What is our power to detect device effects in animal tracking studies?

The use of bio-logging devices to track animal movement continues to grow as technological advances and device miniaturisation allow researchers to study animal behaviour in unprecedented detail. Balanced against the remarkable data that bio-loggers can provide is a need to understand the impact of devices on animal behaviour and welfare.
Recent meta-analyses have demonstrated impacts of device attachment on animal behaviour, but there is concern about the frequency and clarity with which device effects are reported. One aspect lacking in many studies is assessment of the statistical power of tests of device effects, yet such information would assist the interpretation of results. We address this issue by providing an overview of the statistical power, as well as the Type M (magnitude) and Type S (sign) error rate, of tests of device effects within the avian tracking literature across a range of assumed effect sizes.
The median power of statistical tests ranged from 9% to 65% across a range of assumed effect sizes corresponding to benchmark values for small, moderate and large effects (d = 0.2, 0.5, 0.8 respectively). Moreover, when using effect sizes derived from previous a meta-analysis (d = 0.1) median power was only 6%.  When assuming smaller effect sizes, statistical tests were characterised by high Type M and Type S error rates, suggesting that statistically significant results of device effects will tend to exaggerate the size of such effects and may estimate the sign of an effect in the wrong direction.
Well-designed tracking studies will reduce device effects to low levels and consequently issues associated with low power will be commonplace. Nevertheless, assessment of device effects remains important, particularly when embarking on novel tracking studies. We recommend that statistical tests of device effects are reported clearly and are routinely accompanied by assessment of statistical power, including Type M and Type S errors, based upon realistic external estimates of effect size. Reporting the statistical power can help avoid the pitfalls of overstating results from individual studies, shift the emphasis to accurate reporting of effect sizes and guide decisions about the ethical impacts of device attachment.  

Methods
Data covers extraction of effect sizes from avian tracking literature with effect sizes ultimately reported using absolute values of Cohen's d together with the associated standard error around this effect size. Standard errors were subsequently used within the R 'retrodesign' package to estimate statistical power, Type M error rate and Type S error rate to a range of assumed effect sizes (d = 0.1, 0.2, 0.5 & 0.8).

随着技术进步与设备小型化发展，研究者得以以前所未有的精细尺度探究动物行为，用于追踪动物运动的生物记录设备（bio-logging devices）的应用亦持续扩张。但生物记录设备所能提供的优质数据背后，亟需厘清这类设备对动物行为与福利的影响。

近期的元分析（meta-analyses）已证实设备挂载会对动物行为造成影响，但当前研究对设备效应的报告频率与清晰程度仍存在不足。诸多研究均缺失的一环，便是对设备效应检验的统计效力进行评估，而这类信息可有效辅助研究结果的解读。为此，我们针对鸟类追踪相关文献中不同预设效应量场景下的设备效应检验，综述了其统计效力，以及M型错误（Type M error）与S型错误（Type S error）的率值情况。

在对应小、中、大效应量的基准值（分别为d=0.2、0.5、0.8）的一系列预设效应量下，统计检验的中位效力介于9%至65%之间。此外，当采用先前元分析得出的效应量（d=0.1）时，中位效力仅为6%。当预设较小的效应量时，统计检验往往伴随较高的M型与S型错误率，这意味着设备效应的统计学显著性结果往往会夸大这类效应的实际量值，甚至可能错误估计效应的作用方向。

设计精良的追踪研究会将设备效应控制在较低水平，因此低效力相关问题会较为普遍。但即便如此，设备效应的评估仍至关重要，尤其是在开展新型追踪研究时。我们建议，应清晰报告设备效应的统计检验结果，并常规结合基于合理外部效应量估计的统计效力评估，包括M型与S型错误率。报告统计效力有助于避免过度解读单个研究结果的陷阱，将研究重点转向效应量的精准报告，并为设备挂载的伦理影响决策提供参考。

方法
本研究的数据提取自鸟类追踪相关文献，最终报告的效应量采用科恩d值（Cohen's d）的绝对值，搭配该效应量对应的标准误。随后借助R语言的'retrodesign'包，基于一系列预设效应量（d=0.1、0.2、0.5与0.8）估算统计效力、M型错误率与S型错误率。