Generalised Additive Models and Random Forest Approach as effective methods for predictive species density and functional species richness

Abstract

AbstractSpecies distribution modelling (SDM) is a family of statistical methods where species occurrence/density/richness are combined with environmental predictors to create predictive spatial models of species distribution. However, it often turns out that due to complex multi-level interactions between predictors and the response function, different types of models can detect different numbers of important predictors and also vary in their predictive ability. This is why we decided to explore differences in the predictive power of two most common methods, such as the Generalised Additive Model (GAM) and the Random Forest (RF) on the example of the Great Spotted Woodpecker Dendrocopos major and the Great Grey Shrike Lanius excubitor, as well as on the taxonomic and functional species richness. For each of the two bird species’ densities and for two measurements of biodiversity, two sets of SDMs were generated: One based on the GAM, and the other on the RF. According to the out-of-bag, the Akaike Information Criterion (AIC) and an independent evaluation, we demonstrated that the GAM is the best method for predicting density of the Great Spotted Woodpecker and taxonomic species richness, whereas the RF has the lowest prediction error for the density of the Great Grey Shrike and functional species richness. It also becomes apparent that the GAM is responsive to taxonomic species richness and species with broad tolerance to environmental factors, i.e. the Great Spotted Woodpecker, while the RF detects more subtle relationships between density and environmental variables, rendering it more suitable for functional species richness and species with a narrow tolerance range to habitats factors, i.e. the Great Grey Shrike. Thus, effective predictive modelling of animal distribution requires considering several different analytical approaches to produce biologically realistic predictions.