Methodological research on species distribution modelling (SDM) has so far largely focused on the choice of appropriate modelling algorithms and variable selection approaches, but the consequences of choosing amongst different sources of environmental data has scarcely been investigated. Bioclimatic variables are commonly used as predictors in SDMs. Currently, several online databases offer the same sets of bioclimatic variables, but they differ in underlying source of raw data and method of data processing (extrapolation and downscaling). In this paper, we asked whether predictive performance and spatial transferability of SDMs are affected by the choice of two different bioclimatic databases viz. WorldClim 2 and Chelsa 1.2. We used presence-absence data of the invasive plant Ageratina adenophora from the Western Himalaya for training SDMs and a set of independently-collected presence-only datasets from the Central and Eastern Himalaya to evaluate the transferability of the SDMs beyond the training range. We found that the performance of SDMs was, to a large degree, affected by the choice of the climatic dataset. Models calibrated on Chelsa 1.2 outperformed WorldClim 2 in terms of internal evaluation on the calibration dataset. However, when the model was transferred beyond the calibration range to the Central and Eastern Himalaya, models based on WorldClim 2 performed substantially better. We recommend that, in addition to the choice of predictor variables, the choice of predictor datasets with these variables should not be based merely on subjective decision whenever several options are available. Instead, such decisions should be based on robust evaluation of the most appropriate dataset for a given geographic region and species being modelled. Moreover, decisions could also depend on the objective of the study, i.e. projecting within the calibration range or beyond. Therefore, a quantitative evaluation of predictor datasets from alternative sources should be routinely performed as an integral part of the modelling procedure.

中文翻译：

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气候数据的来源可以决定物种分布模型的可转移性

迄今为止，物种分布建模（SDM）的方法学研究主要集中在选择合适的建模算法和变量选择方法上，但是很少研究在不同环境数据源中进行选择的后果。生物气候变量通常用作SDM中的预测变量。当前，几个在线数据库提供相同的生物气候变量集，但是它们在原始数据的基础来源和数据处理方法（外推和缩小比例）方面有所不同。在本文中，我们询问两个不同生物气候数据库的选择是否会影响SDM的预测性能和空间可传递性。WorldClim 2和Chelsa 1.2。我们使用喜马拉雅西部入侵植物紫茎泽兰的不存在数据来训练SDM，并使用喜马拉雅中部和东部独立收集的仅存在数据集来评估SDM在训练范围之外的可转移性。我们发现，SDM的性能在很大程度上受到气候数据集选择的影响。在对校准数据集进行内部评估方面，在Chelsa 1.2上进行校准的模型优于WorldClim 2。但是，当模型超出校准范围转移到喜马拉雅中部和东部时，基于WorldClim 2的模型的性能要好得多。我们建议，除了选择预测变量之外，只要有几种选择，对具有这些变量的预测数据集的选择就不应仅基于主观决定。相反，此类决策应基于对给定地理区域和正在建模的物种的最合适数据集的稳健评估。此外，决策还可能取决于研究目的，即在校准范围内或超出范围进行预测。因此，应定期对替代来源的预测变量数据集进行定量评估，作为建模程序的组成部分。超出校准范围或超出校准范围。因此，应定期对替代来源的预测变量数据集进行定量评估，作为建模程序的组成部分。在校准范围内或超出范围。因此，应定期对替代来源的预测变量数据集进行定量评估，作为建模程序的组成部分。