Labeling Ecological Niche Models

The ongoing biodiversity crisis is pushing ecologists and conservation biologists to develop models to foretell the effects of human-induced transformation of natural resources on the distribution of species, although ecology and biogeography still lacks a paradigmatic body of theory to fully understand the drivers of biodiversity patterns. Two decades of research on ecological niche models and species distributions have been characterized by technical development and discussions on a plethora of methods or algorithms to infer and predict species distributions. Here we suggest a metaphorical classification scheme for some of the most popular models based on their complexity, interpretability and suitability for specific applications in ecology and conservation biology. Our purpose is not to compare methods by their capacity to accurately predict the observed distribution of species, nor to criticize how they are commonly used in applied studies. Instead, we believe that a simple classification scheme can potentially highlight how some methods are more suited for specific applications in ecology and conservation biology. Envelope and distance-based models are grouped into the "fish bowl" category, for their transparency and simplicity. Statistical models are classified as "turbine" models, because of their hidden complexity and general applicability. Finally, machine-learning models are classified as "vault" models, for their high complexity and lack of interpretability of fit parameters. We conclude that the diversity of species distribution models used today is expected for a young research field, but the choice of modeling strategy depends on the purpose of the study. We provide some general guidelines for choosing models for studies of conservation planning and climate change mitigation and suggest models of intermediate complexity for conservation planning and forecast of climate change effects on biodiversity as they provide a good balance between interpretability, predictive power and robustness to model over-fit.