Boris Leroy

Murilo S Dias

Emilien Giraud

Bernard Hugueny

Céline Jézéquel

Fabien Leprieur

Thierry Oberdorff

Pablo A Tedesco

Global biogeographical regions of freshwater fish species

Keywords: Biogeographical regions, biogeography, bioregionalisation, bioregions, dispersal, freshwater fish, actinopterygians, vicariance, zoogeographical regions, transition zones

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Introduction

For almost two centuries, biogeographers have classified continental areas of the world into distinct biogeographical regions on the basis of organism distributions across the Earth [START_REF] Wallace | The geographical distribution of animals; with a study of the relations of living and extinct faunas as elucidating the past changes of the Earth's surface[END_REF][START_REF] Cox | The biogeographic regions reconsidered[END_REF][START_REF] Holt | An update of Wallace's zoogeographic regions of the world[END_REF]. Indeed, early biogeographers observed that many organisms share constellated distributions of endemics in particular regions. Furthermore, they observed that these patterns of endemism are often similar for distinct groups of organisms, resulting in very similar biogeographical regions. This marked similarity has led to the hypothesis that these regions reflect a shared history of diversification among taxa and have been conditioned by geography, geology and climate [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF][START_REF] Lomolino | Biogeography 5th edition[END_REF].

The earliest classifications outlined six major biogeographic regions for birds [START_REF] Sclater | On the general Geographical Distribution of the Members of the Class Aves. (Continued.)[END_REF] and non-flying mammals [START_REF] Wallace | The geographical distribution of animals; with a study of the relations of living and extinct faunas as elucidating the past changes of the Earth's surface[END_REF] (Nearctic, Neotropical, Palearctic, Ethiopian, Oriental and Australian). During recent years, these major regions have been confirmed by an upsurge in bioregionalisation studies. This upsurge was facilitated by the increase in quality and quantity of large-scale datasets, as well as the development of new analytical tools [START_REF] Kreft | A framework for delineating biogeographical regions based on species distributions[END_REF][START_REF] Vilhena | A network approach for identifying and delimiting biogeographical regions[END_REF][START_REF] Edler | Infomap Bioregions: Interactive Mapping of Biogeographical Regions from Species Distributions[END_REF]. Consequently, multiple studies have tried to identify the major biogeographical regions for birds [START_REF] Procheş | The World's Zoogeographical Regions Confirmed by Cross-Taxon Analyses[END_REF][START_REF] Rueda | Identifying global zoogeographical regions: Lessons from Wallace[END_REF][START_REF] Holt | An update of Wallace's zoogeographic regions of the world[END_REF], mammals [START_REF] Kreft | A framework for delineating biogeographical regions based on species distributions[END_REF][START_REF] Procheş | The World's Zoogeographical Regions Confirmed by Cross-Taxon Analyses[END_REF][START_REF] Rueda | Identifying global zoogeographical regions: Lessons from Wallace[END_REF][START_REF] Holt | An update of Wallace's zoogeographic regions of the world[END_REF], amphibians [START_REF] Procheş | The World's Zoogeographical Regions Confirmed by Cross-Taxon Analyses[END_REF][START_REF] Rueda | Identifying global zoogeographical regions: Lessons from Wallace[END_REF][START_REF] Holt | An update of Wallace's zoogeographic regions of the world[END_REF][START_REF] Vilhena | A network approach for identifying and delimiting biogeographical regions[END_REF][START_REF] Edler | Infomap Bioregions: Interactive Mapping of Biogeographical Regions from Species Distributions[END_REF] and reptiles [START_REF] Procheş | The World's Zoogeographical Regions Confirmed by Cross-Taxon Analyses[END_REF]. The result of this upsurge was a debate on the precise limits of biogeographical regions. Indeed, some studies explicitly defined transition zones as distinct regions (e.g., [START_REF] Holt | An update of Wallace's zoogeographic regions of the world[END_REF], whereas others included transition zones in major regions [START_REF] Kreft | A framework for delineating biogeographical regions based on species distributions[END_REF]. This question of transition zones was settled to some extent in the major synthesis of [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF], proposing that transition zones should not be considered as distinct regions, but rather as transitional boundaries between major regions. Indeed, some regions share sharp boundaries, reflecting a long history of isolation by tectonics [START_REF] Ficetola | Global determinants of zoogeographical boundaries[END_REF], whereas others share diffuse boundaries, reflecting recent interchanges, generally limited by mountain or climatic barriers [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF][START_REF] Ficetola | Global determinants of zoogeographical boundaries[END_REF]. [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF] proposed that five major transition zones emerged from anterior works, which could be explained by a vicariance-dispersal model based on tectonic history. This synthetic model can be considered as a general framework to test for biogeographical regions.

However, the recent upsurge in continental bioregionalisation studies has concentrated exclusively on terrestrial vertebrates, which represent but a fraction of the continental organisms. There are other continental organisms with constraints to their dispersal and ecology that are fundamentally distinct from terrestrial vertebrates and for which one might expect distinct biogeographical regions. For example, terrestrial plants are generally characterised by higher degrees of endemism than animals, because they are more constrained than animals in terms of dispersal and tolerance to surmount climatic and other physical barriers [START_REF] Lomolino | Biogeography 5th edition[END_REF]. Hence, major phytogeographical regions were . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/319566 doi: bioRxiv preprint first posted online [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF] described as manifold small regions [START_REF] De Candolle | Essai Elémentaire de Géographie Botanique[END_REF][START_REF] De Candolle | Géographie botanique raisonnée[END_REF][START_REF] Takhtajan | Floristic Regions of the World[END_REF]. However, [START_REF] Cox | The biogeographic regions reconsidered[END_REF] later proposed a handful of large floral regions comparable to biogeographical regions, thus suggesting that the major biogeographical regions are universal across the tree of life. A second example concerns human microbial diseases whose biogeography has also been shown recently to match terrestrial vertebrate biogeography [START_REF] Murray | Global biogeography of human infectious diseases[END_REF].

Another possibility concerns strictly freshwater organisms (i.e., organisms that live and disperse exclusively in freshwaters) as they have lower dispersal abilities than terrestrial vertebrates, and are geographically isolated in drainage basins usually flowing to the oceans.

Terrestrial boundaries and salt waters represent strong barriers to dispersal, hence drainage basins have been considered as 'island-like' systems for strictly freshwater organisms [START_REF] Rahel | Biogeographic barriers, connectivity and homogenization of freshwater faunas: It's a small world after all[END_REF][START_REF] Hugueny | Community Ecology of River Fishes: A Large-Scale Perspective[END_REF][START_REF] Tedesco | Patterns and processes of global riverine fish endemism[END_REF][START_REF] Dias | Global imprint of historical connectivity on freshwater fish biodiversity[END_REF].

Dispersal can occur actively or passively via underground waters, stream captures, exceptional floods, glacier melting causing stream overflow, confluence during sea-level lowering, and displacement by other organisms or typhoons (see also discussion in [START_REF] Capobianco | Vicariance and dispersal in southern hemisphere freshwater fish clades: a palaeontological perspective[END_REF]. However, such dispersal events are rare, therefore immigration and speciation presumably occur on similar time-scales [START_REF] Oberdorff | Global and Regional Patterns in Riverine Fish Species Richness: A Review[END_REF].

Consequently, one might expect that, because of peculiarities of riverscape changes through geological times, strictly freshwater organisms have been subject to different histories of diversification from those of terrestrial vertebrates [START_REF] Rahel | Biogeographic barriers, connectivity and homogenization of freshwater faunas: It's a small world after all[END_REF] and thus have original biogeographical boundaries. Because dispersal is physically constrained, a higher degree of provincialism and endemism could be anticipated for such organisms, resulting potentially in smaller and more numerous biogeographic regions.

In this paper, we focussed on the global biogeography of strictly freshwater actinopterygian fishes (i.e., excluding marine and amphidromous families of fish), hereafter called freshwater fishes. Several studies delineated biogeographical regions of freshwater fishes at regional to continental scales (e.g., [START_REF] Unmack | Biogeography of Australian freshwater fishes[END_REF][START_REF] Oikonomou | Biogeography of freshwater fishes of the Balkan Peninsula[END_REF], and studies conducted at the global scale also focussed on subregional provinces (ecoregions) based on a combination of data and expert decisions [START_REF] Abell | Freshwater Ecoregions of the World: A New Map of Biogeographic Units for Freshwater Biodiversity Conservation[END_REF][START_REF] Lévêque | Global diversity of fish (Pisces) in freshwater[END_REF]. Only one work hinted at nine potential freshwater fish biogeographic regions that covered the same biogeographical regions as terrestrial vertebrates [START_REF] Matthews | Patterns in freshwater fish ecology[END_REF], but this work was based on a coarse geographic scale (52 approximate drainage basins for the whole world) and a low taxonomic resolution (family level). In addition, [START_REF] Matthews | Patterns in freshwater fish ecology[END_REF] included marine and diadromous fish families, which could conceal the effect of long-term isolation on freshwater fish endemicity patterns. Consequently, whether Sclater-Wallace's biogeographical regions are also applicable to freshwater fishes and to other freshwater organisms remains unresolved, and a global-scale quantitative bioregionalisation would represent an important step forward.

In this study, we aimed to define the major biogeographical regions for strictly freshwater fish species at the global scale. To delineate biogeographical regions, we capitalised on the recent development of a comprehensive dataset on freshwater fish distributions in drainage basins covering more than 80% of the Earth surface [START_REF] Tedesco | A global database on freshwater fish species occurrence in drainage basins[END_REF]. First, we identified the large biogeographical regions of freshwater fishes using a recently developed hierarchical . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/319566 doi: bioRxiv preprint first posted online [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF] approach based on networks [START_REF] Vilhena | A network approach for identifying and delimiting biogeographical regions[END_REF], recommended for bioregionalisation studies [START_REF] Edler | Infomap Bioregions: Interactive Mapping of Biogeographical Regions from Species Distributions[END_REF][START_REF] Bloomfield | A comparison of network and clustering methods to detect biogeographical regions[END_REF][START_REF] Rojas | Global biogeography of Albian ammonoids: A network-based approach[END_REF]. Then, we mapped the transition zones between regions and investigated species distributed across region boundaries. Finally, we compared biogeographical regions with terrestrial vertebrate biogeographical regions and discussed our findings in light of the synthetic biogeographical model proposed by [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF].

. CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/319566 doi: bioRxiv preprint first posted online [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF]

Methods

Distribution data

We based our bioregionalisation on the most comprehensive global database on freshwater fish species occurrence in drainage basins [START_REF] Tedesco | A global database on freshwater fish species occurrence in drainage basins[END_REF]. This database comprises 110 331 occurrence records for 14 953 species in 3 119 drainage basins of the world. Species names in the database were validated according to FishBase [START_REF] Froese | [END_REF] and the Catalogue of Fishes [START_REF] Fricke | CATALOG OF FISHES: GENERA, SPECIES[END_REF], and occurrence records were screened by the team developing the database (see details in [START_REF] Tedesco | A global database on freshwater fish species occurrence in drainage basins[END_REF]. We applied additional filters and corrections to the database. Since our aim was to describe the natural biogeographical regions resulting from long-term isolation of freshwater ichthyofaunas, we excluded documented records of introduced species, but included species considered to be recently extinct in their historical river basins. Additionally, to exclude most species that could disperse through marine waters, we retained only families having less than 10% of their species occurring in marine waters. This filter retained all "primary" and almost all "secondary" families of fishes (only Pseudomugilidae and Fundulidae were excluded), i.e. families with respectively no or limited salt tolerant species, as well as 22 families that had never been classified (based on Table 2 of [START_REF] Berra | Freshwater fish distribution[END_REF]. It also included eight families with marine ancestors, seven of which had no species classified as tolerating salt water. Finally, we removed all diadromous species, according to FishBase. Additionally, we detected a few errors that were corrected in the database, mostly related to the native/introduced status for some species. The database used in this publication is available in Appendix S1 in Supporting Information. The resulting dataset included 59 373 records of 11 295 species in 2 581 basins (Figure 1).

To define our bioregions, we worked at the species level and used drainage basins as geographical units. Indeed, (1) in the absence of a unified phylogeny for actinopterygian fishes, species is the most standard unit available and (2) contrary to terrestrial vertebrates, (for which gridded distribution data of reliable quality are is available), the most precise distribution data available for actinopterygian fishes is at the drainage basin unit. However, it is important to note that even if drainage basins are uneven in size, they are biogeographically meaningful for freshwater organisms because water bodies are generally connected within basins but not between basins [START_REF] Hugueny | Community Ecology of River Fishes: A Large-Scale Perspective[END_REF].

. CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/319566 doi: bioRxiv preprint first posted online [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF] Figure 1. Global distribution of freshwater fish species richness per drainage basin based on the global database on freshwater fish species occurrence in drainage basins [START_REF] Tedesco | A global database on freshwater fish species occurrence in drainage basins[END_REF]. Grey-shaded areas correspond to basins without records of native strictly freshwater species.

Delineation of biogeographical regions

Until recently, the prevailing procedure for bioregionalisation has been based on hierarchical clustering methods applied to compositional dissimilarity [START_REF] Kreft | A framework for delineating biogeographical regions based on species distributions[END_REF][START_REF] Procheş | The World's Zoogeographical Regions Confirmed by Cross-Taxon Analyses[END_REF][START_REF] Holt | An update of Wallace's zoogeographic regions of the world[END_REF]. Since then, an approach based on biogeographical networks was introduced by [START_REF] Vilhena | A network approach for identifying and delimiting biogeographical regions[END_REF], and has been recommended for delineating biogeographic regions [START_REF] Edler | Infomap Bioregions: Interactive Mapping of Biogeographical Regions from Species Distributions[END_REF][START_REF] Bloomfield | A comparison of network and clustering methods to detect biogeographical regions[END_REF][START_REF] Rojas | Global biogeography of Albian ammonoids: A network-based approach[END_REF]. A network is composed of a series of nodes which can be connected to each other by links (or edges). In bioregionalisation, the network is composed of both sites (i.e., drainage basins here) and species, which constitutes a bipartite network. When a taxon is known to occur at a particular site, a link is drawn between the taxon and the site. A site cannot be connected to another site, and a taxon cannot be connected to another taxon. By definition, site-site and species-species links are not allowed in this type of analysis. Our final network had 13 876 nodes (11 295 species and 2 581 basins) and 59 373 links. We handled the network under Gephi 0.9.2, with the ForceAtlas2 algorithm. This software groups nodes that are tightly interconnected (such as groups of sites and species from the same biogeographical region) and separates groups of nodes that are not interconnected (distinct biogeographical regions). Such a graphical representation is useful for analysing and exploring the network.

We applied a community-detection algorithm to the entire network in order to group nodes into clusters (i.e. biogeographical regions). We applied the Map Equation algorithm (www.mapequation.org, [START_REF] Rosvall | Maps of random walks on complex networks reveal community structure[END_REF] because it has been tested and recommended to identify biogeographical regions [START_REF] Vilhena | A network approach for identifying and delimiting biogeographical regions[END_REF][START_REF] Edler | Infomap Bioregions: Interactive Mapping of Biogeographical Regions from Species Distributions[END_REF][START_REF] Rojas | Global biogeography of Albian ammonoids: A network-based approach[END_REF] and it features hierarchical clustering. Clusters are identified by the algorithm as having high intra-group but low inter-group connectivity, which corresponds well to the definition of biogeographical regions, i.e. regions of distinct assemblages of endemic taxa. We ran Map Equation (version Sat Oct 28 2017) with 100 trials to find the optimal clustering. We ran the hierarchical clustering (i.e., multi-level) in order to test whether larger regions have a nested hierarchy of subregions. It is important to note that a hierarchy of regions identified at the species level illustrates how biogeographical regions (i.e., distinct assemblages of endemic taxa) are currently spatially nested, but does not represent a historical (i.e., evolutionary) hierarchy of how these regions emerged.

The biogeographical network approach presents several advantages over distance-based approaches that were instrumental in our choice. Foremost, species identities are not lost, i.e., they are not abstracted into dissimilarity matrices between sites. Consequently, the network approach allows one to map how sites are connected by individual species, which presents an unquestionable asset to investigate between-and within-regions structures, such as potential dispersal pathways or barriers. A second practical novelty is that the algorithm assigns each species to a specific bioregion, which enables species-level descriptions (e.g. for online databases such as FishBase) and analyses. Lastly, the Map Equation algorithm is robust to differences in sampling intensities, making the removal of basins with low species richness unnecessary. On the other hand, distance-based approaches have limitations (see e.g., Leprieur & Oikonomou, 2014) and can produce inconsistent results when transforming such large occurrence datasets into a single dimension during the clustering procedure (see Appendix S2).

However, we provide clustering results using two additional methods in Appendix S2 for comparison: another network-based algorithm (Simulated Annealing, [START_REF] Bloomfield | A comparison of network and clustering methods to detect biogeographical regions[END_REF] and a distance-based method (following the framework of [START_REF] Kreft | A framework for delineating biogeographical regions based on species distributions[END_REF].

Sensitivity analysis

We analysed the robustness of the identified regions by randomly extirpating a percentage of species (random value between 0.01 and 10.00% of the total number of species in the database) and re-running the whole bioregionalisation process. This process was repeated 200 times. Then, for each region, we quantified the percentage of each region initial area that was retrieved in each simulation (Appendix S3).

Transition zones and species shared between regions

We calculated the participation coefficient [START_REF] Guimerà | Functional cartography of complex metabolic networks[END_REF][START_REF] Bloomfield | A comparison of network and clustering methods to detect biogeographical regions[END_REF] for each node of the biogeographical network. The participation coefficient indicates the degree to which a node is connected to different regions. A high participation coefficient for a given basin indicates that it contains species from different regions and can be assimilated to a transition zone between regions. A low participation coefficient indicates that all species in . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/319566 doi: bioRxiv preprint first posted online [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF] the basin belong to the same region. The participation coefficient of a node is calculated as follows:

ܲ ൌ 1 െ ൬ ݇ ௦ ݇ ൰ ଶ ே ௦ୀଵ
where Pi is the participation coefficient of node i, kis is the number of links of node i to region s, k i is the total number of links of node i, and N m is the total number of regions. We calculated the participation coefficient at each level of the biogeographical structure identified by Map Equation.

We also summarised the list of species that were shared between the major regions (i.e., excluding tiny clusters) and their distribution characteristics.

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Results

The Map Equation algorithm identified a hierarchy of biogeographical regions with up to six nested levels. For this global-scale study, we investigated the first three levels, termed as supercontinental regions, Regions and Subregions. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/319566 doi: bioRxiv preprint first posted online [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF] regions (Old World and New World) and (b) six major regions (Nearctic, Palearctic, Neotropical, Ethiopian, Sino-Oriental, Australian) and a minor cluster (Madagascan). Some very small clusters of a few drainage basins that do not share species with any other basin were corrected based on expert interpretation and literature (see details in Appendix S5).

Supercontinental regions

At the first level, we found that the world of freshwater fishes was divided into two supercontinental regions that we named New World (Americas) and Old World (Eurasia, Africa and Australian) (Figure 2a). Each supercontinental region contained nearly half the world's 11 295 species with virtually 100% of endemic species (Table 1). Only two species occurred in both supercontinental regions (Figure 3 and Appendix S4): (1) Esox lucius, an Old World species that occurred in 32 basins of northern New World, and (2) Catostomus catostomus, a New World species that occurred in nine basins of northern Old World. These two supercontinental regions hosted 99% of endemic genera and around 80% of endemic families. At this first level, we also found 14 tiny clusters of 49 basins (exclusively located in the Old World) without endemic families and genera but with endemic species (40 species in total). These tiny clusters were most often composed of species-poor basins located in remote islands (e.g., Madagascan) or isolated arid areas (e.g., Arabian Peninsula). Therefore, we post-hoc assigned these clusters to the Old World supercontinental region (see details in Appendix S5).

Regions

At the second level, we found six major regions spatially nested within the two supercontinental regions (Figure 2b), that we named following [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF]. In the Old World supercontinental region, we found four regions and a minor cluster (Figure 2b). The richest one (Table 1) was the Ethiopian region with nearly 50% of Old World species, covering the entire African continent and including areas north from the Sahara and a few basins in the Arabic peninsula. The second richest one was the Sino-Oriental region which included south-eastern Asia from India to Borneo, most of China and Mongolia, Korea and Japan. The third one was the Palearctic region with less than 10% of Old World species, covering Europe, Central Asia (up to Pakistan and Kazakhstan) and Siberia. The fourth one, the poorest in species, was the Australian region, with only 80 species in total, covering Australia, Tasmania and Papouasia-New Guinea. Last, we identified Madagascan as a distinct minor cluster of the Old World, with 100% of endemic species and genera. Within the New World supercontinental region, we found two major regions. The first one was the Neotropical region, containing 85% of New World species and 42% of the world's known freshwater fish species (Table 1). The Neotropical region covered the whole of South America and Mesoamerica up to Southern Mexico. The second one was the Nearctic region, covering North America and northern Mexico. Finally, a tiny cluster composed of four basins of . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/319566 doi: bioRxiv preprint first posted online [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF] Central America was identified at this level, which we post-hoc assigned to the Nearctic region (see details in Appendix S5).

Most regions had very high degrees of endemism (Table 1b), above 96% for all regions except the Palearctic (88%). These high degrees of endemism are apparent on the biogeographical network through a low number of links between regions (Figure 3, Appendix S6). In other words, all regions shared only a very limited number of species (see Appendix S4). The degree of endemism was lower for genera, ranging from 56% for the Palearctic to 98% for the Neotropical, and was much lower for families, with values below or equal to 50% for all regions except the Neotropical one.

Figure 3. Global biogeographical network of freshwater fishes. In this network, both species and drainage basins are represented as nodes. When a species is known to occur in a drainage basin, a link between the species and the basin is drawn. The network is very complex because of the high number of nodes (13 876 nodes corresponding to 11 295 species and 2 581 basins) and links (59 373 occurrences). We spatialised the network in Gephi with the ForceAtlas 2 algorithm in order to group nodes that are strongly interconnected (i.e., basins that share species in common) and spread away from all other nodes that are not interconnected (i.e., basins that have few or no species in common).

We coloured species and basin nodes according to the regions identified with the Map Equation algorithm and highlighted in grey the two supercontinental region (New World and Old World). To simplify the network, we masked here all nodes with less than ten links. A zoomable version of the full network with species and basin names is available in Appendix S6.

. CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/319566 doi: bioRxiv preprint first posted online May. 11, 2018; Interestingly, our clustering results are concordant with both Simulated Annealing (Figure S2.1 in Appendix S2) and beta-diversity methods (Figure S2.2). The only notable discrepancies concerned the Nearctic region which was split in four regions with the betadiversity approach, and a portion of the Central Asian part of the Sino-Oriental which was attributed to the Palearctic by the Simulated Annealing method (see Appendix S2). Likewise, our sensitivity analysis on the Map Equation algorithm confirmed that regions were stable to random extirpations of species (Appendix S3), except for the Sino-Oriental one, which split in two regions in half of the simulations. This split produced the Oriental and Sinean regions with respectively ~85% and ~72% of endemics. These two subregions are visible on the network with two apparent clusters of nodes in the Sino-Oriental cluster (Figure 3). We also observed other minor changes, such as some clusters of basins appearing as small distinct regions, e.g. in Central Asia. . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

Subregions

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Transition zones and species shared between regions

At the supercontinental region level, we obtained participation coefficients of basins between 0.0 and 0.5, with transition zones (i.e., basins with high participation coefficients) located in north-eastern Siberia as well as in northern North America. At the regional level, we observed participation coefficients also ranging from 0.0 to 0.5 (Figure 4). Unsurprisingly, we found the same transition zones between the Nearctic and the Palearctic as for supercontinental regions. However, the major transition zones were located at the boundaries between the Palearctic and Sino-Oriental regions: the high participation coefficients of basins at their boundaries indicated that these boundaries were diffuse (Figure 4). These diffuse boundaries are reflected on the network by the high number of links between multiple species and multiple basins from both regions (Figure 3, Appendix S5). We identified another transition zone between the Nearctic and the Neotropical regions, with high participation coefficients of basins at their boundary (Figure 4). A dozen species of each region also incurred in the other region with similar distribution patterns (Appendix S4).

Overall, the species that were distributed across boundaries could be separated in two broad categories (Appendix S4). First, we found that the majority of shared species had restricted distributions close to regional boundaries, with occasional occurrences beyond. For example, the two-spot livebearer Heterandria bimaculata has a distribution endemic to Central America at the northernmost part of the Neotropical region and incurred in two basins of the Nearctic. Second, we found a limited number of species with large spatial distributions that were able to incur in multiple basins of other regions. The best example is the Northern pike Esox lucius which is one of the two species distributed across both supercontinental regions.

Another example is the Eurasian minnow Phoxinus phoxinus that is widespread in the Palearctic with multiple occurrences in the Sino-Oriental region.

On the other hand, we found almost no transition zones at the boundaries of the Ethiopian or the Australian regions. For the Ethiopian region, only a few basins in northern Africa and the Middle East shared species between the Ethiopian and Palearctic regions (Figure 4) as illustrated by the limited number of links on the network (Figure 3). Only nine species were shared between these two regions (Appendix S4). For the Australian region, only three species dispersed through the Wallace-Huxley boundaries (Figure 3, Appendices S4 and S6): two species assigned to the Oriental region (Aplocheilus panchax and Datnioides polota) occur in Papuasia, and one species assigned to the Australian region (Ophisternon bengalense) was distributed in Australia, Papuasia and Southeastern Asia.

Discussion

Here, we provide the first global bioregionalisation of freshwater fishes based on a quantitative analysis of species distributions. We found that the freshwater fish world is first divided into two supercontinental regions, the Old World and New World. Nested within these two supercontinental regions we found six major biogeographical regions with very high degrees of endemism: the Old World was divided in the Sino-Oriental, Ethiopian, Palearctic and Australian regions, whereas the New World was composed of the Neotropical and Nearctic regions. Nested within each of these major biogeographical regions we found subregions with varying degrees of endemism. Some regions had a few large subregions, while others had numerous subregions of heterogeneous size, species diversity and endemism. We found transition zones between the Nearctic and Neotropical regions, and between the Palearctic and Sino-Oriental regions. Furthermore, we found the Ethiopian and Australian regions to have almost no transition zones with other regions. The fish species that were distributed across regions were of two types: (i) species with ranges that were restricted to the proximity of the regional boundaries with occasional overlaps across boundaries and (ii) species with large distributions that expanded their range beyond boundaries.

Our results compellingly contradict our initial hypothesis that freshwater fish may have biogeographical regions different from the terrestrial vertebrate scheme of Sclater-Wallace because of their restricted dispersal abilities and the specific spatial-temporal dynamics of riverscapes. Indeed, we found a total of six major biogeographical regions that were similar in size and location to the six biogeographical regions identified by [START_REF] Wallace | The geographical distribution of animals; with a study of the relations of living and extinct faunas as elucidating the past changes of the Earth's surface[END_REF]. The only differences were the locations of several boundaries. More surprisingly, our regions were also similar to the coarse biogeographical regions based only on freshwater fish families identified by [START_REF] Matthews | Patterns in freshwater fish ecology[END_REF] and that also included diadromous fish species. Our Neotropical region is the only major difference from results obtained by [START_REF] Matthews | Patterns in freshwater fish ecology[END_REF] who identified a distinct cluster south to the Andes. This difference may be explained by the fact that this area was mostly colonised by the family of Galaxiidae that migrates between freshwater and oceans, and consequently have not been included in our analysis.

In addition, we also identified transition zones broadly following the bioregionalisation model of [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF], suggesting that freshwater fish regions were shaped by vicariance and geodispersal events similar to other groups. However, we observed extremely high rates of endemism for each region, more than 96% of endemic species for all regions except the Palearctic (89%). These endemism rates far exceed the endemism rates for other continental vertebrates such as birds (11 to 84% endemism in major regions, see Appendix S8), mammals (31% to 90%), herptiles (amphibian and reptiles: 46 to 95%; amphibian only: 66 to 98%), as calculated for regions of Procheş & Ramdhani (2012, Table S8.1 in Appendix S8) and Holt et al. (2013, Table S8.2 in Appendix S8). Freshwater fish endemism rates also exceed rates reported in marine biogeographical realms (17 to 84%, [START_REF] Costello | Marine biogeographic realms and species endemicity[END_REF]. Therefore, we conclude here that freshwater fishes are likely to have among the highest rates of species endemism for major biogeographical regions.

The two major supercontinental regions we identified might seem to contradict Morrone's biogeographical kingdoms (2015). Indeed, [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF] hypothesised that three major kingdoms could be derived from formerly disconnected land masses (i.e., Holarctic, Holotropical and Austral). This apparent contradiction is explained by our analyses at the species level which was not designed to reflect ancient biogeographical kingdoms resulting from the Gondwana split. Rather, the two supercontinental regions described here suggest that (1) the six major biogeographical regions have merged into two super-continents with biotic exchanges and (2) that strictly freshwater faunas have been isolated between these two super-continents for the past 10 to 20 million years. Only two species are shared between supercontinental regions, and genetic studies indicate that the only species documented, the pike Esox lucius, likely expanded its range after recent glaciations [START_REF] Maes | Indications for a recent Holarctic expansion of pike based on a preliminary study of mtDNA variation[END_REF][START_REF] Jacobsen | Microsatellite DNA analysis of northern pike (Esox lucius L.) populations: Insights into the genetic structure and demographic history of a genetically depauperate species[END_REF]. This current pattern of isolation of the two supercontinental regions will have far-reaching implications for the future evolution of freshwater fish faunas. The imprint of this isolation on fish faunas will be visible for millions of years, likewise to the historical imprint of the split of Pangea that can be seen today on phylogenetically-informed bioregionalisations (or bioregionalisations at a higher taxonomic level such as families; e.g. [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF]. This pattern is, as far as we know, specific to strictly freshwater fishes. We can expect a different pattern for diadromous fishes which can disperse through marine waters and thus occur in both the Nearctic and the Palearctic (e.g., species in genera Salmo, Salvelinus, Cottus, Lotta, Pungitius, Gasterosteus). Therefore, our hierarchy of supercontinental regions, regions and subregions should not be seen as the hierarchy of freshwater fish diversification history (i.e., our species-level supercontinental regions do not bear the same meaning as kingdoms in [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF], but rather as the recent biogeography of strictly freshwater fish faunas. This recent biogeography illustrates spatially-nested supercontinental regions, regions and subregions all characterised by very high degrees of endemism. These very high degrees of endemism reflect the strong isolation of strictly freshwater fish faunas because of the lack of dispersal during the Neogene (caused by the isolation of the different land masses during the late Cretaceous to Paleogene) and the limited magnitude of fish dispersal events that occurred after land masses merged during the late Miocene to early Pleistocene. Only 86 (0.76%) of the world's 11 295 species can be found across supercontinental or regional boundaries Therefore, 99.24% of the world's freshwater fishes occur in a single supercontinental region and a single region, which ascertains the biological reality of our delineated clusters.

Regions and transition zones

The Neotropical and Nearctic regions of freshwater fishes are very similar to the Neotropical and Nearctic regions highlighted for terrestrial mammals, amphibians and bird species [START_REF] Kreft | A framework for delineating biogeographical regions based on species distributions[END_REF][START_REF] Procheş | The World's Zoogeographical Regions Confirmed by Cross-Taxon Analyses[END_REF][START_REF] Holt | An update of Wallace's zoogeographic regions of the world[END_REF]. These two regions are therefore in agreement with the synthetic biogeographical regionalisation of [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF]. As for other groups of organisms, we identified a Mexican transition zone between Nearctic and Neotropical regions, suggesting that these organisms were affected by biotic interchange between the Americas, but to a much lesser extent than terrestrial vertebrates [START_REF] Bussing | Patterns of distribution of the Central American Icthyofauna[END_REF] . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/319566 doi: bioRxiv preprint first posted online May. 11, 2018; [START_REF] Smith | The biogeography of lower Mesoamerican freshwater fishes[END_REF]. Indeed, we found that only 25 species with restricted distributions were shared between Nearctic and Neotropical regions, as illustrated by the restricted area of the transition zone (Figure 4). These species belong to both primary and secondary freshwater families, which colonised Mesoamerica separately, as suggested by molecular analyses [START_REF] Smith | The biogeography of lower Mesoamerican freshwater fishes[END_REF]. Secondary freshwater fishes probably dispersed through Mesoamerica before the formation of the Panama isthmus, during periods of high runoff leading to temporary freshwater or brackish-water bridges in marine waters coupled with northward discharge of the proto-Amazon during the Miocene ~18-15 Ma [START_REF] Smith | The biogeography of lower Mesoamerican freshwater fishes[END_REF][START_REF] Hoorn | Amazonia through time: Andean uplift, climate change, landscape evolution, and biodiversity[END_REF]. Later, primary freshwater families dispersed through Mesoamerica during the Isthmus formation via landscape diffusion [START_REF] Smith | The biogeography of lower Mesoamerican freshwater fishes[END_REF]. Subsequent changes in the landscape led to increasing isolation of basins within Mesoamerica as well as occasional connectivity events, which in turn shaped the regional patterns of dispersal and diversification [START_REF] Smith | The biogeography of lower Mesoamerican freshwater fishes[END_REF][START_REF] Dias | Global imprint of historical connectivity on freshwater fish biodiversity[END_REF] that probably drove the restricted extent of the Mexican transition zone for freshwater fishes.

The Ethiopian region of freshwater fish resembles the Ethiopian regions of terrestrial vertebrates except for its northern limit. We found that this region expanded beyond the limits of the Sahara up to the Mediterranean sea, similarly to flightless terrestrial mammals [START_REF] Kreft | A framework for delineating biogeographical regions based on species distributions[END_REF]. However, flightless terrestrial mammals were also found to expand their Ethiopian boundary beyond the Arabian Peninsula into Central Asia, which was not the case here. Moreover, most studies for terrestrial vertebrates located the northern boundary of the Ethiopian region south of the Sahara [START_REF] Procheş | The World's Zoogeographical Regions Confirmed by Cross-Taxon Analyses[END_REF][START_REF] Rueda | Identifying global zoogeographical regions: Lessons from Wallace[END_REF][START_REF] Holt | An update of Wallace's zoogeographic regions of the world[END_REF][START_REF] Vilhena | A network approach for identifying and delimiting biogeographical regions[END_REF]. This discrepancy may be explained by several factors.

First, most studies on vertebrates identified the Sahara and the northern coast of Africa as transition zones between the Palearctic and Ethiopian regions [START_REF] Holt | An update of Wallace's zoogeographic regions of the world[END_REF][START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF]. This transition zone was not identified in our analyses: Palearctic fishes only anecdotally crossed the Mediterranean Sea, while their African counterparts merely ventured into the Middle-East. Therefore, we can hypothesise that the Mediterranean Sea was an insurmountable barrier for freshwater fishes, contrary to terrestrial vertebrates that could disperse through Straits of Gibraltar. Dispersal during the desiccation of the Mediterranean Sea during the Messinian salinity crisis about 6 Ma has been proposed with the Lago Mare hypothesis [START_REF] Bianco | Potential role of the palaeohistory of the Mediterranean and Paratethys basins on the early dispersal of Euro -Mediterranean freshwater fishes[END_REF]. This hypothesis stated that, during the refilling of the Mediterranean Sea, a freshwater or brackish phase occurred which would have permitted large scale dispersal of freshwater fishes across the Mediterranean basin. However this hypothesis received limited support in the light of phylogenetic studies [START_REF] Levy | Historical biogeography of European leuciscins (Cyprinidae): Evaluating the Lago Mare dispersal hypothesis[END_REF][START_REF] Perea | Phylogenetic relationships and biogeographical patterns in Circum-Mediterranean subfamily Leuciscinae (Teleostei, Cyprinidae) inferred from both mitochondrial and nuclear data[END_REF]. Hence, freshwater fish dispersal during the Messinian salinity was not the same as that of other groups (e.g., [START_REF] Veith | From Europe to Africa and vice versa: Evidence for multiple intercontinental dispersal in ribbed salamanders (Genus Pleurodeles)[END_REF][START_REF] Agustí | Evidence for African-Iberian exchanges during the Messinian in the Spanish mammalian record[END_REF].

A probable dispersal pathway for freshwater fishes was through diffusion (sensu [START_REF] Lomolino | Biogeography 5th edition[END_REF] between the Nile and Mediterranean basins of the Middle East (illustrated by the distributions of Clarias gariepinus and four African cichlid species). This Middle-East dispersal pathway corroborates to some extent the Saharo-Arabian transition zone proposed by [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF]. This dispersal pathway was identified for the early colonisation of . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/319566 doi: bioRxiv preprint first posted online [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF] Cyprinidae from the Sino-Oriental to Ethiopian region [START_REF] Gaubert | Genus-level supertree of Cyprinidae (Actinopterygii: Cypriniformes), partitioned qualitative clade support and test of macroevolutionary scenarios[END_REF].

Second, drainage basins north of the Sahara are extremely poor in freshwater fish species, notably because of their arid nature. Northernmost basins were probably colonised by Ethiopian species during repeated periods of aquatic connectivity across the Sahara region, the most recent being the Holocene African Humid period (~11 to 8ka) [START_REF] Drake | Ancient watercourses and biogeography of the Sahara explain the peopling of the desert[END_REF].

The Sino-Oriental region is a distinctive feature of freshwater fishes, for two reasons. Firstly, the northern boundary between Sino-Oriental and Palearctic is located beyond the Himalayas and the Gobi desert to the North-West, and goes up to the Stanovoï mountains located north of China, whereas all other groups are limited to a boundary extending longitudinally from the Himalayas to the China Sea [START_REF] Cox | The biogeographic regions reconsidered[END_REF][START_REF] Procheş | The World's Zoogeographical Regions Confirmed by Cross-Taxon Analyses[END_REF][START_REF] Rueda | Identifying global zoogeographical regions: Lessons from Wallace[END_REF][START_REF] Holt | An update of Wallace's zoogeographic regions of the world[END_REF][START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF]. Secondly, this region is composed of two major subregions that were frequently identified as distinct regions in our sensitivity analysis. In particular, the Sinean subregion hosting a rich freshwater fish fauna (963 species) with 72% of endemics seems unique to freshwater fishes, and has been identified more as a transition zone for other groups [START_REF] Procheş | The World's Zoogeographical Regions Confirmed by Cross-Taxon Analyses[END_REF][START_REF] Holt | An update of Wallace's zoogeographic regions of the world[END_REF][START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF]. We can speculate that this characteristic Sino-Oriental region for fishes arose from several factors. Firstly, the entire region was prone to fish speciation since the Eocene (55My), probably because of the very high diversity of aquatic habitats combined with the repeated rearrangement of rivers through capture and glaciation melting [START_REF] Dias | Global imprint of historical connectivity on freshwater fish biodiversity[END_REF][START_REF] Kang | Mapping China's freshwater fishes: Diversity and biogeography[END_REF][START_REF] Kang | Palaeolake isolation and biogeographical process of freshwater fishes in the Yellow River[END_REF][START_REF] Xing | Freshwater fishes of China: Species richness, endemism, threatened species and conservation[END_REF]. For example, the Cyprinidae family, which accounts for 45% of Sino-Oriental species, originated from the Indo-Malaysian tropical region and has likely radiated into Asia since the Eocene [START_REF] Gaubert | Genus-level supertree of Cyprinidae (Actinopterygii: Cypriniformes), partitioned qualitative clade support and test of macroevolutionary scenarios[END_REF]. Secondly, the northern boundary is located farther North than from other groups, suggesting that mountain barriers were more important in defining boundaries for fishes than for other groups, whose boundaries appeared to be rather defined by a combination of tectonics and climate [START_REF] Ficetola | Global determinants of zoogeographical boundaries[END_REF]. Consequently, the fish transition zone between Sino-Oriental and Palearctic is not located near areas of recent tectonic merging as reported for other groups [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF]. In addition, this transition zone is asymmetrically distributed towards the Palearctic (Figure 4) and possibly exceeding the asymmetry reported for other groups [START_REF] Sanmartín | Patterns of animal dispersal, vicariance and diversification in the Holarctic[END_REF], probably because of the extreme differences in fish richness between both regions. Given the mountainous nature of the boundary, dispersal pathways probably emerged at river confluences when the sea level dropped [START_REF] Dias | Global imprint of historical connectivity on freshwater fish biodiversity[END_REF], both at the Northeastern and Southwestern parts of the Sino-Oriental boundaries [START_REF] Gaubert | Genus-level supertree of Cyprinidae (Actinopterygii: Cypriniformes), partitioned qualitative clade support and test of macroevolutionary scenarios[END_REF].

The Australian region is the most depauperate of all fish regions, probably owing to the combination of the complete isolation of this region for the last 60 million years [START_REF] Scotese | PALEOMAP PaleoAtlas for GPlates and the PaleoData Plotter Program, PALEOMAP Project[END_REF] and the dryness of the Australian continent. The boundary between Australian and Sino-Oriental encompasses the entire Wallacea: the Sino-Oriental extends to the limits of the Sunda shelf, whereas the Australian extends to the limits of the Sahul shelf [START_REF] Lomolino | Biogeography 5th edition[END_REF]. Only three species occur on both sides of the boundary. Two Sino-Oriental species (Aplocheilus panchax and Datnioides polota) are Sino-Oriental species known from one . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/319566 doi: bioRxiv preprint first posted online [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF] drainage basin of the Australian region, and one species (Ophisternon bengalense) is distributed in 10 and 11 basins of Australian and Sino-Oriental respectively. O. bengalense lives in estuaries and is tolerant to brackish waters, and thus may have dispersed through Wallacea (e.g., [START_REF] Capobianco | Vicariance and dispersal in southern hemisphere freshwater fish clades: a palaeontological perspective[END_REF]. On the other hand, the distributions of the other two species are more surprising, and the absence of data in all islands of Wallacea makes hypotheses highly speculative. These apparent disjoint distributions could be linked to unknown exceptional dispersal, undocumented species introductions, or misidentification (i.e. two species misidentified as a single species).

Subregions

At subregional and finer levels, we found multiple clusters with varying degrees of endemism, with a substantial number of species distributed between clusters (see Figure 3). While it indicates that strictly freshwater fish species display strong endemism patterns at subregional spatial scales, it also suggests a reticulated history of river basins. The differences in number and endemism of subregions among major regions may be explained by the combination of habitat size and diversity, past climate change, and paleoconnectivity during the Last Glacial Maximum (LGM, see [START_REF] Leprieur | Partitioning global patterns of freshwater fish beta diversity reveals contrasting signatures of past climate changes[END_REF][START_REF] Tedesco | Patterns and processes of global riverine fish endemism[END_REF][START_REF] Dias | Global imprint of historical connectivity on freshwater fish biodiversity[END_REF]. Past climate change had an enormous impact on high-latitude regions, such as the Nearctic and northern parts of the Palearctic [START_REF] Leprieur | Partitioning global patterns of freshwater fish beta diversity reveals contrasting signatures of past climate changes[END_REF]. Most of these areas were covered by ice sheets during the LGM. These Northern areas were colonised by species after the LGM (e.g., [START_REF] Rempel | Postglacial fish dispersal from the Mississippi refuge to the Mackenzie River basin[END_REF]) from refuges located in the southernmost parts of these regions (Mississipi basin for the Nearctic, Danube basin for the Palearctic). Consequently, the relatively recent re-colonisation explains these large species-poor subregions. On the other hand, such climate events were less extreme in tropical regions thereby allowing lineages to thrive for a long period [START_REF] Tedesco | Evidence of history in explaining diversity patterns in tropical riverine fish[END_REF]. The combination of this prosperity with the high diversity and size of habitats in tropical regions, the long-term isolation of drainage basins during the Pleistocene [START_REF] Dias | Global imprint of historical connectivity on freshwater fish biodiversity[END_REF] as well as stable climatic history and favourable climatic conditions [START_REF] Wright | Thermal energy and the rate of genetic evolution in marine fishes[END_REF] probably generated conditions favourable to divergence and radiation processes in tropical subregions. This last hypothesis may explain the numerous tropical subregions with high diversity and endemism we found. The only apparent contradiction could arise from the two large subregions with high endemism of the Sino-Oriental. However, these two subregions probably reflect the uplift of the Tibetan plateau that led to their isolation [START_REF] Kang | Mapping China's freshwater fishes: Diversity and biogeography[END_REF]. In turn, these two subregions included numerous smaller ones with high diversity and endemism (see Figure S7.15) similar to the other tropical regions.

Robustness of findings

This first global quantitative analysis of the biogeography of freshwater fishes is based on a large scale database compiling occurrence data from thousands of sources and is thus inevitably subject to errors and incomplete data [START_REF] Tedesco | A global database on freshwater fish species occurrence in drainage basins[END_REF]. To minimise errors, a careful screening and correction procedure has been implemented for this database (see [START_REF] Tedesco | A global database on freshwater fish species occurrence in drainage basins[END_REF]. Reassuringly, the results obtained from other clustering methods as . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/319566 doi: bioRxiv preprint first posted online [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF] well as our sensitivity analysis suggested that the regions we identified are robust. Furthermore, regions were all spatially coherent (even though no spatial information was provided at any stage of the process) for the first two levels, with high degrees of endemism, indicating the quality of both the dataset and the bioregionalisation approach.

The network method assigns clusters to species (as explained in the methods), which is an asset over distance-based clusters. However, one major caveat needs to be acknowledged.

Species are assigned to the region where their present-day distribution is largest -this region is not necessarily the region where they originated from. A perfect example is the Characidae family at the transition between Nearctic and Neotropical: Astyanax mexicanus was assigned to the Nearctic, although its lineage is assumed to have colonised Mesoamerica during the Panama Isthmus formation [START_REF] Smith | The biogeography of lower Mesoamerican freshwater fishes[END_REF].

Our results at the subregional scales have several limits. Firstly, while species introductions are relatively well documented between major continents or regions, we can expect that some human-assisted translocations of species among basins have not been documented at smaller spatial scales, thereby blurring subregional patterns of endemism. Secondly, heterogeneity in land topology led to vast differences in size and number of drainage basins within different geographical areas of the world. Furthermore, drainage basins (especially large ones) may have a reticulated history challenging their validity as biogeographical units at sub-regional levels (e.g., see Dagosta & Pinna, 2017 and references therein). Third, for areas with numerous small basins, species lists were not necessarily available for all of them, and thus identification of provinces beyond the sub-regional scale would be speculative. Likewise, finescale data was not available in similar quantity or quality within different regions of the world (e.g., remote areas of Africa or Papuasia remain poorly sampled compared to other regions).

Last, the Map Equation is expected to identify transition zones as distinct clusters [START_REF] Vilhena | A network approach for identifying and delimiting biogeographical regions[END_REF][START_REF] Bloomfield | A comparison of network and clustering methods to detect biogeographical regions[END_REF]. We did not observe this pattern at large scales, except for a few basins at the transitions between Nearctic and Neotropical regions or between Sino-Oriental and Palearctic or Australian regions. However, at sub-regional and finer scales, transitions are likely to stand out as separate zones [START_REF] Bloomfield | A comparison of network and clustering methods to detect biogeographical regions[END_REF], which may not necessarily be an appealing property since the participation coefficient is informative enough to describe transition zones. For all these reasons, we deemed preferable not to investigate our results below the third level, as such fine-scale provinces would be better studied in regional studies (e.g., [START_REF] Smith | The biogeography of lower Mesoamerican freshwater fishes[END_REF][START_REF] Kang | Mapping China's freshwater fishes: Diversity and biogeography[END_REF].

Concluding remarks

This first quantitative study of freshwater fish bioregions revealed that their biogeography was probably shaped by the same major drivers as other continental groups of organisms, with peculiar exceptions such as the Sino-Oriental region. These regions identified with species distributions probably reflect relatively recent processes of dispersal and isolation. Ancient processes will be explored in future studies thanks to the newly available dated phylogenies of actinopterygian fishes [START_REF] Rabosky | An inverse latitudinal gradient in speciation rate for marine fishes[END_REF].

. CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/319566 doi: bioRxiv preprint first posted online [START_REF] Morrone | Biogeographical regionalisation of the world: A reappraisal[END_REF] We found that freshwater fishes, in addition to being the most diverse group of continental vertebrates, have extremely high rates of endemism, above 96% for all regions except the Palearctic. Furthermore, we found that tropical regions have a myriad of subregions with very high endemism and richness. These figures compellingly bespeak that freshwater fishes ought to be considered in hotspot analyses and raise many questions about the biogeographical consequences of the current high rates of freshwater fish introductions and extirpations [START_REF] Villéger | From current distinctiveness to future homogenization of the world's freshwater fish faunas[END_REF].

Figure 2 .

 2 Figure 2. Biogeographical regions of freshwater fishes defined at the species level with the Map Equation clustering algorithm. We identified (a) two major supercontinental

Table 1 .

 1 Characteristics of the first two levels of biogeographical regions of freshwater fishes identified with the Map Equation algorithm.

  At the third level, we observed different patterns among regions. Three regions (Sino-Oriental, Nearctic and Australian) had only two to three main subregions (Appendix S7) that were spatially coherent and had high degrees of endemism (68.7-91.5% of endemic species, Appendix S7). The other three regions (Ethiopian, Palearctic and Neotropical) were characterised by a high number of subregions, which were also generally spatially coherent (Appendix S7). The Ethiopian and Neotropical subregions were generally characterised by a high number of species and endemics (Figures S7.9 and S7.14). The Palearctic subregions were characterised by a low number of species and generally low endemicity (Figure S7.11).

Figure 4 .

 4 Figure 4. Map of transition zones between regions (level 2) characterised by the participation coefficient of basins, i.e the proportion of species in a basin that come from regions other than the region of this basin. Thick black lines indicate frontiers, which are shared between two neighbouring regions.

Acknowledgements

We thank Céline Bellard and Philippe Keith for useful discussions, and Aldyth Nyth and Lissette Victorero for English editing. We thank François-Henri Dupuich from derniercri.io for figure editing. Laboratoire Evolution et Diversité Biologique is part of the French Laboratory of Excellence projects ''LABEX TULIP'' and ''LABEX CEBA'' (ANR-10-LABX-41, ANR-10-LABX-25-01).

Biosketch

Boris Leroy is lecturer at the Muséum National d'Histoire Naturelle of Paris. He is interested in the biogeography of aquatic organisms and how it is or will be impacted by global changes such as climate change and invasive alien species.


Clement Dutriez
email: clement.dutriez@inria.fr

Benoît Verhaeghe
email: benoit.verhaeghe@berger-levrault.com

Mustapha Derras
email: mustapha.derras@berger-levrault.com

Switching of GUI framework: the case from Spec to Spec 2

Keywords: GUI, Modernization, Spec, Spec 2

Developers used frameworks to create their User Interface. Those frameworks are developed in a specific version of a language and can be used until the retro-compatibility is broken. In such case, developers need to migrate their software systems to a new GUI framework. We proposed a three-step approach to migrate the front-end of an application. This approach includes an importer, a GUI meta-model and an exporter. We validate our experiment on 6 projects in Pharo. We are able to migrate 5 out of 6 projects that all conserve their widgets organization.

Introduction

Programing languages evolve at a fast pace. For example, since 2017 they were 4 versions of Java, 4 versions of Angular and 2 major versions of Pharo. As many frameworks are developed on top of programming languages, the evolution of these languages may break the compatibility of frameworks based on them. This problem is known as backward compatibility problem. Once a software system has used a legacy framework, i.e. a framework that can not be used directly in the current version of the programming language used for the company application, the developers need to develop solutions to run their applications in the last development environment. One solution is to migrate the applications to the last environment using another framework. In the case of a legacy Graphical User Interface (GUI) framework, the developers need to switch framework. For example, Swing, AWT and JavaFX have been removed from the last Java version and a new version of Spec is in progress.

Those evolution force companies to update their software systems regularly to avoid being stuck in old technologies. To reduce the time induced by such modernization many research papers proposed solutions to migrate the GUI part of applications [START_REF] Erfani | Reverse Engineering iOS Mobile Applications[END_REF][START_REF] Samir | Swing2Script: Migration of Java-Swing applications to Ajax Web applications[END_REF][START_REF] Shah | Reverse-engineering user interfaces to facilitate porting to and across mobile devices and platforms[END_REF][START_REF] Verhaeghe | GUI Migration using MDE from GWT to Angular 6: An Industrial Case[END_REF]. None of them proposed a solution adapted for the Spec framework.

To support the migration of Spec GUI, we present a threestep approach. It includes a GUI meta-model, a strategy to generate the model, and another to create the target GUI. To validate this approach, we developed a tool which migrates Spec applications to Spec 2. Then, we validated our approach on 6 real projects that used both simple and complex user interfaces. Our migration succeeded for 5 projects out of 6. Since Spec 2 is still in active development, we only tested our approach on projects that can be written with the basic components already implemented in Spec 2 e.g. input, text, list.

First, in Section 2, we review the literature on GUI metamodeling. Section 3 describes the differences between Spec framework and the other GUI framework. Section 4 describes our migration approach. We present our implementation in Section 5. In Section 7, we present our results. Finally, in Section 8, we discuss the results obtained with our tool and future work.

Related Works

In the following we present meta-models found in the literature to represent GUI.

The OMG designed the Knowledge Discovery Metamodel (KDM) standard to support the evolution of software. The standard defined a meta-model to represent a piece of software at a high level of abstraction. It includes, among other things, a UI package which represents the components and behavior of a GUI.

The main entity of the UI meta-model is UIResource. It can be refined as UIDisplay or UIField. UIDisplay corresponds to the physical support on which the interface will be displayed, e.g. a computer screen, a printed report, etc. UIField corresponds to a user interface widget, e.g. a form, a text field, a panel, etc. An UIResource might be composed of other UIResources to represent the DOM, and UIActions to represent the behavior of the user interface.

The Interaction Flow Modeling Language (IFML) is specialized in modeling applications with a GUI. The aim of IFML [START_REF] Brambilla | Interaction flow modeling language: Model-driven UI engineering of web and mobile apps with IFML[END_REF] is to provide tools to describe the visible parts of an application. In the IFML meta-model, the visible elements of the GUI are called ViewElements. A ViewElement can be refined as a ViewContainer or a ViewComponent. ViewContainer represents a container of other ViewContainers or ViewComponents, e.g. a window, an HTML Page, a pane, etc. The authors use this relation of containment to represent the DOM.

A ViewComponent corresponds to a widget which displays its content, e.g. a form, a data grid, an image gallery etc. It can be linked to multiple ViewComponentParts to represent the data inside the component. The data can be an input field inside a form, an image element of a gallery, etc.

In their studies, Gotti and Mbarki [START_REF] Gotti | Java Swing Modernization Approach -Complete Abstract Representation based on Static and Dynamic Analysis[END_REF] and Sánchez Ramón et al. [START_REF] Sánchez Ramón | Model-driven reverse engineering of legacy graphical user interfaces[END_REF] used the KDM meta-models. Both authors added the Attribute entity to the meta-model.

Fleurey et al. [START_REF] Fleurey | Model-Driven Engineering for Software Migration in a Large Industrial Context[END_REF] did not explicitly describe the GUI metamodel, but we extracted information from their navigation meta-model. They have, among other things, three elements in their UI meta-model that represent the main window of the application, the graphical entities and their associated events to represent the behavioral part of the application.

The UI meta-model of Garcés et al. [START_REF] Garcés | White-box modernization of legacy applications: The oracle forms case study[END_REF] differs from the previous ones. In their representation, a widget corresponds to data found in a database directly displayed in a table. This difference is due to the context of their work. They migrated User Interface of Oracle Forms applications which are really different from modern GUI framework.

Memon et al. [START_REF] Memon | GUI ripping: reverse engineering of graphical user interfaces for testing[END_REF] designed a meta-model with two entities: widgets and attributes. An UI window is composed of a set of widgets that can have attributes.

Samir et al. [START_REF] Samir | Swing2Script: Migration of Java-Swing applications to Ajax Web applications[END_REF] worked on the migration of Java-Swing applications to Ajax web applications. They created a metamodel to represent the UI of the original application. This meta-model is stored in a XUL (XML-based User interface Language) file and represents the widgets with their attributes and the layout. These widgets belong to a Window and can fire events when an input is performed. The input and the event correspond to the Action and the Event entities of the KDM model. The XUL format has been discontinued.

Shah and Tilevich [START_REF] Shah | Reverse-engineering user interfaces to facilitate porting to and across mobile devices and platforms[END_REF] used a tree architecture to represent the UI. Each node corresponds to a component with its attributes. The root of the tree is the top-level component of the represented GUI.

Joorabchi and Mesbah [START_REF] Erfani | Reverse Engineering iOS Mobile Applications[END_REF] represented a user interface with a set of UI elements. Those elements are basic widgets such as text, button, input. Each element can be linked to multiple attributes and actions.

Mesbah et al. [START_REF] Mesbah | Crawling Ajax-Based Web Applications through Dynamic Analysis of User Interface State Changes[END_REF] did not present directly their meta-model for the user interface. However, they used a DOM-tree representation to analyze different web pages. The meta-model also includes an event entity for the behavioral part. They used different instances of their UI meta-model to represent the web pages of the application.

All the authors used the notion of widget that represents a visual entity of the user interface. And, most of them have an entity attribute that represents a characteristic of a widget. Finally, they used a tree representation and the root of this tree represent the main frame of the GUI.

Comparison of Spec and Spec 2

Spec and Spec 2 are frameworks that allow developers to write GUI for Pharo applications. Both divides the GUI definition into three steps:

• Widgets organization. Spec and Spec 2 frameworks define the GUI organization in one method in which is defined the layout of the application. At this step, apart from the layout, all widgets are abstracts but are defined using a symbol. The properties of the layout components, for example, the height, are also defined in this section.

• Widgets initialization. In this step a concrete User

Interface component is created for each abstract widget defined in the previous step. It also defines the properties of the widget such as its label, its color etc. • Events declaration. Developers should define the user interactions, e.g. click, hover, with the widgets in this step.

Even though the GUI definition is done in the same way for both frameworks, there is one notable difference. In Spec, the organization of the widgets is defined using only one class which can be used to define the layouts of the GUI. In Spec 2, the layouts are defined using multiple classes that are also User Interface elements.

Migration approach

This section presents the migration approach we designed. First, Section 4.1 describes the migration process. Section 4.2 presents our GUI meta-model.

Migration process

From the state of the art, we designed an approach for the migration. 1. Source code model extraction. We build a model representing the source code of the original application. It produces a FAMIX [START_REF] Ducasse | MSE and FAMIX 3.0: an Interexchange Format and Source Code Model Family[END_REF] smalltalk model which allows one to easily query the former application for the next step. 2. GUI model extraction. We analyze the source code model to detect the visual code elements describing the GUI. The GUI meta-model is described Section 4.2. 3. Export. We re-create the GUI in the target language.

Migrated application

This step creates the classes and the necessary configuration classes to run the exported user interface.

GUI Meta-model

To represent the User Interface of an application, we designed a GUI meta-model presented Figure 2. In the following, we present the entities of the meta-model. A container is a widget that can contain other widgets, e.g. a panel, a fieldset, a window. It is essential to represent the DOM heavily used in the literature. A leaf corresponds to a basic visible UI element, e.g. an input, a textfield.

Widget

A page is a type of container that represents a window. It corresponds to the root of an application GUI.

Finally, each widget is linked to multiple attributes and events. Attributes represent properties of the widget, e.g. its color, its size. Events represent all user interactions that the widget can handle, e.g. click, double-click, mouse hover.

Implementation

To test our approach, we implemented a migration tool. It is developed in Pharo using the Moose platform and allows one to migrate from Spec to Spec 2. Note that we do not migrate the behavior of the application. This will be the focus of future works.

Import

As presented Section 4.1, the extraction of the GUI model is divided into two steps: the source code model extraction and the GUI model extraction.

For the source code model extraction, we rely on a FAMIX Smalltalk importer that allows one to create a Smalltalk model for source code. Because an image does not include only the code of the old application, we must provide the list of packages in which the application is designed.

For the second step, our tool creates the GUI model form the source code model.

First, we provide to the tool the main window to extract. It is one page of the application. As presented in Section 3, a Spec UI is defined in three steps: the widgets organization; the widgets definition; and the events declaration, that will be the focus of future works.

Figure 3. Widgets organization creation in Spec

For the widget organization, the tool analysis the method defaultSpec. It uses the FAMIX navigation query system to explore the DOM. Figure 3 presents an example of organization creation using Spec.

First, line 3, the tool detects the invocation of newRow:, so it creates a horizontal panel entity in the model, which is a type of container. Then, line 4 and 5, it extracts the affectations of two widgets inside the previous panel. Once the widgets are created, the tool adds them inside the panel. Once the affectation of the widget is detected in widgets definition, our tool analyzes instantiate invocations to determine what type of widget should be created. To do so, it uses two heuristics: using a keyword that corresponds to a basic widgets, e.g. newButton, newLabel, newTextInput, or using the method instantiate: with the widget type as argument. This latter allows one to create a basic component or to create a customized one such as another user interface. Line 5, self instantiate: LabelPresenter corresponds to the creation of a text widget. Unlike the first example, the widget creation line 2, self newButton, uses the short way to create a basic widget element. So in the example, our tool creates a button widget.

Finally, our tool analyzes invocations on widgets to create their attributes. Lines 2 and 3, it creates the attributes label, with for the latter the value false, and add them to the button widget. Line 6, it creates a label attribute for the label widget.

Export

Once the GUI model is generated, it is possible to export the application. To generate the code, the tool has two visitors that respectively export the widgets organization and the widgets definition part. It also creates the minimal methods to be able to launch the application from the User Interface of Pharo.

Validation

In this section we present the applications we migrated with our tool to validate our approach Section 6.1. Section 6.2 presents the metrics used to evaluate our approach.

Case study

In the following, we present the 6 projects we migrated to validate our tool and approach.

• Refactoring is an application used by developers to refactor their code. It comes with a GUI that show the elements concerned by the refactoring and allows the developers to select which refactor actions they want to apply. Its GUI contains multiple type of widgets such as text, text input, check box, slider. To create the DemoForm GUI, the developers have used multiple pages with 2 pages inside the main one.

• DBManager provides a GUI to manage the connections between Pharo and databases. Its user interface is divided into multiple pages.

Validation metrics

We validate our approach in two steps: First, we check that all entities of interest are extracted; Second, we validate that we can re-export these widgets and that the visual aspect of the migrated application is correct, i.e. similar to the original one.

For the first validation, we manually count and check the entities of the user interface. Table 1 presents a summary of those numbers.

For the second validation, we check that the entities of the original applications are exported correctly. Each Page corresponds to a Pharo class that implements the necessary methods for GUI definition as presented Section 3.

We also check that the exported pages have the same visual aspect as the original ones. It is a subjective evaluation, and we are looking for an automatic solution.

Results and Discussion

In Section 7.1, we present the results for both the import and the export of Spec applications to Spec 2. Then, Section 7.2, we discuss the obtained results.

Results

Table 1 summarizes the extraction results.

For the import, our tool extracts 100% of the widgets for all the projects except the Critic one. For this latter, the widget definition does not follow the regular way to create User Interface. So, our tool cannot analyze the project.

For the Attributes extraction, our tool is less efficient. It extracts less than 25% of the attributes of the Refactoring and the Setting applications, 80% for the DemoForm and Critic projects, 74% for the DemoButton application, and 89% for the DBManager project.

For the export, we visually compared the result of the export with the original application. We present in the two following comparisons that show the main differences. Figure 6 presents the visual differences for the DBManager application. Again on the left-hand side there is the original page, on the right-hand side the page after the migration. There are many differences in this example. The text of the buttons at the left of the image is not present, the buttons at the right of the image are enabled but are disabled in the original application and are not correctly placed. Apart the last difference that comes from an application developers hack, all the differences are due to attribute extraction problems. Finally, the drop-box input has visual differences between the original and the exported application. It is due to widgets implementation. We discussed this point Section 7.2.2.

Discussion

Section 7.2.1 discuses the applications chosen for the validation. Section 7.2.2 presents how the future developments of Spec 2 can impact our solution.

User Interface definition

To validate our tool, we selected projects that used the Spec standard way to express widget definition. However, it is possible in Pharo to define the User Interface not extending the methods defined by the Spec framework. In this case, our tool is not able to extract the widgets of the application.

Widget implementations

Our tool exports the GUI with a similar aspect. This is eased because the visual aspect of basic components does not change between Spec and Spec 2. If a component is implemented differently in the target framework, the visual aspect is impacted. It is the case of the drop-box input presented Figure 6.

This lead to difficulties for automatic validation of the migrated GUI produced by our tool. It is possible to count the number of widgets and attributes but it may not be sufficient because of different visual aspect implementation.

Conclusion and Future works

In this paper, we exposed a preliminary work on the GUI migration problem. We proposed an approach based on a GUI meta-model and a migration process in three steps. We implemented this process in a tool to perform the migration from Spec applications to Spec 2. Then, we validated our tool on 6 Pharo projects.

We were able to extract correctly the widget organization of 5 projects out of 6. However, our tool does not extract all the attributes and works only with applications using the Spec defined methods. Dealing with all non-standard Spec application is our next challenge.

Figure 1 .

 1 Figure 1. Our GUI migration process

Figure 2 .

 2 Figure 2. GUI meta-model

Figure 4 .

 4 Figure 4. Widgets definition creation in Spec

•

  Setting has a GUI to configure the settings for the Pharo code formatting configuration. • CriticToolbar represents the toolbar of an application. It is composed of 3 buttons. • DemoButton is part of the Demo package of Pharo. It is used to show the different configurations of a Spec button. • DemoForm is also part of the Demo package of Pharo.

Figure 5 .

 5 Figure 5. Visual comparison of CriticToolbar Figure 5 presents the visual differences between the original (Spec) version, left hand, and the migrated (Spec 2) one, right-hand. We can see that there is only one minimal difference. The buttons do not have the same height in the exported version. This difference is due to current Spec 2 limitation

Figure 6 .

 6 Figure 6. Visual comparison of DBManager

Table 1 .

 1 Extraction results

		Refactoring Setting CriticToolbar DemoButton	DemoForm	DBMananer
	Widgets number	7	11	5	15	41	37
						(multiple pages) (multiple pages)
	Widgets imported	100%	100%	error	100%	100%	100%
	Attributes number	8	8	5	19	20	29
	Attributes imported	12.5%	25%	80%	74%	80%	89%

(a) DBManager original (b) DBManager exported

IWST'19, August 27-29, 2019, Cologne, Germany 2019.