Using nonlinear hierarchical models for analyzing annulus-based size-at-age data

Keywords

fish, growth model, growth analysis, Utah chub

Abstract

Size-at-age data for fish (derived from otoliths or other structures) are valuable but statistically messy. The data are typically serially correlated and unbalanced, with both time-independent and time-varying covariates. Appropriate growth models are typically nonlinear, with an unknown functional form. We recommend the use of nonlinear hierarchical models for the analysis of such data. We illustrate the use of these methods by applying the recently introduced SAS procedure NLMIXED (SAS Institute Inc., Cary, N.C.) to otolith-based estimated standard lengths of Utah chub (Gila atraria) collected in four locations with predators and four locations without predators.

Original Publication Citation

Schaalje, G.B., Shaw, J.L., and M.C. Belk. 2002. Using nonlinear hierarchical models for analyzing annulus-based size-at-age data. Canadian Journal of Fisheries and Aquatic Sciences 59:1524-1532.

Document Type

Peer-Reviewed Article

Publication Date

2002-9

Permanent URL

http://hdl.lib.byu.edu/1877/8209

Publisher

Canadian Journal of Fisheries and Aquatic Sciences

Language

English

College

Life Sciences

Department

Biology

University Standing at Time of Publication

Full Professor

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