Effects of age and size on spawning and egg production in gag and scamp grouper off the southeastern United States. (5th October 2019)

• Record Type: Journal Article
• Authors: Gamboa-Salazar, Keilin R; Wyanski, David M; Bubley, Walter J; Klibansky, Nikolai
• Editors: Anderson, Emory
• Is Part Of: ICES journal of marine science. Volume 77:Number 1(2020)
• Page Start: 290
• Page End: 299
• Publication Date: 2019-10-05
• Subjects: fecundity -- hermaphrodite -- reproduction -- spawning duration -- spawning frequency -- stock productivity; Ocean -- Periodicals; Fisheries -- Periodicals; Fishes -- Periodicals; Marine biology -- Bibliography -- Periodicals; 551.4605
• DOI: 10.1093/icesjms/fsz174
• Languages: English
• ISSNs: 1054-3139

Abstract

Abstract: Stock assessments historically have used age- and size-independent methods to estimate reproductive output in marine fishes. Recent assessments have used total egg production (TEP), often assuming an invariant spawning frequency (SF) for all females. However, spawning parameters vary with age and size, which invalidates this assumption. This study investigated the age- and size-dependence of female spawning fraction, duration, and frequency in two economically valuable species, gag ( Mycteroperca microlepis ) and scamp ( Mycteroperca phenax ), in Atlantic waters off the southeastern United States, with the ultimate objective of quantifying the effect of these dependencies on TEP estimates. Spawning parameters had moderate-to-strong dome-shaped relationships with age and size ( r 2 = 0.63–0.94), peaking at intermediate ages and sizes. As larger, older females approach sexual transition, they may increase their lifetime reproductive success by shifting energy resources away from female reproductive investment (i.e. eggs) towards investment that will promote future male reproductive success (i.e. gonad transformation). The estimate of TEP based on age-dependent SF was 38% lower for gag and 41% lower for scamp than the estimate based on age-independent (AI) SF; therefore, the AI method overestimates stock productivity. Incorporating age- and size-based reproductive data into assessments can lead to more reliable stock productivity estimates and potentially improved management.