MMI: Multimodel inference or models with management implications?

By J. Fieberg, Douglas H. Johnson. 2015

87 citations recorded by the source corpus.

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.

Subfields: Ecological Modeling, Ecology

View Paper: https://www.semanticscholar.org/paper/cfd4f173d46fa9fdf9e475a161c946b449e7cc55

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In Ecological Modeling

In Ecology

Citations in the corpus

A brief introduction to mixed effects modelling and multi-model inference in ecology (2018, background): “Such an approach requires a huge amount of data (Dochterman and Jenkins 2011), but cross-887 validation to validate a model’s predictive ability is rare and should result in more robust inference (see also Fieberg & Johnson 2015).”
Evaluating population viability and efficacy of conservation management using integrated population models (2018, background): “…explicitly estimating the impact of merlins on seasonal survival rates of plovers are needed (e.g. before and after control studies), as the manipulation of a given variable in the system may result in both direct and indirect effects on population demographic rates (Fieberg & Johnson, 2017).”