Original scientific paper

https://doi.org/10.14798/73.2.816

PREDICTING FACTORS THAT INFLUENCE FISH GUILD COMPOSITION IN FOUR COASTAL RIVERS (SOUTHEAST IVORY COAST) USING ARTIFICIAL NEURAL NETWORKS

Koffi Félix Konan ; Department of Environment, University Jean Lorougnon Guédé, BP 150 Daloa, Ivory Coast
Kotchi Yves Bony ; Department of Environment, University Jean Lorougnon Guédé, BP 150 Daloa, Ivory Coast
Oi Edia Edia ; Department of Sciences and Environment Managment, University Nangui Abrogoua, 02 BP 801 Abidjan 02, Ivory Coast
N’guessan Gustave Aliko ; Department of Environment, University Jean Lorougnon Guédé, BP 150 Daloa, Ivory Coast
Allassane Ouattara ; Department of Sciences and Environment Managment, University Nangui Abrogoua, 02 BP 801 Abidjan 02, Ivory Coast
Germain Gourene ; Department of Sciences and Environment Managment, University Nangui Abrogoua, 02 BP 801 Abidjan 02, Ivory Coast

Abstract

The present study is focused on small coastal rivers in southeast Ivory
Coast, aimed to predict species richness of fish guilds and to test contribution
of environmental variables for explaining guild structure with Self-
Organizing Map (SOM) and Backpropagation (BP) algorithms. The former
method was applied to pattern the samples based on the richness of six
major fish guilds observed (benthivores, invertivores, detritivores, piscivores,
herbivores and omnivores). Four clusters were identified: cluster I
was characterised by benthivores, cluster II was distinguished by invertivores,
detritivores, piscivores and omnivores, cluster III had high richness of
benthivores, invertivores and herbivores, and cluster IV had high numbers
of omnivore, detritivore and piscivore species. The BP showed high predictability
(0.89 for benthivores, 0.85 for omnivores and Odonata, 0.84 for
herbivores). There was high correlation between observed and estimated
values for piscivores (0.77) and detritivores (0.72); the poorest fit was for
invertivores (0.63). The frequency histogram of residuals showed that most
residuals lie around zero for all guilds. The most contributing variables in
predicting the six fish trophic guilds were water temperature, conductivity,
total dissolved solids, dissolved oxygen, depth, width, canopy and distance
from source. This underlines the crucial influence of both instream characteristics
and riparian environment.

Keywords

Fish trophic guilds; Artificial neural network; Prediction; West Africa

Hrčak ID:

141246

URI

https://hrcak.srce.hr/141246

Publication date:

14.5.2015.

Article data in other languages: croatian

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