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：样本大小、位置不确定性和抽样偏差问题


事实证明，物种分布模型（SDM）对于填补我们对物种发生的了解的空白非常有价值。然而，尽管 SDM 具有广泛的适用性，但由于物种发生数据的限制，SDM 仍表现出严重的缺点。这些限制特别包括与样本大小、位置不确定性和抽样偏差相关的问题。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。尽管大量研究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，如果没有全面了解它们的单独和综合影响，我们预测这些问题对模型物种-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、抽样偏差和物种生态对 SDM 输出影响的研究。我们以他们的发现为基础，为对用于 SDM 的物种数据进行严格评估提供建议。