Knowing the salmon semen
Atlantic salmon (Salmo salar) is a cultured fish species with a high economic value, which generates significant revenue from both wild catches and fish farming (Hindar et al.2006). However, the total annual catch of wild Atlantic salmon in the North Atlantic has shown a marked decline during recent decades from approximately 12 000 t in 1973 down to 1200 t in 2016 (NASCO 2016). The commercial response to this problem is in the conservation, restoration, enhancement and rational management of wild salmon in the North Atlantic. By focusing on biology, the aims of scientific projects are to increase fish survival and improve their behavioural adaptation to natural conditions. In a conservation program, it is essential to secure the revival of the species by ensuring genetic variability (Rurangwa et al.2004), using wild broodstock from the local habitat. The priority in reproduction for restocking is to ensure that all males contribute towards successful fertilisation. However, it is important to know the differences in sperm motility among males in order to maintain the genetic integrity of the broodstock used (McGinnity et al.1997, 2003; Rurangwa et al.2004). In addition, knowledge of sperm characteristics (velocity, optimal activation time and subpopulation structures) will help improve fertilization procedures (Bobe and Labbe´ 2010; Fauvel et al.2010).
In fish species with external fertilisation, the quest for reproductive success among competing males can lead to several adaptations, including behavioural, morphological and physiological, to enhance the competitiveness of their spermatozoa (Beatty et al.1969). The physiological quality of spermatozoa differs between competing males because of differences in investment in gametes and sperm production (Ball and Parker 1996). Sperm quality is generally correlated with fertilising ability, which is often used as a determining factor in studies on sperm competition (Levitan 2000). During spawning, the number of spermatozoa released and their swimming speed can affect the probability of fertilisation (Stoltz and Neff 2006). Faster swimming cells may be able to reach the egg first, increasing the probability of successful fertilization (Gage et al.2004; Stoltz and Neff 2006). Atlantic salmon is an anadromous species that migrates up rivers from the sea in order to breed. In the case of this species, spawning males are characterised by intense sexual competition, which confers strong selective pressure on their reproductive physiology (Vladic and Järvi 2001). The wild salmon population can exhibit a wide natural variation in sperm traits leading to sperm competition (Gage et al.2004). However, mature parr males also participate in the spawning, which means that they need to invest more in sperm production than anadromous males (Parker 1998).
Sperm quality can be assessed using simple methods, such as individual analysis of motility (Billard and Cosson 1992) and morphology (Holstein et al.1988), or sophisticated approaches involving molecular tools (Cabrita et al.2014). Motility is the parameter that is most used to assess sperm quality because it directly reflects the fertilising ability of the spermatozoa. Initially, the percentage of motile spermatozoa and/or the duration of their motility were evaluated using subjective visual estimates. However, computer-aided sperm analysis (CASA-Mot) systems were developed to provide more reliable, repeatable and objective measurements of sperm movement (Rurangwa et al.2004). Even though CASA technology was designed for mammalian species (Rurangwa et al. 2004), it is well adapted to fish spermatozoa (Gallego et al. 2013; Kime et al.2001), which are characterised by a short period of vigorous motility after activation. In general, fish spermatozoa remain active for less than 2 min in most aquatic species and, in the case of salmonids specifically, the duration of sperm motility is around 20–30 s (Kime et al.2001).
CASA-Mot systems provide a large amount of data based on the kinematic parameters of each spermatozoon. Applying subpopulation analysis to such data allows for the analysis of groups of spermatozoa with similar motility features and to estimate of sperm quality for each male (Soler et al. 2014). A subpopulation characterised by rapid linear movement has been proposed as an indicator of high-quality spermatozoa (Ferraz et al.2014). Variations in subpopulation distributions have been reported for several species, including boar (Flores et al.2009), bull (Valverde et al.2016), red deer (Martínez-Pastor et al.2005), stallion (Quintero-Moreno et al.2003), cat (Gutiérrez-Reinoso and García-Herreros 2016), dog (Núñez-Martínez et al.2006), fowl (García-Herreros 2016), rooster (García-Herreros 2016), human(Vásquez et al.2016), gilthead seabream (Beirao et al.2011) and steelhead (Kanuga et al.2012). This statistical methodology has improved knowledge regarding spermquality, although it is still used primarily as a research tool (Gil Anaya et al.2015).
In a study in which one of our members participated, the objective of the work was to study sperm subpopulation structure and motility patterns in wild anadromous males and farmed male Atlantic salmon parr. Salmon sperm samples were collected from wild anadromous salmon (WS) and two generations of farmed parr males. Sperm samples were collected from sexually mature males and sperm motility was analysed at different times after activation (5 and 35 s). Differences among the three groups were analysed using statistical techniques based on Cluster analysis the Bayesian method. Atlantic salmon were found to have three sperm subpopulations, and the spermatozoa in ejaculates of mature farmed parr males had a higher velocity and larger size than those of WS males. This could be an adaptation to high sperm competition because salmonid species are naturally adapted to this process. Motility analysis enables us to identify sperm subpopulations, and it may be useful to correlate these sperm subpopulations with fertilisation ability to test whether faster-swimming spermatozoa have a higher probability of success.
Our CASA system works with many different fish species, such as sea bass, salmon, sole, sturgeon, eel, zebrafish, sea urchin, and many more. And if it's not adapted to the species you need, we can customize it accordingly.
RESEARCH ARTICLE:
https://doi.org/10.1071/RD17466
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