Testing methods in species distribution modelling using virtual species: what have we learnt and what are we missing?
5 June 2019Meynard, Christine; Leroy, Boris; Kaplan, David
Species distribution models (SDMs) have become one of the major predictive tools in ecology. However, multiple methodological choices are required during the modelling process, some of which may have a large impact on forecasting results. In this context, virtual species, i.e., the use of simulations involving a fictitious species for which we have perfect knowledge of its occurrence-environment relationships and other relevant characteristics, have become increasingly popular to test SDMs. This approach provides for a simple virtual ecologist framework under which to test model properties, as well as the effects of the different methodological choices, and allows teasing out the effects of targeted factors with great certainty. This simplification is therefore very useful in setting up modelling standards and best practice principles. As a result, numerous virtual species studies have been published over the last decade. The topics covered include differences in performance between statistical models, effects of sample size, choice of threshold values, methods to generate pseudo-absences for presence-only data, among many others. These simulations have therefore already made a great contribution to setting best modelling practices in SDMs. Recent software developments have greatly facilitated the simulation of virtual species, with at least 3 different packages published to that effect. However, the simulation procedure has not been homogeneous, which introduces some subtleties in the interpretation of results, as well as differences across simulation packages. Here we (1) review the main contributions of the virtual species approach in the SDM literature; (2) compare the major virtual species simulation approaches and software packages; and (3) propose a set of recommendations for best simulation practices in future virtual species studies in the context of SDMs.