Using 2-dimensional dispersal kernels to identify the dominant influences on larval dispersal on continental shelves

Abstract

Pelagic larval dispersal is thought to be the main mechanism connecting many marine populations and is an important determinant both of an individual's success and a population's distribution and spatial structure. Thus, quantitative estimates of the retention or dispersion of larvae from spawning grounds is important for the determination of recruitment success in fisheries. Models can be used to study connectivity through a dispersal curve or dispersal kernel: the probability that a larva will settle at a given distance from its release location. We applied a 3-dimensional circulation model and a Lagrangian particle tracking model to the southeast US continental shelf to describe dispersal kernels in 2 dimensions. We used a fully orthogonal design to assess the importance of factors that influence the dispersal kernel, including spawning time, spawning location, larval behavior (vertical position in the water column), larval duration, and turbulent dispersal. Our results indicate that adult behavior, in the form of spawning time and location, may be more important than larval behavior in determining larval dispersal on the inner-and mid-shelves in this region.KEY WORDS: Dispersal kernels · Larval fish · Lagrangian particle tracking · Southeast US continental shelf Resale or republication not permitted without written consent of the publisher

Pelagic Larval Duration and Dispersal Distance Revisited

“…Our observations of the importance of behavior in determining dispersal distance have largely been ignored. Since Shanks et al (2003a) was published, a number of papers have presented models of larval dispersal that have attempted to estimate dispersal distance using PD coupled with oceanographic models with various degrees of sophistication (Siegel et al, 2003(Siegel et al, , 2008Kinlan et al, 2005;Edwards et al, 2007). Many of these models have assumed that larvae are passive; they present the null hypothesis of passive dispersal.…”

Natural variability in hard-bottom communities and possible drivers assessed by a time-series study in the SW Baltic Sea: know the noise to detect the change

“…In our case, PCA has been used in a simple direction to determine the first two principle components from a bivariate data set with latitudes and longitudes of the starting positions of the particles. The spatial extension and density of the starting positions of the particles is defined as the dispersal kernel (Edwards et al, 2007). By using this approach, we have quantitatively estimated the extension of the particle release area by calculating their dispersal kernels (Edwards et al, 2007) in terms of variance ellipses.…”

“…The spatial extension and density of the starting positions of the particles is defined as the dispersal kernel (Edwards et al, 2007). By using this approach, we have quantitatively estimated the extension of the particle release area by calculating their dispersal kernels (Edwards et al, 2007) in terms of variance ellipses. The calculation is based on the variance of the spatial components (longitudes and latitudes of the starting positions of the particles) as well as on their covariance.…”

“…The latter enables us to find a principle angle describing the variations of particles around a mean position along a set of directions other than those of the longitudinal and latitudinal axis of the particle distributions (Preisendorfer, 1988). The dispersal kernels were found to be Gaussian in form, and similar to Edwards et al, (2007) we calculated four parameters: (1) the mean of the starting positions of particles, (2) the major and (3) minor axis of variability of the spatial starting positions of particles and (4) the principal angle of the orientation of the major axis. The identification of the dispersal scale (extension of the particle distribution) is given by the size of the variance ellipse as calculated from the major and minor axes sizes.…”

Inter-annual and inter-specific differences in the drift of fish eggs and yolksac larvae in the North Sea: A biophysical modeling approach

“…Edwards et al (2007) quantitatively estimated the retention or dispersion of larval fish from spawning grounds by calculating dispersal kernels, the density of settling particles at a given location normalized to the number of released particles. They calculated five parameters: 1) mean distance traveled, 2) the direction relative to the starting position, variances of 3) the major and 4) the minor axis of variability of the spatial distribution of a larval cohort, and 5) the principal angle or the orientation of the major axis.…”