MMI: Multimodel inference or models with management implications?

Journal of Wildlife Management
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Abstract

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.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title MMI: Multimodel inference or models with management implications?
Series title Journal of Wildlife Management
DOI 10.1002/jwmg.894
Volume 79
Issue 5
Year Published 2015
Language English
Publisher Wildlife Society
Publisher location Washington, D.C.
Contributing office(s) Northern Prairie Wildlife Research Center
Description 11 p.
First page 708
Last page 718
Online Only (Y/N) N
Additional Online Files (Y/N) N