Using measurement error models to account for georeferencing error in species distribution models
Using measurement error models to account for georeferencing error in species distribution models
复制标题
使用测量误差模型来解释物种分布模型中的地理配准误差
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
S. Munch
中科院分区：
文献类型：
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作者：
J. Velásquez;C. Graham;S. Munch
地理参考误差在用于模拟物种分布的数据集中普遍存在，导致与物种出现相关的协变量值的不确定性，从而导致出现概率估计的偏差。传统上，这一误差是在数据一级处理的，方法是只使用误差水平可接受的记录(过滤)，或通过使用中心趋势衡量标准(平均)汇总抽样单位的协变量。在这里，我们将这些以前的方法与带有测量误差(ME)的贝叶斯Logistic回归的新实现进行了比较，ME是物种分布建模中很少使用的方法。我们表明，ME模型在以下两个方面优于数据级别的方法：1)专业物种；2)当任何一个样本量较小时，地理参考误差较大，或者当所有地理参考出现具有固定的误差水平时。因此，对于某些类型的物种和数据集，ME模型是一种有效的方法，可以减少发生概率估计的偏差，并考虑地理参考误差产生的不确定性。我们的方法可以扩展到只使用存在数据，也可以包括物种分布模型中的其他不确定因素。
Georeferencing error is prevalent in datasets used to model species distributions, inducing uncertainty in covariate values associated with species occurrences that result in biased probability of occurrence estimates. Traditionally, this error has been dealt with at the data-level by using only records with an acceptable level of error (filtering) or by summarizing covariates at sampling units by using measures of central tendency (averaging). Here we compare those previous approaches to a novel implementation of a Bayesian logistic regression with measurement error (ME), a seldom used method in species distribution modeling. We show that the ME model outperforms data-level approaches on 1) specialist species and 2) when either sample sizes are small, the georeferencing error is large or when all georeferenced occurrences have a fixed level of error. Thus, for certain types of species and datasets the ME model is an effective method to reduce biases in probability of occurrence estimates and account for the uncertainty generated by georeferencing error. Our approach may be expanded for its use with presence-only data as well as to include other sources of uncertainty in species distribution models.