Mixed effects: a unifying framework for statistical modelling in fisheries biology

Authored by James T Thorson, Coilin Minto

Date Published: 2015

DOI: 10.1093/icesjms/fsu213

Sponsors: No sponsors listed

Platforms: R

Model Documentation: Other Narrative Mathematical description

Model Code URLs: https://github.com/James-Thorson/mixed-effects/tree/master/State-space_model

Abstract

Fisheries biology encompasses a tremendous diversity of research questions, methods, and models. Many sub-fields use observational or experimental data to make inference about biological characteristics that are not directly observed (called ``latent states{''}), such as heritability of phenotypic traits, habitat suitability, and population densities to name a few. Latent states will generally cause model residuals to be correlated, violating the assumption of statistical independence made in many statistical modelling approaches. In this exposition, we argue that mixed-effect modelling (i) is an important and generic solution to non-independence caused by latent states; (ii) provides a unifying framework for disparate statistical methods such as time-series, spatial, and individual-based models; and (iii) is increasingly practical to implement and customize for problem specific models. We proceed by summarizing the distinctions between fixed and random effects, reviewing a generic approach for parameter estimation, and distinguishing general categories of non-linear mixed-effect models. We then provide four worked examples, including state-space, spatial, individual-level variability, and quantitative genetics applications (with working code for each), while providing comparison with conventional fixed-effect implementations. We conclude by summarizing directions for future research in this important framework for modelling and statistical analysis in fisheries biology.

Tags

time-series Recruitment Fish Age Ecological models Pacific salmon Species abundance Hierarchical-models Stock assessments Bayesian methods