Quantifying intermediary processes in ecology using causal mediation analyses

Ecologists seek to understand the intermediary ecological processes through which changes in one attribute in a system affect other attributes. Yet, quantifying the causal effects of these mediating processes in ecological systems is challenging. Researchers must define what they mean by a “mediated effect”, determine what assumptions are required to estimate mediation effects without bias, and assess whether these assumptions are credible for a study. To address these challenges, scholars in fields outside of ecology have made significant advances in mediation analysis over the past three decades. Here, we bring these advances to the attention of ecologists, for whom understanding mediating processes and deriving causal inferences are important for testing theory and developing resource management and conservation strategies. To illustrate both the challenges and the advances in quantifying mediation effects, we use a hypothetical ecological study. 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, so we also describe procedures that researchers 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 will provide ecological researchers with approaches to clearly communicate the causal assumptions necessary for valid inferences and examine potential violations to these assumptions, which will enable rigorous and reproducible explanations of intermediary processes in ecology.