Fisheries Oceanography · 2015 · 28 citations · 29 references
Life SciencesEcologyZoologyFisheries BiologyMarine EcologyPhysical OceanographyMarine Population DynamicsSea Surface TemperatureStock - Recruitment RelationshipsStock-recruitment RelationshipsZooplankton EcologyEnvironmental Variability AssessmentZooplankton Community DynamicsZooplankton DynamicsZooplankton Population DynamicsZooplankton Species Composition
Abstract The objective of this study was to assess the effect of environmental variability on the dynamics of the Atlantic mackerel ( Scomber scombrus L.) stock in the Gulf of St. Lawrence ( GSL ). We first described the dominant modes of physical and biological (zooplankton) variability using Principal Components Analyses of 40 variables. Two principal modes of variability were identified, a long‐term mode (15–20 yr) associated with a warming of the GSL and a second mode describing alternating cold and warm periods at a higher frequency (5–10 yr). A strong link between physical forcing and the dynamics of zooplankton species known to be important for mackerel was shown. Second, a set of Generalized Additive Models ( GAM ) was developed to explore how these environmental variations could influence mackerel condition (Fulton's K) and recruitment success ( R s ). Optimal GAM s including variations in abundance, species composition and phenology of key copepods improved model performance by 40–50% relative to those considering only physical environmental conditions. The results are consistent with the match–mismatch hypothesis and illustrate the key role of zooplankton dynamics in modulating variations in mackerel K and R s . Finally, this study showed that large variations in R s could be caused by varying environmental conditions independently of the influence of stock biomass. Our results strongly indicate that the effect of environmental variability should be considered in the implementation of an ecosystem‐based approach to Atlantic mackerel stock management.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
nlme : Linear and nonlinear mixed effects models
Saikat DebRoy · Medical Entomology and Zoology · 2006 · 10.2K citations