Effects of process and/or observation errors on the stock–recruitment curve and the validity of the proportional model as a stock–recruitment relationship

Sakuramoto, Kazumi; Suzuki, Naoki

doi:10.1007/s12562-011-0438-4

Cited by 15 publications

(17 citation statements)

References 27 publications

(30 reference statements)

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“…However, the results are a matter of course, because the same values in the left-hand side were used in the right-hand side in equation (11). That is, the minimum AIC does not always indicate a valid model and does not show the validity of the existing of density-dependent effect [9]. The last two models in Table 3 are the cases when ln (R) was used as the dependent variable (Models 8 and 9).…”

Section: Discussionmentioning

confidence: 97%

Case Study: A Simulation Model of the Spawning Stock Biomass of Pacific Bluefin Tuna and Evaluation of Fisheries Regulations

Sakuramoto¹

2016

AJCC

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This study proposes a simulation model that well reproduces the spawning stock biomass of Pacific bluefin tuna. Environmental factors were chosen to estimate the recruitment per spawning stock biomass, and a simulation model that well reproduced the spawning stock biomass was developed. Then, effects of various fisheries regulations were evaluated using the simulation study. The results were as follows: 1) arctic oscillations, Pacific decadal oscillations and the recruitment number of the Pacific stock of Japanese sardine were chosen as the environmental factors that determined the recruitment per spawning stock biomass; 2) spawning stock biomass could be well reproduced using a model that reproduced the recruitment per spawning stock biomass and the survival process of the population that included the effect of fishing; and 3) the effects of various fisheries regulation could be evaluated using the simulation model mentioned above. The effective regulation in the simulations conducted in this paper was a prohibition of fishing for 0-and 1-year-old fish in terms of recovering the spawning stock biomass. The reduction of fishing mortality coefficients for all age fish to 50% of actual values also showed a good performance. The recent reductions of the recruitment and spawning stock biomass were likely caused by heavy harvesting, especially of immature fish, since 2004.

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Section: Discussionmentioning

confidence: 97%

Case Study: A Simulation Model of the Spawning Stock Biomass of Pacific Bluefin Tuna and Evaluation of Fisheries Regulations

Sakuramoto¹

2016

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“…Generally, these errors would have the effect of hiding the real relationship between R and S [17]. Therefore, those factors must appear to have no relationship between R and S.…”

Section: Discussionmentioning

confidence: 99%

A Stock-Recruitment Relationship Applicable to Pacific Bluefin Tuna and the Pacific Stock of Japanese Sardine

Sakuramoto¹

2015

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Abstract(environmental factors), where Rt and St−1 denote the recruitment in year t and spawning stock biomass in year t − 1, and f(.) is a function that evaluates the effect of environmental factors in year t. The simulations showed that when the fluctuation in environmental factors cyclically changed, 1) the shape of the apparent SRR assumed clockwise loops for the shorter maturity age of fish, and 2) the apparent SRR comprised scattered anticlockwise loops for the longer maturity age of fish. These features coincided well with those observed. This finding gives us a new paradigm in SRR, which is far different from the concept that has predominated in the field for more than 60 years.

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“…Upon selection of the model with two regimes, Cahuin et al [11] fit the RPS data with the model of Ricker. Sakuramoto and Suzuki [15] explain in detail that when the actual model for a stock-recruitment relationship is a proportional model, observational and/or process errors can result in the selection of a Ricker [17] or Beverton and Holt [19] model. On the contrary, when the actual model is a Ricker [17] or Beverton and Holt [19] model, observational and/or process errors seldom resulted in the selection of a proportional model.…”

Section: Discussionmentioning

confidence: 99%

“…When the data were adjusted for observational errors and regression analysis was applied to the full data series, the slope of the regression line did not differ significantly from unity indicating the absence of density-dependent effects as well as the absence of two different carrying capacities or regimes. Sakuramoto and Suzuki [15] provide a detailed report on the effect of observation and/or process error which in most cases results from environmental variables on recruitment and/ or SSB in the selection of a stock-recruitment relationship.…”

Section: Introductionmentioning

confidence: 99%

Model for stock-recruitment dynamics of the Peruvian anchoveta (<i>Eugraulis ringens</i>) off Peru

Singh¹,

Sakuramoto

Suzuki³

2014

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This paper was aimed at reexamining the validity of the results from Cahuin et al. (Estuar. Coast. Shelf Sci. 84, 2009) and identifying a model to describe the stock-recruitment relationship of the Peruvian anchoveta (Eugraulis ringens). Regression analysis was used to determine if density-dependent effects were present. The analysis did not show the existence of any densitydependent effects. It is important to use environmental factors and take observational and process errors into account when attempting to identify density-dependent effects in fish populations. Sea surface temperature (SST) and Southern Oscillation Index (SOI) were used as independent variables to fit the recruitment dynamics of the anchoveta. Both SST and SOI were found to be significantly important parameters in structuring anchoveta dynamics according to Akaike Information Criterion (AIC) and R 2 values. The results of this study do not correlate with the findings of Cahuin et al., (2009), where density-dependent effects and the presence of regimes were detected. In conclusion, the recruitment R t is essentially determined in proportion to spawning stock biomass S t , and then environmental factors in year t further change the recruitments. This mechanism is completely same with that for Japanese sardine proposed by Sakuramoto (The Open Fish. Sci. 5, 2012).

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Effects of process and/or observation errors on the stock–recruitment curve and the validity of the proportional model as a stock–recruitment relationship

Cited by 15 publications

References 27 publications

Case Study: A Simulation Model of the Spawning Stock Biomass of Pacific Bluefin Tuna and Evaluation of Fisheries Regulations

Case Study: A Simulation Model of the Spawning Stock Biomass of Pacific Bluefin Tuna and Evaluation of Fisheries Regulations

A Stock-Recruitment Relationship Applicable to Pacific Bluefin Tuna and the Pacific Stock of Japanese Sardine

Model for stock-recruitment dynamics of the Peruvian anchoveta (<i>Eugraulis ringens</i>) off Peru

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Effects of process and/or observation errors on the stock–recruitment curve and the validity of the proportional model as a stock–recruitment relationship

Cited by 15 publications

References 27 publications

Case Study: A Simulation Model of the Spawning Stock Biomass of Pacific Bluefin Tuna and Evaluation of Fisheries Regulations

Case Study: A Simulation Model of the Spawning Stock Biomass of Pacific Bluefin Tuna and Evaluation of Fisheries Regulations

A Stock-Recruitment Relationship Applicable to Pacific Bluefin Tuna and the Pacific Stock of Japanese Sardine

Model for stock-recruitment dynamics of the Peruvian anchoveta (&lt;i&gt;Eugraulis ringens&lt;/i&gt;) off Peru

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Product

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Model for stock-recruitment dynamics of the Peruvian anchoveta (<i>Eugraulis ringens</i>) off Peru