Stock-recruitment models for Pacific sardine (<i>Sardinops sagax</i>)

Jacobson, Larry D.; MacCall, Alec D.

doi:10.1139/f95-057

Cited by 174 publications

(163 citation statements)

References 19 publications

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“…Time series of recruitment at age 3 from 1908 to 1998 were taken from Toresen & Østvedt (2000) and the temperature recorded at the Kola section were averaged over the months January to April, from 1905to 1995(Tereshchenko 1996 (Fig. 3).…”

Section: Atlanto-scandian Herringmentioning

confidence: 99%

Quantile regression models for fish recruitment–environment relationships: four case studies

Planque

Buffaz²

2008

Mar. Ecol. Prog. Ser.

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Understanding and modelling the environmental control of fish recruitment has been a central question in fish population ecology for the last century. Most environment -recruitment models have primarily been developed to model mean recruitment using conventional regression techniques which assume that all environmental parameters are included and that the residual unexplained variability is unstructured. However, the complexity of environmental controls and the empirical evidence that many relationships have failed when retested suggest that these assumptions are generally not met. Most environmental controls may be considered as limiting factors to recruitment and act in interaction with other factors (often not measured or not known). We used quantile regression modelling, which is specifically designed to model limiting relationships, to reanalyse environment -recruitment relationships that have been published for 4 fish stocks: (1) Northeast Arctic cod (Barents Sea), (2) Atlanto-Scandian herring, (3) Bay of Biscay anchovy and (4) Pacific sardine. The method was adapted to the specific case of autocorrelated time series, a common feature of most environmental signals. The results from quantile regression were not straightforward extensions of conventional regressions. For Northeast Arctic cod and Pacific sardine, the original relationships with temperature were not statistically significant in the quantile model. For Atlanto-Scandian herring the relationship was confirmed and temperature clearly appeared as a limiting factor to recruitment. The published relationship for the Bay of Biscay anchovy with upwelling was not confirmed, but the previously undetected relationship with river runoff was established. In this specific case, it was only by using a quantile model that the relationship could be detected as statistically significant. These results confirm the ability of quantile regression models to provide robust interpretation of environment -recruitment relationships and to produce environmentally based advance warning when recruitment is expected to be low.

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Section: Atlanto-scandian Herringmentioning

confidence: 99%

Quantile regression models for fish recruitment–environment relationships: four case studies

Planque

Buffaz²

2008

Mar. Ecol. Prog. Ser.

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“…While environmental variability has been recognized as exerting a substantial influence on the recruitment and productivity of fish stocks (Vert-pre et al, 2013;Szuwalski et al, 2014), this information has not been widely incorporated into fish population models and projections. However, recent examples have demonstrated that population projections used to set catch quotas and rebuilding plans can be more reliable for some stocks if environmental factors, particularly temperature, are considered in projections (Jacobson and McClatchie, 2013;Hill et al, 2014;Pershing et al, 2015;Tommasi et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Seasonal Timing of Maine's Lobster Fishery

Mills

Pershing

Hernández³

2017

Front. Mar. Sci.

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The fishery for American lobster is currently the highest-valued commercial fishery in the United States, worth over US$620 million in dockside value in 2015. During a marine heat wave in 2012, the fishery was disrupted by the early warming of spring ocean temperatures and subsequent influx of lobster landings. This situation resulted in a price collapse, as the supply chain was not prepared for the early and abundant landings of lobsters. Motivated by this series of events, we have developed a forecast of when the Maine (USA) lobster fishery will shift into its high volume summer landings period. The forecast uses a regression approach to relate spring ocean temperatures derived from four NERACOOS buoys along the coast of Maine to the start day of the high landings period of the fishery. Tested against conditions in past years, the forecast is able to predict the start day to within 1 week of the actual start, and the forecast can be issued 3-4 months prior to the onset of the high-landings period, providing valuable lead-time for the fishery and its associated supply chain to prepare for the upcoming season. Forecast results are conveyed in a probabilistic manner and are updated weekly over a 6-week forecasting period so that users can assess the certainty and consistency of the forecast and factor the uncertainty into their use of the information in a given year. By focusing on the timing of events, this type of seasonal forecast provides climate-relevant information to users at time scales that are meaningful for operational decisions. As climate change alters seasonal phenology and reduces the reliability of past experience as a guide for future expectations, this type of forecast can enable fishing industry participants to better adjust to and prepare for operating in the context of climate change.

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“…Year-to-year variability in temperature has been shown to be linked to changes in fish year-class strength for a number of species (Mysak et al 1982, Murray et al 1983, Swartzman et al 1983, Hansen & Buch 1986, Jacobson & MacCall 1995. Atlantic cod Gadus morhua is widely distributed in the waters of the North Atlantic and surrounding seas and occurs in water temperatures in the range -1 to 12°C.…”

Section: Introductionmentioning

confidence: 99%

Interannual variability in temperature and the recruitment of Irish Sea cod

Planque¹,

Fox²

1998

Mar. Ecol. Prog. Ser.

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A short review shows that in cold, arctic regions of the North Atlantic positive relationships are generally observed between cod recruitment and water temperatures whilst negative relationships are observed in warm-temperate regions. According to this relationship, cod recruitment in the Irish Sea should be negat~vely related to sea temperature. Using 3 ~ndependent sources of longterm temperature records we test this hypothesis and show a highly significant connection between the recruitment of cod and temperature signals. We suggest that this relationship should be taken into account, in addition to spawning stock biornass, in any future attempt at understanding cod recruitment in this area.

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Stock-recruitment models for Pacific sardine (Sardinops sagax)

Cited by 174 publications

References 19 publications

Quantile regression models for fish recruitment–environment relationships: four case studies

Quantile regression models for fish recruitment–environment relationships: four case studies

Forecasting the Seasonal Timing of Maine's Lobster Fishery

Interannual variability in temperature and the recruitment of Irish Sea cod

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