A large array of species distribution model (SDM) approaches has been developed for explaining and predicting the occurrences of individual species or species assemblages. Given the wealth of existing models, it is unclear which models perform best for interpolation or extrapolation of existing data sets, particularly when one is concerned with species assemblages. We compared the predictive performance of 33 variants of 15 widely applied and recently emerged SDMs in the context of multispecies data, including both joint SDMs that model multiple species together, and stacked SDMs that model each species individually combining the predictions afterward. We offer a comprehensive evaluation of these SDM approaches by examining their performance in predicting withheld empirical validation data of different sizes representing five different taxonomic groups, and for prediction tasks related to both interpolation and extrapolation. We measure predictive performance by 12 measures of accuracy, discrimination power, calibration, and precision of predictions, for the biological levels of species occurrence, species richness, and community composition. Our results show large variation among the models in their predictive performance, especially for communities comprising many species that are rare. The results do not reveal any major trade‐offs among measures of model performance; the same models performed generally well in terms of accuracy, discrimination, and calibration, and for the biological levels of individual species, species richness, and community composition. In contrast, the models that gave the most precise predictions were not well calibrated, suggesting that poorly performing models can make overconfident predictions. However, none of the models performed well for all prediction tasks. As a general strategy, we therefore propose that researchers fit a small set of models showing complementary performance, and then apply a cross‐validation procedure involving separate data to establish which of these models performs best for the goal of the study.

中文翻译：

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在物种和群落水平上对33种物种分布模型的预测性能进行全面评估

已经开发了各种各样的物种分布模型（SDM）方法，用于解释和预测单个物种或物种集合的发生。鉴于现有模型的丰富性，目前尚不清楚哪种模型对现有数据集的内插或外推效果最佳，尤其是在涉及物种集合时。我们在多物种数据的背景下比较了15种广泛应用和最近出现的SDM的33个变体的预测性能，其中包括将多个物种建模在一起的两个联合SDM，以及将每个物种分别组合后对预测进行建模的堆叠SDM。我们通过检查这些SDM方法在预测代表5个不同分类组的不同大小的预扣实证数据时的性能，以及与内插法和外推法有关的预测任务的性能，对这些SDM方法进行全面评估。对于物种发生，物种丰富度和群落组成的生物学水平，我们通过12种准确性，区分力，校准和预测精度来衡量预测性能。我们的结果表明，模型之间的预测性能差异很大，尤其是对于包含许多稀有物种的群落而言。结果并未显示出模型性能测度之间的任何重大权衡；相同的模型在准确性，辨别力和校准方面通常表现良好，以及单个物种的生物学水平，物种丰富度和社区组成。相反，给出最精确的预测的模型没有得到很好的校准，这表明性能较差的模型可能会做出过分自信的预测。但是，对于所有的预测任务，没有一个模型表现良好。因此，作为一般策略，我们建议研究人员拟合一小组显示出互补性能的模型，然后应用涉及单独数据的交叉验证程序来确定这些模型中哪个模型最适合研究目标。没有一个模型对所有预测任务都表现良好。因此，作为一般策略，我们建议研究人员使用一小组展示互补性能的模型，然后应用包含单独数据的交叉验证程序来确定这些模型中哪个模型最适合研究目标。没有一个模型对所有预测任务都表现良好。因此，作为一般策略，我们建议研究人员拟合一小组显示出互补性能的模型，然后应用涉及单独数据的交叉验证程序来确定这些模型中哪个模型最适合研究目标。