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

Abstract

The poor understanding of changes in mollusc ecology along rivers, especially in West Africa, hampers the implementation of management measures. We used a self–organizing map, indicator species analysis, linear discriminant analysis and a random forest model to distinguish mollusc assemblages, to determine the ecological preferences of individual mollusc species and to associate major physicochemical variables with mollusc assemblages and occurrences in the Sô River Basin, Benin. We identified four mollusc assemblages along an upstream–downstream gradient. Dissolved oxygen (DO), biochemical oxygen demand (BOD), salinity, calcium (Ca), total nitrogen (TN), copper (Cu), lead (Pb), nickel (Ni), cadmium (Cd) and mercury (Hg) were the major physicochemical variables responsible for structuring these mollusc assemblages. However, the physicochemical factors responsible for shaping the distribution of individual species varied per species. Upstream sites (assemblage I) showed high DO and low BOD and mineral compounds (i.e., TN, salinity, and Ca), which are primarily responsible for structuring the occurrences of bivalves (Afropisidium pirothi, Etheria elliptica, Sphaerium hartmanni) and the gastropod Lanistes varicus. Sites along the middle reach (assemblage II) were characterised by a high degree of organic pollution but low heavy metal pollution; we detected no specific mollusc indicator species. Downstream sites (assemblage III) displayed high mineral and heavy metal concentrations and a fauna without specific indicator species. Finally, downstream sites associated with brackish water (assemblage IV) displayed important levels of organic and heavy metal pollution. These sites are dominated by diverse gastropods (i.e., Bulinus spp., Gabbiella africana, Indoplanorbis exustus, Pachymelania fusca, Radix natalensis, Stenophysa marmorata and Tympanotonos fuscatus). Our results highlight that mollusc communities in the Sô River Basin are structured by key physicochemical variables related to the river–estuary continuum. Habitats that are progressively more downstream are confronted with increasing anthropogenic stress. Conservation and management plans should focus on downstream habitats.

Keywords

• Artificial neural network
• Ecology
• Freshwater biodiversity
• Modelling
• Mollusc community
• Tropical river systems