A larval ‘recruitment kernel’ to predict hatching locations and quantify recruitment patterns

A backward-in-time particle tracking model was applied here to predict larval hatching locations by releasing particles at larval nursery locations. Based on the p.d.f. of linear recruitment distance, a larval recruitment kernel was first proposed to quantify recruitment patterns in the case where spawning/hatching sources are unknown. The proposed recruitment kernel holds distinct ecological significance compared to the dispersal kernel. Characteristics of the recruitment kernel (e.g., the self-recruitment) is distinct from those of the dispersal kernel (e.g., the well-known local retention), providing important supplements to population connectivity research. The dataset provides data for all the figures in the paper text.