Potential distribution models (PDMs) are widely applied to understand and predict biogeographic patterns. PDM evaluation, however, presents major challenges, including (1) matches of predictions with observed absences and presences being treated similarly and (2) treatment of predicted presences falling outside the observations as errors, while a major motivation of PDMs is to identify such locations. Our aim was to construct a family of model performance metrics to measure the reliability and transferability of PDMs while providing solutions to the problems mentioned above.

中文翻译：

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歧视的置信度和一致性：潜在分布模型的一系列新的评估指标

电位分布模型（PDM）广泛应用于理解和预测生物地理模式。然而，PDM 评估提出了重大挑战，包括 (1) 预测与观察到的缺席和存在的匹配进行类似处理；(2) 将超出观察范围的预测存在视为错误，而 PDM 的主要动机是识别这些位置。我们的目标是构建一系列模型性能指标来衡量 PDM 的可靠性和可转移性，同时为上述问题提供解决方案。