National assessments of species vulnerability to climate change strongly depend on selected data sources

Aim Correlative species distribution models (SDMs) are among the most frequently used tools for conservation planning under climate and land use changes. Conservation‐focused climate change studies are often conducted on a national or local level and can use different sources of occurrence records (e.g., local databases, national biodiversity monitoring) collated at different geographic extents. However, little is known about how these restrictions in geographic space (i.e., Wallacean shortfall) can lead to restrictions in environmental space (i.e., Hutchinsonian shortfall) and accordingly affect conclusions about a species’ vulnerability to climate change. Location Americas with a focus on Mexico. Methods We present an example study constructing SDMs for three Mexican tree species ( Alnus acuminata , Liquidambar styraciflua and Quercus xalapensis ) using datasets collated at a global (Americas), national (Mexico) and local (cloud forests of eastern Mexico) level to demonstrate the potential effects of a Wallacean shortfall on the estimation of the environmental niche—and thus on a Hutchinsonian shortfall—its projection in space and time and, consequently, on species’ potential vulnerability to climate change. Results The consequence of using the three datasets was species‐specific and strongly depended on the extent to which the Wallacean shortfall affected estimations of environmental niches (i.e., Hutchinsonian shortfall). Where restrictions in geographic space lead to an underestimation of the environmental niche, vulnerability to climate change was estimated to be substantially higher. Additionally, the restrictions in geographic space may increase the likelihood of issues with non‐analogue climates, increasing model uncertainty. Main conclusion We recommend assessing the extent to which a species’ entire realized environmental niche is captured within the target conservation area, and increasing the geographic extent, if needed, to account for environments and occurrences reflecting potential future conditions. This way, the risk of underestimating the climatic potential of the species (i.e., Hutchinsonian shortfall), as well as the errors induced by extrapolation into “locally novel” climates, can be minimized.