Are experiment sample sizes adequate to detect biologically important interactions between multiple stressors?
Are experiment sample sizes adequate to detect biologically important interactions between multiple stressors?
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DOI：
10.1002/ece3.9289
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发表时间：
2022-09
影响因子：
2.6
通讯作者：
Murrell, David J.
中科院分区：
文献类型：
--
作者：
Burgess, Benjamin J.;Jackson, Michelle C.;Murrell, David J.
关键词：
由于大多数生态系统都受到多种共同发生的压力源的挑战，一个重要的挑战是了解和预测压力源如何相互作用以影响生物反应。一种流行的方法是设计因子实验，测量对压力源的生物反应，并将观察到的反应与零模型预期进行比较。不幸的是，我们认为实验样本量不足以检测大多数非零压力源交互反应，这极大地阻碍了进展。使用真实的和模拟数据，我们表明许多实验的典型样本量（<6）只能（i）检测到与加性零模型的非常大的偏差，这意味着许多重要的非零应激源对相互作用被遗漏，（ii）可能导致大多数统计异常值被报告。提供了在加性或乘性空模型下模拟数据的计算机代码，以估计用户定义的响应和样本量的统计功效，我们建议将其用于辅助实验设计和结果解释。我们怀疑，大多数实验可能需要20个或更多的重复每个处理有足够的权力来检测非加性。然而，需要在考虑最小感兴趣的相互作用的同时进行功率估计，即，生物学重要相互作用的下限，可能是系统特异性的，这意味着通用指南不可用。我们讨论的方式，可以选择最小的相互作用的利益，以及如何可以增加样本量。我们的主要分析涉及添加剂零模型，但我们发现类似的问题发生乘法零模型，我们鼓励类似的调查统计权力的其他零模型和推理方法。如果不了解现有统计工具的检测能力或最小有意义的相互作用的定义，我们无疑将继续错过重要的生态系统压力源相互作用。为调查多个生态系统压力源如何相互作用的实验确定适当的样本量需要考虑（i）成本;（ii）检测偏离零期望的统计能力;以及（iii）预先确定什么构成生物学上重要的偏离。目前，只考虑成本，这导致实验设计可能会错过重要的压力源相互作用。
As most ecosystems are being challenged by multiple, co‐occurring stressors, an important challenge is to understand and predict how stressors interact to affect biological responses. A popular approach is to design factorial experiments that measure biological responses to pairs of stressors and compare the observed response to a null model expectation. Unfortunately, we believe experiment sample sizes are inadequate to detect most non‐null stressor interaction responses, greatly hindering progress. Using both real and simulated data, we show sample sizes typical of many experiments (<6) can (i) only detect very large deviations from the additive null model, implying many important non‐null stressor‐pair interactions are being missed, and (ii) potentially lead to mostly statistical outliers being reported. Computer code that simulates data under either additive or multiplicative null models is provided to estimate statistical power for user‐defined responses and sample sizes, and we recommend this is used to aid experimental design and interpretation of results. We suspect that most experiments may require 20 or more replicates per treatment to have adequate power to detect nonadditive. However, estimates of power need to be made while considering the smallest interaction of interest, i.e., the lower limit for a biologically important interaction, which is likely to be system‐specific, meaning a general guide is unavailable. We discuss ways in which the smallest interaction of interest can be chosen, and how sample sizes can be increased. Our main analyses relate to the additive null model, but we show similar problems occur for the multiplicative null model, and we encourage similar investigations into the statistical power of other null models and inference methods. Without knowledge of the detection abilities of the statistical tools at hand or the definition of the smallest meaningful interaction, we will undoubtedly continue to miss important ecosystem stressor interactions. Determining adequate sample size for experiments investigating how multiple ecosystem stressors interact need to consider (i) costs; (ii) statistical ability to detect a deviation from the null expectation; and (iii) prior determination of what constitutes a biologically important deviation. Currently, only costs have been considered and this leads to experimental designs that are likely to miss important stressor interactions.
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