Energy availability and habitat heterogeneity predict global riverine fish diversity

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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.


  1. 1

    Murray, A. F. Application of Neural Networks (Kluwer Academic, Boston, 1995).

  2. 2

    Edwards, M. & Morse, D. R. The potential for computer-aided identification in biodiversity research. TREE 10, 153–158 (1995).

  3. 3

    Wright, D. H. Species energy theory: an extension of species-area theory. Oikos 41, 495–506 (1983).

  4. 4

    Kerr, J. T. & Packer, L. Habitat heterogeneity as a determinant of mammal species richness in high-energy regions. Nature 385, 252–254 (1997).

  5. 5

    Brown, J. H. Macroecology (Chicago University Press, Chicago, 1995).

  6. 6

    Rosenzweig, M. L. Species Diversity in Space and Time (Cambridge University Press, Cambridge, 1995).

  7. 7

    MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton University Press, Princeton, NJ, 1967).

  8. 8

    Wright, D. H., Currie, D. J. & Maurer, B. A. Species Diversity in Ecological Communities (eds Ricklefs, R.E. & Schluter, D.) 66–74 (University of Chicago Press, Chicago, 1993).

  9. 9

    Whittaker, R. H. Evolution of species diversity in land communities. Evol. Biol. 10, 1–67 (1977).

  10. 10

    Oberdorff, T., Guégan, J.-F. & Hugueny, B. Global scale patterns of fish species richness in rivers. Ecography 18, 345–352 (1995).

  11. 11

    Turner, J. R. G., Lennon, J. J. & Lawrenson, J. A. British bird species distributions and the energy theory. Nature 335, 539–541 (1988).

  12. 12

    Turner, J. R. G., Gatehouse, C. M. & Corey, C. A. Does solar energy control organic diversity? Butterflies, moths and British climate. Oikos 48, 195–205 (1987).

  13. 13

    Currie, D. J. Energy and large scale patterns of animal and plant species richness. Am. Nat. 137, 27–49 (1991).

  14. 14

    Currie, D. J. & Paquin, V. Large-scale biogeographical patterns of species richness of trees. Nature 329, 326–327 (1987).

  15. 15

    Oberdorff, T., Hugueny, B. & Guégan, J.-F. Is there an influence of historical events on contemporary fish species richness in rivers? Comparisons between Western Europe and North America. J. Biogeogr. 24, 461–467 (1997).

  16. 16

    Welcomme, R. L. Status of fisheries in South American rivers. Interciencia 15, 337–345 (1990).

  17. 17

    Hugueny, B. West African rivers as biogeographic islands: species richness of fish communities. Oecologia 79, 235–243 (1989).

  18. 18

    Hugueny, B. Geographical range of west African freshwater fishes: role of biological characteristics and stochastic processes. Acta Oecologica 11, 351–375 (1990).

  19. 19

    Scardi, M. Artificial neural networks as empirical models for estimating phytoplankton production. Marine Ecol. Progr. Ser. 139, 289–299 (1996).

  20. 20

    Ehrman, J. M., Clair, T. A. & Bouchard, A. Using neural networks to predict pH changes in acidified Eastern Canadian lakes. Artif. Intell. Appl. 10, 1–8 (1996).

  21. 21

    Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J. & Aulanier, S. Application of neural networks to modelling non-linear relationships in ecology. Ecol. Model. 90, 39–52 (1996).

  22. 22

    Barinaga, M. Arecipe for river recovery? Science 273, 1648–1650 (1996).

  23. 23

    Naiman, R. J., Magnuson, J. J., McKnight, D. M., Stanford, J. A. & Karr, J. R. Freshwater ecosystems and their management: a national initiative. Science 270, 584–585 (1995).

  24. 24

    Ludwig, D., Hilborn, R. & Walters, C. Uncertainty, resource exploitation, and conservation: lessons from history. Science 260, 17and 36 (1993).

  25. 25

    Johnson, B. L., Richardson, W. B. & Naimo, T. J. Past, present, and future concepts in large river ecology. BioScience 45, 134–141 (1995).

  26. 26

    Beattie, M. An ecosystem approach to fish and wildlife conservation. Ecol. Appl. 6, 696–698 (1996).

  27. 27

    Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating error. Nature 323, 533–536 (1986).

  28. 28

    Kohavi, R. Astudy of cross-validation and bootstrap for estimation and model selection. Proc. 14th Int. Joint Conf. on Artif. Intell. 1137–1143 (Kaufman, Montreal, 1995).

  29. 29

    Garson, G. D. Interpreting neural-network connection weights. Artif. Intell. Exp. 6, 47–51 (1991).

  30. 30

    Goh, A. T. C. Back-propagation neural networks for modelling complex systems. Artif. Intell. Engng 9, 143–151 (1995).

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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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