German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103, Leipzig, Germany.
School of Biological Sciences, University of Reading, RG6 6EX, Reading, UK.
Biol Rev Camb Philos Soc. 2023 Aug;98(4):983-1002. doi: 10.1111/brv.12939. Epub 2023 Mar 1.
Ecologists routinely use statistical models to detect and explain interactions among ecological drivers, with a goal to evaluate whether an effect of interest changes in sign or magnitude in different contexts. Two fundamental properties of interactions are often overlooked during the process of hypothesising, visualising and interpreting interactions between drivers: the measurement scale - whether a response is analysed on an additive or multiplicative scale, such as a ratio or logarithmic scale; and the symmetry - whether dependencies are considered in both directions. Overlooking these properties can lead to one or more of three inferential errors: misinterpretation of (i) the detection and magnitude (Type-D error), and (ii) the sign of effect modification (Type-S error); and (iii) misidentification of the underlying processes (Type-A error). We illustrate each of these errors with a broad range of ecological questions applied to empirical and simulated data sets. We demonstrate how meta-analysis, a widely used approach that seeks explicitly to characterise context dependence, is especially prone to all three errors. Based on these insights, we propose guidelines to improve hypothesis generation, testing, visualisation and interpretation of interactions in ecology.
生态学家通常使用统计模型来检测和解释生态驱动因素之间的相互作用，目的是评估在不同环境中是否存在感兴趣的效应在符号或幅度上发生变化。在假设、可视化和解释驱动因素之间的相互作用时，两个基本的相互作用特性经常被忽视：度量尺度——响应是在加性还是乘性尺度上进行分析，例如比率或对数尺度；以及对称性——是否在两个方向上考虑依赖性。忽略这些特性可能会导致以下三种推理错误中的一种或多种：对（i）检测和幅度（Type-D 错误）和（ii）效应修饰的符号（Type-S 错误）的误解；以及（iii）对潜在过程的错误识别（Type-A 错误）。我们用广泛应用于实证和模拟数据集的一系列生态问题来说明这些错误。我们展示了元分析如何特别容易犯所有三种错误，元分析是一种广泛使用的方法，旨在明确描述语境依赖性。基于这些见解，我们提出了改善生态中相互作用的假设生成、测试、可视化和解释的指导方针。
