registering the traits' evolutionary history and time of all speciation/extinction events, which we used to build a phylogenetic tree of each process. This tree consisted of extinct lineages, and living good and incipient species (Fig. 1f). By pruning the latter

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and extinct lineages, we obtained a reconstructed tree and a vector of present-day trait values (Fig. 1f). Codes for running the simulations are available in RPANDA (function sim.MCBD; [START_REF] Morlon | RPANDA: An R package for macroevolutionary analyses on phylogenetic trees[END_REF]. We simulated 100 processes for each different parameter setting, discarding simulations that produced reconstructed trees with less than 5 tips or extinct radiations (although these rarely occurred).

In order to easily explore the effects of changing competition strength on diversification, we ran all our simulations with the same values for the effect of traitdriven competition on extinction (a1) and trait evolution (a2). We expect a1 and a2 to co-vary, as extinction and trait evolution are both driven by the same individual-level process, namely reduced survival of individuals experiencing strong competition (Dieckman & Doebeli 1999). Thereafter we note a=a1=a2 , and refer to a as the competition strength. We ran a first set of simulations with no bounds in trait space.

We set b = 0.6, m = 0.2 and varied competition strength (a = 0-0.1). We ran simulations without and with extinction. For those with extinction, we set good and incipient species background extinction rates to µbg1 = 0.01 and µbg2 = 0.016, allowing for traitdependent extinction rates (competitive exclusion) by setting µ0 to 50 times µbg1 and µbg2 values, respectively. We set higher incipient extinction rates to account for potentially lower population sizes in incipient lineages. Population size will affect the background extinction rate through both stochastic demographic fluctuations [START_REF] Lande | Risks of population extinction from demographic and environmental stochasticity and random catastrophes[END_REF] and genetic factors such as inbreeding depression [START_REF] Frankham | Genetics and extinction[END_REF]. It will also affect the competition-driven extinction rate as the response to directional selection (and the resulting effect on individuals' fitness if adaptation is too slow) depends on effective population size (Wright, 1931).

We fixed σ 2 = 0.5 to achieve a good balance between the stochastic and deterministic components of trait evolution. We then ran a second set of simulations with increasingly larger symmetric hard bounds on trait space around the root value and with extinction; fixing competition strength (a= 0.04). All other parameter values were the same as in the unbounded simulations. These values were selected through preliminary exploration of the behavior of the model as to obtain biological realism.

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For example, ensuring trees of reasonable size for adaptive radiations (i.e. < 30 tips when possible; [START_REF] Harmon | Early bursts of body size and shape evolution are rare in comparative data[END_REF] and extinction rates that were not too high (e.g. a maximum turnover rate of 0.4 in the most strongly competitive case).

Model output

To assess the effect of competition on species richness, trait diversity, and the dynamics of macroevolutionary rates under the MCBD model, we recorded for each radiation and at each time step species richness, phenotypic disparity, realized speciation, extinction and net diversification rates, speciation duration, and trait evolution rates (Appendix S1).

To investigate the footprint that competition leaves on phylogenetic trait data under the MCBD model, we applied a set of comparative tools commonly used for detecting adaptive radiations. For each reconstructed tree we computed the γ statistic [START_REF] Pybus | Testing macro-evolutionary models using incomplete molecular phylogenies[END_REF], a metric used to test for declines in diversification rates Comparative analyses were performed with the R packages RPANDA, geiger [START_REF] Pennell | geiger v2. 0: an expanded suite of methods for fitting macroevolutionary models to phylogenetic trees[END_REF], DDD (Etienne et al. 2012), and phytools [START_REF] Revell | phytools: an R package for phylogenetic comparative biology (and other things)[END_REF].

RESULTS

Dynamics of speciation, extinction and species richness

There were only few conditions under which competition increased the number of species in a clade (Fig. 2a,b & Fig. 3a,b). Even in the absence of both trait limits and extinction, species richness was highest at intermediate (not at highest) competition strength (Fig. 2a,b). As expected from adaptive radiation theory, competition spurred diversification early in the radiation, but then generated fast declines in speciation rates (Fig. 2c). Indeed, competition accelerated speciation completion at the beginning of the radiation and then slowed it down toward the present (Fig. 2d). Hard trait limits

were not required to obtain such patterns, as incipient species experienced the constraints imposed by coexisting species (Fig. S1a), which was sufficient to slow down speciation completion and limit species richness.

Competition had a strong effect on the intensity and temporal dynamics of extinction (Fig. 3c), which had cascading effects on other aspects of the radiation (compare Fig. 3 with Fig. 2). Extinction rates of good species were overall higher for stronger competition and increased nearly linearly with time as trait space became more packed (Fig. 3c). Extinction rates of incipient species were also higher for stronger competition. This erased the negative effect of competition on the time to complete speciation towards the present (compare Fig. 3d with Fig. 2d), as only fast diverging incipient lineages escaped extinction and became good species. Speciation rates therefore remained higher under strong competition throughout the radiation (Fig. 3c), even if they declined slightly through time as a result of frequent incipient species extinction (Fig. 3c). The combined effect of competition on extinction and speciation rates led to net diversification rates that were higher early in the radiation

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and declined faster later on (Fig. 3c). This resulted in a lower species richness (Fig. 3a,b).

Finally, limiting trait space led to markedly fewer species compared to the unbounded case (Fig. 4a,b). This resulted from overall lower speciation rates caused by increasing failed speciation in narrower trait spaces, as there was almost no effect on speciation duration or extinction rates (Fig. 4c-d). Limiting trait space did not have a marked effect on temporal diversification dynamics: as in the unbounded case, speciation slowed down and extinction increased strongly, resulting in a net diversification rate slow down (Fig. 4c-d).

Dynamics of trait disparity and evolution rates

While competition did not necessarily increase clade size, it generated clades with high trait disparity (Fig. 2e & 3e), unless this disparity was limited by hard trait bounds (Fig. 4e). Even low levels of competition lead to an exponential increase in trait disparity through time, compared to a linear increase in the absence of competition (Fig. 2e &3e). In unbounded trait spaces, even though competition generated all the ecological constraints on evolution mentioned above, this did not result in a slowdown in the accumulation of trait disparity. The accumulation of trait disparity only progressively slowed down in bounded trait spaces, and only once bounds were hit (which occurred increasingly late as boundaries got wider, Fig. 4e).

Contrary to what happened for species diversification dynamics, we rarely observed slowdowns in mean trait evolution rates (Fig. 2f, 3f and 4f). There was an initial drop down very early in the radiation, in the period preceding the first speciation event, when trait space was empty and the only two existing lineages could freely evolve away from each other. After this short period, mean trait evolution rates were either slightly increasing (in unbounded radiations without extinction, Fig. 2f) or constant (in bounded and unbounded radiations with extinction, Fig. 3f &4f). These average rate patterns hide a strong heterogeneity across lineages (Fig. S2). Coherent with ideas of ecological opportunity and ecological limits, directional evolution in

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competition-driven radiations was faster in lineages near empty zones of trait space (i.e. edges in unbounded radiations), with near-stasis in the crowded, constrained middle (Fig. S2a,b). In the absence of extinction, this increase in evolutionary rate at edges overcompensated the decrease towards the middle, leading to the observed slight increase in mean rates (Fig. 2f & S2a). Extinction erased this increase by moderating the competitive pressure experienced by lineages (Fig. 3f andS2b). Even in the presence of hard trait limits, when bounds were reached and diversity approximated equilibrium, mean rates did not decline (Fig. 4f &S2c). Lineages near the bounds experienced slow rates as they could not evolve further, but intense competition drove many species extinct, constantly freeing up trait space. Overall, these cycles of extinction, branching and reoccupation of trait space leveled off evolutionary rate declines imposed by bounds.

Patterns left in contemporary data γ did not appear as a robust signal of competition (Fig. 5, first column). Without extinction, reconstructed trees showed increasingly negative γ values with increasing competition strength (Fig. 5a), but neutral radiations also exhibited negative γ values, indicating that at least part of this signal stemmed from the protracted nature of the speciation process. Moreover, γ values between competitive and neutral radiations only began to diverge late in time (Fig. S3a). In the presence of extinction, γ values increased rather than decreased with competition strength, as radiations with more intense competition experienced more extinctions (Fig. 5b). Finally, trees from bounded competition-driven radiations had γ values that became less negative as trait space became narrower (Fig. 5c).

Diversification models were not particularly useful either for detecting competition-driven radiations (Fig. 5, second column). Without extinction, both neutral and competition-driven radiations produced an important proportion of reconstructed trees that best supported models with declining (mostly linearly) speciation rates. Extinction progressively erased the signal of diversification rate

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decline as competition increased: trees from neutral radiations indicated support for models of declining speciation and increasing extinction rates, while for competitiondriven radiations support increased toward models of constant speciation-extinction (Fig. 5b). Support for models with declining speciation and increasing extinction decreased further in the presence of bounds and as bounds got narrower (Fig. 5c).

Phylogenetic signal (K) in tip trait data provided a more consistent signature of competition (Fig. 5, third column). K values were higher for competition-driven than for neutral radiations, indicating that as morphospace is progressively occupied, competition constrains closely related species to a limited portion of this space (Fig. S2). In the absence of bounds, the signature of competition provided by K became stronger as the radiation proceeded (Fig. S3a,b). In bounded radiations, K was highest at the time when the bounds were hit, and then remained stable or slowly decreasing (Fig. S3c). K therefore decreased with increasingly tight trait spaces, but remained generally higher than under neutral radiations (Fig. 5c, the only exception occurred in the most constrained trait space).

The other consistent signature of competition was the statistical support for the MC model (Fig. 5, last column), which is noteworthy as it is different in several aspects from the generating model (Appendix S2). Trait model fitting generally recovered the MC model as the best for all competition-driven radiations, while the BM model was recovered for most neutral radiations (Fig. 5a,b). Support for the MC model was however progressively lost as bounds got tighter (Fig. 5c). Models with decreasing rates of evolution (e.g. diversity or time dependent rates) were never selected over MC.

When we nevertheless examined the recovered rate change parameter for the ACDC model in competition-driven radiations, we found that it was generally negative (representing a model with declining rates), the only exception being tightly bounded radiations where this parameter was positive (Fig. S4).

DISCUSSION

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We investigated the macroevolutionary consequences of interspecific competition during radiations, and the signal left on contemporary data under several scenarios. We found four main results: first, competition-driven radiations promote trait diversity, but not necessarily species richness; second, competition-driven radiations show declines in speciation and net diversification rates, even in unbounded trait spaces, but not in trait evolution rates, even in bounded trait spaces; third, testing for early burst patterns of species and/or trait diversification in comparative data does not provide a good test of adaptive radiations; fourth, phylogenetic signal in trait data and support for matching competition models may provide promising alternatives for detecting adaptive radiations.

It has been suggested that much of life's diversity, both in terms of species number and body form, originated as adaptive radiations [START_REF] Simpson | The Majors Features of Evolution[END_REF]. Whether this is the case or not, here we find that competition-driven radiations tend to generate phenotypic diversity, but not higher species richness than neutral radiations. While Another general expectation of verbal theories of adaptive radiation is that there are "early bursts" of both speciation and trait evolution, with rates that are highest at early stages of diversification, declining later as competition for ecological space increases [START_REF] Simpson | The Majors Features of Evolution[END_REF][START_REF] Schluter | The ecology of adaptive radiation[END_REF]. We did observe early burst patterns of speciation: rates are initially high as fast trait divergence in an empty ecological space leads to fast speciation, declining afterwards. In the absence of extinction, the decline is driven by a "duration" control of speciation [START_REF] Dynesius | Persistence of within-species lineages: A neglected control of speciation rates[END_REF]: speciation takes more time to complete as incipient lineages have more difficulty diverging ecologically from their parent species in an increasingly packed morphological space.

In the presence of extinction, the decline is instead driven by increasing extinction of

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incipient lineages, as in "ephemeral speciation" models [START_REF] Stanley | Macroevolution, pattern and process[END_REF][START_REF] Futuyma | Evolutionary constraint and ecological consequences[END_REF][START_REF] Rosenblum | Goldilocks meets Santa Rosalia: An ephemeral speciation model explains patterns of diversification across time scales[END_REF]. Bounds in trait space and saturation of ecological space exacerbate competition-driven declines in diversification rates, but are not required:

the constraints in phenotypic space imposed by competing lineages is sufficient by itself to slow down diversification as radiations proceed. Although radiating in multidimensional trait spaces might alleviate these constraints [START_REF] Doebeli | Diversity and coevolutionary dynamics in highdimensional phenotype spaces[END_REF], if species mostly diversify along single or a few phenotypic axes (e.g. lines of least evolutionary resistance; Schluter 1996), the described scenario might be common and invocation of contentious hard ecological limits to diversity not needed, favoring "damped" diversification explanations instead [START_REF] Cornell | Is regional species diversity bounded or unbounded?[END_REF][START_REF] Harmon | Species diversity is dynamic and unbounded at local and continental scales[END_REF].

Conversely, we did not observe early burst patterns of trait evolution in competitive radiations, even in bounded trait spaces. Mean rates tended to remain constant or even slightly increased. As lineages diversify trait space becomes more packed and competitive pressure increases (this occurs even in multidimensional phenotypic spaces; [START_REF] Doebeli | Diversity and coevolutionary dynamics in highdimensional phenotype spaces[END_REF]. In radiations far from (or without) ecological limits and with no extinction, fast competition-driven evolution at edges of occupied trait space overcompensates the effect of the constraints imposed by trait space packing in its central region; this even results in slight increases in mean trait evolution rates. Some empirical studies have indeed found evidence for higher rates at extremes of occupied morphospace (e.g. [START_REF] Cooper | What factors shape rates of phenotypic evolution? A comparative study of cranial morphology of four mammalian clades[END_REF] or increasing evolutionary rates towards the present in adaptive traits (e.g. Venditti et al. 2011;[START_REF] Hopkins | Dynamic evolutionary change in post-Paleozoic echinoids and the importance of scale when interpreting changes in rates of evolution[END_REF]. When increased competition leads to frequent extinctions, these open up trait/niche space, which reduces the competitive pressure to evolve at edges and the constraints imposed by trait space packing in its central region, resulting in nearly constant mean trait evolution rates. These results are consistent with recent paleontological studies that did not find evidence for trait evolution rate declines in adaptive macroevolutionary diversification [START_REF] Hopkins | Dynamic evolutionary change in post-Paleozoic echinoids and the importance of scale when interpreting changes in rates of evolution[END_REF]. Hence, the idea that mean trait evolution rates will decline during adaptive radiations because of

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increased competitive constraints on morphospace occupation might not necessarily hold; instead, we suggest that under some conditions character displacement and high lineage turnover fostered by increased competitive pressure may balance the effect of niche packing. Empirical studies are needed to clarify the potential generality of this process.

As expected (even for constant-rate neutral evolution; Foote 1996), in bounded trait spaces trait disparity reaches its maximum when boundaries are reached. However, competition does not necessarily lead lineages to trait boundaries early in a radiation unless the bounds are small compared to niche width: character displacement pushes species away in trait space, but it does so just enough to escape competition, so that it takes time before lineages reach the boundaries of trait space. Thus, our model predicts that relatively early disparity peaks may only be observed in adaptive radiations that are either very old compared to the moment where they hit bounds, or characterized by a tightly constrained trait space where significant species diversification would be difficult. Hence, if early disparity peaks are repetitively observed across species-rich clades irrespective of their age, as has been observed in the fossil record [START_REF] Hughes | Clades reach highest morphological disparity early in their evolution[END_REF], this suggests that the traits considered evolve in such constrained morphological spaces [START_REF] Foote | Models of morphological diversification[END_REF][START_REF] Gavrilets | Dynamics of clade diversification on the morphological hypercube[END_REF]), but that these traits are not the ones that are involved in competitive interactions and niche partitioning (if such interactions and partitioning occur).

Taken together, our results show a decoupling between rates that are often thought to be coupled. In particular, rates of trait evolution can remain constant (or even increase) despite decreasing diversification rates. And in adaptive radiations that reached ecomorphological limits, rates of trait evolution can remain constant despite disparity reaching a plateau, similarly to the "fly in a tube" effect [START_REF] Felice | A fly in a tube: Macroevolutionary expectations for integrated phenotypes[END_REF].

These two decoupling are both linked to the continuous reoccupation of vacated trait space after extinctions.

Biologists have searched for early burst patterns in comparative data as a signal of adaptive radiations [START_REF] Harmon | Tempo and Mode of Evotutionary Radiation in Iguanian Lizards[END_REF][START_REF] Harmon | Early bursts of body size and shape evolution are rare in comparative data[END_REF][START_REF] Moen | Why does diversification slow down?[END_REF]. Paradoxically,

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despite not producing declines in trait evolution rates, fits of the ACDC model to our simulated competition-driven radiations infer a decelerating rate process, except in tightly bounded trait spaces where an increasing rate process is inferred (Fig. S4).

These later results add to a growing literature suggesting that temporal trends found when fitting an ACDC model to extant trait data should not be used to draw conclusions about the process itself [START_REF] Revell | Phylogenetic signal, evolutionary process, and rate[END_REF][START_REF] Uyeda | Comparative analysis of principal[END_REF]. Our analyses also confirm that early bursts of diversification may be difficult to discriminate from an apparent decline stemming from the protracted nature of speciation [START_REF] Etienne | Prolonging the past counteracts the pull of the present: Protracted speciation can explain observed slowdowns in diversification[END_REF]. Additionally, we corroborate that extinction erases the signature of declining rates in reconstructed trees [START_REF] Rabosky | Explosive evolutionary radiations: Decreasing speciation or increasing extinction through time?[END_REF][START_REF] Liow | When can decreasing diversification rates be detected with molecular phylogenies and the fossil record?[END_REF] While searching for early bursts in comparative data does not seem the most reliable way to detect competition-driven radiations, our results suggest that alternative signals could be exploited. The two clear signatures are a strong phylogenetic signal (as measured by Blomberg's K) and a greater likelihood of phenotypic models incorporating competitive interactions [START_REF] Drury | Estimating the effect of competition on trait evolution using maximum likelihood inference[END_REF]. That competition leads to high phylogenetic signal (indicating that closely related species are more similar than expected) is perhaps unanticipated, as character displacement will tend to increase differences between two competing species [START_REF] Pfennig | Character displacement: ecological and reproductive responses to a common evolutionary problem[END_REF]. Conversely, here we show (similarly to [START_REF] Clarke | Trait Evolution in Adaptive Radiations: Modeling and Measuring Interspecific Competition on Phylogenies[END_REF]) that tree wide competition, while increasing overall disparity, tends to limit the morphospace occupied by subclades, leading to a strongly structured trait distribution in which species tend to occupy the same trait regions as their ancestors (e.g. niche [START_REF] Harmon | Tempo and Mode of Evotutionary Radiation in Iguanian Lizards[END_REF][START_REF] Losos | Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species[END_REF]). The only scenarios under which competition-driven radiations do not generate a high phylogenetic signal are scenarios with tight trait bounds (Fig. 5c; [START_REF] Revell | Phylogenetic signal, evolutionary process, and rate[END_REF]) that produce low species diversity.

F o r R e v i e w O n l y conservatism;
Those are also the only exceptions to the otherwise widespread support for matching competition models [START_REF] Drury | Estimating the effect of competition on trait evolution using maximum likelihood inference[END_REF]. Support for models incorporating competitive interactions is even more informative than phylogenetic signal alone. Indeed, while a declining rate unrelated to competition would also generate phylogenetic signal [START_REF] Revell | Phylogenetic signal, evolutionary process, and rate[END_REF], trait evolution models can distinguish these two scenarios because they induce a different morphospace occupation: gaps in trait space under declining rates, and more even distribution under competition [START_REF] Clarke | Trait Evolution in Adaptive Radiations: Modeling and Measuring Interspecific Competition on Phylogenies[END_REF]. It would be interesting to test if novel statistics that capture finer scale variation in a trait's distribution than Blomberg's K (e.g. Lewitus et al. in review) are also able to distinguish these two scenarios, as well as to check if phylogenetic signal remains a feature of adaptive radiations in multidimensional trait space (Harvey & Rambaut 2007[START_REF] Doebeli | Diversity and coevolutionary dynamics in highdimensional phenotype spaces[END_REF]. Likewise, it would be useful to assess the robustness of the support for Drury et al.'s MC models (2016) with a variety of competition-driven radiation scenarios, as it could be argued that our generating model resembles the MC inference tool. In addition to a strong phylogenetic signal and support for the matching competition model, competition-driven radiations can generate heterogeneous trait evolution rates (and also most likely diversification rates), with higher rates for species at the extremes of occupied morphospace. This pattern could potentially be exploited with recent comparative methods that allow estimating branch-specific rates (Venditti et al. 2011;[START_REF] Rabosky | Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees[END_REF][START_REF] Maliet | A model with many small shifts for estimating species-specific diversification rates[END_REF].

As with any modelling approach, we made a series of simplifying assumptions that may impact our results. In particular, population-level dynamics or landscape features may affect macroevolutionary outcomes [START_REF] Aguilée | Clade diversification dynamics and the biotic and abiotic controls of speciation and extinction rates[END_REF]. Nonetheless, we were able to investigate in a formal framework some fundamental ideas on adaptive radiations that clarify the macroevolutionary consequences of competition and will hopefully help design more precise and powerful tools to investigate them.

SUPPORTING INFORMATION

Additional Supporting Information may be downloaded via the online version of this article at Wiley Online Library (www.ecologyletters.com).

FIGURE LEGENDS

  through time. Negative γ values indicate a slowdown in speciation rate towards the present. We also compared the fit (using the AICc;[START_REF] Burnham | Multimodel inference: Understanding AIC and BIC in model selection[END_REF] of different models of diversification, representing: i) constant rates of speciation/extinction (with and without extinction), ii) time-dependent rates with linear/exponential dependences, or iii) diversity-dependent rates with linear dependency on speciation and constant or not extinction (Appendix S2). For each reconstructed tree and associated trait data, we estimated a measure of the phylogenetic signal (K;[START_REF] Blomberg | Testing for phylogenetic signal in comparative data: behavioral traits are more labile[END_REF]. K = 1 is expected under BM and which K values to expect under competition-driven radiations is not entirely clear (Appendix S2). We also compared the fit of commonly used models of phenotypic evolution representing: random (BM;[START_REF] Felsenstein | Phylogenies and the Comparative Method[END_REF] or constrained (OU; Hansen 1997) evolution, exponentially-changing rates through time (ACDC;[START_REF] Blomberg | Testing for phylogenetic signal in comparative data: behavioral traits are more labile[END_REF], diversity-dependent rates (DD; Weir & Mursleen 2013), and a model incorporating competitive interactions (MC;[START_REF] Drury | Estimating the effect of competition on trait evolution using maximum likelihood inference[END_REF]. We also obtained reconstructed trees and associated γ and K values along the simulations.

  competition-driven character displacement increases speciation rates at the beginning of clades' history, either the ecological constraints in trait space imposed by competing species or competitive exclusion ("Darwinian extinction",[START_REF] Sepkoski | A kinetic model of Phanerozoic taxonomic diversity I. Analysis of marine orders[END_REF] Webb 2003;[START_REF] Pfennig | Character displacement as the "best of a bad situation": fitness trade-offs resulting from selection to minimize resource and mate competition[END_REF]) limit this diversity-generating process once trait space is packed.

Figure 1 -

 1 Figure 1 -An integrated phylogenetic model of traits and species diversification. The speciation completion rate of a lineage depends on the distance with its parental species (A). The extinction rate (B) and the competition-driven component of trait evolution (C) are plotted as a function of the distance between two competing lineages. The a parameters control competition strength. Higher α values model a scenario where more similar phenotypes compete more strongly, implying a strong association between trait values and resource use. Conversely, a scenario where the evolving trait is less important for resource allocation is modeled by lower α values, as competition is less intense and less related to phenotypic differences among lineages. (D, E)

Figure 2 -

 2 Figure 2 -Effect of competition on diversification patterns in an unbounded trait space and no extinction. We performed 100 simulations for each a value. All other parameters were fixed (see methods). (A) Mean species richness (log scale) through time. (B) Final species richness (reconstructed tree size). (C, D) Realized mean speciation rates and speciation duration through time. (E) Mean trait disparity. (F) Mean trait evolution rate. Colored lines in (C,D,F) are a smoothed function fitted to the colored dots (1 Ma bins) for clearer representation. Notice the higher stochasticity at the beginning of the simulations given the fewer lineages present.

Figure 3 -

 3 Figure 3 -Effect of competition on diversification patterns in an unbounded trait space with competitive exclusion. (A) Mean species richness (log scale) through time. (B) Final species richness (reconstructed tree size). (C) Realized mean speciation rates, good species extinction rates, and net diversification rates (speciation minus extinction). (D) Speciation duration. (E) Mean trait disparity. (F) Mean trait evolution rate.

Figure 4 -

 4 Figure 4 -Effect of competition on diversification patterns with bounds on trait space and competitive exclusion. Competition strength was fixed at an intermediate value (a = 0.04). Trait/ecological space was increasingly larger (higher width). (A) Mean species richness (log scale) through time. (B) Final species richness (reconstructed tree size). C) Realized mean speciation rates, good species extinction

Figure 5 -

 5 Figure 5 -Patterns recovered on reconstructed trees and trait tip data using phylogenetic comparative methods. Results for simulated radiations without (A) and with competitive exclusion (B) and no bounds, and with competitive exclusion and bounds (C). From left to right: g statistic for tree shape; species diversification model fit (proportion of trees showing support for a given birth-death model, where B/D indicate speciation/extinction, and cst, lin and exp indicate constant, linearly or exponentially time-changing rates); phylogenetic signal (K) in trait data; and phenotypic evolution model fit (proportion of trees showing support for each of the following models: Matching Competition (MC), single optimum Ornstein-Uhlenbeck (OU), exponentially increasing or decreasing rates through time (ACDC), Brownian motion (BM), and exponential (DDe) or linear (DDl) diversity-dependent rates). See Appendix S2 for details.

A

  No extinction, no bounds With extinction, no bounds Fixed α (0.04), with extinction and bounds % best diversi cation model % best trait model

AKNOWLEDGMENTS

We thank J. P. Drury, M. Manceau, J. Clavel and E. Lewitus for their help and useful comments, and more generally members of the Morlon lab. We also thank J.

Rosindell and three anonymous reviewers whose comments helped to greatly improve the quality of this manuscript. This work was funded by grant PANDA from the European Research Council (ERC-CoG) awarded to HM.