A larval “recruitment kernel” to predict hatching locations and quantify recruitment patterns
Larval recruitment, a critical component of population connectivity, has been under investigated compared to larval dispersal. We developed a backward-in-time Lagrangian particle tracking model to predict larval hatching locations and proposed a larval recruitment kernel, to quantify recruitment patterns. Combining field data and a hydrodynamic model, our backtracking model predicted Lake Whitefish ( Coregonus clupeaformis ) hatching locations in Lake Erie. We found a strong linear correlation ( r = 0.95–0.98) between travel distance (i.e., distance along a trajectory) and pelagic larval duration (PLD), and a moderate correlation ( r = 0.66–0.68) between linear distance (i.e., displacement) and PLD. This questions the wide use of PLD as a proxy for dispersal distance. We defined the recruitment kernel using the probability density function of the linear recruitment distance. Characteristics of the recruitment kernel, such as theoretical self-recruitment, median-recruitment distance, long-distance recruitment, and openness convey significant information about population connectivity that are distinct from those derived using the well-known dispersal kernel (e.g., theoretical local retention).