Relating fish recruitment to stock biomass and physical environment in the Black Sea using generalized additive models

Daskalov, Georgi

doi:10.1016/s0165-7836(99)00006-5

Cited by 121 publications

(103 citation statements)

References 20 publications

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“…This is remarkable since attempts to empirically relate recruitment strength to environmental factors have been followed before and go back to the early work of Ricker (1958). Recent attempts extend mathematically beyond the traditional Ricker spawner-recruit model by using generalized additive modeling approaches (Daskalov, 1999 for Sprat, Anchovy, Whiting and Horse Mackerel in the Black Sea), applying fuzzy logic (Nishida et al, 2007 for Bigeye Tuna in the Indian Ocean) or using neural network analysis (Chen and Hare, 2006 for Pacific Halibut). While the above and other modernized versions have allowed to incorporate environmental variability and to improve the fit to the data compared to the traditional empirical Ricker model, "breaking relations", nonlinear interdependencies between environmental, physiological and biotic factors and internal feed back processes have often limited the success of these empirical approaches and the question arises, why the here presented model may be an exception.…”

Section: Concluding Remarks and Novelty Of Approachmentioning

confidence: 99%

A catch forecast model for the Peruvian scallop (Argopecten purpuratus) based on estimators of spawning stock and settlement rate

Wolff

Taylor

Mendo

et al. 2007

Ecological Modelling

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a b s t r a c tThe Peruvian Bay scallop (Argopecten purpuratus) fishery in Independencia bay (Southern Peru) is being subjected to great inter-annual variability in catch and effort. This is mainly due to the ENSO (El Niño-Southern oscillation)-caused changes in the population dynamics of the stock, which greatly proliferated during the El Niño events 1983 and 1998. As a consequence "gold rush" conditions arose and resource users profited from a multi-million dollar export business. After the El Niño booms, the system normalized and catches dropped to normal levels. This boom and bust situation has made a rational management of the resource difficult, and annual catches are considered unpredictable, just like the stochastic environment. This paper attempts to provide a catch forecast model to enable the scallop fishery to better prepare for and adapt to the ever-changing conditions of the scallop stock.The model proposes that annual catches are mainly the result of the recruitment success of the incoming new cohort, which is a function of adult spawning stock size and the number of settlers to the sea bottom. The latter is considered a function of the larval mortality rate and the temperature-dependent development time to the settlement stage, while the former is proportional to the catches taken over the spawning period (November-April). Using monthly catch and temperature data for the period 1983-2005, we constructed a regression model to predict the catch for the year after the recruitment period (July-June) as a function of (a) the catch during the spawning period (as a proxy for spawning stock biomass) and (b) the settlement factor that was derived from the mean water temperature over the spawning period, an assumed instantaneous larval mortality rate, and the relationship between temperature and larval period to settlement. The resulting multiple regression (R 2 = 0.930) proves that both factors can explain a large part of the inherent variability of the data. The model reveals that annual catches greatly depend on the spawning stock size when temperatures are low, while this factor decreases in importance with increasing temperatures, at which the settlement factor is much more influential instead. These findings are relevant for the stock management: at low temperatures, the maintenance of a large enough spawning stock over the spawning period (November-April) is decisive for the yield of the post-recruitment fishing period thereafter, while at increasing spawning temperatures, spawning stock size is of decreasing importance for determining the yield.

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Section: Concluding Remarks and Novelty Of Approachmentioning

confidence: 99%

A catch forecast model for the Peruvian scallop (Argopecten purpuratus) based on estimators of spawning stock and settlement rate

Wolff

Taylor

Mendo

et al. 2007

Ecological Modelling

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“…Incorporating environmental variables can enhance the performance of stock-recruitment models (Fiksen and Slotte 2002, Marjomäki 2004, Keyl and Wolff 2008 and GAM models are a flexible tool for incorporating non-linear environmental effects (Daskalov 1999, Chen et al 2005, Megrey et al 2005, Keyl and Wolff 2008. Even though the pikeperch stock-recruitment model presented here performs well, with most data points within the 95% confidence limit of the model (Fig.…”

Section: Discussionmentioning

confidence: 90%

Challenges and Opportunities of Local Fisheries Management: Pikeperch, Sander Lucioperca (Actinopterygii: Perciformes: Percidae), in Pärnu Bay, Northern Gulf of Riga, Baltic Sea

Müller‐Karulis¹,

Arula²,

Balode³

et al. 2013

Acta Icth et Piscat

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“…The technique is highly flexible for fitting and estimating the variation appropriately when there is a lack of information (Daskalov 1999;Piet 2002;Venables and Dichmont 2004). Megrey et al (2005) compared various modeling methods and concluded that the GAM is useful for identifying the relationships between recruitment and influential factors and can be applied first to the predictor and response variables to ascertain the functional form empirically from the data.…”

Section: Discussionmentioning

confidence: 99%

“…To examine the stock-environment-recruitment relationship properly, various models, such as linear regression models (Sparholt 1996), generalized additive models (GAM) (Daskalov 1999;Megrey et al 2005), principal component analysis (PCA) (Yatsu et al 2005), artificial neural networks (ANN) (Arregui et al 2006) etc. have been applied.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Effect of Environmental Factors on the Ricker Stock-Recruitment Relationship for North Pacific Albacore Using Generalized Additive Models

Shih¹,

Chen

Hsu³

2014

Terr. Atmos. Ocean. Sci.

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This study investigates how the inclusion of El Niño episodes affects the modeling of recruitment in North Pacific albacore. The relationship between spawning stock size and recruitment for the 1976 -2004 period, including environmental variables (Southern Oscillation Index and sea surface temperature anomalies), was conducted by general linear and generalized additive models (GAM). The results indicate that the Southern Oscillation Index and the interaction between spawning stock size and sea surface temperature anomalies have significant effects on the recruitment of North Pacific albacore. GAM fit the original data better than general linear models, according to the Akaike information criterion. The recruitment declined when El Niño episodes occurred, but increased when La Niño episodes occurred. The recruitment also increased when the spawning stock size density was above 11.21 million tons with a -0.3°C sea surface temperature anomaly. El Niño was observed to have both long-period (above 10 -15 years) and short-period (1 -2 years) effects on North Pacific recruitment.

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Relating fish recruitment to stock biomass and physical environment in the Black Sea using generalized additive models

Cited by 121 publications

References 20 publications

A catch forecast model for the Peruvian scallop (Argopecten purpuratus) based on estimators of spawning stock and settlement rate

A catch forecast model for the Peruvian scallop (Argopecten purpuratus) based on estimators of spawning stock and settlement rate

Challenges and Opportunities of Local Fisheries Management: Pikeperch, Sander Lucioperca (Actinopterygii: Perciformes: Percidae), in Pärnu Bay, Northern Gulf of Riga, Baltic Sea

Modeling the Effect of Environmental Factors on the Ricker Stock-Recruitment Relationship for North Pacific Albacore Using Generalized Additive Models

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