物种分布模型中样本偏差校正的效果评估
Assessing the effect of sample bias correction in species distribution models
摘要：
开源生物多样性数据库包含大量的物种发生记录，但这些记录往往存在空间偏差，影响了基于这些记录的物种分布模型的可靠性。样本偏差校正技术包括以记录数为代价的数据过滤或需要相当多的额外采样工作。然而，独立的数据很少，校正技术的评估必须依赖于使用唯一可用（有偏差）数据的子集计算的性能指标，这可能会产生误导。在这里，我们评估在何种程度上，一个公认的样本偏差校正技术可能会提高模型的能力，在没有独立的数据来预测物种分布。我们评估了由修正和模型随机性引起的模型预测的变化。我们提出了一个指数的影响相对于模型的随机性，相对重叠指数（ROI）的校正。我们测试了ROI是否比经典的性能指标和绝对重叠指标更好地代表校正的效果，使用64种脊椎动物和21种虚拟物种与生成的样本偏差。当基于绝对重叠和交叉验证性能指标时，除了cAUC外，我们没有发现校正的影响。当考虑其影响相对于模型的随机性，校正的效果取决于网站和物种。虚拟物种使我们能够验证，校正实际上提高了分布预测和生物相关性的选定变量在网站上有一个明确的梯度样本偏差，当物种分布预测与样本偏差模式不相关。
英文摘要：
Open-source biodiversity databases contain a large amount of species occurrence records, but these are often spatially biased, which affects the reliability of species distribution models based on these records. Sample bias correction techniques include data filtering at the cost of record numbers or require considerable additional sampling effort. However, independent data are rarely available and assessment of the correction technique must rely on performance metrics computed with subsets of the only available (biased) data, which may be misleading. Here we assess the extent to which an acknowledged sample bias correction technique is likely to improve models' ability to predict species distributions in the absence of independent data. We assessed the variation in model predictions induced by the correction and model stochasticity. We present an index of the effect of correction relative to model stochasticity, the Relative Overlap Index (ROI). We tested whether the ROI better represented the effect of correction than classic performance metrics and absolute overlap metrics using 64 vertebrate species and 21 virtual species with a generated sample bias. When based on absolute overlaps and cross-validation performance metrics, we found no effect of correction, except for cAUC. When considering its effect relative to model stochasticity, the effect of correction depended on the site and the species. Virtual species enabled us to verify that the correction actually improved distribution predictions and the biological relevance of the selected variables at the sites with a clear gradient of sample bias, and when species distribution predictors are not correlated with sample bias patterns.
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