- KOREAN
- P-ISSN2287-8327
- E-ISSN2288-1220
- SCOPUS, KCI
ISSN : 2287-8327
Evaluation of Environmental Factors to Determine the Distribution of Functional Feeding Groups of Benthic Macroinvertebrates Using an Artificial Neural Network
Sovan Lek (EDB, UMR 5174)
Piet F.M. Verdonschot (Alterra, Green World Research)
Abstract
Functional feeding groups (FFGs) of benthic macroinvertebrates are guilds of invertebrate taxa that obtain food in similar ways, regardless of their taxonomic affinities. They can represent a heterogeneous assemblage of benthic fauna and may indicate disturbances of their habitats. The proportion of different groups can change in response to disturbances that affect the food base of the system, thereby offering a means of assessing disruption of ecosystem functioning. In this study, we used benthic macroinvertebrate communities collected at 650 sites of 23 different water types in the province of Overijssel, The Netherlands. Physical and chemical environmental factors were measured at each sampling site. Each taxon was assigned to its corresponding FFG based on its food resources. A multilayer perceptron (MLP) using a backpropagation algorithm, a supervised artificial neural network, was applied to evaluate the influence of environmental variables to the FFGs of benthic macroinvertebrates through a sensitivity analysis. In the evaluation of input variables, the sensitivity analysis with partial derivatives demonstrates the relative importance of influential environmental variables on the FFG, showing that different variables influence the FFG in various ways. Collector-filterers and shredders were mainly influenced by Ca²+ and width of the streams, and scrapers were influenced mostly with Ca²+ and depth, and predators were by depth and pH. Ca²+ and depth displayed relatively high influence on all four FFGs, while some variables such as pH, %gravel, %silt, and %bank affected specific groups. This approach can help to characterize community structure and to ecologically assess target ecosystems.
- keywords
- Artificial neural network, Benthic macroinvertebrates, Contribution evaluation, Functional feeding groups, Prediction, Sensitivity analysis
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