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A larval “recruitment kernel” to predict hatching locations and quantify recruitment patterns

Wei Shi·Leon Boegman·Shiliang Shan·Yingming Zhao·Josef D. Ackerman·Zachary A. Amidon·Aidin Jabbari·Edward Roseman
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Abstract

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).

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Wei Shi, Leon Boegman, Shiliang Shan, Yingming Zhao, Josef D. Ackerman, Zachary A. Amidon, Aidin Jabbari, Edward Roseman. 2024-05-06. A larval “recruitment kernel” to predict hatching locations and quantify recruitment patterns. https://doi.org/10.1029/2023wr036099

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