Designing causal mediation analyses to quantify intermediary processes in ecology

Ecologists seek to understand the intermediary ecological processes through which changes in one attribute in a system affect other attributes. A causal understanding of mediating processes is important for testing theory and developing resource management and conservation strategies. Yet, quantifying the causal effects of these mediating processes in ecological systems is challenging, because it requires defining what we mean by a "mediated effect", determining what assumptions are required to estimate mediation effects without bias, and assessing whether these assumptions are credible in a study. To address these challenges, scholars have made significant advances in research designs for mediation analysis. Here, we review these advances for ecologists. To illustrate both the advances and the challenges in quantifying mediation effects, we use a hypothetical ecological study of drought impacts on grassland productivity. With this study, we show how common research designs used in ecology to detect and quantify mediation effects may have biases and how these biases can be addressed through alternative designs. Throughout the review, we highlight how causal claims rely on causal assumptions, and we illustrate how different designs or definitions of mediation effects can relax some of these assumptions. In contrast to statistical assumptions, causal assumptions are not verifiable from data, and so we also describe procedures that we can use to assess the sensitivity of a study's results to potential violations of its causal assumptions. The advances in causal mediation analyses reviewed herein equip ecologists to communicate clearly the causal assumptions necessary for valid inferences, and to examine and address potential violations to these assumptions using suitable experimental and observational designs, which will enable rigorous and reproducible explanations of intermediary processes in ecology.