Modeling gene flow and local adaptation in sockeye salmon

A fundamental goal of ecology is to understand factors affecting population productivity and viability.  Although microevolutionary forces are known to contribute to intraspecific variation and local adaptation, their interactions are complex, making their influence on productivity difficult to predict.  Here, we are developing a stochastic, individual-based model to study effects of gene flow, genetic drift and selection on local adaptation and population dynamics in interconnected salmon populations.  In particular, we will consider how varying dispersal rates between phenotypically distinct populations may affect local adaptation, a scenario that may arise when climate change or other human-induced activity alters population connectivity.  Phenotypes of individual salmon and their offspring are determined using a quantitative genetic model, which is linked to an age-structured population dynamics model.  We are calibrating the model using empirical data on migration rates, reproductive success and selection differentials from wild populations of sockeye salmon (Oncorhynchus nerka) in the Wood River system, Alaska, USA.  Genetic components of variation will be estimated using an animal model and pedigree data from these populations. We will test the hypothesis that high gene flow reduces local adaptation and consequently reduces population productivity, whereas adaptive divergence may still be maintained at lower levels of gene flow.  We aim to generalize the model so that it may be re-parameterized and used in other systems as a tool to assess the effects of evolutionary interactions among populations on demography and viability.