In marine ecosystems, rising atmospheric CO2 and climate change are associated with concurrent shifts in temperature, circulation, stratification, nutrient input, oxygen content, and ocean acidification, with potentially wide-ranging biological effects. Population-level shifts are occurring because of physiological intolerance to new environments, altered dispersal patterns, and changes in species interactions. Together with local climate-driven invasion and extinction, these processes result in altered community structure and diversity, including possible emergence of novel ecosystems. Impacts are particularly striking for the poles and the tropics, because of the sensitivity of polar ecosystems to sea-ice retreat and poleward species migrations as well as the sensitivity of coral-algal symbiosis to minor increases in temperature. Midlatitude upwelling systems, like the California Current, exhibit strong linkages between climate and species distributions, phenology, and demography. Aggregated effects may modify energy and material flows as well as biogeochemical cycles, eventually impacting the overall ecosystem functioning and services upon which people and societies depend.

Population genetics is a powerful tool for measuring important larval connections between marine populations [1-4]. Similarly, oceanographic models based on environmental data can simulate particle movements in ocean currents and make quantitative estimates of larval connections between populations possible [5-9]. However, these two powerful approaches have remained disconnected because no general models currently provide a means of directly comparing dispersal predictions with empirical genetic data (except, see [10]). In addition, previous genetic models have considered relatively simple dispersal scenarios that are often unrealistic for marine larvae [11-15], and recent landscape genetic models have yet to be applied in a marine context [16-20]. We have developed a genetic model that uses connectivity estimates from oceanographic models to predict genetic patterns resulting from larval dispersal in a Caribbean coral. We then compare the predictions to empirical data for threatened staghorn corals. Our coupled oceanographic-genetic model predicts many of the patterns observed in this and other empirical datasets; such patterns include the isolation of the Bahamas and an east-west divergence near Puerto Rico [3, 21-23]. This new approach provides both a valuable tool for predicting genetic structure in marine populations and a means of explicitly testing these predictions with empirical data.

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.

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