Dispersal modeling of fish early life stages: sensitivity with application to Atlantic cod in the western Gulf of Maine

By M. Huret, J. Runge, Changsheng Chen, Geoffrey W. Cowles, Qichun Xu, J. Pringle. 2007

114 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/92e89b93431236640fa7a7f843fdd8b1ccc01ff2

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Citations in the corpus

Larval dispersal and marine population connectivity. (2009, background): “…when biological models are combined with regional and local hydrodynamic models, repeated scenarios can be computed under variable forcing (e.g., real wind fields) to generate spatially explicit connectivity matrices (e.g., James et al. 2002, Bode et al. 2006, Cowen et al. 2006, Huret et al. 2007).”
Fine-scale spatial and temporal genetic structure of Atlantic cod off the Atlantic coast of the USA (2010, background): “Smithsonian Institute Press, Washington, DC Conover DO, Clarke LM, Munch SB, Wagner GN (2006) Spatial and temporal scales of adaptive divergence in marine fishes and the implications for conservation.”
Model sensitivity and robustness in the estimation of larval transport: A study of particle tracking parameters (2013, background): “…studies of uncertainty in biophysical models examine the sensitivity of larval transport predictions to larval mortality, growth, and behavior for a particular species or set of species (e.g. Huret et al., 2007, 2010; Paris et al., 2007; Peck and Hufnagl, 2012; Schmidt and Hinrichsen, 2008).”
Modeling fish growth and reproduction in the context of the Dynamic Energy Budget theory to predict environmental impact on anchovy spawning duration (2009, methodology): “…success5 (Werner et al., 2001; Gallego et al., 2007) by showing in particular how spawn-6 ing location and timing influence larval transport (Huret et al., 2007a) and7 how the inter-annual variability of the environmental conditions encountered8 by the larvae determines recruitment…”
Reproductive Timing in Marine Fishes: Variability, Temporal Scales, and Methods (2011, background): “In addition, there is a need to improve our understanding of when fish spawn so that we can assess where they spawn and thus determine how spatiotemporal factors affect recruitment success and the efficacy of spatially explicit approaches for protecting spawning populations (i.e., marine protected a”
Life history and biogeography of Calanus copepods in the Arctic Ocean: An individual-based modeling study (2012, methodology): “The physical ice-ocean model was an updated version of the Arctic Ocean Finite-Volume Community Ocean Model (AO-FVCOM) (Chen et al., 2009) that is based on the spherical coordinate, semiimplicit version of the Finite-Volume Community Ocean Model (FVCOM) fully coupled to an Unstructured Grid version ”