MMI: Multimodel inference or models with management implications?

• Published in: The Journal of wildlife management Vol. 79; no. 5; pp. 708 - 718
• Main Authors: Fieberg, John, Johnson, Douglas H.
• Format: Journal Article
• Language: English
• Published: Bethesda Blackwell Publishing Ltd 01-07-2015; Wiley Subscription Services, Inc
• Subjects: causation; Datasets; Ecological modeling; effect size; Grasses; Inference; Linear regression; mechanism; model selection; Modeling; multicollinearity; multimodel inference; Multimodel Inference Special Section; Natural resource management; observational data; overfitting; Parametric models; prediction; Regression analysis; Regression coefficients; statistics; Wildlife; Wildlife management; USA
• ISSN: 0022-541X; 1937-2817
• DOI: 10.1002/jwmg.894

We consider a variety of regression modeling 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 resource management, the importance of considering causality and accurately estimating effect sizes is greater than simply considering a variety of models. 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 mechanism will provide much better predictions beyond the range of data observed. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.