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A Lagrangian stochastic larva tracking model based on a 3-dimensional (3D) high resolution wind-driven coastal circulation model is used to study the dispersal of benthic larvae. The larva tracking model includes 3D advection and turbulence, and a species-specific larval swimming behaviour that accounts for ontogenic changes, sensitivity to light exposure and inter-individual variability. Larval mortality can also be included. The dispersal model is applied to Owenia fusiformis larvae, whose swimming behaviour description is based on both existing data and new complementary measurements. Larval velocities (resulting from both settling and swimming behaviour) were measured with actographic equipment and ranged between -1 and 0.9 mm s -1. Measured swimming activity rates were lower than 50%. The sensitivity study of larval dispersal in March-April 1999 showed that: (1) the dispersal of neutrally buoyant passive larvae is more sensitive to the physical forcings resolution, because of both advection and diffusion processes, than to the variability of spawning locations within neighbouring grid cells (up to 1 km apart) in Banyuls Bay (France, NW Mediterranean); and (2) a physical barrier, located at 20 m deep in Argelès (France, NW Mediterranean) and 30 m deep in Banyuls Bay, separated nearshore and offshore larval dispersal in 1999. The final positions and local retention of larvae released in Banyuls Bay and Argelès result from: (1) the balance between the 3D turbulence, larval settling velocity (~0.8 mm s -1 ) and swimming activity rate; and (2) natural mortality, although the effect is not proportional to survival rates. High resolution larvae dispersal patterns for O. fusiformis in Banyuls Bay suggest that self-recruitment was low in the Banyuls population during spring 1999 and confirm that post-settlement deposit patterns observed there in May 1999 were insignificant. In addition, interconnections between the Argelès and Banyuls populations can exist. KEY WORDS: Larval dispersal · 3D numerical modeling· Lagrangian/Eulerian models · Owenia fusiformis · Wind-driven currents Resale or republication not permitted without written consent of the publisherMar Ecol Prog Ser 311: [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66] 2006 behaviour, what population dynamics emerge? Benthic population dynamics is a major issue in the management of nearshore regions. Some benthic species are fished directly and others are used to assess the biochemical quality of the environment since their sedentary adult stage integrates bio-chemical environmental changes throughout their lifetime. The use of some of these populations as bio-indicators is questionable if their dynamics are unstable due to larval dispersal.Pioneering larval dispersal studies have tried to identify physical retention structures (which are generally highly predictable) relative to the duration of the animal's larval stage, e.g. tidal estuaries for a short larval stage (de Wolf 1974, Chen et al. 1997 or a general ther...

The objective of this study was to assess the effect of environmental variations on the abundance of Sardinella aurita and Sardinella maderensis in Senegalese waters in the upwelling system. Monthly data indicating the abundance of sardinella were first estimated from commercial statistics, using Generalized Linear Model from 1966 to 2011. Abundance indices (AIs) were then compared with environmental indices, at the local scale, a Coastal Upwelling Index (CUI) and a coastal Sea Surface Temperature (SST) index, and on a large scale, the North Atlantic Oscillation (NAO), the Atlantic Multidecadal Oscillation (AMO) and the Multivariate El Niño Southern Oscillation Index (MEI), using correlations and times series analyses. The results showed that the abundance of sardinella is determined by a strong seasonal pattern and inter‐annual fluctuations. The abundance of S. aurita peaked in spring and in autumn, whereas that of S. maderensis peaked in the warm season (July–September). The trend of the sardinella abundance was significantly correlated with the CUI, especially in autumn and spring. Interannual fluctuations of S. maderensis and S. aurita abundance are, respectively, driven by the precocity and the duration of the upwelling season that is attributed to distinct migration patterns. Both sardinella species also respond with a delay of around 4 years to the winter NAO index and the autumn CUI, and the AMO index, respectively, both related to migration patterns. The wide variations in sardinella biomass are caused by variations in environmental conditions, which should be considered in the implementation of an ecosystem‐based approach in sardinella stocks management.