Model Selection via Focused Information Criteria for Complex Data in Ecology and Evolution

TL;DR

This paper develops and demonstrates focused information criteria for model selection in complex ecological data, improving the precision of key estimates in ecological studies.

Contribution

It introduces focused model selection criteria tailored for ecology and evolution, enhancing estimation accuracy for interest parameters.

Findings

Focused criteria improve estimate precision over traditional methods.

Application to bird abundance data shows better model selection.

Case study on whale body condition demonstrates practical utility.

Abstract

Datasets encountered when examining deeper issues in ecology and evolution are often complex. This calls for careful strategies for both model building, model selection, and model averaging. Our paper aims at motivating, exhibiting, and further developing focused model selection criteria. In contexts involving precisely formulated interest parameters, these versions of FIC, the focused information criterion, typically lead to better final precision for the most salient estimates, confidence intervals, etc. as compared to estimators obtained from other selection methods. Our methods are illustrated with real case studies in ecology; one related to bird species abundance and another to the decline in body condition for the Antarctic minke whale.