Abstract
Society increasingly demands accurate predictions of complex ecosystem processes under novel conditions to address environmental challenges. Obtaining the process-level knowledge required to do so does not necessarily align with the burgeoning use in ecology of correlative model selection criteria such as Akaike’s Information Criterion. These criteria select models based on their ability to reproduce outcomes, not accurately representing causal effects. Causal understanding does not require matching outcomes. Instead, it involves identifying model forms and parameter values that accurately describe processes. We argue that researchers can reach incorrect conclusions about cause-and-effect relationships by relying on information criteria. We illustrate via a concrete example that inference extending beyond prediction into causality can be badly misled by information-theoretic evidence. Finally, we identify a solution space to bridge the gap between the correlative inference provided by model selection criteria and a process-based understanding of ecological systems.