Model performance for the determination of appropriate harvest levels in the case of data-poor stocks

Wetzel, Chantell R.; Punt, André E.

doi:10.1016/j.fishres.2011.04.024

Cited by 40 publications

(35 citation statements)

References 16 publications

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“…The last five years have seen an increase in the development and testing of assessment methods to estimate sustainable yields and harvest levels for data-limited stocks (Rosenberg et al, 2014;Martell and Froese, 2013;Wetzel and Punt, 2011;MacCall, 2009). The category of data-limited stocks, in the sense of stocks without an analytical assessment, includes stocks with a considerable amount of information, although not always available or compiled.…”

Section: Introductionmentioning

confidence: 99%

Harvest control rules for data limited stocks using length-based reference points and survey biomass indices

2015

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a b s t r a c tThere are a large number of commercially exploited stocks lacking quantitative assessments and reliable estimates of stock status. Providing MSY-based advice for these data-limited stocks remains a challenge for fisheries science. For many data-limited stocks, catch length composition and/or survey biomass indices or catch-per-unit effort (cpue) are available. Information on life history traits may also be available or borrowed from similar species/stocks. In this work we present three harvest control rules (HCRs), driven by indicators derived from key monitoring data. These were tested through simulation using two exploitation scenarios (development and over-exploitation) applied to 50 stocks (pelagic, demersal, deep sea species and Nephrops). We examine the performance of the HCRs to deliver catch-based advice that is risk adverse and drives stocks to MSY. The HCR with a biomass index-adjusted status quo catch, used to provide catch-based advice for several European data-limited stocks, showed the poorest performance, keeping the biomass at low or very low levels. The HCRs that adjust the status quo catch based on the variability of the biomass index time series was able to drive most of the stocks to MSY, showing low to moderate biological risk. The recovery of biomass required asymmetric confidence intervals for the biomass index and larger decreases in status quo catch than increases. The HCR based on length reference points as proxies for the F SQ /F MSY ratio was able to reverse the decreasing trend in biomass but with levels of catch below MSY. This HCR did not prevent some of the stocks declining when subject to overexploitation. For data-limited stocks, the empirical HCRs tested in this work can provide the basis for catch advice. Nevertheless, applications to real life cases require simulation testing to be carried out to tune the HCRs. Our approach to simulation testing can be used for such analysis.

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Section: Introductionmentioning

confidence: 99%

Harvest control rules for data limited stocks using length-based reference points and survey biomass indices

2015

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“…For data-poor stocks, however, implementation of the P* approach is impossible, and setting ABCs that prevent overfishing for these stocks is challenging (Wetzel and Punt 2011). Recently, a number of approaches for setting ABCs for data-poor stocks have been developed.…”

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confidence: 99%

“…Wetzel and Punt (2011) conducted a simulation analysis to explore how well DB-SRA and DCAC estimated the catch that achieves F MSY (called the overfishing limit, or OFL) for species with life histories typical of groundfishes, principally flatfishes (order Pleuronectiformes) and members of the genus Sebastes, found off the western USA. They found that both DB-SRA and DCAC generally produced estimates of the OFL at or below the true values.…”

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An Evaluation of Harvest Control Rules for Data‐Poor Fisheries

Wiedenmann

Wilberg

Miller

2013

N American J Fish Manag

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For federally managed fisheries in the USA, National Standard 1 requires that an acceptable biological catch be set for all fisheries and that this catch avoid overfishing. Achieving this goal for data‐poor stocks, for which stock assessments are not possible, is particularly challenging. A number of harvest control rules have very recently been developed to set sustainable catches in data‐poor fisheries, but the ability of most of these rules to avoid overfishing has not been tested. We conducted a management strategy evaluation to assess several control rules proposed for data‐poor situations. We examined three general life histories (“slow,” “medium,” and “fast”) and three exploitation histories (under‐, fully, and overexploited) to identify control rules that balance the competing objectives of avoiding overfishing and maintaining high levels of harvest. Many of the control rules require information on species life history and relative abundance, so we explored a scenario in which unbiased knowledge was used in the control rule and one in which highly inflated estimates of stock biomass were used. Our analyses showed that no single control rule performed well across all scenarios, with those that performed well in the unbiased scenario performing poorly in the biased scenarios and vice versa. Only the most conservative data‐poor control rules limited the probability of overfishing across most of the life history and exploitation scenarios explored, but these rules typically required very conservative catches under the unbiased scenarios.Received June 22, 2012; accepted May 22, 2013

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“…In this regard, this paper is similar to recent work by Wetzel and Punt (2011) that evaluates the performance of other data-poor fisheries assessment methods (MacCall, 2009;Dick and MacCall, 2011).…”

Section: Discussionmentioning

confidence: 56%

Evaluating methods that classify fisheries stock status using only fisheries catch data

2012

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Model performance for the determination of appropriate harvest levels in the case of data-poor stocks

Cited by 40 publications

References 16 publications

Harvest control rules for data limited stocks using length-based reference points and survey biomass indices

Harvest control rules for data limited stocks using length-based reference points and survey biomass indices

An Evaluation of Harvest Control Rules for Data‐Poor Fisheries

Evaluating methods that classify fisheries stock status using only fisheries catch data

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