Influence of seasonal changes in relative liver size, condition, relative gonad size and variability in ovarian development in multiple spawning fish species used in environmental monitoring programmes

Abstract

Basic biological information was collected to assist in understanding the biology of selected small-bodied fishes for use in environmental monitoring programmes. Gonado-somatic index (I G ) and hepato-somatic index (I H ) profiles of female blacknose dace Rhinichthys atratulus, mummichog Fundulus heteroclitus and golden shiner Notemigonus crysoleucas were similar, decreasing gradually during the spawning season. I G and I H profiles of female northern redbelly dace Phoxinus eos peaked twice suggesting oocyte recruitment was continuous throughout the spawning season. Regressions of ovary mass to adjusted body mass (M A ) were more variable in dace relative to the other fish species. For female blacknose dace, variability in the relationship between ovary mass and M A was minimized by selecting either 2 year-old fish and fish that weighed 2-4 g.

Energy density of sub-Antarctic fishes from the Beagle Channel

“…The increase in the HSI and the GSI values during the oocyte yolk-laden period (Fig. 2b, GSI \ 28) would correspond to vitellogenin synthesis in the liver and its transfer to the ovary, something that has also been suggested for other fishes (Galloway and Munkittrick 2006). The continuous increase of GSI values (Fig.…”

Seasonal changes in oocyte development, growth and population size distribution of Percilia gillissiand Trichomycterus areolatus in the Itata basin, Chile

“…The presence of mature oocytes in the gonad of both species simultaneously with oocytes at all other different developmental stages indicates a multiple spawning reproductive strategy with continuous preparation of the gonad for development and maturation of oocytes in groups (batches) (Galloway & Munkittrick 2006), that are spawned over a period of time in a few or many events as described for other species (Barrett & Munkittrick 2010). The highest observed abundance of oocytes in advanced stages of maturity, were consistent with data of highest gonadosomatic indices (GSI) during spring and early summer, previously reported for both species (Chiang et al 2011a) and to the presence of mature female (external examination) between September and December (T. areolatus) (Montoya et al 2012).…”

“…The histological analysis of the oocytes in T. areolatus is protracted from what has been reported by Manríquez et al (1988) using macroscopic observations, which describes this species with a synchronous development of batches of oocytes in the gonad between spring and early summer. Histology allows us to clearly differentiate the stages of maturity of the gonad (Galloway & Munkittrick 2006), as we identifi ed mature eggs from July to January, demonstrating a long spawning season and supported by Montoya et al (2012) that found spawned eggs of P. gillissi between November and January; while for T. areolatus they found no eggs (maybe because, siluriform eggs are mainly demersal, Huaquin et al 2002) they described larvae and postlarvae/juveniles stages along late spring to early autum in other river systems.…”

“…For monitoring purposes, collections should be made prior to spring for these species as the large adults initiated spawning early, increasing variability in GSI during earlier proposed monitoring seasons (Chiang et al 2011a). This information is key to designing effective biological monitoring programs, which allows us to assess threats and understand the possible causes of deterioration of the populations exposed to stressors of different origins and help in the restoration and/or protection of these species (Galloway & Munkittrick 2006). …”

Genomic investigation of year‐long and multigenerational exposures of fathead minnow to the munitions compound RDX

“…Analysis of reproductive endpoints was restricted to time periods when a strong relationship between gonad size and body weight existed among sampled females (n ¼ 5). This approach accounts for within-sample variability and maximizes sensitivity for detecting differences among treatments [32]. In our current design, sample size was not flexible to compensate for increased variability.…”