Centre for Ecology and Conservation, University of Exeter, Penryn TR10 9FE, UK.
Philos Trans R Soc Lond B Biol Sci. 2021 Aug 16;376(1831):20200227. doi: 10.1098/rstb.2020.0227. Epub 2021 Jun 28.
Recent advances in tagging and biologging technology have yielded unprecedented insights into wild animal physiology. However, time-series data from such wild tracking studies present numerous analytical challenges owing to their unique nature, often exhibiting strong autocorrelation within and among samples, low samples sizes and complicated random effect structures. Gleaning robust quantitative estimates from these physiological data, and, therefore, accurate insights into the life histories of the animals they pertain to, requires careful and thoughtful application of existing statistical tools. Using a combination of both simulated and real datasets, I highlight the key pitfalls associated with analysing physiological data from wild monitoring studies, and investigate issues of optimal study design, statistical power, and model precision and accuracy. I also recommend best practice approaches for dealing with their inherent limitations. This work will provide a concise, accessible roadmap for researchers looking to maximize the yield of information from complex and hard-won biologging datasets. This article is part of the theme issue 'Measuring physiology in free-living animals (Part II)'.
近年来，标记和生物遥测技术的进步为野生动物生理学研究提供了前所未有的深入见解。然而，由于这些野外追踪研究的数据具有独特的性质，通常在样本内和样本间表现出很强的自相关性，样本量小且随机效应结构复杂，因此此类研究产生的时间序列数据在分析上面临着诸多挑战。要从这些生理数据中得出可靠的定量估计值，并准确了解与之相关的动物的生活史，需要仔细而有针对性地应用现有的统计工具。我使用模拟和真实数据集的组合，突出了分析来自野生监测研究的生理数据时所涉及的关键陷阱，并研究了最佳研究设计、统计功效以及模型精度和准确性等问题。我还为处理其内在局限性推荐了最佳实践方法。这项工作将为希望从复杂且来之不易的生物遥测数据集获得最大信息量的研究人员提供一份简明、易懂的路线图。本文是“测定自由生活动物的生理学（第二部分）”主题专刊的一部分。
