1. Bioclimatic variables (BCVs) are routinely used in potential distribution models, typically without considering their calculation options in detail. We aimed at studying the impact of a decision, yet unexamined, on the calculation of BCVs, namely whether the identity of specific months/quarters in the calculation of BCVs should be updated for the future periods (temporal context). Effects on the performance of potential distribution models and on their projections were investigated. Additionally, we also aimed at comparing the impact of month/quarter shifts to that of climate model selection and covariate selection.
2. Potential natural vegetation models encompassing eight habitat types and the whole territory of Hungary were created using boosted regression trees. We tested multiple initial covariate sets to compare the impact of the temporal context to that of covariate selection. The resulting models were applied to the reference and one future time period (with data from two regional climate models). The effect of the BCV calculation approach was tested by linear mixed‐effects models and model goodness‐of‐fit measures in a comprehensive framework of 192 predictions. Area under the ROC curve (AUC) and true positive rate (TPR) curves were used to evaluate the models.
3. Our results show that (a) temporal context of BCVs in interaction with covariate selection had a strong effect on model structure as well as on projections; (b) no evidence supporting the superiority of the widely applied calculation approach of BCVs was found. However, we found notable differences under the two approaches and examples of projection artefacts when applying the widespread way of calculation.
4. We conclude that (a) more attention and more transparent communication is needed when BCVs are used as covariates in distribution models; (b) not only ecophysiology but also the way covariates are calculated should be considered when preselecting covariates for potential distribution models.

中文翻译：

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计算生物气候变量的方式会对潜在的分布模型产生影响

1. 生物气候变量（BCV）通常在潜在的分布模型中使用，通常不考虑其详细计算选项。我们旨在研究一项尚未决定的决定对BCV的影响，即是否应在将来的时期（时间范围）更新BCV的计算中特定月份/季度的身份。研究了对潜在分布模型的性能及其预测的影响。此外，我们还旨在比较月/季度变化对气候模型选择和协变量选择的影响。
2. 使用增强的回归树创建了涵盖八种栖息地类型和匈牙利整个领土的潜在自然植被模型。我们测试了多个初始协变量集，以比较时态与协变量选择的影响。将得到的模型应用于参考和一个未来时间段（包含来自两个区域气候模型的数据）。在192个预测的综合框架中，通过线性混合效应模型和模型拟合优度测度测试了BCV计算方法的效果。使用ROC曲线（AUC）和真实阳性率（TPR）曲线下的面积评估模型。
3. 我们的研究结果表明：（a）BCV与协变量选择交互作用的时态对模型结构以及预测有很大影响；（b）没有证据支持广泛使用的BCV计算方法的优越性。但是，当采用广泛的计算方法时，我们发现两种方法和投影伪影示例之间存在显着差异。
4. 我们得出的结论是：（a）当将BCV用作分布模型的协变量时，需要更多的关注和更透明的沟通；（b）在为潜在的分布模型预先选择协变量时，不仅应考虑生态生理，而且应考虑协变量的计算方式。