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Fisheries typically experience large changes over time in fishing effort. The size of the area fished may also change substantially over time, mimicking the trend in fishing effort, and may have major effects on the population dynamics and fishing process. We extend a biomass dynamics model to incorporate fished area and relative fish density in fished and unfished areas. The fishable population is defined as those individuals in the fished area and those that are sufficiently close to the fished area that they could potentially move into fished area during the fishing season. We estimate fishable biomass using three models assuming different level of population mixing between fished and unfished areas (i.e., partial mixing, full mixing, and no mixing). The models are implemented within a hierarchical Bayesian framework. Model performance is explored using simulations, and the approach is illustrated using logbook data for two tiger prawn species in Australia's Northern Prawn Fishery. The partial mixing model that involves estimating a mixing parameter performs better than the models that assume no or full mixing. The methods could be applied to other fisheries where the area fished has changed substantially over the history of the fishery. Les peches commerciales connaissent typiquement de grands changements temporels de leur effort de peche. La surface de la zone de peche peut aussi changer de maniere substantielle dans le temps, mimant la tendance de l'effort de peche, et peut avoir un effet majeur sur la dynamique de la population et sur le processus de peche. Nous faisons l'extension d'un modele de la dynamique de la biomasse afin d'inclure la surface pechee et la densite relative des poissons dans les zones pechees et non pechees. Nous definissons la population capturable comme contenant les individus dans la zone pechee et ceux qui sont suffisamment proches de la zone de peche qu'ils pourraient penetrer dans la zone de peche durant la saison de capture. Nous estimons la biomasse capturable a l'aide de trois modeles qui presupposent des niveaux differents de melange entre les zones pechees et non pechees (c.-a-d. melange partiel, melange total et melange nul). Les modeles sont utilises dans un cadre hierarchique bayesien. Nous explorons la performance des modeles a l'aide de simulations et illustrons la methode a partir de donnees tirees des journaux de peche sur deux especes de crevettes tigrees de la peche commerciale de crevettes du nord de l'Australie. Le modele de melange partiel qui estime un parametre de melange fonctionne mieux que les modeles qui presupposent un melange complet ou nul. Ces methodes peuvent s'appliquer a d'autres pecheries commerciales dans lesquelles la zone de peche a considerablement change durant l'histoire de cette peche. [Traduit par la Redaction], Introduction Industrial fisheries typically experience substantial changes in fishing effort and catch over their history. This has been conceptualized as the 'fisheries cycle' (Food and Agriculture Organization of the United [...]

Catchability and natural mortality are key quantities in fisheries stock assessment. However, it is difficult to estimate these two parameters simultaneously using only fishery catch and effort data. A Bayesian state-space modified delay-difference model is outlined that can estimate time series of catchability by fleet as well as natural mortality. This model, and three variants thereof, is fitted to data for grooved tiger prawns (Penaeus semisulcatus) in Australia's Northern Prawn Fishery during the period of the year when there is little recruitment. A model that allows for both observation and process error and estimates natural mortality is best, in terms of model selection criteria as well as fit diagnostics. The posterior median estimate for catchability for the primary target fleet ranges from 6.15 X [10.sup.-4] to 1.09 X [10.sup.-4] during 1980-2007, while the posterior median estimate for catchability for a fleet with P. semisulcatus as its byproduct is about 20% of that for the primary fleet. Fishing efficiency increased at approximately 2% annually during 1980-2007, while the weekly natural mortality is estimated to be 0.053 [week.sup.-1]. La capturabilite et la mortalite naturelle sont deux quantites essentielles dans revaluation des stocks de peche. Il est, cependant, difficile d'estimer ces deux variables simultanement a partir de seulement les donnees de capture et d'effort de peche. Nous presentons un modele bayesien modifie d'etat-espace et de delai difference qui peut estimer des series chronologiques de capturabilite par la flotte de peche, ainsi que la mortalite naturelle. Nous ajustons ce modele et trois de ses variae la peche commerciale nordique de crevettes (Penaeus semisulcatus) de l'Australie durant la periode de l'annee ou il y a peu de recrutement. Un modele qui tient compte a la fois des erreurs d'observation et de traitement et qui estime la mortalite naturelle fonctionne le mieux, en ce qui a trait aux criteres de selection des modeles et aux caracteres diagnostics de l'ajustement. L'estimation a posteriori de la mediane de la capturabilite par la flotte qui cible principalement les crevettes varie de 6,15 X [10.sup.-4] a 1,09 X [10.sup.-4] entre 1980 et 2007, alors que l'estimation a posteriori de la mediane de la capturabilite par la flotte qui recolte P. semisulcatus comme capture accessoire est d'environ 20 % de celle de la flotte principale. L'efficacite de la peche a augmente d'environ 2 % par annee de 1980 a 2007, alors que la mortalite naturelle hebdomadaire est estimee a 0,053 [semaine.sup.-1]. [Traduit par la Redaction], Introduction Stock assessments provide the basis for applying harvest control rules and hence providing fisheries management advice. In general, stock assessment involves fitting population dynamics models to available data using [...]

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Sharks (class Chondrichthyes, subclass Elasmobranchii) typically have a long lifespan, slow growth rate and low fecundity, leading to low productivity and hence relatively high vulnerability to fishing. Information for managing fisheries catches of elasmobranchs is often lacking, because elasmobranchs are usually non‐target species, often with low abundance. It is more feasible to develop management reference points for elasmobranchs based on their life‐history information than through traditional stock assessments. The natural mortality rate (M) is the leading life‐history parameter (LHP) required by many methods for developing reference points and is itself often derived indirectly from other LHPs. In this paper, we evaluate nine M estimators, using 15 shark stocks in the Western and Central Pacific Ocean as examples. We then compare four methods for developing fishing mortality reference points Fmsy, including empirical modelling, the Euler‐Lotka equation, an M‐productivity‐based method and the spawning potential rate converted method. Our analyses show that popular M estimators developed mainly from teleost data resulted in large deviations from the average and were not suitable for elasmobranchs. All four methods for estimating Fmsy performed similarly. However, the empirical method is very simple and cost‐effective and tended to produce smaller deviations from the average than the other three methods. Nonetheless, it is recommended that multiple methods should be used to minimize possible bias and reflect uncertainty, if the required LHPs are available. [ABSTRACT FROM AUTHOR]

Understanding recruitment variability in marine fisheries has benefits for the stock management and dependent fishers' ability to plan their income. Here, we overview past and new research on the complex recruitment dynamics of redleg banana prawns Penaeus indicus in Australia's Joseph Bonaparte Gulf to assess themes dating back to the time of Hjort and identify new challenges. During 2015 and 2016, redleg prawn catches and catch per unit effort decreased to anomalously low levels, suggesting a substantial decrease in prawn biomass. We hypothesized that low catches could be explained by temporary drops in sea level and rainfall potentially reducing the ability of postlarvae to reach their nursery ground. We contend that very bad prawn catch years may be predicted using two variables that are possible drivers of recruitment—the January Southern Oscillation Index and the combined January to February cumulative rainfall. However, due to challenges in verifying and defining such environmental relationships for inclusion in a stock assessment, we propose development of a harvest strategy framework to support management recommendations. Our study highlights the increasing role of anthropogenic climate change in exaggerating the impacts of environmental drivers on recruitment processes and the need to also focus on multidisciplinary research. [ABSTRACT FROM AUTHOR]