Dispersive currents from narrow windows of time explain patterns of population connectivity in an ecologically and economically important fish

Identifying the drivers of population connectivity remains a fundamental question in ecology and evolution. Answering this question can be challenging in aquatic environments where dynamic lake and ocean currents, high variance in reproductive success, and above average rates of dispersal and gene flow can increase noise. We developed a novel, integrative approach that couples detailed biophysical models with eco-genetic individual-based models to generate predictive values of genetic differentiation. We also used RAD-Seq to genotype 960 yellow perch (Perca flavescens), a species with an [~]30-day pelagic larval duration (PLD), collected from 20 sites circumscribing Lake Michigan. By comparing predictive and empirical values of genetic differentiation, we estimated the relative contributions for known drivers of population connectivity (e.g., currents, behavior, PLD). For the main basin populations (i.e., the largest contiguous portion of the lake), we found that high gene flow led to low overall levels of genetic differentiation among populations (FST = 0.003). By far the best predictors of genetic differentiation were connectivity matrices that 1. came from a specific week and year, and 2. resulted in high population connectivity. Thus, these narrow windows of time during which highly dispersive currents occur are driving the patterns of population connectivity in this system. We also found that populations from the northern and southern main basin are slightly divergent from one another, while those from Green Bay and the main basin are highly divergent (FST = 0.11). By integrating biophysical and eco-genetic models with genome-wide data, we illustrate that the drivers of population connectivity can be identified in high gene flow systems.