Abstract Species distribution modelling (SDM) is an important tool for ecologists, but different algorithms and different sampling strategies produce different results. Using virtual species with differing characteristics, this study investigated the effect of sampling strategy choices on SDM predictions across multiple algorithms and species, including the impacts of different sample size and prevalence choices, and the effects of validating models using presence and background data as opposed to true absences. We also assessed the consistency of predictions between algorithms, and investigated the effectiveness of using stability assessment of spatial predictions in geographic space to evaluate SDM predictions. Maxent performed most consistently under all scenarios both in regards to performance metrics and spatial prediction stability, and should be considered for most scenarios either on its own or as part of a model ensemble, in particular when true absences are not available. A key recommendation of this study is the use of metrics to assess agreement between replicate predictions as a measure of spatial stability, rather than relying solely on performance metrics such as area under the curve (AUC).

中文翻译：

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仅存在物种分布模型对样本流行率敏感：使用空间预测稳定性和准确性指标评估模型

摘要 物种分布建模（SDM）是生态学家的重要工具，但不同的算法和不同的采样策略会产生不同的结果。本研究使用具有不同特征的虚拟物种，研究了采样策略选择对跨多个算法和物种的 SDM 预测的影响，包括不同样本大小和流行选择的影响，以及使用存在和背景数据验证模型的影响，而不是真正的缺席。我们还评估了算法之间预测的一致性，并研究了使用地理空间中空间预测的稳定性评估来评估 SDM 预测的有效性。Maxent 在所有场景下在性能指标和空间预测稳定性方面的表现最为一致，并且应该在大多数情况下单独或作为模型集合的一部分考虑，特别是当真正的缺席不可用时。本研究的一个关键建议是使用度量来评估重复预测之间的一致性，作为空间稳定性的度量，而不是仅仅依赖于曲线下面积 (AUC) 等性能度量。