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At small spatial and temporal scales, genetic differentiation is largely controlled by constraints on gene flow, while genetic diversity across a species' distribution is shaped on longer temporal and spatial scales. We assess the hypothesis that oceanographic transport and other seascape features explain different scales of genetic structure of giant kelp, Macrocystis pyrifera. We followed a hierarchical approach to perform a microsatellite-based analysis of genetic differentiation in Macrocystis across its distribution in the northeast Pacific. We used seascape genetic approaches to identify large-scale biogeographic population clusters and investigate whether they could be explained by oceanographic transport and other environmental drivers. We then modelled population genetic differentiation within clusters as a function of oceanographic transport and other environmental factors. Five geographic clusters were identified: Alaska/Canada, central California, continental Santa Barbara, California Channel Islands and mainland southern California/Baja California peninsula. The strongest break occurred between central and southern California, with mainland Santa Barbara sites forming a transition zone between the two. Breaks between clusters corresponded approximately to previously identified biogeographic breaks, but were not solely explained by oceanographic transport. An isolation-by-environment (IBE) pattern was observed where the northern and southern Channel Islands clustered together, but not with closer mainland sites, despite the greater distance between them. The strongest environmental association with this IBE pattern was observed with light extinction coefficient, which extends suitable habitat to deeper areas. Within clusters, we found support for previous results showing that oceanographic connectivity plays an important role in the population genetic structure of Macrocystis in the Northern hemisphere., (© 2015 John Wiley & Sons Ltd.)

Patterns of spatial genetic structure (SGS), typically estimated by genotyping adults, integrate migration over multiple generations and measure the effective gene flow of populations. SGS results can be compared with direct ecological studies of dispersal or mating system to gain additional insights. When mismatches occur, simulations can be used to illuminate the causes of these mismatches. Here, we report a SGS and simulation-based study of self-fertilization in Macrocystis pyrifera, the giant kelp. We found that SGS is weaker than expected in M. pyrifera and used computer simulations to identify selfing and early mortality rates for which the individual heterozygosity distribution fits that of the observed data. Only one (of three) population showed both elevated kinship in the smallest distance class and a significant negative slope between kinship and geographical distance. All simulations had poor fit to the observed data unless mortality due to inbreeding depression was imposed. This mortality could only be imposed for selfing, as these were the only simulations to show an excess of homozygous individuals relative to the observed data. Thus, the expected data consistently achieved nonsignificant differences from the observed data only under models of selfing with mortality, with best fits between 32% and 42% selfing. Inbreeding depression ranged from 0.70 to 0.73. The results suggest that density-dependent mortality of early life stages is a significant force in structuring Macrocystis populations, with few highly homozygous individuals surviving. The success of these results should help to validate simulation approaches even in data-poor systems, as a means to estimate otherwise difficult-to-measure life cycle parameters., (© 2013 John Wiley & Sons Ltd.)

Ocean currents are expected to be the predominant environmental factor influencing the dispersal of planktonic larvae or spores; yet, their characterization as predictors of marine connectivity has been hindered by a lack of understanding of how best to use oceanographic data. We used a high-resolution oceanographic model output and Lagrangian particle simulations to derive oceanographic distances (hereafter called transport times) between sites studied for Macrocystis pyrifera genetic differentiation. We build upon the classical isolation-by-distance regression model by asking how much additional variability in genetic differentiation is explained when adding transport time as predictor. We explored the extent to which gene flow is dependent upon seasonal changes in ocean circulation. Because oceanographic transport between two sites is inherently asymmetric, we also compare the explanatory power of models using the minimum or the mean transport times. Finally, we compare the direction of connectivity as estimated by the oceanographic model and genetic assignment tests. We show that the minimum transport time had higher explanatory power than the mean transport time, revealing the importance of considering asymmetry in ocean currents when modelling gene flow. Genetic assignment tests were much less effective in determining asymmetry in gene flow. Summer-derived transport times, in particular for the month of June, which had the strongest current speed, greatest asymmetry and highest spore production, resulted in the best-fit model explaining twice the variability in genetic differentiation relative to models that use geographic distance or habitat continuity. The best overall model also included habitat continuity and explained 65% of the variation in genetic differentiation among sites., (© 2011 Blackwell Publishing Ltd.)