Hostname: page-component-7c8c6479df-94d59 Total loading time: 0 Render date: 2024-03-29T12:34:19.626Z Has data issue: false hasContentIssue false

Role of some environmental variables in trout abundance models using neural networks

Published online by Cambridge University Press:  15 January 1996

Sovan Lek
Affiliation:
UMR 9964, Équipe de Biologie quantitative, Université Paul Sabatier, 118, route de Narbonne, 31062 Toulouse Cedex, France
Alain Belaud
Affiliation:
Laboratoire d'Ingénierie Agronomique, Équipe Environnement Aquatique et Aquaculture, École Nationale Supérieure Agronomique de Toulouse, 145, avenue de Muret, 31076 Toulouse Cedex, France
Philippe Baran
Affiliation:
Laboratoire d'Ingénierie Agronomique, Équipe Environnement Aquatique et Aquaculture, École Nationale Supérieure Agronomique de Toulouse, 145, avenue de Muret, 31076 Toulouse Cedex, France
Ioannis Dimopoulos
Affiliation:
UMR 9964, Équipe de Biologie quantitative, Université Paul Sabatier, 118, route de Narbonne, 31062 Toulouse Cedex, France
Marc Delacoste
Affiliation:
Laboratoire d'Ingénierie Agronomique, Équipe Environnement Aquatique et Aquaculture, École Nationale Supérieure Agronomique de Toulouse, 145, avenue de Muret, 31076 Toulouse Cedex, France
Get access

Abstract

Neural networks provide a "black box" model for explaining and predicting trout abundance with 8 environmental variables. This work investigates the specific effect of each variable, by inputting fictitious configurations of explanatory variables and by checking the responses of the model. The comparison between this response of the model to environmental variables on one hand, and results from field observations on the other hand, shows similarities and indicates neural network modelling can be trusted. The elevation appears to be the major explanatory factor. The influence of shelters, bottom velocity and Froude number also play an important role. When considered separately, depth does not have a notable influence on the density of trout. Such confirmations of field observations suggest that these models can be used to obtain a clear identification and hierarchization of the factors influencing the abundance of trout and the mode of action of the factors. This approach can be extended to other applications in quantitative ecology in which non-linear relationships are usually observed.

Type
Research Article
Copyright
© IFREMER-Gauthier-Villars, 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)