DOI: 10.5061/dryad.qnk98sfg5
title: Data from: National assessments of species vulnerability to climate change strongly depend on selected data sources

description: 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-analog climates, increasing model uncertainty. Main Conclusion:
 We recommend to assess 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 minimised.
description: The global dataset was created by downloading all occurrence
 records of the target species from GBIF (GBIF.org 14 January 2020, GBIF
 Occurrence Download https://doi.org/10.15468/dl.g2yss3) and then cleaning
 the data by removing those with no geographical coordinates, coordinate
 uncertainty greater than 1000 m, or incorrect or duplicate coordinates; or
 with the observation dated prior to 1950. We therefore only kept records
 with no known coordinate issues, and for which the basis of observation in
 GBIF was reported as “human observation”, “observation”, “specimen”,
 “living specimen”, “literature occurrence”, and “material sample”. All
 records outside of the Americas were removed as these represent non-native
 locations often associated with (botanical) gardens and parks (e.g.,
 Vetaas, 2002) or other urban areas (Booth, 2017). These highly
 human-modified environments (e.g. watering or shelter from extreme
 climate) rather represent the fundamental niche and are often not well
 reflected by global climate data, and would therefore introduce other
 niche dimensions or bias to the model. The national
 dataset was identical to the global but with occurrence records restricted
 to Mexico. The local dataset was compiled by more
 intensive resource collection, based on previous studies developed in the
 Laboratory of Plant-Atmosphere Interaction from the Institute of Ecology,
 National Autonomous University of Mexico (UNAM), the National Commission
 for the Knowledge and Use of Biodiversity (CONABIO), the National Forestry
 Commission (CONAFOR), and the United States Forest Service (USDA Forest
 Service). This local dataset is focused on the cloud forests of eastern
 Mexico (Figure S1).  To limit the effects of spatial
 autocorrelation and sampling bias, we disaggregated the occurrence records
 in all datasets by removing occurrences closer than 5 km from each other
 using the R-package spThin (Aiello-Lammens et al., 2015). The final
 numbers of occurrences per species and dataset are given in Table
 1.
description: See readme.txt for details on data.