Spatio-temporal analysis of compositional data: increased precision and improved workflow using model-based inputs to stock assessment

Stock assessment models are fitted to abundance-index, fishery catch, and age/length/sex composition data that are estimated from survey and fishery records. Research has developed spatio-temporal methods to estimate abundance indices, but there is little research regarding model-based methods to generate age/length/sex composition data. We demonstrate a spatio-temporal approach to generate composition data and a multinomial sample size that approximates the estimated imprecision. A simulation experiment comparing spatio-temporal and design-based methods demonstrates a 32% increase in input sample size for the spatio-temporal estimator. A Stock Synthesis assessment used to manage lingcod in the California Current also shows a 17% increase in sample size and better model fit using the spatio-temporal estimator, resulting in smaller standard errors when estimating spawning biomass. We conclude that spatio-temporal approaches are feasible for estimating both abundance-index and compositional data, thereby providing a unified approach for generating inputs for stock assessments. We hypothesize that spatio-temporal methods will improve statistical efficiency for composition data in many stock assessments, and recommend that future research explore the impact of including additional habitat or sampling covariates.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author