Quantifying the probability of larval exchange among marine populations is key to predicting local population dynamics and optimizing networks of marine protected areas. The pattern of connectivity among populations can be described by the measurement of a dispersal kernel. However, a statistically robust, empirical dispersal kernel has been lacking for any marine species. Here, we use genetic parentage analysis to quantify a dispersal kernel for the reef fish Elacatinus lori, demonstrating that dispersal declines exponentially with distance. The spatial scale of dispersal is an order of magnitude less than previous estimates-the median dispersal distance is just 1.7 km and no dispersal events exceed 16.4 km despite intensive sampling out to 30 km from source. Overlaid on this strong pattern is subtle spatial variation, but neither pelagic larval duration nor direction is associated with the probability of successful dispersal. Given the strong relationship between distance and dispersal, we show that distance-driven logistic models have strong power to predict dispersal probabilities. Moreover, connectivity matrices generated from these models are congruent with empirical estimates of spatial genetic structure, suggesting that the pattern of dispersal we uncovered reflects long-term patterns of gene flow. These results challenge assumptions regarding the spatial scale and presumed predictors of marine population connectivity. We conclude that if marine reserve networks aim to connect whole communities of fishes and conserve biodiversity broadly, then reserves that are close in space (<10 km) will accommodate those members of the community that are shortdistance dispersers.population connectivity | dispersal kernel | parentage analysis | marine protected areas | biological oceanography Q uantifying patterns of marine larval dispersal is a major goal of ecology and conservation biology (1-3). Many marine species have a bipartite life cycle that is characterized by a dispersive larval phase and a relatively sedentary adult phase. Thus, larval dispersal drives the exchange of individuals and alleles (i.e., connectivity) among populations within many marine metapopulations (4). In turn, connectivity influences population dynamics, microevolutionary processes, and the design of effective networks of marine reserves.Ecologists have long recognized that dispersal kernels offer a useful approach to quantifying patterns of dispersal (5, 6). Here, an empirical dispersal kernel is defined as a probability density function (p.d.f.) that can be integrated to yield the probability of successful dispersal over a given distance. Estimating a dispersal kernel requires that sampling be spatially extensive to capture longdistance dispersal (LDD) events-the tail of the kernel. Capturing the tail is essential to understanding ecological and evolutionary processes that are driven by LDD (7). Sampling must also be intensive to tighten the confidence intervals (CIs) associated with low-frequency LDD events. Despite a decades-long resea...

Characterizing patterns of larval dispersal is essential to understanding the ecological and evolutionary dynamics of marine metapopulations. Recent research has measured local dispersal within populations, but the development of marine dispersal kernels from empirical data remains a challenge. We propose a framework to move beyond point estimates of dispersal towards the approximation of a simple dispersal kernel, based on the hypothesis that the structure of the seascape is a primary predictor of realized dispersal patterns. Using the coral reef fish Elacatinus lori as a study organism, we use genetic parentage analysis to estimate self-recruitment at a small spatial scale (<1 km). Next, we determine which simple kernel explains the observed self-recruitment, given the influx of larvae from reef habitat patches in the seascape at a large spatial scale (up to 35 km). Finally, we complete parentage analyses at six additional sites to test for export from the focal site and compare these observed dispersal data within the metapopulation to the predicted dispersal kernel. We find 4.6% self-recruitment (CI95% : ±3.0%) in the focal population, which is explained by the exponential kernel y = 0.915(x) (CI95% : y = 0.865(x) , y = 0.965(x)), given the seascape. Additional parentage analyses showed low levels of export to nearby sites, and the best-fit line through the observed dispersal proportions also revealed a declining function y = 0.77(x). This study lends direct support to the hypothesis that the probability of larval dispersal declines rapidly with distance in Atlantic gobies in continuously distributed habitat, just as it does in the Indo-Pacific damselfishes in patchily distributed habitat.

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The availability of genomic data for an increasing number of species makes it possible to incorporate evolutionary processes into conservation plans. Recent studies show how genetic data can inform spatial conservation prioritization (SCP), but they focus on metrics of diversity and distinctness derived primarily from neutral genetic data sets. Identifying adaptive genetic markers can provide important information regarding the capacity for populations to adapt to environmental change. Yet, the effect of including metrics based on adaptive genomic data into SCP in comparison to more widely used neutral genetic metrics has not been explored. We used existing genomic data on a commercially exploited species, the giant California sea cucumber (Parastichopus californicus), to perform SCP for the coastal region of British Columbia (BC), Canada. Using a RAD-seq data set for 717 P. californicus individuals across 24 sampling locations, we identified putatively adaptive (i.e., candidate) single nucleotide polymorphisms (SNPs) based on genotype-environment associations with seafloor temperature. We calculated various metrics for both neutral and candidate SNPs and compared SCP outcomes with independent metrics and combinations of metrics. Priority areas varied depending on whether neutral or candidate SNPs were used and on the specific metric used. For example, targeting sites with a high frequency of warm-temperatureassociated alleles to support persistence under future warming prioritized areas in the southern coastal region. In contrast, targeting sites with high expected heterozygosity at candidate loci to support persistence under future environmental uncertainty prioritized areas in the north. When combining metrics, all scenarios generated intermediate solutions, protecting sites that span latitudinal and thermal gradients. Our results demonstrate that distinguishing between neutral and adaptive markers can affect conservation solutions and emphasize the importance of defining objectives when choosing among various genomic metrics for SCP.