Assessing the reliability of species distribution projections in climate change research

TLDR: In this paper, the authors provide an overview of common modelling practices in the field and assess model predictions reliability using a virtual species approach and three commonly applied SDM algorithms (GLM, MaxEnt and Random Forest) to assess the estimated and actual predictive performance of models parameterized with different modelling settings and violations of modelling assumptions.

Abstract: Aim Forecasting changes in species distribution under future scenarios is one of the most prolific areas of application for species distribution models (SDMs). However, no consensus yet exists on the reliability of such models for drawing conclusions on species distribution response to changing climate. In this study we provide an overview of common modelling practices in the field and assess model predictions reliability using a virtual species approach. Location Global Methods We first provide an overview of common modelling practices in the field by reviewing the papers published in the last 5 years. Then, we use a virtual species approach and three commonly applied SDM algorithms (GLM, MaxEnt and Random Forest) to assess the estimated (cross-validated) and actual predictive performance of models parameterized with different modelling settings and violations of modelling assumptions. Results Our literature review shows that most papers that model species distribution under climate change rely on single models (65%) and small samples ( Main conclusions Our study calls for extreme caution in the application and interpretation of SDMs in the context of biodiversity conservation and climate change research, especially when modelling a large number of species where species-specific model settings become impracticable.