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Shaping sustainable harvest boundaries for marine populations despite estimation bias

Goto, Daisuke and Devine, Jennifer A. and Umar, Ibrahim and Fischer, Simon H. and De Oliveira, Jose A. A. and Howell, Daniel and Jardim, Ernesto and Mosqueira, Iago and Ono, Kotaro (2022). Shaping sustainable harvest boundaries for marine populations despite estimation bias. Ecosphere. 13 :2 , e3923
[Research article]

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Abstract

Biased estimates of population status are a pervasive conservation problem. This problem has plagued assessments of commercial exploitation of marine species and can threaten the sustainability of both populations and fisheries. We develop a computer-intensive approach to minimize adverse effects of persistent estimation bias in assessments by optimizing operational harvest measures (harvest control rules) with closed-loop simulation of resource-management feedback systems: management strategy evaluation. Using saithe (Pollachius virens), a bottom water, apex predator in the North Sea, as a real-world case study, we illustrate the approach by first diagnosing robustness of the existing harvest control rule and then optimizing it through propagation of biases (overestimated stock abundance and underestimated fishing pressure) along with select process and observation uncertainties. Analyses showed that severe biases lead to overly optimistic catch limits and then progressively magnify the amplitude of catch fluctuation, thereby posing unacceptably high overharvest risks. Consistent performance of management strategies to conserve the resource can be achieved by developing more robust control rules. These rules explicitly account for estimation bias through a computational grid search for a set of control parameters (threshold abundance that triggers management action, B-trigger, and target exploitation rate, F-target) that maximize yield while keeping stock abundance above a precautionary level. When the biases become too severe, optimized control parameters-for saithe, raising B-trigger and lowering F-target-would safeguard against a overharvest risk (<3.5% probability of stock depletion) and provide short-term stability in catch limit (<20% year-to-year variation), thereby minimizing disruption to fishing communities. The precautionary approach to fine-tuning adaptive risk management through management strategy evaluation offers a powerful tool to better shape sustainable harvest boundaries for exploited resource populations when estimation bias persists. By explicitly accounting for emergent sources of uncertainty, our proposed approach ensures effective conservation and sustainable exploitation of living marine resources even under profound uncertainty.

Authors/Creators:Goto, Daisuke and Devine, Jennifer A. and Umar, Ibrahim and Fischer, Simon H. and De Oliveira, Jose A. A. and Howell, Daniel and Jardim, Ernesto and Mosqueira, Iago and Ono, Kotaro
Title:Shaping sustainable harvest boundaries for marine populations despite estimation bias
Series Name/Journal:Ecosphere
Year of publishing :2022
Volume:13
Number:2
Article number:e3923
Number of Pages:14
Publisher:WILEY
ISSN:2150-8925
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Fish and Aquacultural Science
(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 405 Other Agricultural Sciences > Fish and Wildlife Management
Keywords:decision-making, environmental stochasticity, fisheries management, management procedure, management strategy evaluation, measurement error, precautionary principle, retrospective pattern, risk analysis, state-space model, stock assessment, trade-offs
URN:NBN:urn:nbn:se:slu:epsilon-p-116490
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-116490
Additional ID:
Type of IDID
DOI10.1002/ecs2.3923
Web of Science (WoS)000760264100007
ID Code:27533
Deposited By: SLUpub Connector
Deposited On:12 Apr 2022 13:25
Metadata Last Modified:12 Apr 2022 13:31

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