@article{fdi:010012665,
  title = {{E}nergy availability and habitat heterogeneity predict global riverine fish diversity},
  author = {{G}u{\'e}gan, {J}ean-{F}ran{\c{c}}ois and {L}ek, {S}. and {O}berdorff, {T}hierry},
  editor = {},
  language = {{ENG}},
  abstract = {{P}rocesses governing patterns of richness of riverine fish species at the global level can be modelled using artificial neural network ({ANN}) procedures. {T}hese {ANN}s are the most recent development in computer-aided identification and are very different from conventional techniques. {H}ere we use the potential of {ANN}s to deal with some of the persistent fuzzy and nonlinear problems that confound classical statistical methods for species diversity prediction. {W}e show that riverine fish diversity patterns on a global scale can be successfully predicted by geographical patterns in local river conditions. {N}onlinear 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. {T}hese findings highlight the dominant effect of energy availability and habitat heterogeneity on patterns of global fish diversity. {O}ur results reinforce the species-energy theory and contrast with those from a recent study on {N}orth {A}merican mammal species, but, more interestingly, they demonstrate the applicability of {ANN} methods in ecology. ({R}{\'e}sum{\'e} d'auteur)},
  keywords = {{POISSON} {D}'{EAU} {DOUCE} ; {DIVERSITE} {SPECIFIQUE} ; {ESTIMATION} ; {MODELISATION} ; {RESEAU} {NEURONAL}},
  booktitle = {},
  journal = {{N}ature},
  volume = {391},
  numero = {6665},
  pages = {382--384},
  ISSN = {0028-0836},
  year = {1998},
  DOI = {10.1038/34899},
  URL = {https://www.documentation.ird.fr/hor/fdi:010012665},
}