Accounting for spatial dependence improves relative abundance estimates in a benthic marine species structured as a metapopulation

Aug 2, 2026

Upload by Jiven

Sea urchin (Loxechinus albus) supports an important benthic fishery in Chile, yet its local abundance recovery depends on larval dispersal across a large, spatial metapopulation structure. Classical stock assessment approaches often standardize CPUE and assume hyperstability for the total population, which can ignore spatial dependence among fishing sites. The study develops a Bayesian catch standardization model with explicit spatial dependence, improving statistical performance versus a non-spatial alternative. Leave-one-out cross-validation and predictive distributions indicate consistent parameter estimation, implying better relative abundance indices for assessment and management.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Abstract

1. Introduction

CPUE and stock assessment assumptions

Metapopulation structure and sea urchin context

This is an electronic reprint of the original article. This reprint may differ from the original in pagination and typographic detail. Cavieres, Joaquin; Monnahan, Cole C. ; Vehtari, Aki Accounting for spatial dependence improves relative abundance estimates in a benthic marine species structured as a metapopulation Published in: FISHERIES RESEARCH DOI: 10.1016/j.fishres.2021.105960 Published: 01/08/2021 Document Version Peer reviewed version Published under the following license: CC BY-NC-ND Please cite the original version: Cavieres, J. , Monnahan, C. C. , & Vehtari, A. (2021) . Accounting for spatial dependence improves relative abundance estimates in a benthic marine species structured as a metapopulation. FISHERIES RESEARCH, 240,[105960] . [https://doi.org/10.1016/j.fishres.2021.105960](https://doi.org/10.1016/j.fishres.2021.105960) This material is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you foryour research use or educational purposes in electronic or print form. You must obtain permission for anyother use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user. Accounting for spatial dependence improves relative abundance estimates in a benthic marine species structured as a metapopulation Joaquin Cavieresa,, Cole C. Monnahanb , Aki Vehtaric a Department of Statistics, University of Valpara􀀓􀀐so, Chile b Resource Ecology and Fisheries Management, National Marine Fisheries Service (NOAA), Seattle, Washington, United States c Department of Computer Science, Aalto University, Finland Abstract 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 e􀀋ort (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 􀀌shing 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. Keywords: Metapopulation, catch per unit e􀀋ort (CPUE), spatial model, Bayesian inference, Probabilistic modelling 1 1. Introduction 2 Catch per unit e􀀋ort (CPUE) is a crucial variable in 􀀌shery science and often assumped propor- 3 tional to the abundance of a particular 􀀌shery resource over time. But nevertheless many factors can 4 alter this relationship. For example, the assumption of proportionality does not hold when factors 5 a􀀋ect catchability but not abundance ([1][2]) . Hence, it is necessary to incorporate variables that are 6 not related to population abundance (e.g., spatial variation in e􀀋ort and temporal gear e􀀎ciency) 7 and should be accounted for in CPUE standardization ([2]) . CPUE is a key source of information for 8 changes in abundance in assessment models, an

FAQs

Why is spatial dependence important for estimating local abundance in a metapopulation?

Local abundance recovery depends on distance and hydrodynamic characteristics because subpopulations are interconnected by larval dispersion across space.

What limitation exists in classical CPUE-based stock assessment models?

They often assume hyperstability for the total population and ignore spatial dependence among fishing sites, which can produce unreliable relative abundance indices.

How does the proposed approach improve relative abundance estimation?

It uses a Bayesian catch standardization model with explicit spatial dependence, showing better performance than a non-spatial model via leave-one-out cross-validation and consistent predictive distributions.

Aug 2, 2026

Upload by Jiven