Energy availability and habitat heterogeneity predict global riverine fish diversity

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Abstract

Processes governing patterns of richness of riverine fish species at the global level can be modelled using artificial neural network (ANN) procedures. These ANNs are the most recent development in computer-aided identification and are very different from conventional techniques1,2. Here we use the potential of ANNs to deal with some of the persistent fuzzy and nonlinear problems that confound classical statistical methods for species diversity prediction. We show that riverine fish diversity patterns on a global scale can be successfully predicted by geographical patterns in local river conditions. Nonlinear relationships, fitted by ANN methods, adequately describe the data, with up to 93 per cent of the total variation in species richness being explained by our results. These findings highlight the dominant effect of energy availability and habitat heterogeneity on patterns of global fish diversity. Our results reinforce the species-energy theory3 and contrast with those from a recent study on North American mammal species4, but, more interestingly, they demonstrate the applicability of ANN methods in ecology.

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Figure 1: Three-layer feed-forward artificial neural network (ANN) structure used in this work.
Figure 2: Prediction of fish species richness (SR) using the 3-5-1 artificial neural network (ANN) model shown in Fig. 1.
Figure 3: Contribution of the three independent variables (SDA, FR and NPP) used in the 3-5-1 ANN model.
Figure 4: ‘Leave-one-out’ cross-validation test for the 183 rivers analysed in this study.

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Acknowledgements

We thank B. Hugueny, D. Fournier, S. Morand, F. Renaud, P. Bourret, P. Auriol, H.Descamps, members of the CESAC group at Toulouse and SMEL group at Sète for helpful comments on the manuscript; S. L. Pimm for discussion and encouragement; and M. Hochberg and M. Hewison for improving the English of the text. This study was funded by ORSTOM, Cayenne and Paris (thanks are due to the administrative staff), UMR 5556, and SMEL (J.F.G.), CNRS UMR 5576 University of Toulouse (S.L.) and CSP-MNHN (T.O.).

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Correspondence to Jean-François Guégan.

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