Publication

Society increasingly demands accurate predictions of complex ecosystem processes under novel conditions to address environmental challenges. However, 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 information criterion. These criteria select models based on their ability to reproduce outcomes, not on their ability to accurately represent causal effects. Causal understanding does not require matching outcomes, but rather involves identifying model forms and parameter values that accurately describe processes. We contend that researchers can arrive at incorrect conclusions about cause-and-effect relationships by relying on information criteria. We illustrate via a specific example that inference extending beyond prediction into causality can be seriously 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.