Bioclimatic envelope models are commonly used to assess the influence of climate change on species' distributions and biodiversity patterns. Understanding how methodological choices influence these models is critical for a comprehensive evaluation of the estimated impacts. Here we systematically assess the performance of bioclimatic envelope models in relation to the selection of predictors, modeling technique, and pseudo‐absences. We considered (a) five different predictor sets, (b) seven commonly used modeling techniques and an ensemble model, and (c) three sets of pseudo‐absences (1,000 pseudo‐absences, 10,000 pseudo‐absences, and the same as the number of presences). For each combination of predictor set, modeling technique, and pseudo‐absence set, we fitted bioclimatic envelope models for 300 species of mammals, amphibians, and freshwater fish, and evaluated the predictive performance of the models using the true skill statistic (TSS), based on a spatially independent test set as well as cross‐validation. On average across the species, model performance was mostly influenced by the choice of predictor set, followed by the choice of modeling technique. The number of the pseudo‐absences did not have a strong effect on the model performance. Based on spatially independent testing, ensemble models based on species‐specific nonredundant predictor sets revealed the highest predictive performance. In contrast, the Random Forest technique yielded the highest model performance in cross‐validation but had the largest decrease in model performance when transferred to a different spatial context, thus highlighting the need for spatially independent model evaluation. We recommend building bioclimatic envelope models according to an ensemble modeling approach based on a nonredundant set of bioclimatic predictors, preferably selected for each modeled species.

中文翻译：

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关于预测因子选择，建模技术和假缺数对生物气候包络模型性能的重要性

生物气候包络模型通常用于评估气候变化对物种分布和生物多样性模式的影响。了解方法选择如何影响这些模型对于全面评估估计的影响至关重要。在这里，我们系统地评估了与预测因子，建模技术和假性缺失的选择有关的生物气候包络模型的性能。我们考虑了（a）五种不同的预测变量集，（b）七种常用的建模技术和一个整体模型，以及（c）三组伪缺席（1,000个伪缺席，10,000个伪缺席，并且与数字相同）存在）。对于预测变量集，建模技术和伪缺集的每种组合，我们为300种哺乳动物，两栖动物，和淡水鱼，并基于空间独立的测试集和交叉验证，使用真实技能统计（TSS）评估了模型的预测性能。平均而言，整个物种的模型性能主要受预测变量集的选择影响，其次是建模技术的选择。伪缺失的数量对模型性能没有很大影响。基于空间独立的测试，基于物种特定的非冗余预测变量集的集成模型显示出最高的预测性能。相比之下，随机森林技术在交叉验证中产生了最高的模型性能，但是当转移到不同的空间环境时，模型性能的下降最大，因此突出了需要空间独立的模型评估。