TY - JOUR
T1 - Accounting for spatial dependence improves relative abundance estimates in a benthic marine species structured as a metapopulation
AU - Cavieres, Joaquin
AU - Monnahan, Cole C.
AU - Vehtari, Aki
N1 - Funding Information:
We thank Aalto University for supporting and financing part of this research. The university's Probabilistic Machine Learning Group has been especially helpful. Additionally, we thank the Instituto de Fomento Pesquero (IFOP) for providing the data for this study. We also thank two anonymous reviewers for helpful feedback on an earlier version of the manuscript.
Publisher Copyright:
© 2021 Elsevier B.V.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/8
Y1 - 2021/8
N2 - Sea urchin (Loxechinus albus) is one of the most important benthic resource in Chile. Due to their large-scale spatial metapopulation structure, sea urchin subpopulations are interconnected by larval dispersion, so the recovery of local abundance depends on the distance and hydrodynamic characteristics of their spatial domain. Currently, this resource is evaluated with classical stock assessment models, using standardized catch per unit effort (an index of relative abundance) as a key piece of information to determine catch quotas and achieve sustainability. However, these estimates assume hyperstability for the total population, ignoring spatial dependence among fishing sites, which is a fundamental concept for populations structured as metapopulation. We develop a Bayesian catch standardization model with explicit spatial dependence to better address the structure of this population. The proposed model performs statistically better compared to a model without spatial dependence, based on leave-one-out cross-validation, and predictive distributions also show that parameter estimation is consistent with the data. We argue that incorporating spatial structure improves the estimated relative abundance index in a population structured as a metapopulation. Our improved index of abundance will lead to better assessments and management advice, thus improving the sustainability of the stock.
AB - Sea urchin (Loxechinus albus) is one of the most important benthic resource in Chile. Due to their large-scale spatial metapopulation structure, sea urchin subpopulations are interconnected by larval dispersion, so the recovery of local abundance depends on the distance and hydrodynamic characteristics of their spatial domain. Currently, this resource is evaluated with classical stock assessment models, using standardized catch per unit effort (an index of relative abundance) as a key piece of information to determine catch quotas and achieve sustainability. However, these estimates assume hyperstability for the total population, ignoring spatial dependence among fishing sites, which is a fundamental concept for populations structured as metapopulation. We develop a Bayesian catch standardization model with explicit spatial dependence to better address the structure of this population. The proposed model performs statistically better compared to a model without spatial dependence, based on leave-one-out cross-validation, and predictive distributions also show that parameter estimation is consistent with the data. We argue that incorporating spatial structure improves the estimated relative abundance index in a population structured as a metapopulation. Our improved index of abundance will lead to better assessments and management advice, thus improving the sustainability of the stock.
KW - Bayesian inference
KW - Catch per unit effort (CPUE)
KW - Metapopulation
KW - Probabilistic modelling
KW - Spatial model
UR - http://www.scopus.com/inward/record.url?scp=85104423947&partnerID=8YFLogxK
U2 - 10.1016/j.fishres.2021.105960
DO - 10.1016/j.fishres.2021.105960
M3 - Article
AN - SCOPUS:85104423947
VL - 240
JO - FISHERIES RESEARCH
JF - FISHERIES RESEARCH
SN - 0165-7836
M1 - 105960
ER -