Leroy, B. | Search bioRxiv

bioRxiv2018

Global biogeographical regions of freshwater fishes

AimTo define the major biogeographical regions and transition zones for freshwater fish species.\n\nTaxonStrictly freshwater species of actinopterygian fish (i.e., excluding marine and amphidromous fish families).\n\nMethodsWe based our bioregionalisation on a global database of freshwater fish species occurrences in drainage basins, which, after filtering, includes 11 295 species in 2 581 basins. On the basis of this dataset, we generated a bipartite (basin-species) network upon which we applied a hierarchical clustering algorithm (the Map Equation) to detect regions. We tested the robustness of regions with a sensitivity analysis. We identified transition zones between major regions with the participation coefficient, indicating the degree to which a basin has species from multiple regions.\n\nResultsOur bioregionalisation scheme showed two major supercontinental regions (Old World and New World, 50% species of the world and 99.96% endemics each). Nested within these two supercontinental regions lie six major regions (Nearctic, Neotropical, Palearctic, Ethiopian, Sino-Oriental and Australian) with extremely high degrees of endemism (above 96% except for the Palearctic). Transition zones between regions were of limited extent compared to other groups of organisms. We identified numerous subregions with high diversity and endemism in tropical areas (e.g. Neotropical), and a few large subregions with low diversity and endemism at high latitudes (e.g. Palearctic).\n\nMain conclusionsOur results suggest that regions of freshwater fish species were shaped by events of vicariance and geodispersal which were similar to other groups, but with freshwater-specific processes of isolation that led to extremely high degrees of endemism (far exceeding endemism rates of other continental vertebrates), specific boundary locations, and limited extents of transition zones. The identified bioregions and transition zones of freshwater fish species reflect the strong isolation of freshwater fish faunas for the past 10 to 20 million years. The extremely high endemism and diversity of freshwater fish fauna raises many questions about the biogeographical consequences of current introductions and extinctions.

ecology↗

bioRxiv2017

Theoretical solutions for the evaluation of discrimination capacity of species distribution models

The discriminating capacity (i.e., ability to correctly classify presences and absences) of species distribution models (SDMs) is commonly evaluated with metrics such as the Area Under the Receiving Operating Characteristic Curve, the Kappa statistic and the True Skill Statistic (TSS). AUC and Kappa have been repeatedly criticised, but the TSS has fared relatively well since its introduction, mainly because it has been considered as independent of prevalence. In addition, discrimination metrics have been contested because they should be calculated on presence-absence data, but are often used on presence-only or presence-background data. Here, we investigate the TSS and an alternative set of metrics -similarity indices, also known as F-measures. We first show that even in ideal conditions (i.e., perfectly random presence-absence sampling), TSS can be misleading because of its dependence on prevalence, whereas similarity/F-measures provide adequate estimations of model discrimination capacity. Second, we show that in real-world situations where sample prevalence is different from true species prevalence (i.e., biased sampling or presence-pseudoabsence), no discrimination capacity metric provide adequate estimations of model discrimination capacity, including metrics specifically designed for presence-pseudoabsence. Our conclusions are twofold. First, they unequivocally appeal SDM users to understand the potential shortcomings of discrimination metrics when quality presence-absence data are lacking, and we provide recommendations to obtain such data. Second, in the specific case of virtual species, which are increasingly used to develop and test SDM methodologies, we strongly recommend the use of similarity/F-measures, which were not biased by prevalence, contrary to TSS.

ecology↗