|Título||Isolation by oceanographic distance explains genetic structure for Macrocystis pyrifera in the Santa Barbara Channel.|
|Publication Type||Journal Article|
|Authors||Alberto, F, Raimondi, PT, Reed, DC, Watson, JR, Siegel, DA, Mitarai, S, Coelho, N, Serrão, EA|
|Year of Publication||2011|
|Date Published||2011 Jun|
|Palavras-chave||California, Gene Flow, Genetic Variation, Genetics, Population, Macrocystis, Models, Genetic, Oceanography, Regression Analysis, Seasons, Seawater, Spores, Time Factors, Water Movements|
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.
|Alternate Journal||Mol. Ecol.|