Success and failure assessing gonad maturity in sequentially hermaphroditic fishes: comparisons between macroscopic...

For two protogynous hermaphrodite fish species, the performance of visual gonad analysis techniques was evaluated to determine when the use of macroscopic methods was sufficient and when microscopic techniques were necessary. Simple macroscopic gonad analysis was found to be a powerful tool for distinguishing sex and whether or not females were spawning capable or ripe for black sea bass Centropristis striata (n = 1443) and red porgy Pagrus pagrus (n = 980), often producing results that were in close agreement with more complex and expensive microscopic techniques. Estimates of key reproductive variables, such as size-dependent sex-change ogives, spawning season duration, spawning fraction and batch number, were also very similar or equal between methods. Apparent seasonal spawning activity was also predicted similarly by each method and the patterns were highly correlated with seasonal patterns in gonado-somatic indices. In contrast, distinguishing between immature females and those that were mature, but inactive, proved difficult when using macroscopic methods and, in these cases, predictions often differed from those produced microscopically. In turn, maturity ogives differed significantly between methods for C. striata (maturity ogives could not be generated for P. pagrus as nearly all fish encountered were mature). Agreement rates among male phases were also very low. Macroscopic methods were able to identify signs of sex transition in very advanced specimens, but early signs were only evident microscopically. While much more detail is visible microscopically, here several population-scale parameters important for fisheries management were estimated equally well with the unaided eye for C. striata and P. pagrus. For comprehensive, fishery-independent surveys and long-term research programmes in particular, determining when microscopic techniques are and are not necessary can greatly improve efficiency and reduce costs without compromising data quality.