Deciphering ecology from statistical artefacts: Competing influence of sample size, prevalence and habitat specialization on species distribution models and how small evaluation datasets can inflate metrics of performance
Deciphering ecology from statistical artefacts: Competing influence of sample size, prevalence and habitat specialization on species distribution models and how small evaluation datasets can inflate metrics of performance
复制标题
从统计人工制品中解读生态学：样本量、流行率和栖息地专业化对物种分布模型的相互影响，以及小型评估数据集如何夸大绩效指标
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
W. Robinson
中科院分区：
文献类型：
--
作者：
Tyler A. Hallman;W. Robinson
样本大小和物种特征，包括流行率和栖息地专业化，可以影响物种分布模型（SDM）的预测性能。然而，对于使用哪种模型性能指标却没有达成一致。在这里，我们通过分析物种性状和样本量对SDM性能的影响，直接比较AUC和部分ROC作为SDM性能的指标。
Sample size and species characteristics, including prevalence and habitat specialization, can influence the predictive performance of species distribution models (SDMs). There is little agreement, however, on which metric of model performance to use. Here, we directly compare AUC and partial ROC as metrics of SDM performance through analyses on the effects of species traits and sample size on SDM performance.