Species distribution models are generic empirical techniques that have a number of applications. One of these applications is to determine which environmental conditions are most important for a species. The calculation of this variable importance depends on a number of assumptions, including that the observations that are used to estimate the models are independent of each other. Spatial autocorrelation, which is a common feature most environmental factors confounds this assumption. Besides, many species distribution models are trained using a number of explanatory variables that have different levels of spatial autocorrelation. In this study we quantified the effects of differences in spatial autocorrelation in explanatory variables and the type of species responses to environmental gradients on variable importance estimations in species distribution models. We simulated data for both environmental predictors and species, so that we were in control of the true contribution of every variable in the model and the importance that could be estimated after fitting the models. We found that spatial autocorrelation in the predictors inflated the variable importance estimates, but only when the response of species to the environmental gradients is linear. This inflation effect was larger when the environmental preferences of species coincided with the dominant environmental conditions in a study site. Additionally we find that unimodal responses to the predictors yield systematically a higher variable importance compared to linear responses. We conclude that the type of response to environmental conditions and the relative levels of spatial autocorrelation in the environmental variables cause most bias in relative variable importance estimations. In this way, this study helps to clarify in a systematic and controlled approach how to make proper inferences about variable importance in species distribution models.

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物种分布建模中的变量重要性估计何时受到空间相关性的影响？

物种分布模型是具有许多应用程序的通用经验技术。这些应用之一是确定哪种环境条件对一个物种最重要。变量重要性的计算取决于许多假设，包括用于估计模型的观察值彼此独立。空间自相关是大多数环境因素都会混淆的一个常见特征。此外，许多物种分布模型是使用许多具有不同水平空间自相关性的解释变量进行训练的。在这项研究中，我们量化了解释变量中空间自相关的差异以及物种对环境梯度的响应类型对物种分布模型中变量重要性估计的影响。我们模拟了环境预测因子和物种的数据，因此我们可以控制模型中每个变量的真实贡献以及在拟合模型后可以估算的重要性。我们发现，预测变量中的空间自相关夸大了变量重要性估计值，但仅当物种对环境梯度的响应是线性的时才如此。当物种的环境偏好与研究地点的主要环境条件一致时，这种通货膨胀效应会更大。此外，我们发现，与线性响应相比，对预测变量的单峰响应系统地产生了更高的变量重要性。我们得出的结论是，对环境条件的响应类型以及环境变量中空间自相关的相对水平在相对变量重要性估计中引起最大的偏差。这样，本研究有助于以系统和受控的方式阐明如何对物种分布模型中的变量重要性做出适当的推断。