Comparison of approaches to combine species distribution models based on different sets of predictors

Distribution models should take into account the different limiting factors that simultaneously influence species ranges. Species distribution models built with different explanatory variables can be combined into more comprehensive ones, but the resulting models should maximize complementarity and avoid redundancy. Our aim was to compare the different methods available for combining species distribution models. We modelled 19 threatened vertebrate species in mainland Spain, producing models according to three individual explanatory factors: spatial constraints, topography and climate, and human influence. We used five approaches for model combination: Bayesian inference, Akaike weight averaging, stepwise variable selection, updating, and fuzzy logic. We compared the performance of these approaches by assessing different aspects of their classification and discrimination capacity. We demonstrated that different approaches to model combination give rise to disparities in the model output...物种分布模型（Species Distribution Model）需综合考量同时影响物种分布范围的各类限制因子。基于不同解释变量构建的物种分布模型可整合为更全面的模型，但最终得到的整合模型应最大化互补性并避免冗余。本研究旨在对比各类可用于整合物种分布模型的方法。我们针对西班牙本土的19种受威胁脊椎动物构建物种分布模型，基于三类独立解释因子分别生成模型：空间限制因子、地形与气候因子以及人类活动影响因子。我们采用五种模型整合方案：贝叶斯推断（Bayesian inference）、赤池权重平均法（Akaike weight averaging）、逐步变量选择法（stepwise variable selection）、模型更新法（updating）以及模糊逻辑法（fuzzy logic）。我们通过评估各类方法在分类与判别能力的不同维度，对比了它们的模型表现。本研究表明，不同的模型整合方法会导致模型输出结果存在显著差异……