Estimation of lake-scale stock-recruitment models for Great Lakes sea lampreys

Jean V. Adams and Michael L. Jones

Ecological Modelling, 2022, vol. 467, issue C

Abstract: Understanding recruitment dynamics is an essential part of effective fisheries management, whether the focus is on conservation, harvest policy development, or invasive species control. We developed a model that estimates lake-wide Ricker stock-recruitment relations for invasive sea lampreys (Petromyzon marinus) in each of the five Laurentian Great Lakes to inform future control efforts. We fit adult-to-adult models, taking advantage of a long time series of lake-wide, adult, sea lamprey abundance estimates. We incorporated proportional contributions at age for the stock as well as additional explanatory variables sea lamprey weight, as a surrogate for fecundity, and lampricide quantity applied, as a surrogate for anthropogenic mortality, to explain residual recruitment variability. The best model incorporated equal cohort contributions from the adult stock (that matured 5, 6, and 7 years prior to recruitment), a single productivity parameter (α) common to all five lakes, lake-specific carrying capacity parameters (βj), and coefficients for sea lamprey weight and lampricide quantity applied. The precision of the estimated Ricker parameters compared favorably to those estimated by adult-to-larva models, a promising development in the pursuit of sea lamprey recruitment prediction. The model should be useful to fisheries managers in the Great Lakes wishing to consider various recruitment overfishing strategies in the control of invasive sea lampreys, reaffirming that even models built on a single life stage can inform our understanding of ecological interactions and explorative management scenarios.

Keywords: Recruitment; Population dynamics; Sea lamprey; Great lakes; Lampricide Control; Invasive species (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:467:y:2022:i:c:s0304380022000394

DOI: 10.1016/j.ecolmodel.2022.109916

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