DOI: 10.14798/73.2.816
Bibliography
PREDICTING FACTORS THAT INFLUENCE FISH GUILD COMPOSITION IN FOUR COASTAL RIVERS (SOUTHEAST IVORY COAST) USING ARTIFICIAL NEURAL NETWORKS
2015, 73 (2) p. 48-57
Koffi Félix Konan, Kotchi Yves Bony, Oi Edia Edia, N’guessan Gustave Aliko, Allassane Ouattara, Germain Gourene
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