Species distribution models (SDMs) have become a common tool in studies of species–environment relationships but can be negatively affected by positional uncertainty of underlying species occurrence data. Previous work has documented the effect of positional uncertainty on model predictive performance, but its consequences for inference about species–environment relationships remain largely unknown. Here we use over 12 000 combinations of virtual and real environmental variables and virtual species, as well as a real case study, to investigate how accurately SDMs can recover species–environment relationships after applying known positional errors to species occurrence data. We explored a range of environmental predictors with various spatial heterogeneity, species' niche widths, sample sizes and magnitudes of positional error. Positional uncertainty decreased predictive model performance for all modeled scenarios. The absolute and relative importance of environmental predictors and the shape of species–environmental relationships co-varied with a level of positional uncertainty. These differences were much weaker than those observed for overall model performance, especially for homogenous predictor variables. This suggests that, at least for the example species and conditions analyzed, the negative consequences of positional uncertainty on model performance did not extend as strongly to the ecological interpretability of the models. Although the findings are encouraging for practitioners using SDMs to reveal generative mechanisms based on spatially uncertain data, they suggest greater consequences for applications utilizing distributions predicted from SDMs using positionally uncertain data, such as conservation prioritization and biodiversity monitoring.

中文翻译：

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受物种发生位置不确定性影响的物种分布模型仍然可以在生态学上进行解释

物种分布模型 (SDM) 已成为研究物种与环境关系的常用工具，但可能会受到潜在物种发生数据位置不确定性的负面影响。以前的工作已经记录了位置不确定性对模型预测性能的影响，但它对推断物种-环境关系的影响在很大程度上仍然未知。在这里，我们使用超过 12000 种虚拟和真实环境变量和虚拟物种的组合，以及一个真实的案例研究，来研究 SDM 在将已知位置误差应用于物种发生数据后恢复物种-环境关系的准确性。我们探索了一系列具有各种空间异质性、物种生态位宽度、样本大小和位置误差幅度的环境预测因子。位置不确定性降低了所有建模场景的预测模型性能。环境预测因子的绝对和相对重要性以及物种-环境关系的形状与位置不确定性水平共同变化。这些差异比观察到的整体模型性能差异要弱得多，尤其是对于同质预测变量。这表明，至少对于所分析的示例物种和条件，位置不确定性对模型性能的负面影响并未强烈扩展到模型的生态可解释性。尽管这些发现对于使用 SDM 揭示基于空间不确定数据的生成机制的从业者来说是令人鼓舞的，