Seascape genetics along a steep cline: using genetic patterns to test predictions of marine larval dispersal - HEATHER M. GALINDO; ANNA S. PFEIFFER‐HERBERT; MARGARET A. McMANUS; YI CHAO; FEI CHAI; STEPHEN R. PALUMBI (Molecular Ecology, 2010)
Coupled biological and physical oceanographic models are powerful tools for studying connectivity among marine populations because they simulate the movement of larvae based on ocean currents and larval characteristics. However, while the models themselves have been parameterized and verified with physical empirical data, the simulated patterns of connectivity have rarely been compared to field observations. We demonstrate a framework for testing biological‐physical oceanographic models by using them to generate simulated spatial genetic patterns through a simple population genetic model, and then testing these predictions with empirical genetic data. Both agreement and mismatches between predicted and observed genetic patterns can provide insights into mechanisms influencing larval connectivity in the coastal ocean. We use a high‐resolution ROMS‐CoSINE biological‐physical model for Monterey Bay, California specifically modified to simulate dispersal of the acorn barnacle, Balanus glandula . Predicted spatial genetic patterns generated from both seasonal and annual connectivity matrices did not match an observed genetic cline in this species at either a mitochondrial or nuclear gene. However, information from this mismatch generated hypotheses testable with our modelling framework that including natural selection, larval input from a southern direction and/or increased nearshore larval retention might provide a better fit between predicted and observed patterns. Indeed, moderate selection and a range of combined larval retention and southern input values dramatically improve the fit between simulated and observed spatial genetic patterns. Our results suggest that integrating population genetic models with coupled biological‐physical oceanographic models can provide new insights and a new means of verifying model predictions.