Mitigating pseudoreplication and bias in resource selection functions with autocorrelation‐informed weighting
Mitigating pseudoreplication and bias in resource selection functions with autocorrelation‐informed weighting
复制标题
通过自相关-知情加权减轻资源选择函数中的伪复制和偏差
DOI：
10.1111/2041-210x.14025
复制
发表时间：
2023
影响因子：
6.6
通讯作者：
Calabrese, Justin M.
中科院分区：
文献类型：
--
作者：
Alston, Jesse M.;Fleming, Christen H.;Kays, Roland;Streicher, Jarryd P.;Downs, Colleen T.;Ramesh, Tharmalingam;Reineking, Björn;Calabrese, Justin M.
资源选择函数是基础动物生态学和应用动物生态学中最常用的统计工具之一。它们通常使用动物跟踪数据进行参数化，动物跟踪技术的进步导致此类数据集中位置之间的自相关水平不断提高。由于随机选择函数假设数据是独立同分布的，这种自相关会导致误导性的置信区间变窄和参数估计偏差，数据细化、广义估计方程和步长选择函数（SSFs）被认为是缓解自相关带来的统计问题的技术，但这些方法有显著的局限性，包括统计效率低，充分的统计独立性、输入数据的限制和（在SSF的情况下）规模依赖性推断的目标不明确或任意。为了解决这些问题，我们引入了一种动物位置的似然加权方法，以减轻自相关对RSFs的负面影响。在这项研究中，我们证明了这种方法根据其非独立性水平对动物运动轨迹中的每个观察位置进行加权，扩大置信区间，减少移动轨迹中缺失数据时可能出现的偏差。生态学家和保护生物学家可以使用该方法来提高从RSF导出的推断的质量。我们还提供了一个完整的，带注释的分析工作流程，以帮助新用户应用我们的方法，他们自己的动物跟踪数据使用thectmm Rpackage。
Resource selection functions (RSFs) are among the most commonly used statistical tools in both basic and applied animal ecology. They are typically parameterized using animal tracking data, and advances in animal tracking technology have led to increasing levels of autocorrelation between locations in such data sets. Because RSFs assume that data are independent and identically distributed, such autocorrelation can cause misleadingly narrow confidence intervals and biased parameter estimates.Data thinning, generalized estimating equations and step selection functions (SSFs) have been suggested as techniques for mitigating the statistical problems posed by autocorrelation, but these approaches have notable limitations that include statistical inefficiency, unclear or arbitrary targets for adequate levels of statistical independence, constraints in input data and (in the case of SSFs) scale‐dependent inference. To remedy these problems, we introduce a method for likelihood weighting of animal locations to mitigate the negative consequences of autocorrelation on RSFs.In this study, we demonstrate that this method weights each observed location in an animal's movement track according to its level of non‐independence, expanding confidence intervals and reducing bias that can arise when there are missing data in the movement track.Ecologists and conservation biologists can use this method to improve the quality of inferences derived from RSFs. We also provide a complete, annotated analytical workflow to help new users apply our method to their own animal tracking data using thectmm Rpackage.