TY - JOUR

T1 - Shaping sustainable harvest boundaries for marine populations despite estimation bias

AU - Goto, Daisuke

AU - Devine, Jennifer A.

AU - Umar, Ibrahim

AU - Fischer, Simon H.

AU - De Oliveira, José A. A.

AU - Howell, Daniel

AU - Jardim, Ernesto

AU - Mosqueira, Iago

AU - Ono, Kotaro

PY - 2022/2/6

Y1 - 2022/2/6

N2 - 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, Btrigger, and target exploitation rate, Ftarget) that maximize yield while keeping stock abundance above a precautionary level. When the biases become too severe, optimized control parameters?for saithe, raising Btrigger and lowering Ftarget?would safeguard against a overharvest risk (

AB - 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, Btrigger, and target exploitation rate, Ftarget) that maximize yield while keeping stock abundance above a precautionary level. When the biases become too severe, optimized control parameters?for saithe, raising Btrigger and lowering Ftarget?would safeguard against a overharvest risk (

KW - decision-making

KW - environmental stochasticity

KW - fisheries management

KW - management procedure

KW - management strategy evaluation

KW - measurement error

KW - precautionary principle

KW - retrospective pattern

KW - risk analysis

KW - state-space model

KW - stock assessment

KW - trade-offs

U2 - 10.1002/ecs2.3923

DO - 10.1002/ecs2.3923

M3 - Article

VL - 13

JO - Ecosphere

JF - Ecosphere

SN - 2150-8925

IS - 2

M1 - e3923

ER -