MMI: Multimodel Inference or Models With Management Implications?

Weconsider a variety of regressionmodeling strategies for analyzingobservational data associated with typical wildlife studies, including all subsets and stepwise regression, a single full model, and Akaike’s InformationCriterion (AIC)-basedmultimodel inference.Although there are advantages and disadvantages to each approach, we suggest that there is no unique best way to analyze data. Further, we argue that, although multimodel inference canbeuseful innatural resourcemanagement, the importance of considering causality and accurately estimating effect sizes is greater than simply considering a variety ofmodels.Determining causation is far more valuable than simply indicating how the response variable and explanatory variables covaried within a data set, especially when the data set did not arise from a controlled experiment. Understanding the causal mechanismwill providemuchbetter predictionsbeyond the rangeof data observed.Published 2015.This article is a U.S. Government work and is in the public domain in the USA.