Along-shore larval dispersal kernels in a numerical ocean model of the central Chilean coast

Abstract

Dispersal kernels provide a useful way to quantify the average spatial distribution of propagules originating from a given point in space. Consequently, dispersal kernels have been used in analytical and numerical studies of short-and long-distance dispersal of marine invertebrates and fish with pelagic larval stages. In most cases, the shape of dispersal kernels is pre-determined and parameterised with knowledge of larval duration or mean current velocities homogeneously across space. Here, the characteristics of planktonic larval dispersal for near-shore species in a realistic coastal ocean flow are investigated through the use of a numerical ocean model of a section of the central Chilean coast. The 3-dimensional primitive equation model was forced by 4 yr of observed winds from Las Cruces. Planktonic larval dispersal was simulated by advecting passive drifters using the evolving model velocity field. No a priori assumptions were made about diffusion-advection statistics. Drifters were released daily from regularly spaced locations along the coast and were considered to have settled if found within 1 km of the coast 30 d after release. Observed dispersal kernels were then calculated for each release location, and their variability in space and time was examined. This variability was found to be substantial over spatial scales less than a typical larval-advection scale, and, as a result, a spatially and temporally averaged dispersal kernel was inadequate as a global model of settlement. Large along-shore variation in the shape of dispersal kernels led to significant variation in the spatial pattern of connectivity among local sites, with some acting as net sources and some as net sinks within scales of 10s of kilometres. These results are linked to the alongshore and seasonal variability in ocean circulation, in particular close to shore. Both local and global dispersal kernels were found to be non-Gaussian, with their distribution related to that of the ocean velocity field. It is concluded that, in realistic flows with complicated coastal geometry, considerable departure from the expected Gaussian dispersal kernels based on homogeneous flow conditions can lead to complex spatial patterns of connectivity and successful settlement along a relatively simple but real coastline.

Variabilidad ambiental y recursos pesqueros en el Pacifico suroriental: estado de la investigacion y desafios para el manejo pesquero

“…Aiken et al (2007), mostraron que ejes de dispersión genéricos son variables y asimétricos a lo largo de la costa (33º-34°S), y que la dependencia temporal de los mismos es importante. Sus resultados apoyan el uso de modelos numéricos hidrodinámicos y biológicos acoplados más que estadísticas de flujo anual y modelos simples de difusión-advección para abordar el tema de conectividad.…”

A coupled ecological–hydrodynamic model for the spatial distribution of sessile aquatic species in thermally forced basins

“…In our simple approach, we consider that dispersing larvae are passively transported by currents, i.e., that they are unable to swim, orient themselves or perform vertical migrations, apart from the capability to settle at the bottom of the water body in the location where they reach maturity (e.g., Bode et al, 2006). Although the assumption of passive transport is quite common in the description of larval dispersal (e.g., James et al, 2002;Aiken et al, 2007), it should be noted that active movements can also play a remarkable role in determining the mean distance traveled by larvae (Steneck, 2006;Cowen et al, 2006;Paris et al, 2007;Werner et al, 2007;Cowen and Sponaugle, 2009). …”

“…We have verified the robustness of the results with respect to this hypothesis (see Section 5.3 below). The computation of Lagrangian steps is accomplished by integrating the equations of motion for the propagules between 0 and a final time t L , which represents the mean duration of the larval stage (assumed here to be constant; see, e.g., Bode et al, 2006;Aiken et al, 2007). Technically, numerical integration has been carried out by means of a standard explicit Euler method.…”

Inverse approach to estimating larval dispersal reveals limited population connectivity along 700 km of wave-swept open coast

“…We used these data to estimate a onedimensional dispersal kernel, a simple representation of larval settlement along a linear coastline. The kernel is a probability density function that describes the probability of dispersal from a natal site to any other location along the coast [33,[37][38][39]. The parameters of a dispersal kernel (mean and standard deviation of dispersal distance) could be estimated directly from observing the movement and settlement of propagules in the coastal ocean, either real or simulated via Lagrangian circulation models (e.g.…”