Age and Size Dependency of Spawning, and Effects on Egg Production, in Gag and Scamp Grouper off the Southeastern U.S.A.
Gamboa, Keilin Rebeca
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Stock assessments have historically used spawning stock biomass as a proxy for reproductive output in marine fishes, assuming an invariant number of batches spawned per female fish. The number of batches spawned has been shown to vary with age and size, which invalidates this assumption, and assessments have begun to use total egg production (TEP) as a measure of reproductive output. This study utilized samples collected by the Southeast Reef Fish Survey (n=8,683) to investigate the age- and size-dependency of spawning fraction, duration, and frequency in Gag (<i>Mycteroperca microlepis</i>) and Scamp (<i>M. phenax</i>), and to estimate the effects of these dependencies on TEP estimates. Spawning parameters had moderate to strong dome-shaped relationships with age and size (R<sup>2</sup>=0.38-0.90). Generalized additive models (GAMs) showed that both age and size are highly significant predictors of spawning probability (p<0.0001), with the smoother functions providing further support to the dome-shape of these relationships. This can be explained by preparation for sexual transition; it may serve the lifetime reproductive success of the largest, oldest females to reduce their reproductive investment as females and invest in somatic growth to increase their future male reproductive success. Including year as a covariate in the spawning models showed that there is significant temporal variation in these relationships (p<0.0001), which could be acting as a proxy for environmental variation or be a result of changes in the population demographics. Age- and size-dependent TEP estimates were 3-99% lower than those based on a constant spawning frequency, with the largest differences in the youngest and oldest females. Age-dependent population-wide TEP estimates were 37% lower for Gag and 30% lower for Scamp than age-independent ones. Incorporating age- and size-based reproductive data into assessments can lead to more reliable stock productivity estimates, and potentially improved management.