Data from: 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-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.

Methods
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.

Usage Notes
See readme.txt for details on data.

目标：物种分布相关性模型（SDM）是应对气候和土地利用变化进行保护规划中最常使用的工具之一。以保护为导向的气候变化研究通常在国家或地方层面进行，并可能利用不同地理范围汇集的发生记录来源（例如，地方数据库、国家生物多样性监测）。然而，关于这些地理空间限制（即华莱士赤字）如何导致环境空间限制（即休钦森赤字）并相应地影响关于物种对气候变化脆弱性的结论，知之甚少。位置：美洲，重点关注墨西哥。方法：我们通过构建三个墨西哥树木物种（尖叶槭（Alnus acuminata）、香树脂液态洋（Liquidambar styraciflua）和墨西哥橡树（Quercus xalapensis））的SDM来展示如何使用全球（美洲）、国家（墨西哥）和地方（墨西哥东部云林）层面汇集的数据集，以此为例，探讨华莱士赤字对环境生态位估计（从而影响休钦森赤字）的潜在影响，及其在空间和时间上的预测，以及因此对物种对气候变化潜在脆弱性的影响。结果：使用三个数据集的后果具有物种特异性，并且强烈依赖于华莱士赤字影响环境生态位估计（即休钦森赤字）的程度。当地理空间的限制导致环境生态位的低估时，对气候变化的脆弱性估计显著增加。此外，地理空间的限制可能会增加出现非类似气候问题的可能性，从而增加模型的不确定性。主要结论：我们建议评估物种整个实际环境生态位在目标保护区域内的覆盖程度，并在必要时增加地理范围，以考虑反映潜在未来条件的环境和发生情况。这样，可以最小化低估物种气候潜力的风险（即休钦森赤字），以及由“局部新颖”气候外推引起的误差。方法：全球数据集是通过从GBIF（GBIF.org 2020年1月14日，GBIF发生记录下载 https://doi.org/10.15468/dl.g2yss3）下载目标物种的所有发生记录创建的，然后通过删除没有地理坐标、坐标不确定性大于1000米、坐标不正确或重复的坐标，或观察日期早于1950年的记录来清洗数据；因此，我们只保留了没有已知坐标问题的记录，以及GBIF中报告的观察基础为“人类观察”、“观察”、“标本”、“活标本”、“文献发生”和“材料样本”的记录。所有美洲以外的记录都被删除，因为这些代表非本地地点，通常与（植物学）花园和公园（例如，Vetaas，2002）或其他城市区域（Booth，2017）相关。这些高度人为改造的环境（例如，浇水或极端气候下的庇护所）更代表基本生态位，并且通常不会被全球气候数据很好地反映，因此会引入其他生态位维度或偏差到模型中。国家数据集与全球数据集相同，但发生记录仅限于墨西哥。地方数据集是通过墨西哥国立自治大学（UNAM）植物-大气相互作用实验室、国家生物多样性知识与利用委员会（CONABIO）、国家林业委员会（CONAFOR）和美国农业部森林服务局（USDA Forest Service）的前期研究进行的更密集的资源收集编译的。这个地方数据集专注于墨西哥东部的云林（图S1）。为了限制空间自相关和采样偏差的影响，我们使用R软件包spThin（Aiello-Lammens等，2015年）通过删除彼此距离小于5公里的发生记录来对所有数据集中的发生记录进行分解。每个物种和每个数据集的最终发生记录数量在表1中给出。使用说明：请参阅readme.txt以获取数据详细信息。