Species distribution modeling to inform transboundary species conservation and management under climate change: promise and pitfalls

Spatially explicit biogeographic models are among the most used methods in conservation biogeography, with correlative species distribution models (SDMs) being the most popular among them. SDMs can identify the potential for species’ and community range shifts under climate change, and thus can inspire, inform, and guide complex and adaptive conservation management planning efforts such as collaborative transboundary conservation frameworks. However, SDMs are rarely developed collaboratively, which would be ideal for conservation applications of such models. Further, SDMs that are applied to conservation often do not follow best practices of the field, which are particularly important for applications in climate change contexts for which model extrapolation into potentially novel climates is necessary. Thus, while there is substantial promise, particularly among machine-learning based SDM approaches, there are also many pitfalls to consider when applying SDMs to conservation, and especially in the context of transboundary management under climate change. Here, we summarize these pitfalls and the key steps to mitigate them and maximize the promise of applying SDMs to facilitate transboundary conservation planning under climate change. We argue that conservation modeling capacity must be elevated among practitioners such that they can easily implement best practices when using SDMs, especially regarding: 1) avoiding model overcomplexity, 2) addressing input data bias, and 3) accounting for uncertainty in model extrapolations and projections. While our discussion centers mainly on the pitfalls and opportunities of applying the most popular correlative SDM algorithm, Maxent, our suggestions can also be generalized to a range of other SDM tools. Overall, improved training in, tools for, and implementation of best practices in biogeographic models such as SDMs hold great promise to