Uncertainty in Bioeconomic Modelling

Conrad, Jon M.

doi:10.1007/978-0-387-71815-6_12

Cited by 14 publications

(11 citation statements)

References 63 publications

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“…). The survey of bioeconomic models by Nøstbakken & Conrad () found that studies exploring the impacts of uncertainties in bioeconomic optimization models have focused on errors related to random variation and parameters. The potential effects of model error on predictions are generally overlooked (Hilborn & Mangel ).…”

Section: Introductionmentioning

confidence: 99%

Why model assumptions matter for natural resource management: interactions between model structure and life histories in fishery models

Hoshino

Milner‐Gulland

Hillary

2014

Journal of Applied Ecology

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Summary1. Bioeconomic models are increasingly used to provide benchmarks for harvest levels in wildlife and natural resource management, yet uncertainties related to model structure are underexplored. We investigate the importance of a range of uncertainties with a focus on model structure and life histories when estimating bioeconomic target reference point (TRPs) and assess the policy implications of ignoring these uncertainties. 2. We use three contrasting case studies to investigate the interactions between model, observational and process errors related to life-history parameters: the short-lived Japanese common squid Todarodes pacificus and Pacific saury Cololabis saira, and the slow-growing Patagonian toothfish Dissostichus eleginoides. We developed a simulation framework to test the harvest strategies resulting from bioeconomic TRPs under various assumptions about model structures and parameters. 3. We found the relative importance of different types of uncertainties affecting precision and accuracy of the model outputs varied according to the life-history traits. Little difference in TRP estimates was found between simple vs. complex population models for saury, while large differences were found for toothfish. The assumptions made about stock structure for squid not only resulted in different TRP estimates (generally, smaller for the multistock models), but also different economic outcomes depending on the balance of effort allocation between stocks. 4. Synthesis and applications. We use models similar to those used in the actual management of three case study species to explore the effects of interacting uncertainties on the management advice. We show that the interactions between structural elements of the models lead to very different management advice, depending on the life history of the species concerned. For the long-lived toothfish, life-history and gear selectivity parameters interacted strongly. For the short-lived squid which is managed as two stocks, spatial fishing effort allocation, correlation of environmental drivers between stocks and differential stock productivity interacted, producing very poor economic performance if assumptions about stock structure are incorrect. The key message for model-based natural resource management is that it is vital to investigate the major uncertainties related to model structure, process and estimation errors simultaneously, because they interact to produce non-intuitive results.

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Section: Introductionmentioning

confidence: 99%

Why model assumptions matter for natural resource management: interactions between model structure and life histories in fishery models

Hoshino

Milner‐Gulland

Hillary

2014

Journal of Applied Ecology

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“…Problems in fishery resource management are not the exception . Nøstbakken and Conrad reviewed uncertainties encountered in bio‐economic modeling, focusing on dynamic programming as the central tool for their resolution. Trade‐offs between costs and benefits of fishery resource management can be formulated in this context …”

Section: Introductionmentioning

confidence: 99%

Optimal harvesting policy of an inland fishery resource under incomplete information

Yoshioka

Yaegashi

Yoshioka

et al. 2019

Appl Stoch Models Bus & Ind

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A new mathematical model for finding the optimal harvesting policy of an inland fishery resource under incomplete information is proposed in this paper. The model is based on a stochastic control formalism in a regime‐switching environment. The incompleteness of information is due to uncertainties involved in the body growth rate of the fishery resource: a key biological parameter. Finding the most cost‐effective harvesting policy of the fishery resource ultimately reduces to solving a terminal and boundary value problem of a Hamilton‐Jacobi‐Bellman equation: a nonlinear and degenerate parabolic partial differential equation. A simple finite difference scheme for solving the equation is then presented, which turns out to be convergent and generates numerical solutions that comply with certain theoretical upper and lower bounds. The model is finally applied to the management of Plecoglossus altivelis, a major inland fishery resource in Japan. The regime switching in this case is due to the temporal dynamics of benthic algae, the main food of the fish. Model parameter values are identified from field measurement results in 2017. Our computational results clearly show the dependence of the optimal harvesting policy on the river environmental and biological conditions. The proposed model would serve as a mathematical tool for fishery resource management under uncertainties.

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“…However, most of the economic and biological processes take place in an uncertain environment in reality (Charles & Munro, 1985). Uncertainties in fishery include stock measurement error, parameter estimation errors, environmental variability influencing the growth of fish stocks, structural uncertainty and model error (Charles, 1998;Sethi, Costello, Fisher, Hanemann, & Karp, 2005;Nøstbakken & Conrad, 2007;Roughgarden & Smith, 1996;Poudel, Sandal, & Kvamsdal, 2015;Kvamsdal, Poudel, & Sandal, 2016). Most of the extant literature that evaluates long-term stock management does not consider such uncertainties sufficiently.…”

Section: Introductionmentioning

confidence: 99%

Greed is Good: From Super-Harvest to Recovery in a Stochastic Predator-Prey System

Sandal

Kvamsdal

et al. 2019

SSRN Journal

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This paper demonstrates a predator-prey system of cod and capelin that confronts a possible scenario of prey extinction under the first-best policy in a stochastic world. We discover a novel 'super-harvest' phenomenon that the optimal harvest of the predator is even higher than the myopic policy, or the 'greedy solution', on part of the state space. This intrinsic attempt to harvest more predator to protect the prey is a critical evidence supporting the idea behind 'greed is good'. We ban prey harvest and increase predator harvest in a designated state space area based on the optimal policy. Three heuristic recovery plans are generated following this principle. We employ stochastic simulations to analyse the probability of prey recovery and evaluate corresponding costs in terms of value loss percentage. We find that the alternative policies enhance prey recovery rates mostly around the area of 50% recovery probability under the optimal policy. When we scale up the predator harvest by 1.5, the prey recovery rate escalates for as much as 28% at a cost of 5% value loss. We establish two strategies: modest deviation from the optimal on a large area or intense measure on a small area. It seems more cost-effective to target the stock space with accuracy than to simply boost predator harvest when the aim is to achieve remarkable improvement of prey recovery probability.

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Uncertainty in Bioeconomic Modelling

Cited by 14 publications

References 63 publications

Why model assumptions matter for natural resource management: interactions between model structure and life histories in fishery models

Why model assumptions matter for natural resource management: interactions between model structure and life histories in fishery models

Optimal harvesting policy of an inland fishery resource under incomplete information

Greed is Good: From Super-Harvest to Recovery in a Stochastic Predator-Prey System

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