|Document Type:||Journal Article|
|Title:||Mixed effects: a unifying framework for statistical modelling in fisheries biology|
|Author:||James T. Thorson, Coilin Minto|
|Journal:||ICES Journal of Marine Science|
|Keywords:||mixed-effect model, state-space, integration, hierarchical, latent variable, spatial model, individual-level variability, measurement error,|
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.
|Full Text URL:||http://icesjms.oxfordjournals.org/content/72/5/1245.abstract?etoc|
|URL1:||Journal article location|
|Theme:||Recovery and rebuilding of marine and coastal species|
Characterize the population biology of species, and develop and improve methods for predicting the status of populations.
Develop methods to use physiological, biological and behavioral information to predict population-level processes.