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Title: Accounting for spatio-temporal and sampling dependence in survey and CPUE biomass indices: simulation and Bayesian modeling framework
Authors: Fuster-Alonso, Alba; Conesa, David; Cousido-Rocha, Marta; Izquierdo, Francisco; Paradinas, Iosu; Cervino, Santiago; Pennino, Maria Grazia
Abstract: Estimating changes in the biomass of a fish stock is crucial for successful management. However, fishery assessment may be affected by the quality of the inputs used in stock assessment models. Survey biomass indices derived from fishery-independent and catch per unit effort (CPUE) biomass indices derived from fishery-dependent data are key inputs for model calibration. These indices have biases that could compromise the accuracy of the stock assessment models results. Therefore, there are plenty proposed methods to standardize survey or CPUE biomass data. From simpler models like generalized linear models (GLMs) to more complex models that take into account spatio-temporal correlation, like geostatistical models, and sampling dependence, like marked point processes. But many of them do not consider the underlying spatio-temporal or sampling dependence of these data. Hence, the goal of the study is to present a spatio-temporal simulation and Bayesian modeling framework to assess the impact of applying models that do not consider spatio-temporal and sampling dependence. Results indicate that geostatistical models and marked point processes achieve the lowest measures of error. Hence, to capture the underlying spatio-temporal process of the survey and CPUE biomass indices and data sampling preferentiality, it is essential to apply models that consider the spatio-temporal and sampling dependence.
Keywords: survey biomass indices; CPUE biomass indices; simulation; statistical modeling; preferential sampling and spatio-temporal effects; STOCK ASSESSMENT; STANDARDIZATION; CATCH; INFERENCE; PACKAGE; CAUGHT
Issue Date: 2024
Publisher: OXFORD UNIV PRESS
Type: Article; Early Access
Language: 
DOI: 10.1093/icesjms/fsae056
URI: http://dspace.azti.es/handle/24689/1895
ISSN: 1054-3139
E-ISSN: 1095-9289
Funder: ERDF, Ministry of Science, Innovation and Universities - State Research Agency [RTI2018-099868-B-I00]
European UnionNext Generation EU
GAIN [Agencia Gallega de Innovacion] - Xunta de Galicia, GRC-MERVEX [IN607A 2022/04]
Ministerio de Ciencia, Innovacion y Universidades of Spain (MCIN/AEI/FEDER, UE) [PID2022-136455NB-I00]
European Regional Development Fund
Generalitat Valenciana [CIAICO/2022/165]
Spanish project ProOceans (Ministerio de Ciencia e Innovacion, Proyectos de I + D + I) [RETOSPID2020-118097RBI00]
Ministerio de Ciencia e Innovacion from the project ProOeans [PRE2021-099287, PID2020-118097RB-I00]
Barcelona municipal government through the iMARES research group at the Institute of Marine Sciences (ICM-CSIC) in Barcelona
Appears in Publication types:Artículos científicos



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