The use of artificial neural networks to assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake

The use of artificial neural networks to assess fish abundance and spatial occupancy in the... The present work describes a comparison of the ability of multiple linear regression (MLR) and artificial neural networks (ANN) to predict fish spatial occupancy and abundance in a mesotrophic reservoir. Models were run and tested with 306 observations obtained by the sampling point abundance method using electrofishing. For each of the 306 samples, the relationships between physical parameters and the abundance and spatial occupancy of various fish species were studied. For the 15 fish species occurring in the lake, six main fish populations were retained to perform comparisons between ANN and MLR models. Each of the six MLR and ANN models had eight independent environmental variables (i.e. depth, distance from the bank, slope of the bottom, flooded vegetation cover, percentage of boulders, percentage of pebbles, percentage of gravel and percentage of mud) and one dependent variable (fish density for the considered population). To determine the population assemblage, principal component analysis (PCA) was performed on the partial coefficients of the MLR and on the relative contribution of each independent variable of ANN models (determined using Garson's algorithm). The results stress that ANN are more suitable for predicting fish abundance at the population scale than MLR. In the same way, a higher level of ecological complexity, i.e. community scale, was reliably obtained by ANN whereas MLR presented serious shortcomings. These results show that ANN are an appropriate tool for predicting population assemblage in ecology. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecological Modelling Elsevier

The use of artificial neural networks to assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake

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Publisher
Elsevier
Copyright
Copyright © 1999 Elsevier Science B.V.
ISSN
0304-3800
eISSN
1872-7026
D.O.I.
10.1016/S0304-3800(99)00110-6
Publisher site
See Article on Publisher Site

Abstract

The present work describes a comparison of the ability of multiple linear regression (MLR) and artificial neural networks (ANN) to predict fish spatial occupancy and abundance in a mesotrophic reservoir. Models were run and tested with 306 observations obtained by the sampling point abundance method using electrofishing. For each of the 306 samples, the relationships between physical parameters and the abundance and spatial occupancy of various fish species were studied. For the 15 fish species occurring in the lake, six main fish populations were retained to perform comparisons between ANN and MLR models. Each of the six MLR and ANN models had eight independent environmental variables (i.e. depth, distance from the bank, slope of the bottom, flooded vegetation cover, percentage of boulders, percentage of pebbles, percentage of gravel and percentage of mud) and one dependent variable (fish density for the considered population). To determine the population assemblage, principal component analysis (PCA) was performed on the partial coefficients of the MLR and on the relative contribution of each independent variable of ANN models (determined using Garson's algorithm). The results stress that ANN are more suitable for predicting fish abundance at the population scale than MLR. In the same way, a higher level of ecological complexity, i.e. community scale, was reliably obtained by ANN whereas MLR presented serious shortcomings. These results show that ANN are an appropriate tool for predicting population assemblage in ecology.

Journal

Ecological ModellingElsevier

Published: Aug 17, 1999

References

  • Electrofishing for fish larvae and juveniles: equipment modifications for increased efficiency with short fishes
    Copp, G.H
  • Modelling spatial dynamics of fish
    Giske, J; Huse, G; Fiksen, O
  • The use of artificial neural networks to predict the presence of small-bodied fish in a river
    Mastrorillo, S; Lek, S; Dauba, F; Belaud, A
  • Predicting grassland community changes with an artificial neural network model
    Tan, S.S; Smeins, F.E

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