What if stock assessment is as simple as a linear model? The a4a
                    initiative

Jardim, Ernesto; Millar, Colin P.; Mosqueira, Iago; Finlay, Scott; Osio, Giacomo Chato; Ferretti, M.; Alzorriz, Nekane; Orio, Alessandro

doi:10.1093/icesjms/fsu050

Cited by 28 publications

(17 citation statements)

References 6 publications

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“…For this, an a4a stock assessment model was fitted using Markov chain Monte Carlo (MCMC). a4a is a statistical catch‐at‐age model implemented in R making use of the FLR platform and using automatic differentiation implemented in the Automatic Differentiation Model Builder (ADMB) as the optimization engine (Jardim et al, 2014). This process generated both structural uncertainty (through the models chosen for fishing mortality, recruitment and survey catchability) and estimation uncertainty (through the MCMC fit).…”

Section: Methodsmentioning

confidence: 99%

Selectivity metrics for fisheries management and advice

et al. 2020

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Fish and Fisheries. 2020;21:621-638. | 621 wileyonlinelibrary.com/journal/faf | INTRODUC TI ONAll fishing is fundamentally selective. Depending on what fishers want to catch, they choose gear type (e.g., longlines, set nets, trawls), gear specifications (e.g., mesh size, hook size), and the time and place to deploy these gears. These choices are influenced by the regulatory framework, that is gear regulations, limitations on the catch size and composition, and seasonal and temporal restrictions.Fisheries selectivity relates to desirable species (species selectivity) or sizes (size selectivity). There are three types of size selectivity: AbstractFisheries management typically aims at controlling exploitation rate (e.g., Fbar) to ensure sustainable levels of stock size in accordance with established reference points (e.g., F MSY , B MSY ). Population selectivity ("selectivity" hereafter), that is the distribution of fishing mortality over the different demographic components of an exploited fish stock, is also important because it affects both Maximum Sustainable Yield (MSY) and F MSY , as well as stock resilience to overfishing. The development of an appropriate metric could make selectivity operational as an additional lever for fisheries managers to achieve desirable outcomes. Additionally, such a selectivity metric could inform managers on the uptake by fleets and effects on stocks of various technical measures. Here, we introduce three criteria for selectivity metrics:(a) sensitivity to selectivity changes, (b) robustness to recruitment variability and (c) robustness to changes in Fbar. Subsequently, we test a range of different selectivity metrics against these three criteria to identify the optimal metric. First, we simulate changes in selectivity, recruitment and Fbar on a virtual fish stock to study the metrics under controlled conditions. We then apply two shortlisted selectivity metrics to six European fish stocks with a known history of technical measures to explore the metrics' response in real-world situations. This process identified the ratio of F of the first recruited age-class to Fbar (Frec/Fbar) as an informative selectivity metric for fisheries management and advice.

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Section: Methodsmentioning

confidence: 99%

Selectivity metrics for fisheries management and advice

et al. 2020

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“…The stock assessment model used here ( a4a [ 9 ]) is age-based making it necessary to convert the length-based indices and stock data to be age-based. This was done using a simple length-slicing method (see below) based on the von Bertalanffy growth equation [ 22 ] (the following methods are also appropriate for alternatives such as the Gompertz model [ 23 ]).…”

Section: Methodsmentioning

confidence: 99%

“…The a4a statistical catch-at-age stock assessment model was used to assess both of the age-based stocks [ 9 ]. The a4a model requires setting up three submodels for the fishing mortality (the fmodel ), the index catchability (the qmodel , one for each index) and recruitment (the rmodel ).…”

Section: Methodsmentioning

confidence: 99%

“…Generating the suite of candidate assessments is made more straightforward through the use of a flexible stock assessment framework that can include multiple sources of uncertainty. The assessment for all ( a4a ) initiative presents has been developed to allow uncertainty about biological processes such as growth and natural mortality to be taken into account and their uncertainties propagated through to the estimates of population abundance, fishing mortality and reference points by the stock assessment model [ 9 ]. To develop a range of assumptions on the biological processes the a4a approach encourages the use of information from diverse sources such as scientific papers, Ph.D. theses, Fishbase, other stocks, etc., and also the use of generic information on life history invariants to derive generic priors, as suggested by [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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An Applied Framework for Incorporating Multiple Sources of Uncertainty in Fisheries Stock Assessments

et al. 2016

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Estimating fish stock status is very challenging given the many sources and high levels of uncertainty surrounding the biological processes (e.g. natural variability in the demographic rates), model selection (e.g. choosing growth or stock assessment models) and parameter estimation. Incorporating multiple sources of uncertainty in a stock assessment allows advice to better account for the risks associated with proposed management options, promoting decisions that are more robust to such uncertainty. However, a typical assessment only reports the model fit and variance of estimated parameters, thereby underreporting the overall uncertainty. Additionally, although multiple candidate models may be considered, only one is selected as the ‘best’ result, effectively rejecting the plausible assumptions behind the other models. We present an applied framework to integrate multiple sources of uncertainty in the stock assessment process. The first step is the generation and conditioning of a suite of stock assessment models that contain different assumptions about the stock and the fishery. The second step is the estimation of parameters, including fitting of the stock assessment models. The final step integrates across all of the results to reconcile the multi-model outcome. The framework is flexible enough to be tailored to particular stocks and fisheries and can draw on information from multiple sources to implement a broad variety of assumptions, making it applicable to stocks with varying levels of data availability The Iberian hake stock in International Council for the Exploration of the Sea (ICES) Divisions VIIIc and IXa is used to demonstrate the framework, starting from length-based stock and indices data. Process and model uncertainty are considered through the growth, natural mortality, fishing mortality, survey catchability and stock-recruitment relationship. Estimation uncertainty is included as part of the fitting process. Simple model averaging is used to integrate across the results and produce a single assessment that considers the multiple sources of uncertainty.

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“…Methot and Wetzel, 2013), using splines to obtain a more flexible selection (e.g. Aarts and Poos, 2009;Butterworth, Ianelli, and Hilborn, 2003;Jardim et al, 2015) while keeping the number of parameters low, or allowing penalized deviances with fixed penalties (e.g. Methot and Wetzel, 2013).…”

Section: Evolution Of Stock Assessment Modelsmentioning

confidence: 99%

Review of State-Space Models for Fisheries Science

Aeberhard

Flemming

Nielsen³

2018

Annu. Rev. Stat. Appl.

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What if stock assessment is as simple as a linear model? The a4a initiative

Cited by 28 publications

References 6 publications

Selectivity metrics for fisheries management and advice

Selectivity metrics for fisheries management and advice

An Applied Framework for Incorporating Multiple Sources of Uncertainty in Fisheries Stock Assessments

Review of State-Space Models for Fisheries Science

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