Choosing presence‐only species distribution models

International audienceOver the past two decades, species distribution models (SDMs) have become one of the most popular modelling tools in biogeographical studies. SDMs try to quantify the relationship between a taxon and its environment, for example, to predict its geographical distribution, to assess potential impacts of climate or land use change, or to explore biogeographical hypotheses. In practice, SDMs generally correlate species distribution data in the form of spatially explicit presences and absences, to environmental predictors, such as climatic variables. In cases where presences and absences are difficult to obtain in quantity and quality—that is, for the majority of biodiversity—it is possible to use SDMs with presence data alone. These are dedicated approaches requiring the generation of additional data points (called ‘background points’ or ‘pseudoabsences’). Overall, the concept of SDMs is simple; however, their implementation is complex because a large number of decisions are required throughout the multiple steps of the process (Figure 1). Each of these decisions must be weighed carefully by the users because they have a strong influence on the outcomes of SDMs and their interpretation. Guidance on how to make these decisions can be found in methodological or pedagogical papers and books (e.g. Elith et al., 2006; Guillera-Arroita et al., 2015; Guisan et al., 2017; Guisan & Thuiller, 2005; Phillips et al., 2006; Thuiller et al., 2009). However, for the majority of these decisions, there is still a high degree of uncertainty, because of shortfalls in our knowledge (see my perception of this degree of uncertainty in Figure 1). This uncertainty often leads to either making arbitrary decisions or costly sensitivity analyses when preparing SDMs. Furthermore, the profusion of methodological studies makes it easy for users (especially new users) to either miss guidance or caveats relevant to their study, or to lack the ability to understand them.Two main issues in SDM implementation require further guidance: uncertainty in decision-making and inaccessibility of guidelines. Uncertainty can be addressed by studies comprehensively investigating a specific methodological issue, providing established guidelines for decision-making. Inaccessibility can be addressed by studies synthesising the methodological progress with sufficient pedagogy to propagate good practices in the field. Here, I appraise a recent study which has combined these two characteristics in such an outstanding way that it should be extremely helpful to both new and experienced users (Valavi et al., 2021). First, Valavi and colleagues comprehensively addressed the choice of modelling techniques in presence-only situations, which has been a prominent issue so far. Second, they detailed all their methodological choices pedagogically, explaining the underlying reasons, and thus providing accessible guidelines throughout the multiple steps of the modelling process. In the following text, I first explain the context of model choice and pinpoint the major progress provided by Valavi and colleagues, and then I explain why, beyond this progress, their study will improve practices in the field. Finally, I conclude with an outlook on the uncertain decisions in SDMs