Further Information on Spawning Stock Size and Resultant Production for Skeena Sockeye

1964 ◽  
Vol 21 (5) ◽  
pp. 1329-1331 ◽  
Author(s):  
M. P. Shepard ◽  
F. C. Withler ◽  
J. McDonald ◽  
K. V. Aro
Keyword(s):  
Stock Size ◽  
Spawning Stock

not available

scholarly journals An explicit solution for calculating optimum spawning stock size from Ricker’s stock recruitment model

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1623 ◽  
Author(s):  
Mark D. Scheuerell
Keyword(s):  
Explicit Solution ◽  
Model Fitting ◽  
Maximum Sustainable Yield ◽  
Reference Points ◽  
Stock Size ◽  
Points Of Interest ◽  
Spawning Stock ◽  
Stock Recruitment ◽  
Explicit Formulae ◽  
Recruitment Model

Stock-recruitment models have been used for decades in fisheries management as a means of formalizing the expected number of offspring that recruit to a fishery based on the number of parents. In particular, Ricker’s stock recruitment model is widely used due to its flexibility and ease with which the parameters can be estimated. After model fitting, the spawning stock size that produces the maximum sustainable yield (SMSY) to a fishery, and the harvest corresponding to it (UMSY), are two of the most common biological reference points of interest to fisheries managers. However, to date there has been no explicit solution for either reference point because of the transcendental nature of the equation needed to solve for them. Therefore, numerical or statistical approximations have been used for more than 30 years. Here I provide explicit formulae for calculating bothSMSYandUMSYin terms of the productivity and density-dependent parameters of Ricker’s model.

scholarly journals Evaluation of the absolute levels of acoustic estimates of the 1983 year class of Norwegian spring-spawning herring

2003 ◽  
Vol 60 (3) ◽  
pp. 480-485 ◽  
Author(s):  
Ingolf Røttingen ◽  
Sigurd Tjelmeland
Keyword(s):  
Working Group ◽  
Assessment Model ◽  
Stock Size ◽  
Blue Whiting ◽  
Realistic Picture ◽  
Absolute Sense ◽  
Spawning Stock ◽  
The Absolute

Abstract The 1983 year class of Norwegian spring-spawning herring was large and, in retrospect, increased the spawning stock by more than two million tons when it recruited in 1987–1988. This paper summarizes and evaluates the acoustic estimates of the 1983 year class in the period 1983–2001. The key to the evaluation is a minimum stock based on the number of the 1983 year class caught in the international fishery and the year-class estimates made by the ICES Northern Pelagic and Blue Whiting Fisheries Working Group in 2002 using the SeaStar assessment model. The period of analysis covers a change in instrumentation, around 1990, from the SIMRAD EK400/Nord integrator to the EK500/BEI integrator system. The application of the acoustic estimates in the assessment and management of this herring stock is reviewed. It is concluded that the stock size was underestimated when the acoustic estimates were used in an absolute sense in the 1980s. In the 1990s the acoustic estimates were tuned to stock-size indices obtained from other methodologies and this approach seems to have given a realistic picture of the development of the 1983 year class.

Relations between Egg Production, Larval Production and Spawning Stock Size in Clyde Herring

1974 ◽  
pp. 129-138 ◽  
Author(s):  
A. Saville ◽  
I. G. Baxter ◽  
D. W. McKay
Keyword(s):  
Egg Production ◽  
Stock Size ◽  
Spawning Stock ◽  
Larval Production

Improving the stock-recruitment relationship in Icelandic cod (Gadus morhua) by including age diversity of spawners

1998 ◽  
Vol 55 (6) ◽  
pp. 1372-1377 ◽  
Author(s):  
Gudrun Marteinsdottir ◽  
Kristjan Thorarinsson
Keyword(s):  
Gadus Morhua ◽  
Extended Period ◽  
Shannon Index ◽  
Age Composition ◽  
Stock Size ◽  
Age Diversity ◽  
Spawning Stock ◽  
Stock Recruitment ◽  
Mature Fish

The size of the Icelandic cod stock has been gradually declining since the middle of this century. Recruitment has been poor over an extended period of time and much below the long-term average since 1985. Except for the concurrent decrease in stock size and recruitment during this period, the stock size - recruitment relationship is weak. This relationship is improved by including the age composition of the spawning stock. Spawning stock age diversity in each year from 1955 to 1992 was estimated with the Shannon index using the number of mature fish in each age group. By including information on age composition, 31% of the total variation in recruitment was accounted for by the model with stock size, age diversity, and the interaction between the two, compared with less than 15% by single factor models of either age diversity or stock size. These results indicate that age diversity is an important component in stock-recruitment models and that one of the management goals for fish species should be to maintain high age diversity in the spawning stocks.

Bias in Estimation of Stock-Recruit Function Parameters Caused by Nonrandom Environmental Variability

1988 ◽  
Vol 45 (3) ◽  
pp. 554-557 ◽  
Author(s):  
Michael J. Armstrong ◽  
Peter A. Shelton
Keyword(s):  
Monte Carlo ◽  
Random Distribution ◽  
Environmental Variability ◽  
Random Component ◽  
Periodic Forcing ◽  
Stock Size ◽  
Spawning Stock ◽  
The Mean ◽  
Parameter Precision ◽  
Parameter Values

Parameter estimation for stock–recruit models normally assumes a random distribution of residuals around the underlying function. Monte Carlo simulations, in which departures from the mean stock-recruit function were determined by periodic forcing with a random component, showed that bias may occur in the estimation of average parameter values if randomness is assumed. The bias occurred when the spawning stock size varied in-phase or out-of-phase with the periodic forcing and was greatest when the period was approximately twice the mean age of the spawning stock. In addition to bias, patterning of spawner stock size and recruitment data caused by the periodic variability gave misleading impressions of parameter precision.

Simplified Calculation of Optimum Spawning Stock Size from Ricker's Stock Recruitment Curve

1985 ◽  
Vol 42 (11) ◽  
pp. 1833-1834 ◽  
Author(s):  
Ray Hilborn
Keyword(s):  
Stochastic Variation ◽  
Simple Modification ◽  
Recruitment Curve ◽  
Stock Size ◽  
Spawning Stock ◽  
Stock Recruitment ◽  
Simplified Calculation

The optimum spawning stock size for a Ricker stock recruitment curve was shown to be accurately approximated by the equation Ps = Pr(0.5–0.07a) when 0 < a < 3. A simple modification was also shown to incorporate stochastic variation about the stock recruitment curve into calculations of optimum stock size.

Recruitment variation related to fecundity in marine fishes

2000 ◽  
Vol 57 (1) ◽  
pp. 116-124 ◽  
Author(s):  
S J Rickman ◽  
N K Dulvy ◽  
S Jennings ◽  
J D Reynolds
Keyword(s):  
Interannual Variation ◽  
Empirical Studies ◽  
Marine Fishes ◽  
Comparative Approach ◽  
Fish Stock ◽  
Closely Related Species ◽  
Stock Size ◽  
Evolutionary Relatedness ◽  
Spawning Stock ◽  
The Mean

An understanding of the processes that control recruitment variation is central to explaining the population dynamics of fishes and predicting their responses to exploitation. Theory predicts that interannual variation in recruitment should be positively correlated with the fecundity of fish species, but empirical studies have not supported this hypothesis. Here, we adopt a phylogenetic comparative approach, which accounts for evolutionary relatedness among stocks and species, to investigate this relationship. We calculated the mean fecundity of fishes from 52 stocks at the mean length of maturity and related this to interannual recruitment variation. We found that in 13 of 14 comparisons between stocks or closely related species, the stocks with higher fecundity have higher recruitment variation. This was true whether or not we controlled for spawning stock size. However, when the analyses were repeated using a traditional cross-species approach, which did not account for the evolutionary relatedness of stocks, the relationships were not significant. This is the first empirical study to link fecundity with recruitment variation and suggests that fecundity is an important component of fish stock dynamics.

scholarly journals Predicting Crappie Recruitment in Ohio Reservoirs with Spawning Stock Size, Larval Density, and Chlorophyll Concentrations

2006 ◽  
Vol 26 (1) ◽  
pp. 1-12 ◽  
Author(s):  
David B. Bunnell ◽  
R. Scott Hale ◽  
Michael J. Vanni ◽  
Roy A. Stein
Keyword(s):  
Larval Density ◽  
Stock Size ◽  
Spawning Stock ◽  
Chlorophyll Concentrations

scholarly journals Selecting relative abundance proxies for BMSY and BMEY

2013 ◽  
Vol 71 (3) ◽  
pp. 469-483 ◽  
Author(s):  
André E. Punt ◽  
Anthony D. M. Smith ◽  
David C. Smith ◽  
Geoffrey N. Tuck ◽  
Neil L. Klaer
Keyword(s):  
Fisheries Management ◽  
Carrying Capacity ◽  
Maximum Sustainable Yield ◽  
Commercial Fisheries ◽  
Specific Information ◽  
Stock Size ◽  
Stock Assessments ◽  
Spawning Stock ◽  
Potential Loss ◽  
Over Time

Abstract The objectives for many commercial fisheries include maximizing either yield or profit. Clearly specified management targets are a key element of effective fisheries management. Biomass targets are often specified for major commercial fisheries that are managed using quantitative stock assessments where biomass is calculated and tracked over time. BMSY, the biomass corresponding to Maximum Sustainable Yield, is often used as a target when maximizing yield is important, while BMEY is the biomass target to maximize profit. There are difficulties in estimating both quantities accurately, and this paper explores default proxies for each target biomass, expressed as biomass levels relative to carrying capacity, which are more easily estimated. Integration across a range of uncertainties about stock dynamics and the costs of fishing suggests that a proxy for BMSY in the range of 35–40% of carrying capacity minimizes the potential loss in yield compared with that which would arise if BMSY was known exactly, while a proxy for BMEY of 50–60% of carrying capacity minimizes the corresponding potential loss in profit. These estimates can be refined given stock-specific information regarding productivity (particularly the parameter which defines the resilience of recruitment to changes in spawning stock size) and costs and prices. It is more difficult to find a biomass level that achieves a high expected profit than a biomass level that achieves a high expected catch, because the former is sensitive to uncertainties related to costs and prices, as well as parameters which determine productivity.

Sign in / Sign up

Export Citation Format

Share Document