Habitat-based models for predicting the occurrence of ground-beetles in arable landscapes: two alternative approaches

Abstract

The potential of habitat-based models was explored to predict the occurrence of carabid beetles in arable conditions. It was hypothesised that: (i) the habitats surrounding a location were good predictors of the occurrence of the most common carabid species; (ii) the current knowledge on habitat associations for some individual species was sufficient to develop accurate predictive models. The performance of knowledge-based models was assessed for eight well-studied carabid species. Rule sets were produced using an extensive database describing the nature and condition of the habitats recorded within a 50 m radius of the sampling sites. The performance was compared to a more classical approach based on logistic regression (LR) models, using the same original information summarised into 19 variables by correspondence analysis (CA). The performance of the rule-based (RB) models was higher than expected by chance for species occurring in less than 70% of the sites ( k>0.4) and was relatively consistent across the three areas of England where they were tested. Models developed for widespread species had a high prediction success (PS) but no discriminatory ability (low k value). LR and RB approaches gave comparable results for species of average prevalence (30–70%) while for species occurring in less than 30% of the sampled sites, the RB approach performed significantly better than the LR one. It is suggested that knowledge-based approaches could be used more widely to predict the distribution of invertebrate species. The effect of species prevalence and the potential application of knowledge-based habitat-models in the context of biodiversity assessment are discussed.