Evaluation and comparison of data-driven and knowledge-supported Bayesian Belief Networks to assess the habitat suitability for alien macroinvertebrates

Abstract

Defining habitats vulnerable to invasion is important to support the management of invasive alien species (IAS). We developed and applied data-driven and knowledge-supported data-driven Bayesian Belief Networks (BBNs) to assess the habitat suitability for alien gammarids. Data-driven model development using a Naive Bayes classifier and equal width discretization resulted in a habitat suitability model with a moderate technical performance (CCI = 68% K = 0.33). Although the structure of the knowledge-supported model yielded important ecological insight between environmental and biotic variables and the occurrence of alien gammarids, the performance was lower (CCI = 60% K = 0.19) compared to the purely data-driven model. The lower predictive performance of the knowledge-supported model may be attributed to its higher model complexity. Our study shows that BBNs can support the management of IAS as they are visually appealing, transparent models that facilitate integration of monitoring data and expert knowledge.