Input variable selection with a simple genetic algorithm for conceptual species distribution models: A case study of river pollution in Ecuador

Species distribution models (SDMs) have received increasing attention in freshwater management to support decision making. Existing SDMs are mainly data-driven and often developed with statistical and machine learning methods but with little consideration of hypothetic ecological knowledge. Conceptual SDMs exist, but lack in performance, making them less interesting for decision management. Therefore, there is a need for model identification tools that search for alternative model formulations. This paper presents a methodology, illustrated with the example of river pollution in Ecuador, using a simple genetic algorithm (SGA) to identify well performing SDMs by means of an input variable selection (IVS). An analysis for 14 macroinvertebrate taxa shows that the SGA is able to identify well performing SDMs. It is observed that uncertainty on the model structure is relatively large. The developed tool can aid model developers and decision makers to obtain insights in driving factors shaping the species assemblage.