Date of this Version
The Journal of Wildlife Management
We consider a variety of regressionmodeling strategies for analyzing observational data associated with typical wildlife studies, including all subsets and stepwise regression, a single full model, and Akaike’s Information Criterion (AIC)-based multimodel 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 can be useful in natural 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 providemuch better predictions beyond the range of data observed.