Evaluating alternative estimators of fishery management reference points

Haltuch, Melissa A.; Punt, André E.; Dorn, Martin W.

doi:10.1016/j.fishres.2008.01.008

Cited by 55 publications

(42 citation statements)

References 11 publications

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“…This is often considered for changes in maturity or weights at age or changes in the fishery selectivity patterns by using the most recent estimates, although longer-term considerations can also be made (e.g., Haltuch et al 2009). However, changes in the natural mortality rate generally have a much larger impact on biological reference points (Haltuch et al 2008). As demonstrated above in the Georges Bank yellowtail flounder example, steepness is a function of M when the SR curve is fixed.…”

Section: Discussionmentioning

confidence: 99%

In what direction should the fishing mortality target change when natural mortality increases within an assessment?

Legault

Palmer

2016

Can. J. Fish. Aquat. Sci.

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Traditionally, the natural mortality rate (M) in a stock assessment is assumed to be constant. When M increases within an assessment, the question arises how to change the fishing mortality rate target (F Target ). Per recruit considerations lead to an increase in F Target , while limiting total mortality leads to a decrease in F Target . Application of either approach can result in nonsensical results. Short-term gains in yield associated with high F Target values should be considered in light of potential losses in future yield if the high total mortality rate leads to a decrease in recruitment. Examples using yellowtail flounder (Limanda ferruginea) and Atlantic cod (Gadus morhua) are used to demonstrate that F Target can change when M increases within an assessment and to illustrate the consequences of different F Target values. When a change in M within an assessment is contemplated, first consider the amount and strength of empirical evidence to support the change. When the empirical evidence is not strong, we recommend using a constant M. If strong empirical evidence exists, we recommend estimating F Target for a range of stock-recruitment relationships and evaluating the trade-offs between risk of overfishing and forgone yield.Résumé : Traditionnellement, le taux de mortalité naturelle (M) dans une évaluation de stock est présumé être constant. Quand M augmente dans une évaluation, cela soulève la question à savoir comment changer le taux cible de mortalité par pêche (F Target ). Des considérations relatives aux recrues individuelles mènent à une augmentation de F Target , alors que le fait de limiter la mortalité totale mène à une réduction de F Target . L'application d'une ou l'autre de ces approches peut se traduire par des résultats qui n'ont aucun sens. Les augmentations à court terme du rendement associées à des valeurs de F Target élevées devraient être évaluées par rapport aux réductions potentielles du rendement futur si le taux de mortalité totale élevé se traduit par une baisse du recrutement. Des exemples basés sur la limande à queue jaune (Limanda ferruginea) et la morue franche (Gadus morhua) sont utilisés pour démontrer que F Target peut changer quand M augmente dans une évaluation et pour illustrer les conséquences de différentes valeurs de F Target . Quand la possibilité de changer M dans une évaluation est examinée, il faut d'abord prendre évaluer la quantité et la force des preuves empiriques qui appuient ce changement. Si ces preuves ne sont pas fortes, nous recommandons d'utiliser un M constant. Si les preuves empiriques sont fortes, nous recommandons d'estimer F Target pour une gamme de relations stock-recrutement et d'évaluer les compromis entre le risque de surpêche et le rendement non réalisé. [Traduit par la Rédaction]

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Section: Discussionmentioning

confidence: 99%

In what direction should the fishing mortality target change when natural mortality increases within an assessment?

Legault

Palmer

2016

Can. J. Fish. Aquat. Sci.

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“…structural uncertainty), and the effect of observation and process errors (ICES, 2004;Linton and Bence, 2008;Wetzel and Punt, 2011;Deroba and Schueller, 2013). Much of this previous simulation work, however, was based on generic fish populations or was designed for applications to specific fish stocks (Kell et al, 1999;Haltuch et al, 2008). Some have suggested that results from generic studies are too broad to be valid for specific cases, while, conversely, others have argued that specific applications are too narrow to be generally relevant (ICES, 2012a).…”

Section: Introductionmentioning

confidence: 99%

Simulation testing the robustness of stock assessment models to error: some results from the ICES strategic initiative on stock assessment methods

Deroba¹,

Butterworth

Methot³

et al. 2014

ICES Journal of Marine Science

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The World Conference on Stock Assessment Methods (July 2013) included a workshop on testing assessment methods through simulations. The exercise was made up of two steps applied to datasets from 14 representative fish stocks from around the world.Step 1 involved applying stock assessments to datasets with varying degrees of effort dedicated to optimizing fit.Step 2 was applied to a subset of the stocks and involved characteristics of given model fits being used to generate pseudo-data with error. These pseudo-data were then provided to assessment modellers and fits to the pseudo-data provided consistency checks within (self-tests) and among (cross-tests) assessment models. Although trends in biomass were often similar across models, the scaling of absolute biomass was not consistent across models. Similar types of models tended to perform similarly (e.g. age based or production models). Self-testing and cross-testing of models are a useful diagnostic approach, and suggested that estimates in the most recent years of time-series were the least robust. Results from the simulation exercise provide a basis for guidance on future large-scale simulation experiments and demonstrate the need for strategic investments in the evaluation and development of stock assessment methods.

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“…In keeping with the MSE approach, the present study considered both the "true" simulated population and the "perceived" population; the population that would be estimated to exist from the stock assessment, taking into account measurement error (Haltuch et al, 2008). The PHCR was applied to the "perceived" population while the performance was measured with respect to the "true" population.…”

Section: Methodsmentioning

confidence: 99%

Initial tests of the robustness of the provisional harvest control rule in Canada’s Sustainable Fisheries Policy to process and measurement errors using simulated depleted fish populations

Shelton¹

2017

J. Northw. Atl. Fish. Sci.

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(PHCR) is proposed in the DMF to allow adjustments of the annual total allowable catch based on a scientific assessment of the state of the stock. The DMF defines three spawning stock biomass Zones (Critical, Cautious and Healthy). The PHCR adjusts fishing mortality dependent on the Zone within which the spawning stock biomass is estimated to fall. Elements of the PHCR have been incorporated in the scientific advice and management approaches for a number of Canadian fish stocks. In this study, initial evaluation of the PHCR was carried out on three simulated depleted fish populations with different life histories under a variety of combinations of process error on recruitment and measurement error on spawning stock biomass. The simulations represent "bestcase" scenarios because reference points were assumed to be known exactly and the magnitude of the errors was moderate. The simulation results suggested that fish stocks in the Critical Zone should rebuild to the Healthy Zone under the PHCR with high probability (>0.78) irrespective of life history differences and the combinations of process and observations errors. However, the time to rebuild was up to twice as long as it took in the absence of fishing and the PHCR was not effective in ensuring the DMF requirement of a low probability (<0.1) of the population returning to the Cautious Zone. The PHCR was also not effective in keeping fishing mortality below the level that generates maximum sustainable yield when the stock was in the Cautious Zone and subject to measurement error. Variation in the annual catch generated by the PHCR in the simulations increased with increasing process and observation errors to a maximum CV of 0.6, which may be inconsistent with the fishing industry's desire for low variation in annual catch.

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Evaluating alternative estimators of fishery management reference points

Cited by 55 publications

References 11 publications

In what direction should the fishing mortality target change when natural mortality increases within an assessment?

In what direction should the fishing mortality target change when natural mortality increases within an assessment?

Simulation testing the robustness of stock assessment models to error: some results from the ICES strategic initiative on stock assessment methods

Initial tests of the robustness of the provisional harvest control rule in Canada’s Sustainable Fisheries Policy to process and measurement errors using simulated depleted fish populations

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