Species distribution models (SDMs) have proven valuable in filling gaps in our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to limitations in species occurrence data. These limitations include, in particular, issues related to sample size, positional uncertainty, and sampling bias. In addition, it is widely recognised that the quality of SDMs as well as the approaches used to mitigate the impact of the aforementioned data limitations depend on species ecology. While numerous studies have evaluated the effects of these data limitations on SDM performance, a synthesis of their results is lacking. However, without a comprehensive understanding of their individual and combined effects, our ability to predict the influence of these issues on the quality of modelled species–environment associations remains largely uncertain, limiting the value of model outputs. In this paper, we review studies that have evaluated the effects of sample size, positional uncertainty, sampling bias, and species ecology on SDMs outputs. We build upon their findings to provide recommendations for the critical assessment of species data intended for use in SDMs.

中文翻译：

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优化物种分布模型中的出现数据：样本量、位置不确定性和抽样偏差很重要


物种分布模型 （SDM） 已被证明在填补我们对物种出现的知识空白方面很有价值。然而，尽管 SDM 具有广泛的适用性，但由于物种出现数据的局限性，它们表现出关键的缺陷。这些限制尤其包括与样本量、位置不确定性和抽样偏倚相关的问题。此外，人们普遍认识到，SDM 的质量以及用于减轻上述数据限制影响的方法取决于物种生态学。虽然许多研究已经评估了这些数据限制对 SDM 性能的影响，但缺乏对其结果的综合。然而，如果不全面了解它们的单个和综合影响，我们预测这些问题对建模物种-环境关联质量的影响的能力在很大程度上仍然不确定，从而限制了模型输出的价值。在本文中，我们回顾了评估样本量、位置不确定性、采样偏倚和物种生态学对 SDM 输出影响的研究。我们以他们的发现为基础，为用于 SDM 的物种数据的批判性评估提供建议。