scholarly journals Incorporating Early-Life History Parameters in the Estimation of the Stock-Recruit Relationship of Georges Bank Atlantic Cod (Gadus morhua)

2003 ◽  
Vol 33 ◽  
pp. 191-205 ◽  
Author(s):  
L O'Brien ◽  
P J Rago ◽  
R G Lough ◽  
P Berrien
2013 ◽  
Vol 29 (3) ◽  
pp. 623-629 ◽  
Author(s):  
M.-M. Kroll ◽  
M. A. Peck ◽  
I. A. E. Butts ◽  
E. A. Trippel

PLoS ONE ◽  
2013 ◽  
Vol 8 (9) ◽  
pp. e75889 ◽  
Author(s):  
Ryan R. E. Stanley ◽  
Brad deYoung ◽  
Paul V. R. Snelgrove ◽  
Robert S. Gregory

2006 ◽  
Vol 63 (2) ◽  
pp. 216-223 ◽  
Author(s):  
Håkon Otterå ◽  
Ann-Lisbeth Agnalt ◽  
Knut E. Jørstad

Abstract Several hundred Atlantic cod (Gadus morhua L.) were collected from selected spawning grounds along the Norwegian coast in March 2002. Four areas or regions that represent a wide range of environmental conditions were chosen for our breeding experiments: Porsangerfjord, Tysfjord, Helgeland, and Øygarden. Cod were transported to Øygarden near Bergen, individually tagged, and kept in sea cages. In both 2003 and 2004, a total of 40 family groups (adult pairs) representing the four regions were monitored for their spawning performance in separate tanks. During the spawning period, the quantity and diameter of eggs were recorded. During 2003, the time of peak spawning differed among groups. It was evident that the broodstock from the Øygarden region spawned about one month earlier than the broodstock collected from the Helgeland region. This also occurred in 2004, two years after the cod were collected, suggesting that the difference has a genetic component. Differences in life history parameters between cod populations, such as spawning cycles as described here, could be adaptive and under genetic control. This must be taken into consideration when assessing precautionary means of overcoming the problem with escapees from future cod mariculture.


2016 ◽  
Vol 73 (2) ◽  
pp. 246-256 ◽  
Author(s):  
Bjarte Bogstad ◽  
Natalia A. Yaragina ◽  
Richard D.M. Nash

Recruitment at age 3 of the Northeast Arctic cod (Gadus morhua) is highly variable. It has generally been believed that year-class strength for this stock is determined prior to settlement to the bottom after about 6 months. However, newer observations indicate that year-class strength may change considerably between settlement and recruitment at age 3. Our analyses cover the 1983–2009 year classes where comprehensive data from total egg production (TEP), surveys, and stock assessments were available for a thorough examination of these cohorts. On average, only 6 out of 1 million of a new generation at the TEP stage reaches the age of recruitment to the fishery. The between-cohort variability in abundance is greatest at the ages 0–1 stage. Although the mortality is highest during the first months of life, the year-class strength can also be affected considerably by processes taking place between the 0-group stage (∼6 months) and age 3. The mortality in this period of life seems to be strongly density-dependent, and cannibalism is an important source of mortality.


2014 ◽  
Vol 71 (8) ◽  
pp. 2064-2087 ◽  
Author(s):  
Geir Ottersen ◽  
Bjarte Bogstad ◽  
Natalia A. Yaragina ◽  
Leif Christian Stige ◽  
Frode B. Vikebø ◽  
...  

Abstract The Barents Sea stock of Atlantic cod (Gadus morhua) is currently the world's largest cod stock. It is also a stock for which long time-series are available and much research has been carried out. With this review, we wish to present an overview and evaluation of the knowledge on Barents Sea cod early life dynamics. The focus is on the effects of the biotic and abiotic drivers, which jointly determine the strength of a year class. A stage-by-stage approach is employed. We summarize and assess the significance of the different processes described in the literature to be at play during each specific life stage, from spawning stock, through eggs, larvae, and pelagic juvenile, to demersal juvenile and recruitment at age 3. Also Russian work is included, some of which until now has not been available to non-Russian readers. Physical drivers examined include sea temperature, advection and dispersal, wind-induced turbulence, and light. Biotic mechanisms studied range from maternal effects and skipped spawning in the adult stock through egg quantity and quality, to prey availability for the larvae and effects of cannibalism on the juveniles. Finally, we evaluate the main hypotheses put forth by Johan Hjort a hundred years ago in the light of our synthesis of present knowledge. A main conclusion is that it is unlikely that there is any one single life stage during which recruitment with any generality is determined.


2014 ◽  
Vol 71 (2) ◽  
pp. 203-216 ◽  
Author(s):  
Cóilín Minto ◽  
Joanna Mills Flemming ◽  
Gregory Lee Britten ◽  
Boris Worm

Productivity is a central determinant of population dynamics with consequences for population viability, resilience to exploitation, and extinction. In fish, the strength of a cohort is typically established during early life stages. Traditional approaches to measuring productivity do not allow for interannual variation in the maximum reproductive rate, a parameter governing population productivity. Allowing such process variation provides the ability to track dynamic changes instead of assuming a static productivity regime. Here we develop and evaluate a multivariate stock–recruitment state-space model to simultaneously estimate time-varying stock productivity and synchronicity of dynamics across populations. We apply the method to North Atlantic cod (Gadus morhua) populations, showing that the productivity of early life stages has varied markedly over time, with many populations at historically low productivity. Trends in productivity were similar in some adjacent populations but less regionally coherent than previously thought, particularly in the Northwest Atlantic. Latitudinal variation in the Northeast Atlantic suggests a differential response to environmental change. We conclude that time-varying productivity provides a useful framework that integrates across many dimensions of environmental change affecting early life history dynamics.


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