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