{"refrec":{"BRefID":302594,"RR":"<b>Thorson, J.T.; Haltuch, M.A.</b> (2019). Spatio-temporal analysis of compositional data: increased precision and improved workflow using model-based inputs to stock assessment. <i>Can. J. Fish. Aquat. Sci. 76(3)</i>: 401-414. <a href=\"https://dx.doi.org/10.1139/cjfas-2018-0015\" target=\"_blank\">https://dx.doi.org/10.1139/cjfas-2018-0015</a>","BEntID":294839,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":0,"RefStringPartII":". <i>Can. J. Fish. Aquat. Sci. 76(3)</i>: 401-414. <a href=\"https://dx.doi.org/10.1139/cjfas-2018-0015\" target=\"_blank\">https://dx.doi.org/10.1139/cjfas-2018-0015</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Thorson, J.T.; Haltuch, M.A.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Thorson, J.T.; Haltuch, M.A.","Englishabstract":"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.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Spatio-temporal analysis of compositional data: increased precision and improved workflow using model-based inputs to stock assessment","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-06-12 01:31:49.029735","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":"delta-generalized linear model; spatio-temporal model; length composion","OtherDescriptors":null,"Notes":null,"AnaPub":2019,"MonPub":null,"DateUpdate":"2019-02-27","DateCreate":"2018-10-30","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000459585200006","VABBcode":null,"OpenAcc":0,"DOI":"10.1139/cjfas-2018-0015"},"refs":null,"anarec":{"AnaID":302594,"PubliDate":2019,"Pagination":"401-414","XtraPublOfAnaID":null,"ISBN":null,"Volume":"76","Issue":"3","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":42535,"SerRR":"Canadian Journal of Fisheries and Aquatic Sciences = Journal canadien des sciences halieutiques et aquatiques. 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