A length-based population model and stock–recruitment relationships for red king crab, <i>Paralithodes camtschaticus</i>, in Bristol Bay, Alaska

Zheng, Jie; Murphy, Margaret; Kruse, Gordon H.

doi:10.1139/f95-120

Cited by 94 publications

(117 citation statements)

References 14 publications

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“…Year-specific estimates of M for northern shrimp in Kachemak Bay, Alaska, showed an increasing trend through time, which may have resulted from increasing predation pressure from groundfish (Fu and Quinn 2000). Based on a review of previously published estimates, Zheng et al (1995a) concluded that M was lower for intermediate-size red king crabs than it was for smaller and larger crabs and that M varied substantially over time. Thus, they included effects of sex, size, and year on M for red king crab in a population dynamics model.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, they included effects of sex, size, and year on M for red king crab in a population dynamics model. Concerns about overparameterization of the model led them to develop a more parsimonious version that removed the size dependence in M, but results were similar (Zheng et al 1995b). Siddeek et al (2002) estimated M separately for male and female king crabs, but attempts to estimate M by year were unsuccessful.…”

Section: Discussionmentioning

confidence: 99%

“…Fu and Quinn (2000) estimated M as a parameter within a length-based population dynamics model for northern shrimp Pandalus borealis in Kachemak Bay, Alaska. Zheng et al (1995aZheng et al ( , 1995b reviewed published estimates of M for red king crab and also estimated it as a parameter within a lengthbased population model. Similarly, Siddeek et al (2002) estimated M within length-based and age-based population models for red king crab and golden king crab Lithodes aequispinus.…”

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Direct and Indirect Estimates of Natural Mortality for Chesapeake Bay Blue Crab

et al. 2007

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Abstract.-Analyses of the population dynamics of blue crab Callinectes sapidus have been complicated by a lack of estimates of the instantaneous natural mortality rate (M). We developed the first direct estimates of M for this species by solving Baranov's catch equation for M given estimates of annual survival rate and exploitation rate. Annual survival rates were estimated from a tagging study on adult female blue crabs in Chesapeake Bay, and female-specific exploitation rates for the same stock were estimated by comparing commercial catches with abundances estimated from a dredge survey. We also used eight published methods Our results indicate that natural mortality of blue crab is higher than previously believed, and we consider M values between 0.7 and 1.1 per year to be reasonable for the exploitable stock in Chesapeake Bay. Remaining uncertainty about M makes it necessary to evaluate a range of estimates in assessment models.The estimation of natural mortality rates is one of the most difficult and most critical elements of many fishery stock assessments. The natural mortality rate is a key determinant of the potential productivity of a stock and thus the amount of exploitation a stock can sustain. In general, assuming that natural mortality and harvest mortality are additive, stocks with higher natural mortality rates are more productive and are able to sustain higher rates of exploitation. Lacking evidence to the contrary, most stock assessments assume that natural mortality is constant through time as well as across the sizes or ages of the exploited animals. Thus, a single estimate of the instantaneous natural mortality rate (M) is presumed to apply to the entire exploitable stock.The values used for M in assessment models can have substantial effects on model results, biological conclusions, and management recommendations. For a simple age-structured model, Clark (1999) found that stock abundance and target harvest rates could be severely overestimated when M was overestimated by as little as 0.1 per year or less, especially when fishing mortality was low (F , 0.3 per year). Similarly, harvest policies for U.S. West Coast groundfish based on a catch-at-age model were sensitive to changes in M of less than 0.05 per year (Williams 2002). Using a length-structured model for red king crab Paralithodes camtschaticus in Bristol Bay, Alaska, Zheng et al. (1997a, 1997b found that stock rebuilding and longterm harvest strategies were highly sensitive to changes in M of 0.2-0.3 per year. These and other results indicate that it is desirable to have precise knowledge about M for assessment purposes.Unfortunately, estimates of M used in stock assessment models are often uncertain, partly because it is difficult and expensive to estimate the parameter. In practice, values of M for use in stock assessments are obtained by two types of methods, which we refer to as direct and indirect. Direct methods involve estimating M from data pertaining solely to the species or stock of interest. Direct methods include field ...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Direct and Indirect Estimates of Natural Mortality for Chesapeake Bay Blue Crab

et al. 2007

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“…Обозначим количество животных, погибших по естественным причинам, символом C t ,a , тогда схему эксплуатируемой популяции рыб, согласно уравнениям Виртуально-популяционный анализ динамики численности промысловых ракообразных на основе возрастных когорт не получил широкого применения в силу сложности определения точного возраста животного (ICES, 2001a;ICES, 2001б). Однако с начала 90-х годов прошлого столетия, основные принципы изменения численности поколений, разработанные Ф. И. Барановым, стали с успехом применяться в моделировании динамики численности крабов и креветок (Collie, 1991;Kruse and Collie, 1991;Zheng et al, 1995;Quinn et al, 1998;Cadrin, 2000). Модели этого типа базировались на изменениях численности не поколений, а размерных групп (LBA, length-based analysis) и учитывали линьку и стохастический рост животных.…”

Section: модели популяционной динамикиunclassified

“…Пополнение (R) моделируемой популяции камчатского краба американские исследователи описали с помощью двух параметров: 1) количество молодых крабов, пополняющих популяцию в году t, R t и 2) параметр U l , определяющий стохастическое изменение процесса пополнения (Sullivan et al, 1990;Zheng et al, 1995). Пополнение каждой размерной группы R l, t выражалось через уравнение:…”

Section: модели популяционной динамикиunclassified