Organizing Maps and Machine Learning Models to Assess Mollusc Community Structure in Relation to Physicochemical Variables in a West Africa River–Estuary System
Ecological Indicators 126 (2021) 107706 Contents lists available at ScienceDirect Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind Using self–organizing maps and machine learning models to assess mollusc community structure in relation to physicochemical variables in a West Africa river–estuary system Zinsou Cosme Koudenoukpo a,b,1, Olaniran Hamed Odountan b,c,*,1, Prudenci`ene Ablawa Agboho d, Tatenda Dalu e,f, Bert Van Bocxlaer g, Luc Janssens de Bistoven h, Antoine Chikou a, Thierry Backeljau h,i a Laboratory of Hydrobiology and Aquaculture, Faculty of Agronomic Sciences, University of Abomey–Calavi, 01 BP 526 Cotonou, Benin b Cercle d’Action pour la Protection de l’Environnement et de la Biodiversit´e (CAPE BIO–ONG), Cotonou, Abomey–Calavi, Benin c Laboratory of Ecology and Aquatic Ecosystem Management, Department of Zoology, Faculty of Science and Technics, University of Abomey–Calavi, 01 BP 526 Cotonou, Benin d Centre de Recherche Entomologique de Cotonou (CREC), 06 BP 2604 Cotonou, Benin e School of Biology and Environmental Sciences, University of Mpumalanga, Nelspruit 1201, South Africa f South African Institute for Aquatic Biodiversity, Grahamstown 6140, South Africa g CNRS, Univ. Lille, UMR 8198 – Evo–Eco–Paleo, F–59000 Lille, France h Royal Belgian Institute of Natural Sciences, Vautierstraat 29, B–1000 Brussels, Belgium i Evolutionary Ecology Group, University of Antwerp, Universiteitsplein 1, B–2610 Antwerp, Belgium ARTICLE INFO ABSTRACT Keywords: The poor understanding of changes in mollusc ecology along rivers, especially in West Africa, hampers the Artificial neural network implementation of management measures. We used a self–organizing map, indicator species analysis, linear Ecology discriminant analysis and a random forest model to distinguish mollusc assemblages, to determine the ecological Freshwater biodiversity preferences of individual mollusc species and to associate major physicochemical variables with mollusc as Modelling semblages and occurrences in the So^ River Basin, Benin.
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