EconPapers    
Economics at your fingertips  
 

Transforming Data and Capacity-Limited Stock Assessment: Achieving Risk Equivalence with Hierarchical Assessment Frameworks and Auxiliary Data

Laurence T. Kell (), Massimiliano Cardinale, Iago Mosqueira, Henning Winker and Rishi Sharma
Additional contact information
Laurence T. Kell: Centre for Environmental Policy, Imperial College London, Weeks Building, 16-18 Prince’s Gardens, London SW7 1NE, UK
Massimiliano Cardinale: Department of Aquatic Resources, Institute of Marine Research, Swedish University of Agricultural Sciences, Turistgatan 5, SE-453 30 Lysekil, Sweden
Iago Mosqueira: Wageningen Marine Research, Haringkade 1, 1976 CP IJmuiden, The Netherlands
Henning Winker: Department of Aquatic Resources, Institute of Marine Research, Swedish University of Agricultural Sciences, Turistgatan 5, SE-453 30 Lysekil, Sweden
Rishi Sharma: Food and Agricultural Organization, Fishery and Aquaculture Division, 00153 Rome, Italy

Sustainability, 2025, vol. 17, issue 21, 1-26

Abstract: Ensuring the sustainability of fisheries worldwide requires that scientific advice remain effective even when data and capacity are limited. To address these challenges, we propose a hierarchical assessment framework (HAF) capable of integrating auxiliary information, such as empirical indicators for fishing pressure, within a Bayesian state-space biomass dynamic modelling framework. The aim is to provide risk-equivalent advice to ensure that management does not penalise data-limited fisheries with undue precaution (and loss of potential yield), nor expose them to a higher risk of overexploitation. To achieve this, we evaluated performance using classification skill metrics, such as true skill, for stock status relative to maximum sustainable yield (MSY)-based reference points. Results demonstrate that incorporating auxiliary data, particularly fishing mortality indices from periods of high exploitation, substantially improves the accuracy of stock status classification. Adoption of hierarchical assessment frameworks will support targeted data collection and evidence-based, adaptive fisheries management.

Keywords: Bayesian stock assessment; biomass-based; calibration; classification; length-based indicators; prediction skill; validation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/21/9383/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/21/9383/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:21:p:9383-:d:1777178

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-11-01
Handle: RePEc:gam:jsusta:v:17:y:2025:i:21:p:9383-:d:1777178