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Growth in the brown shrimp Crangon crangon: II. Meta-analysis and modelling
Hufnagl, M.; Temming, A. (2011). Growth in the brown shrimp Crangon crangon: II. Meta-analysis and modelling. Mar. Ecol. Prog. Ser. 435: 155-172. http://dx.doi.org/10.3354/meps09224
In: Marine Ecology Progress Series. Inter-Research: Oldendorf/Luhe. ISSN 0171-8630; e-ISSN 1616-1599, more
Peer reviewed article  

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Keywords
    Cycles > Life cycle
    Fishing > Commercial fishing
    Growth rate
    Moults
    Population functions > Recruitment
    Crangon crangon (Linnaeus, 1758) [WoRMS]
    Marine/Coastal
Author keywords
    Size-at-age

Authors  Top 
  • Hufnagl, M.
  • Temming, A.

Abstract
    Existing laboratory and field data on growth were combined, reanalyzed and discussed to generate a holistic temperature-, length- and gender-dependent growth rate (G, mm d–1) model for North Sea region brown shrimp Crangon crangon (L.). Length (L, mm) and temperature (T, °C) dependent growth rates of Crangon crangon are highly variable within and among studies but decrease with L and increase with T. Applying general nonlinear regression, mean growth was derived as G = 0.02421·T – 0.00115·e0.08492·T·L (r² = 0.860). Applying quantile regression (75th percentile), a growth model describing growth of the fastest growing fraction of the population was derived as Gmax = 0.03054·T – 0.00104·e0.09984·T·L (r² = 0.857). Female growth rates were higher than male growth rates and were similar to Gmax. In a simulation, G and Gmax were used with seasonally varying temperature to generate monthly length trajectories (cohorts). Further, length-based mortality was included and the fraction of each cohort attaining minimal commercial size was calculated. May cohorts (5 mm initial length), representing spring recruitment, grew to 50 mm by November if G was used. Application of the fast growth model (Gmax) allowed for the same length to be reached 2 mo earlier. We conclude that the autumnal peak in adult abundance in the North Sea is most probably due to recruitment from the spring cohort of the same year. Our results suggest that the previous year’s summer cohort contributes little to this autumnal peak because of high cumulative and overwintering mortality.

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