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Understanding the population structure and reproductive behavior of hatchery-produced yellowtail kingfish (Seriola lalandi)
Journal Pre-proof Understanding the population structure and reproductive behavior of hatchery-produced yellowtail kingfish (Seriola lalandi)
P. Dettleff, E. Hernandez, V. Martinez PII:
S0044-8486(19)31634-5
DOI:
https://doi.org/10.1016/j.aquaculture.2020.734948
Reference:
AQUA 734948
To appear in:
aquaculture
Received date:
28 June 2019
Revised date:
8 January 2020
Accepted date:
9 January 2020
Please cite this article as: P. Dettleff, E. Hernandez and V. Martinez, Understanding the population structure and reproductive behavior of hatchery-produced yellowtail kingfish (Seriola lalandi), aquaculture (2020), https://doi.org/10.1016/j.aquaculture.2020.734948
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© 2020 Published by Elsevier.
Journal Pre-proof Understanding the population structure and reproductive behavior of hatchery-produced yellowtail kingfish (Seriola lalandi). Dettleff, P.a, Hernandez, E.a, Martinez, V. a a
FAVET-INBIOGEN, Faculty of Veterinary Sciences, University of Chile, Avda. Santa Rosa 11735, La
Pintana, Santiago, Chile. Corresponding author: V. Martinez. FAVET-INBIOGEN, Faculty of Veterinary Sciences, University of Chile, Avda. Santa Rosa 11735, La Pintana, Santiago, Chile. E-mail address: [email protected].
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Telephone number: +56 2 2978 5641.
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Abstract
The yellowtail kingfish (Seriola lalandi) is a marine endemic fish, and a key species in the national
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programme for the diversification of Chilean aquaculture. Since it has been recently developed
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from wild fish, the biology of this species under production is to a large extent unknown. For example, the structure of the different populations used to create the nati onal breeding
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programme is not well characterised. Moreover, due to the fact that it is not possible to perform stripping of broodstock in yellowtail kingfish, the genetic contributions of individuals are affected
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by the reproductive behaviour of this species. To increase our knowledge of the biology of this species under aquaculture conditions, the objectives of this study were: (i) to identify the
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population structures of wild and commercial populations of yellowtail kingfish obtained from different fisheries off the Chilean coast, and compare these to those of Mexican and Australian
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specimens and (ii) to study the reproductive behaviour of commercial yellowtail kingfish broodstock through paternity testing, to estimate the genetic contributions of individuals throughout the artificial spawning season in captivity. We used a set of 12 highly informative microsatellite markers optimised for paternity testing. The analysis of the population structure showed at least two clusters of yellowtail kingfish, including a single metapopulation from Chile and Australia (possibly explained by the migratory behaviour of this species in the Pacific Ocean), and the other from Mexico (which is most likely composed of California yellowtail, S. dorsalis). Some degree of admixture, albeit small, was observed between the populations from Mexico and Australia. Paternity analysis showed that the average ratio (male/female) contributing in a spawning event was 2.6, confirming the spawning behaviour observed in other species in this genus. Additionally, we observed that males participated in matings regularly during the whole 1
Journal Pre-proof spawning season. Using the results of this research, we recommend modifying the current implementation of yellowtail kingfish breeding programmes to reduce the effects of random genetic drift. This can be achieved by managing the genetic contributions of broodstock in a twostep breeding programme. This study provides useful genetic information for the long-term development and management of the Chilean yellowtail kingfish industry, which involves a species of high importance for the diversification of Chilean aquaculture. Keywords
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Seriola lalandi, paternity analysis, population structure, genetic contributions.
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1. Introduction
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The yellowtail kingfish (Seriola lalandi) is a marine pelagic fish with a worldwide distribution
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(Moran, et al., 2007), including the Pacific Ocean and South Atlantic Ocean (Patel et al., 2016; Sepulveda and Gonzalez, 2017). Due to the commercial value of yellowtail kingfish, this species is cultured by fishery industries in several locations, including in northern Chile (Ottolenghi et al.,
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2004). Additionally, other members of the genus have been traditionally produced in Japan using capture-based aquaculture systems. However, in the last 20 years, several countries have cultured
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this species commercially, including Japan, Australia, New Zealand, USA, Mexico, the Netherlands,
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South Africa, and Chile. Due to an increase in global demand for yellowtail kingfish, this species has recently been designated as one of the key species for the diversification of the aquaculture industry in Chile (Palomino et al., 2014). Since 2006, the Chilean aquaculture diversification programme has established the complete production cycle of yellowtail kingfish, including broodstock assessment and larvae production. To implement the breeding programme, wild yellowtail kingfish specimens were captured. These wild specimens were obtained from different locations to avoid loss of genetic variability. However, little is known about whether these populations shared a common ancestry. Thus, it is important to understand the genetic composition of the populations present along the Pacific coast, and the degree of differentiation of the yellowtail kingfish populations along this coast (An et al., 2014).
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Journal Pre-proof Yellowtail kingfish is a migratory species with a variable distance of migration, and adult migration distances can be as long as 3,000 km (Gillanders et al., 2001). This differential migratory pattern can further affect the yellowtail kingfish population structure, contributing to fragmentation of the populations (Palumbi, 2003). The genetic variability across different subpopulations needs to be considered when establishing a broodstock population. Additionally, knowing the genetic variability of the natural populations is relevant for the conservation of the natural population and fisheries management (Dudgeon et al., 2012). Some previous studies have determined the structure of yellowtail kingfish populations worldwide. The yellowtail kingfish in Japan were
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determined to belong to one population, and the yellowtail kingfish of Australia and New Zealand were identified as a single population (Miller et al., 2011; Nugroho et al., 2001). A further two
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populations have been identified, one in South Africa, in the South Atlantic region, and another in the north-east Pacific region, corresponding to the area encompassing California and Mexico
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(Purcell et al., 2015). Two studies conducted on yellowtail kingfish in the southwest Pacific region
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were inconclusive (Fernandez et al., 2015; Sepulveda and Gonzalez, 2017). Additionally, it is important to understand the relationship between global populations of this species, as this can
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help establish sustainable management of aquaculture and Seriola spp. fisheries in Chile and in other countries along the Pacific coast.
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The production of offspring in captivity is relevant for sustainable aquaculture production of yellowtail kingfish. As such, the implementation of breeding programmes to increase the efficiency
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and sustainability of production is essential for the long-term development of aquaculture industry
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(Abdelrahman et al., 2017). Some studies have shown that even a small increase in an economic trait per generation could generate a major effect in long term (de Oliveira et al., 2016). The implementation of a breeding programme requires phenotypes and pedigree information to predict breeding values (Abdelrahman et al., 2017). However, in yellowtail kingfish, the number of offspring per parent is affected by the reproductive biology of the species. This species shows spontaneous mating behaviour, with repeated spawning events during the reproductive season (Palomino et al., 2014). Therefore, the development of molecular tools to assign the offspring to the corresponding broodstock is needed when evaluating the genetic contribution of each fish, and for the establishment of pedigrees for use in future breeding programmes. Moreover, molecular tools may be used to assess the interaction between cultured and wild specimens in the long term.
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Journal Pre-proof In this study, we evaluated a set of novel microsatellites markers of yellowtail kingfish to (i) identify the population structure of yellowtail kingfish across several locations along the northern Chilean coast, Mexico, and Australia; and (ii) to study the reproductive behaviour of yellowtail kingfish to assess the genetic contributions of different individuals, and to be able to calculate the effective population size. The results provided by this study are relevant for the long-term development and management of the Chilean yellowtail kingfish aquaculture industry, and provide new information on the reproductive behaviour and the population structure of this species along the Pacific coast.
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2. Material and methods
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2.1 Fish samples
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Seventy-one wild yellowtail kingfish were captured from four sampling points at three different locations in northern Chile by local fishermen in February 2017. The sampling sites in the
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Coquimbo region were Punta de Choros (29.2° S; 71.4° W) and Guanaqueros (30.1° S; 71.4° W), while in the Atacama region fish were sampled from different locations near Caldera (27.0° S;
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70.8° W). Additionally, 30 samples of yellowtail kingfish sampled from the Pacific coast of Mexico
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and 64 from Australia were included in this study. The broodstock system comprised four breeding units (R1, R2, R3, and R4), and a single replacement unit (UFA) maintained under recirculation. The broodstock system was located in the
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Acuinor SA Company, located in Caldera, in the Atacama Region of Chile. Each broodstock system
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included 18, 22, 25, 30, and 122 fish for R1, R2, R3, R4, and UFA, respectively. The fish of each unit were maintained in separated indoor tanks with depths of 2.5 m and volumes of 20,000 squared litres. Fish were fed at libitum daily, with different photoperiods and temperature increase s, to ensure the availability of eggs throughout the year (each breeding unit produced larvae for approximately three months of the year). A fin clip sample was obtained from each individual yellowtail kingfish, and stored in ethanol at −20 °C, until further use. “The average size was 111 (±5.8), 101.5 (±5.9), 98 (±7.3), 96.9 (±8) and 94.2 cm (±5.5) for R1, R2, R3, R4 and UFA, respectively. The sex of each broodstock was determined by cannulation (Palomino, et al., 2014). Larvae generated from each breeding unit were sampled randomly at different time points during the spawning season (see Figure 1), and according to the availability of larvae in the production centre (not all spawnings were used for producing larvae). The larvae sampled at each point were 4
Journal Pre-proof stored in ethanol at −20 °C until further use. A subset of larvae collected at each time point was randomly chosen for posterior DNA purification (174, 169, 166, and 66 larvae in total for breeding units R1, R2, R3, and R4, respectively). 2.2 Primer selection After a series of analyses, to ensure cross-amplification, and accounting for the presence of null alleles, 12 (out of 25 analysed) microsatellites markers were used in the study of genetic contribution and population structure analysis (see Fernandez et al., 2015, for details on the
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markers used). For each microsatellite marker, forward and reverse primers were designed in our laboratory or were previously published (Miller et al., 2011; Moran et al., 2007; Patel et al., 2016;
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Sepulveda and Gonzalez, 2017). Primer sequence and allele representation are presented in
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Supplementary Table 1. 2.3 DNA extraction, PCR, and capillary electrophoresis
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Genomic DNA was purified from larvae and fin clips using the NucleoSpin Plant II kit (Macherey-
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Nagel). The samples were quantified using a Qubit fluorometer (Thermo Fisher Scientific) with the Qubit dsDNA BR Assay Kit (Thermo Fisher Scientific). For the PCR reaction, we develop ed and
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optimised multiplex reactions, generating three sets of multiplexes with four microsatellites markers each, with a specific fluorofore (6-FAM, NED, VIC, or PET (Applied Biosystems)). The PCR was performed in a T100 thermal cycler (Bio-Rad) using the following protocol: each 10 µL of PCR
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reaction included 1 µL of PCR Buffer (NH4SO4) (Fermentas), 0.83 mM of dNTPs, 0.75 U of Taq DNA
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Polymerase (Thermo Fisher Scientific), 1 μL of BSA (10 mg/mL) (Promega), 30 mM of MgCL2, 7 mM of fluorophore -forward primer for each microsatellite of the multiplex, 6 nM of reverse primer for each microsatellite of the multiplex, and 20 ng of DNA. The PCR cycling conditions included 7 min of initial denaturation at 95 °C, 21 cycles of amplification (95 °C for 30 s, 58 °C for 30 s, 72 °C for 30 s) and 20 cycles of amplification (95 °C for 30 s, 58 °C for 55 s, 72 °C for 30 s), with a final step of 72 °C for 30 min. The Veterinary Genetics Laboratory of UC Davis (California, USA) performed the capillary electrophoresis in an ABI 3730 as an external service. The analysis of fluorescent DNA fragments was performed with Peak scanner software v1.10 (Thermo Fisher Scientific). 2.4 Genetic analysis
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Journal Pre-proof The genetic parameters, including allele frequencies observed heterozygosity (Ho), expected heterozygosity (He), Fis, Fst, Hardy-Weinberg, and principal coordinates analysis (PCoA) were calculated using GENEPOP on the Web software (Rousset, 2008) and GenAlex software (Peakall and Smouse, 2012). The estimation of the rate of inbreeding (ΔF) and effective population size (Ne) were determined from the genetic contributions according to Brown et al. (2005). The ΔF was calculated as follows:
Where,
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̅
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is the square contribution of each parent (in fractional terms), ̅ is the average
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contribution of males in fractional terms as 1/(2Cm)), and ̅ is the average contribution of females
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(in fractional terms as 1/(2cf)). The Ne was calculated as Ne=1/(2ΔF). The offspring obtained at
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different sampling points were pooled for each broodstock group to improve the reliability of ci. 2.5 Population structure analysis
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The population structure analysis was performed including all available samples, using Structure 2.3.4 software (Pritchard et al., 2000). This analysis was performed assuming between one and
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nine population clusters (K=1 to K=9), performing 20 runs for each K with an ancestry admixture model with a simulation of 10,000 length burn-in period, and 100,000 number of MCMC reps after
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burn-in for each run. This run length was chosen considering the variation of the Markov chain. The determination of the number of clusters in the dataset was performed using Structure
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Harvester software (Evanno et al., 2005). 2.6 Genetic contribution analysis To obtain the genetic contribution of each broodstock during the spawning season, a paternity inference analysis was performed to reconstruct the pedigree using the genotypes of the four sets of broodstock and the genotypes of larvae generated in the different spawning samples collected during the season (Figure 1) using Colony (Jones and Wang, 2010). We used a model including polygamy, medium length of run, a probability of included male or female candidates of 1, and determined the maternity and paternity probability of the first and second most probable parent for each offspring. 3. Results 6
Journal Pre-proof 3.1 Genetic analysis The set of microsatellites markers amplified successfully in all the samples used in this study. The allele frequencies for each population are presented in Supplementary Table 2. The percentage of polymorphic loci in each population was 100% for all populations. The number of alleles (Na) across each marker in each population varied from 2 to 30, while the number of effective alleles (Ne) ranged from 1.03 to 22.7. The average Na, average Ne, average observed heterozygosity (Ho), and average expected heterozygosity (He) across all populations for each microsatellite marker are presented in Table 1. To characterise the population sub-division, we additionally calculated Fis
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and Fit using GENEPOP on the Web software (Table 2). The loci evaluated in the study were found
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to be under Hardy-Weinberg equilibrium within populations (data not shown).
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3.2 Population structure analysis
We estimated Fst values to evaluate populations considering the sampling locations within the
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range of the environments of this species (Punta de Choros, Guanaqueros, Caldera, Mexico, and Australia). The Fst values observed varied from less than 0.01 (between the Chilean populations)
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to values greater than 0.15 (between the Chilean and Mexican populations) (Table 5). These results agree with the PCoA analysis, where all the Chilean population groups together with a
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greater distance from the Australian and Mexican populations (Figure 2) The structure analysis assuming different scenarios with respect to the number of populations
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(K=2 to 8), did not show evidence of more than K=2 (Figure 3). The clusters evidenced one South
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Pacific metapopulation (including the Chilean populations and the Australian population), and a second metapopulation comprising the Mexican population (Figure 4). 3.3 Paternity analysis and genetic contribution of broodstock The dataset of microsatellites markers used in this study showed a probability of exclusion for overall loci of 0.999999, 0.999986, and 0.999999 for estimation in the parental analysis with 1 parent known, 0 parents known, and parent pairs, respectively. The average number of females contributing to each spawning sampled ranged from 2.1 to 2.8, and the average number of males contributing to each spawning ranged from 4.2 to 5.7 (Table 3). The number of males contributing per female was 2.6 ± 1.5 (Table 3).
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Journal Pre-proof The percentages of the contribution of parents during the spawning season showed important differences in terms of contribution both in males as females (Figure 1, Table 3, and Supplementary Figure S1). The results show that several males and females contributed very little, while others contributed many offspring to the next generation. For example, some males and females contributed to 25% and 37% of the offspring in the next generation, respectively ( Figure 5, Table 4). This asymmetry of the genetic contributions was evident in all the spawning groups analysed (Figure 5). It appears that males participated in almost twice as many spawnings as females (4.0 versus 2.5).
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This distribution was observed across the different sets of broodstocks (Table 4), showing that
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males participate in more spawning events than females during the season. All these results suggest that there is a significant asymmetry in the genetic contribution of the
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broodstock (Figure 5), which may give rise to a significant decline in the effective population size.
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The estimated rate of inbreeding was equal to ΔF=0.0398, with an effective population size Ne = 12.54 individuals, considering all four breeding units.
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4. Discussion
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To establish a successful breeding programme using endemic species such as yellowtail kingfish, it is crucial to understand the reproductive behaviour and the genetic composition of the broodstock, and of wild populations present along the Pacific coast (Purcell et al., 2015). This is
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especially important when considering the conservation of endemic fisheries, and the possibility of
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introgression from aquaculture. In this study, we validated a set of polymorphic microsatellite markers that proved to be useful for paternity analysis, and for studying the population genetics of this species (Fernandez et al., 2015). This result is consistent with previous findings (Premachandra et al., 2017; Purcell et al., 2015; Swart et al., 2015). The structure of the endemic fisheries showed that there is little variation within the Chilean populations (both wild and those used in aquaculture), and in those from Australia. Yellowtail kingfish is a migratory species, which could explain the lack of population structure in the South Pacific (Gillanders et al., 2001). Some recent studies showed the existence of three different populations in the Pacific, including the north-west (Japan), north-east (North America), and South Pacific (Australia-New Zealand and Chilean coast) populations (Premachandra et al., 2017; Purcell et al., 2015; Swart et al., 2015).
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Journal Pre-proof In our analysis, the Mexican population appears to be in a different cluster. Recent results have shown that the Mexican population is likely composed of California yellowtail, and should be described as Seriola dorsalis instead (Purcell et al., 2018). This is important considering that there has been some exchange of genetic material between countries (F. Lafarga, personal communication). However, the long term impact of this is not known, and the possibility of mating between yellowtail kingfish and California yellowtail should be considered. We observed some degree of admixture between the populations from Australia and Mexico, which could be explained by migration over large distances by some individuals, behaviour that has been
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observed by tag and recapture studies in populations of the same location, such as the Australian populations (Gillanders et al., 2001). Despite this, more studies should be performed to evaluate
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the possible impact of these long-distance migrant fish in the conformation of the three main
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described populations under aquaculture in the Pacific Ocean.
When using new species for aquaculture, there are several biological issues that need to be
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resolved when designing the production system in practice. For example, in many marine species
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it is not yet technically feasible to perform external fertilisation, and thus it is not possible control the genetic contributions of the broodstock when implementing the breeding programme. We observed an asymmetric contribution of the broodstock within the breeding units of yellowtail
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kingfish, for the different spawning seasons analysed, in which a few males and females dominate. This is consistent with the courtship behaviour described for yellowtail kingfish from Australia
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(Moran et al., 2007). Therefore, it is likely that many of the broodstock would not contribute
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offspring, nor contribute to the number of reproductive partners within the reproductive season (de Oliveira et al., 2016; Renshaw et al., 2007). However, it is crucial that many of the broodstock contribute to the offspring to avoid losses of genetic diversity within the populations, which may lead to a decrease in fitness (Abdelrahman et al., 2017; de Oliveira et al., 2016). The skewed distribution of genetic contributions leads to a relatively large rate of inbreeding when considering all the spawning groups (ΔF=0.04). These results are important when considering the management of the breeding programme, as it is important to avoid short- and long-term loss of genetic variation, by chance, which would lead to unsustainable rates of genetic gain. To consider this problem in practice, some degree of preselection is required at the larval stage to minimise the impact of skewed distributions (Martinez et al., 2006). Based on the results of our analysis, breeding programmes should consider genetic contributions of broodstock. In the Chilean national breeding programme, a two stage breeding programme is used based on preselecting individuals 9
Journal Pre-proof in the first stage based on paternity analysis (Martinez, et al., 2006). The extent to which the number and ratio of males and females contribute to lower rates of inbreeding is still under scrutiny, and further data is required to ascertain which is the best sex ratio within breeding units. Paternity analysis provides valuable information about the reproductive behaviour of fish. For example, our results show that females require more than one male for each spawning event. These results are consistent previous findings describing a sequential courtship behavio ur, in which a single female is approached by at least two different males in a single spawning (Moran et al., 2007; Yang et al., 2016). However, in some cases, the eggs of a single female can be fertili sed
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by more than four males within a single spawning event, which may increase the chance of
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increasing the genetic contributions of some of the sires involved in each breeding unit.
5. Conclusion
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We observed similar spawning behaviour in Chilean populations of yellowtail kingfish as have been
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described in other species in the genus Seriola. Females tend to produce eggs in pulses throughout the spawning season, while males contribute more evenly. In general, eggs of a single female were
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fertilised by two or more males during a single spawning event. The extent of admixture between yellowtail kingfish and California yellowtail should be considered further, especially when considering the possible effects of outbreeding depression. Considering the relatively high rates of
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inbreeding, caused by the asymmetry of the genetic contributions, proper management of the
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broodstock contributions is required to increase the sustainability of yellowtail kingfish aquaculture. This study provides useful genetic information for the long-term development and management of the Chilean yellowtail kingfish industry, which involves a species of high importance for the diversification of Chilean aquaculture with native species. Acknowledgments The authors acknowledge the funding provided by PDACH-CORFO 09PDAC-7020, PTEC-CORFO 15PTEC-45861 and FONDEF IDEA en dos etapas ID14-10125. Some from Mexico were kindly supplied by F. Lafarga, from CICESE, Mexico and 64 samples of yellowtail kingfish from the Pacific coast of Australia (kindly supplied by Dr. G. Partridge, from Murdoch University).
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Journal Pre-proof Figure 1. Distribution of progeny samples within each spawning season obtained for each breeding unit (R1, R2, R3, and R4). Data are presented within breeding units in days from the first spawning day of the season (0).
Figure 2. Principal coordinates analysis based in Fst matrix between populations. Coordinate 1 and 2 represent the 90.38% and 4.99% of the total variation. BROD = broodstock; UFA = candidates broodstock; PUNCH = Punta Choros; GUAN = Guanaqueros; CALD = Caldera; AUS =
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Australia; MEX = Mexico.
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Figure 3. Delta K graph of structure analysis across all K values assumed.
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Figure 4. Cluster analysis from structure across different sample sets assuming K = 2.
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Representative graph across the 20 runs with K=2. Numbers indicate: 1 = Caldera; 2 = Guanaqueros; 3 = Punta Choros; 4 = Broodstock; 5 = broodstock candidates; 6 = Mexican
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population; 7 = Australian population.
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Figure 5. Progeny contributions of sires and dams across breeding units.
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1. References
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Abdelrahman, H., ElHady, M., Alcivar-Warren, A., Allen, S., Al-Tobasei, R., Bao, L., Beck, B., Blackburn, H., Bosworth, B., Buchanan, J., Chappell, J., Daniels, W., Dong, S., Dunham, R., Durland, E., Elaswad, A., Gomez-Chiarri, M., Gosh, K., Guo, X., Hackett, P., Hanson, T., Hedgecock, D., Howard, T., Holland, L., Jackson, M., Jin, Y., Kahlil, K., Kocher, T., Leeds, T., Li, N., Lindsey, L., Liu, S., Liu, Z., Martin, K., Novriadi, R., Odin, R., Palti, Y., Peatman, E., Proestou, D., Qin, G., Reading, B., Rexroad, C., Roberts, S., Salem, M., Severin, A., Shi, H., Shoemaker, C., Stiles, S., Tan, S., Tang, K.F., Thongda, W., Tiersch, T., Tomasso, J., Prabowo, W.T., Vallejo, R., van der Steen, H., Vo, K., Waldbieser, G., Wang, H., Wang, X., Xiang, J., Yang, Y., Yant, R., Yuan, Z., Zeng, Q., Zhou, T., 2017. Aquaculture genomics, genetics and breeding in the United States: current status, challenges, and priorities for future research. Bmc Genomics. 18, 191. An, H.S., Kang, H.W., Han, H.S., Park, J.Y., Hong, C.G., Park, J., Myeong, J.I., An, C.M., 2014. Genetic differences between the wild and hatchery-produced populations of Korean short barbeled grunter (Hapalogenys nitens) determined with microsatellite markers. Genet Mol Res. 13, 8901-8912. Blonk, R.J.W., Komen, J., Kamstra, A., Crooijmans, R.P.M.A., van Arendonk, J.A.M., 2009. Levels of inbreeding in group mating captive broodstock populations of Common sole, (Solea solea), inferred from parental relatedness and contribution. Aquaculture. 289, 26-31. Brown, R.C., Woolliams, J.A., McAndrew, B.J., 2005. Factors influencing effective population size in commercial populations of gilthead seabream, Sparus aurata. Aquaculture. 247, 219-225. de Oliveira, C.A., Ribeiro, R.P., Yoshida, G.M., Kunita, N.M., Rizzato, G.S., de Oliveira, S.N., Dos Santos, A.I., Nguyen, N.H., 2016. Correlated changes in body shape after five generations of selection to improve growth rate in a breeding program for Nile tilapia Oreochromis niloticus in Brazil. Journal of applied genetics. 57, 487-493. Dudgeon, C.L., Blower, D.C., Broderick, D., Giles, J.L., Holmes, B.J., Kashiwagi, T., Kruck , N.C., Morgan, J.A., Tillett, B.J., Ovenden, J.R., 2012. A review of the application of molecular genetics for fisheries management and conservation of sharks and rays. J Fish Biol. 80, 1789-1843. Evanno, G., Regnaut, S., Goudet, J., 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 14, 2611-2620. Fernandez, G., Cichero, D., Patel, A., Martinez, V., 2015. Genetic structure of Chilean populations of Seriola lalandi for the diversification of the national aquaculture in the north of Chile. Lat Am J Aquat Res. 43, 374-379. Gillanders, B.M., Ferrell, D.J., Andrew, N.L., 2001. Estimates of movement and life-history parameters of yellowtail kingfish (Seriola lalandi): how useful are data from a cooperative tagging programme? Mar Freshwater Res. 52, 179-192. Jones, O.R., Wang, J., 2010. COLONY: a program for paternity and sibship inference from multilocus genotype data. Molecular ecology resources. 10, 551-555. Martinez, V., Kause, A., Mantysaari, E., Maki-Tanila, A., 2006. The use of alternative breeding schemes to enhance genetic improvement in rainbow trout: II. Two-stage selection. Aquaculture. 254, 195-202. Miller, P.A., Fitch, A.J., Gardner, M., Hutson, K.S., Mair, G., 2011. Genetic population structure of Yellowtail Kingfish (Seriola lalandi) in temperate Australasian waters inferred from microsatellite markers and mitochondrial DNA. Aquaculture. 319, 328-336. 12
Journal Pre-proof
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Moran, D., Smith, C.K., Gara, B., Poortenaar, C.W., 2007. Reproductive behaviour and early development in yellowtail kingfish (Seriola lalandi Valenciennes 1833). Aquaculture. 262, 95-104. Nugroho, E., Ferrell, D.J., Smith, P., Taniguchi, N., 2001. Genetic divergence of kingfish from Japan, Australia and New Zealand inferred by microsatellite DNA and mitochondrial DNA control region markers. Fisheries Sci. 67, 843-850. Ottolenghi, F., Silvestri, C., Giordano, P., Lovatelli, A., New, M., 2004. CAPTURE-BASED AQUACULTURE THE FATTENING OF EELS, GROUPERS, TUNAS AND YELLOWTAILS. Food and Agriculture Organization of the United Nations, Rome. Palomino, J., Herrera, G., Dettleff, P., Martinez, V., 2014. Growth differentiation factor 9 and bone morphogenetic protein 15 expression in previtellogenic oocytes and during early embryonic development of Yellow-tail Kingfish Seriola lalandi. Biological research. 47, 60. Palumbi, S.R., 2003. Population genetics, demographic connectivity, and the design of marine reserves. Ecol Appl. 13, S146-S158. Patel, A., Dettleff, P., Hernandez, E., Martinez, V., 2016. A comprehensive transcriptome of early development in yellowtail kingfish (Seriola lalandi). Molecular ecology resources. 16, 364376. Peakall, R., Smouse, P.E., 2012. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research--an update. Bioinformatics. 28, 2537-2539. Porta, J.M., Novel, P., Martinez-Rodriguez, G., Alvarez, M.C., Porta, J., 2009. Isolation and characterization of microsatellites from Seriola dumerili (Risso 1810). Aquac Res. 40, 249251. Premachandra, H.K.A., Lafarga-De La Cruz, F., Takeuchi, Y., Miller, A., Fielder, S., O'Connor, W., Frere, C.H., Nguyen, N.H., Bar, I., Knibb, W., 2017. Genomic DNA variation confirmed Seriola lalandi comprises three different populations in the Pacific, but with recent divergence. Sci Rep-Uk. 7. Pritchard, J.K., Stephens, M., Donnelly, P., 2000. Inference of population structure using multilocus genotype data. Genetics. 155, 945-959. Purcell, C.M., Chabot, C.L., Craig, M.T., Martinez-Takeshita, N., Allen, L.G., Hyde, J.R., 2015. Developing a genetic baseline for the yellowtail amberjack species complex, Seriola lalandi sensu lato, to assess and preserve variation in wild populations of these globally important aquaculture species. Conserv Genet. 16, 1475-1488. Purcell, C.M., Seetharam, A.S., Snodgrass, O., Ortega-Garcia, S., Hyde, J.R., Severin, A.J., 2018. Insights into teleost sex determination from the Seriola dorsalis genome assembly. Bmc Genomics. 19, 31. Renshaw, M.A., Patton, J.C., Rexroad, C.E., Gold, J.R., 2007. Isolation and characterization of dinucleotide microsatellites in greater amberjack, Seriola dumerili. Conserv Genet. 8, 1009-1011. Rousset, F., 2008. genepop'007: a complete re-implementation of the genepop software for Windows and Linux. Molecular ecology resources. 8, 103-106. Sepulveda, F.A., Gonzalez, M.T., 2017. Spatio-temporal patterns of genetic variations in populations of yellowtail kingfish Seriola lalandi from the south-eastern Pacific Ocean and potential implications for its fishery management. J Fish Biol. 90, 249-264. Swart, B.L., von der Heyden, S., Bester-van der Merwe, A., Roodt-Wilding, R., 2015. Molecular systematics and biogeography of the circumglobally distributed genus Seriola (Pisces: Carangidae). Molecular phylogenetics and evolution. 93, 274-280. Yang, S.G., Ji, S.C., Lim, S.G., Hur, S.W., Jeong, M., Lee, C.H., Kim, B.S., Lee, Y.D., 2016. Management of Sexual Maturation and Natural Spawning of Captive - Reared Yellowtail 13
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Kingfish, Seriola lalandi, in an Indoor Rearing Tank. Development & reproduction. 20, 141147.
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Journal Pre-proof Tables
Table 1. Average Na, Ne, Ho and He in all sampled populations.
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Mean He 0.92 0.90 0.84 0.92 0.92 0.64 0.75 0.86 0.87 0.93 0.21 0.90
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Mean Ho 0.78 0.75 0.84 0.84 0.84 0.62 0.70 0.83 0.81 0.84 0.14 0.81
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Mean Ne 13.57 10.09 7.01 13.02 12.60 2.89 5.78 7.93 9.28 13.61 1.39 11.71
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Mean Na 22.57 17.71 11.29 21.43 19.14 6.14 11.29 13.43 16.00 23.14 3.86 17.71
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Locus Sequ_233 Sequ_114 Sequ_47 Sequ_216 Sequ_42 Sdu_46 Sequ_41 Sequ_230 Sequ_57 Sequ_77 Sequ_4 Sdu gA3D
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Journal Pre-proof Table 2. Fis and Fit statistics of the microsatellite set in all sampled populations.
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Fwc(it) 0.1762 0.3204 0.0731 0.1081 0.1263 0.2143 0.1942 0.0728 0.0704 0.1171 0.7133 0.109 0.1654
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Fwc(is) 0.1646 0.2974 0.0423 0.0983 0.108 0.0685 0.0864 0.0506 0.0529 0.1059 0.385 0.0968 0.1088
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Locus Sequ_233 Sequ_114 Sequ_47 Sequ_216 Sequ_42 Sdu_46 Sequ_41 Sequ_230 Sequ_57 Sequ_77 Sequ_4 Sdu gA3D All
Table 3. Number of broodstock contributing to each spawning per set and sex ratio. Set 1
Set 2
Set 3
Set 4
Total average
2 7 1
3 7 1
2 8 1
5 6 1
3 7 1
4 4.3
4 5.4
4 4.2
4 5.7
4 4.9
2.1
2.2
2.6
2.7
2.4
2.4±1.3
3.1±1.4
2.2±1.6
2.8±1.9
2.6±1.5
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Parameters
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Minimum number of males contributing per sampling Maximum number of males contributing per sampling Minimum number of females contributing per sampling Maximum number of females contributing per s sampling
Average number of males contributing in each sampling Average number of females contributing in each sampling
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Average number of males per female contributing in each sampling
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Table 4. Percentages of contribution of parents during the spawning season. Min % of Max % of Min % of Max % of Average % of Average % of Average n° Average n° Broodstock contribution contribution contribution contribution contribution contribution of spawning of spawning set of males of males of females of females of males of females per male per female 0
20.1
4.5
35.8
8.3
15.3
Set 2
0
11.9
0
36.9
6.3
10.2
Set 3
0
25.9
0
19.3
5.7
5.7
Set 4
0
15.2
0
37.9
3.1
total
0.0
18.3
1.1
32.5
5.9
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Set 1
9.4
4.0
2.5
3.1 2.3 0.6
Caldera
Guanaqueros
Guanaqueros Punta Choros Broodstock UFAs Mexico
-0.0013 0.0097 0.0103 0.0042 0.1382
0.0028 0.0033 -0.0029 0.1227
Australia
0.1135
Punta Choros
Broodstock
UFAs
0.0061 0.0025 0.1269
0.0038 0.1359
0.1244
0.1017
0.1237
0.1148
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Population
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Table 5. Fst values between populations.
3.8
6.4
6 5.4 3.5 1
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0.1037
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Mexico
0.1514
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Table 3. Number of broodstock contributing to each spawning per set and sex ratio. Set 1
Set 2
Set 3
Set 4
Minimum n° of males contributing per sampling Maximum n° of males contributing per sampling Minimum n° of females contributing per sampling Maximum n° of females contributing per sampling Average n° of males contributing in each sampling Average n° of females contributing in each sampling Average n° of males/female contributing in each sampling
2 7 1 4 4.3 2.1
3 7 1 4 5.4 2.2
2 8 1 4 4.2 2.6
5 6 1 4 5.7 2.7
Total average 3 7 1 4 4.9 2.4
2.4±1.3
3.1±1.4
2.2±1.6
2.8±1.9
2.6±1.5
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Parameter
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Journal Pre-proof Table 4. Percentages of contribution of parents during the spawning season.
Min % of contribution of males
Max % of contribution of males
Min % of contribution of females
Max % of contribution of females
Average % of Average % of Average n° of Averag contribution contribution spawning per spawni of males of females male fem
Set 1
0
20.1
4.5
35.8
8.3
15.3
6
3
Set 2
0
11.9
0
36.9
6.3
10.2
5.4
3
Set 3
0
25.9
0
19.3
5.7
5.7
3.5
2
Set 4
0
15.2
0
37.9
3.1
6.4
1
0
Average
0
18.3
1.1
32.5
9.4
4.0
2
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Broodstock set
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5.9
Journal Pre-proof Highlights.
- The parentage analysis showed that the average relation male/female contributing in a spawning event was 2.6, showing that female of S. lalandi require more than one male for each spawning event, confirming the spawning behavior observation of other studies.
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- Females tend to show pulses of egg production, while males show a more even contribution during the spawning season.
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- Wild and commercial populations evaluated of S. lalandi, are confirmed for at least two different populations, with Chilean populations for one side and Mexican in the other. The Australian population appear to be more admixed.
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Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
P. Dettleff, E. Hernandez, V. Martinez PII:
S0044-8486(19)31634-5
DOI:
https://doi.org/10.1016/j.aquaculture.2020.734948
Reference:
AQUA 734948
To appear in:
aquaculture
Received date:
28 June 2019
Revised date:
8 January 2020
Accepted date:
9 January 2020
Please cite this article as: P. Dettleff, E. Hernandez and V. Martinez, Understanding the population structure and reproductive behavior of hatchery-produced yellowtail kingfish (Seriola lalandi), aquaculture (2020), https://doi.org/10.1016/j.aquaculture.2020.734948
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2020 Published by Elsevier.
Journal Pre-proof Understanding the population structure and reproductive behavior of hatchery-produced yellowtail kingfish (Seriola lalandi). Dettleff, P.a, Hernandez, E.a, Martinez, V. a a
FAVET-INBIOGEN, Faculty of Veterinary Sciences, University of Chile, Avda. Santa Rosa 11735, La
Pintana, Santiago, Chile. Corresponding author: V. Martinez. FAVET-INBIOGEN, Faculty of Veterinary Sciences, University of Chile, Avda. Santa Rosa 11735, La Pintana, Santiago, Chile. E-mail address: [email protected].
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Telephone number: +56 2 2978 5641.
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Abstract
The yellowtail kingfish (Seriola lalandi) is a marine endemic fish, and a key species in the national
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programme for the diversification of Chilean aquaculture. Since it has been recently developed
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from wild fish, the biology of this species under production is to a large extent unknown. For example, the structure of the different populations used to create the nati onal breeding
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programme is not well characterised. Moreover, due to the fact that it is not possible to perform stripping of broodstock in yellowtail kingfish, the genetic contributions of individuals are affected
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by the reproductive behaviour of this species. To increase our knowledge of the biology of this species under aquaculture conditions, the objectives of this study were: (i) to identify the
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population structures of wild and commercial populations of yellowtail kingfish obtained from different fisheries off the Chilean coast, and compare these to those of Mexican and Australian
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specimens and (ii) to study the reproductive behaviour of commercial yellowtail kingfish broodstock through paternity testing, to estimate the genetic contributions of individuals throughout the artificial spawning season in captivity. We used a set of 12 highly informative microsatellite markers optimised for paternity testing. The analysis of the population structure showed at least two clusters of yellowtail kingfish, including a single metapopulation from Chile and Australia (possibly explained by the migratory behaviour of this species in the Pacific Ocean), and the other from Mexico (which is most likely composed of California yellowtail, S. dorsalis). Some degree of admixture, albeit small, was observed between the populations from Mexico and Australia. Paternity analysis showed that the average ratio (male/female) contributing in a spawning event was 2.6, confirming the spawning behaviour observed in other species in this genus. Additionally, we observed that males participated in matings regularly during the whole 1
Journal Pre-proof spawning season. Using the results of this research, we recommend modifying the current implementation of yellowtail kingfish breeding programmes to reduce the effects of random genetic drift. This can be achieved by managing the genetic contributions of broodstock in a twostep breeding programme. This study provides useful genetic information for the long-term development and management of the Chilean yellowtail kingfish industry, which involves a species of high importance for the diversification of Chilean aquaculture. Keywords
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Seriola lalandi, paternity analysis, population structure, genetic contributions.
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1. Introduction
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The yellowtail kingfish (Seriola lalandi) is a marine pelagic fish with a worldwide distribution
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(Moran, et al., 2007), including the Pacific Ocean and South Atlantic Ocean (Patel et al., 2016; Sepulveda and Gonzalez, 2017). Due to the commercial value of yellowtail kingfish, this species is cultured by fishery industries in several locations, including in northern Chile (Ottolenghi et al.,
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2004). Additionally, other members of the genus have been traditionally produced in Japan using capture-based aquaculture systems. However, in the last 20 years, several countries have cultured
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this species commercially, including Japan, Australia, New Zealand, USA, Mexico, the Netherlands,
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South Africa, and Chile. Due to an increase in global demand for yellowtail kingfish, this species has recently been designated as one of the key species for the diversification of the aquaculture industry in Chile (Palomino et al., 2014). Since 2006, the Chilean aquaculture diversification programme has established the complete production cycle of yellowtail kingfish, including broodstock assessment and larvae production. To implement the breeding programme, wild yellowtail kingfish specimens were captured. These wild specimens were obtained from different locations to avoid loss of genetic variability. However, little is known about whether these populations shared a common ancestry. Thus, it is important to understand the genetic composition of the populations present along the Pacific coast, and the degree of differentiation of the yellowtail kingfish populations along this coast (An et al., 2014).
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Journal Pre-proof Yellowtail kingfish is a migratory species with a variable distance of migration, and adult migration distances can be as long as 3,000 km (Gillanders et al., 2001). This differential migratory pattern can further affect the yellowtail kingfish population structure, contributing to fragmentation of the populations (Palumbi, 2003). The genetic variability across different subpopulations needs to be considered when establishing a broodstock population. Additionally, knowing the genetic variability of the natural populations is relevant for the conservation of the natural population and fisheries management (Dudgeon et al., 2012). Some previous studies have determined the structure of yellowtail kingfish populations worldwide. The yellowtail kingfish in Japan were
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determined to belong to one population, and the yellowtail kingfish of Australia and New Zealand were identified as a single population (Miller et al., 2011; Nugroho et al., 2001). A further two
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populations have been identified, one in South Africa, in the South Atlantic region, and another in the north-east Pacific region, corresponding to the area encompassing California and Mexico
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(Purcell et al., 2015). Two studies conducted on yellowtail kingfish in the southwest Pacific region
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were inconclusive (Fernandez et al., 2015; Sepulveda and Gonzalez, 2017). Additionally, it is important to understand the relationship between global populations of this species, as this can
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help establish sustainable management of aquaculture and Seriola spp. fisheries in Chile and in other countries along the Pacific coast.
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The production of offspring in captivity is relevant for sustainable aquaculture production of yellowtail kingfish. As such, the implementation of breeding programmes to increase the efficiency
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and sustainability of production is essential for the long-term development of aquaculture industry
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(Abdelrahman et al., 2017). Some studies have shown that even a small increase in an economic trait per generation could generate a major effect in long term (de Oliveira et al., 2016). The implementation of a breeding programme requires phenotypes and pedigree information to predict breeding values (Abdelrahman et al., 2017). However, in yellowtail kingfish, the number of offspring per parent is affected by the reproductive biology of the species. This species shows spontaneous mating behaviour, with repeated spawning events during the reproductive season (Palomino et al., 2014). Therefore, the development of molecular tools to assign the offspring to the corresponding broodstock is needed when evaluating the genetic contribution of each fish, and for the establishment of pedigrees for use in future breeding programmes. Moreover, molecular tools may be used to assess the interaction between cultured and wild specimens in the long term.
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Journal Pre-proof In this study, we evaluated a set of novel microsatellites markers of yellowtail kingfish to (i) identify the population structure of yellowtail kingfish across several locations along the northern Chilean coast, Mexico, and Australia; and (ii) to study the reproductive behaviour of yellowtail kingfish to assess the genetic contributions of different individuals, and to be able to calculate the effective population size. The results provided by this study are relevant for the long-term development and management of the Chilean yellowtail kingfish aquaculture industry, and provide new information on the reproductive behaviour and the population structure of this species along the Pacific coast.
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2. Material and methods
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2.1 Fish samples
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Seventy-one wild yellowtail kingfish were captured from four sampling points at three different locations in northern Chile by local fishermen in February 2017. The sampling sites in the
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Coquimbo region were Punta de Choros (29.2° S; 71.4° W) and Guanaqueros (30.1° S; 71.4° W), while in the Atacama region fish were sampled from different locations near Caldera (27.0° S;
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70.8° W). Additionally, 30 samples of yellowtail kingfish sampled from the Pacific coast of Mexico
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and 64 from Australia were included in this study. The broodstock system comprised four breeding units (R1, R2, R3, and R4), and a single replacement unit (UFA) maintained under recirculation. The broodstock system was located in the
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Acuinor SA Company, located in Caldera, in the Atacama Region of Chile. Each broodstock system
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included 18, 22, 25, 30, and 122 fish for R1, R2, R3, R4, and UFA, respectively. The fish of each unit were maintained in separated indoor tanks with depths of 2.5 m and volumes of 20,000 squared litres. Fish were fed at libitum daily, with different photoperiods and temperature increase s, to ensure the availability of eggs throughout the year (each breeding unit produced larvae for approximately three months of the year). A fin clip sample was obtained from each individual yellowtail kingfish, and stored in ethanol at −20 °C, until further use. “The average size was 111 (±5.8), 101.5 (±5.9), 98 (±7.3), 96.9 (±8) and 94.2 cm (±5.5) for R1, R2, R3, R4 and UFA, respectively. The sex of each broodstock was determined by cannulation (Palomino, et al., 2014). Larvae generated from each breeding unit were sampled randomly at different time points during the spawning season (see Figure 1), and according to the availability of larvae in the production centre (not all spawnings were used for producing larvae). The larvae sampled at each point were 4
Journal Pre-proof stored in ethanol at −20 °C until further use. A subset of larvae collected at each time point was randomly chosen for posterior DNA purification (174, 169, 166, and 66 larvae in total for breeding units R1, R2, R3, and R4, respectively). 2.2 Primer selection After a series of analyses, to ensure cross-amplification, and accounting for the presence of null alleles, 12 (out of 25 analysed) microsatellites markers were used in the study of genetic contribution and population structure analysis (see Fernandez et al., 2015, for details on the
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markers used). For each microsatellite marker, forward and reverse primers were designed in our laboratory or were previously published (Miller et al., 2011; Moran et al., 2007; Patel et al., 2016;
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Sepulveda and Gonzalez, 2017). Primer sequence and allele representation are presented in
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Supplementary Table 1. 2.3 DNA extraction, PCR, and capillary electrophoresis
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Genomic DNA was purified from larvae and fin clips using the NucleoSpin Plant II kit (Macherey-
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Nagel). The samples were quantified using a Qubit fluorometer (Thermo Fisher Scientific) with the Qubit dsDNA BR Assay Kit (Thermo Fisher Scientific). For the PCR reaction, we develop ed and
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optimised multiplex reactions, generating three sets of multiplexes with four microsatellites markers each, with a specific fluorofore (6-FAM, NED, VIC, or PET (Applied Biosystems)). The PCR was performed in a T100 thermal cycler (Bio-Rad) using the following protocol: each 10 µL of PCR
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reaction included 1 µL of PCR Buffer (NH4SO4) (Fermentas), 0.83 mM of dNTPs, 0.75 U of Taq DNA
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Polymerase (Thermo Fisher Scientific), 1 μL of BSA (10 mg/mL) (Promega), 30 mM of MgCL2, 7 mM of fluorophore -forward primer for each microsatellite of the multiplex, 6 nM of reverse primer for each microsatellite of the multiplex, and 20 ng of DNA. The PCR cycling conditions included 7 min of initial denaturation at 95 °C, 21 cycles of amplification (95 °C for 30 s, 58 °C for 30 s, 72 °C for 30 s) and 20 cycles of amplification (95 °C for 30 s, 58 °C for 55 s, 72 °C for 30 s), with a final step of 72 °C for 30 min. The Veterinary Genetics Laboratory of UC Davis (California, USA) performed the capillary electrophoresis in an ABI 3730 as an external service. The analysis of fluorescent DNA fragments was performed with Peak scanner software v1.10 (Thermo Fisher Scientific). 2.4 Genetic analysis
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Journal Pre-proof The genetic parameters, including allele frequencies observed heterozygosity (Ho), expected heterozygosity (He), Fis, Fst, Hardy-Weinberg, and principal coordinates analysis (PCoA) were calculated using GENEPOP on the Web software (Rousset, 2008) and GenAlex software (Peakall and Smouse, 2012). The estimation of the rate of inbreeding (ΔF) and effective population size (Ne) were determined from the genetic contributions according to Brown et al. (2005). The ΔF was calculated as follows:
Where,
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is the square contribution of each parent (in fractional terms), ̅ is the average
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contribution of males in fractional terms as 1/(2Cm)), and ̅ is the average contribution of females
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(in fractional terms as 1/(2cf)). The Ne was calculated as Ne=1/(2ΔF). The offspring obtained at
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different sampling points were pooled for each broodstock group to improve the reliability of ci. 2.5 Population structure analysis
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The population structure analysis was performed including all available samples, using Structure 2.3.4 software (Pritchard et al., 2000). This analysis was performed assuming between one and
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nine population clusters (K=1 to K=9), performing 20 runs for each K with an ancestry admixture model with a simulation of 10,000 length burn-in period, and 100,000 number of MCMC reps after
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burn-in for each run. This run length was chosen considering the variation of the Markov chain. The determination of the number of clusters in the dataset was performed using Structure
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Harvester software (Evanno et al., 2005). 2.6 Genetic contribution analysis To obtain the genetic contribution of each broodstock during the spawning season, a paternity inference analysis was performed to reconstruct the pedigree using the genotypes of the four sets of broodstock and the genotypes of larvae generated in the different spawning samples collected during the season (Figure 1) using Colony (Jones and Wang, 2010). We used a model including polygamy, medium length of run, a probability of included male or female candidates of 1, and determined the maternity and paternity probability of the first and second most probable parent for each offspring. 3. Results 6
Journal Pre-proof 3.1 Genetic analysis The set of microsatellites markers amplified successfully in all the samples used in this study. The allele frequencies for each population are presented in Supplementary Table 2. The percentage of polymorphic loci in each population was 100% for all populations. The number of alleles (Na) across each marker in each population varied from 2 to 30, while the number of effective alleles (Ne) ranged from 1.03 to 22.7. The average Na, average Ne, average observed heterozygosity (Ho), and average expected heterozygosity (He) across all populations for each microsatellite marker are presented in Table 1. To characterise the population sub-division, we additionally calculated Fis
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and Fit using GENEPOP on the Web software (Table 2). The loci evaluated in the study were found
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to be under Hardy-Weinberg equilibrium within populations (data not shown).
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3.2 Population structure analysis
We estimated Fst values to evaluate populations considering the sampling locations within the
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range of the environments of this species (Punta de Choros, Guanaqueros, Caldera, Mexico, and Australia). The Fst values observed varied from less than 0.01 (between the Chilean populations)
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to values greater than 0.15 (between the Chilean and Mexican populations) (Table 5). These results agree with the PCoA analysis, where all the Chilean population groups together with a
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greater distance from the Australian and Mexican populations (Figure 2) The structure analysis assuming different scenarios with respect to the number of populations
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(K=2 to 8), did not show evidence of more than K=2 (Figure 3). The clusters evidenced one South
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Pacific metapopulation (including the Chilean populations and the Australian population), and a second metapopulation comprising the Mexican population (Figure 4). 3.3 Paternity analysis and genetic contribution of broodstock The dataset of microsatellites markers used in this study showed a probability of exclusion for overall loci of 0.999999, 0.999986, and 0.999999 for estimation in the parental analysis with 1 parent known, 0 parents known, and parent pairs, respectively. The average number of females contributing to each spawning sampled ranged from 2.1 to 2.8, and the average number of males contributing to each spawning ranged from 4.2 to 5.7 (Table 3). The number of males contributing per female was 2.6 ± 1.5 (Table 3).
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Journal Pre-proof The percentages of the contribution of parents during the spawning season showed important differences in terms of contribution both in males as females (Figure 1, Table 3, and Supplementary Figure S1). The results show that several males and females contributed very little, while others contributed many offspring to the next generation. For example, some males and females contributed to 25% and 37% of the offspring in the next generation, respectively ( Figure 5, Table 4). This asymmetry of the genetic contributions was evident in all the spawning groups analysed (Figure 5). It appears that males participated in almost twice as many spawnings as females (4.0 versus 2.5).
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This distribution was observed across the different sets of broodstocks (Table 4), showing that
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males participate in more spawning events than females during the season. All these results suggest that there is a significant asymmetry in the genetic contribution of the
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broodstock (Figure 5), which may give rise to a significant decline in the effective population size.
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The estimated rate of inbreeding was equal to ΔF=0.0398, with an effective population size Ne = 12.54 individuals, considering all four breeding units.
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4. Discussion
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To establish a successful breeding programme using endemic species such as yellowtail kingfish, it is crucial to understand the reproductive behaviour and the genetic composition of the broodstock, and of wild populations present along the Pacific coast (Purcell et al., 2015). This is
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especially important when considering the conservation of endemic fisheries, and the possibility of
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introgression from aquaculture. In this study, we validated a set of polymorphic microsatellite markers that proved to be useful for paternity analysis, and for studying the population genetics of this species (Fernandez et al., 2015). This result is consistent with previous findings (Premachandra et al., 2017; Purcell et al., 2015; Swart et al., 2015). The structure of the endemic fisheries showed that there is little variation within the Chilean populations (both wild and those used in aquaculture), and in those from Australia. Yellowtail kingfish is a migratory species, which could explain the lack of population structure in the South Pacific (Gillanders et al., 2001). Some recent studies showed the existence of three different populations in the Pacific, including the north-west (Japan), north-east (North America), and South Pacific (Australia-New Zealand and Chilean coast) populations (Premachandra et al., 2017; Purcell et al., 2015; Swart et al., 2015).
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Journal Pre-proof In our analysis, the Mexican population appears to be in a different cluster. Recent results have shown that the Mexican population is likely composed of California yellowtail, and should be described as Seriola dorsalis instead (Purcell et al., 2018). This is important considering that there has been some exchange of genetic material between countries (F. Lafarga, personal communication). However, the long term impact of this is not known, and the possibility of mating between yellowtail kingfish and California yellowtail should be considered. We observed some degree of admixture between the populations from Australia and Mexico, which could be explained by migration over large distances by some individuals, behaviour that has been
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observed by tag and recapture studies in populations of the same location, such as the Australian populations (Gillanders et al., 2001). Despite this, more studies should be performed to evaluate
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the possible impact of these long-distance migrant fish in the conformation of the three main
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described populations under aquaculture in the Pacific Ocean.
When using new species for aquaculture, there are several biological issues that need to be
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resolved when designing the production system in practice. For example, in many marine species
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it is not yet technically feasible to perform external fertilisation, and thus it is not possible control the genetic contributions of the broodstock when implementing the breeding programme. We observed an asymmetric contribution of the broodstock within the breeding units of yellowtail
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kingfish, for the different spawning seasons analysed, in which a few males and females dominate. This is consistent with the courtship behaviour described for yellowtail kingfish from Australia
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(Moran et al., 2007). Therefore, it is likely that many of the broodstock would not contribute
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offspring, nor contribute to the number of reproductive partners within the reproductive season (de Oliveira et al., 2016; Renshaw et al., 2007). However, it is crucial that many of the broodstock contribute to the offspring to avoid losses of genetic diversity within the populations, which may lead to a decrease in fitness (Abdelrahman et al., 2017; de Oliveira et al., 2016). The skewed distribution of genetic contributions leads to a relatively large rate of inbreeding when considering all the spawning groups (ΔF=0.04). These results are important when considering the management of the breeding programme, as it is important to avoid short- and long-term loss of genetic variation, by chance, which would lead to unsustainable rates of genetic gain. To consider this problem in practice, some degree of preselection is required at the larval stage to minimise the impact of skewed distributions (Martinez et al., 2006). Based on the results of our analysis, breeding programmes should consider genetic contributions of broodstock. In the Chilean national breeding programme, a two stage breeding programme is used based on preselecting individuals 9
Journal Pre-proof in the first stage based on paternity analysis (Martinez, et al., 2006). The extent to which the number and ratio of males and females contribute to lower rates of inbreeding is still under scrutiny, and further data is required to ascertain which is the best sex ratio within breeding units. Paternity analysis provides valuable information about the reproductive behaviour of fish. For example, our results show that females require more than one male for each spawning event. These results are consistent previous findings describing a sequential courtship behavio ur, in which a single female is approached by at least two different males in a single spawning (Moran et al., 2007; Yang et al., 2016). However, in some cases, the eggs of a single female can be fertili sed
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by more than four males within a single spawning event, which may increase the chance of
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increasing the genetic contributions of some of the sires involved in each breeding unit.
5. Conclusion
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We observed similar spawning behaviour in Chilean populations of yellowtail kingfish as have been
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described in other species in the genus Seriola. Females tend to produce eggs in pulses throughout the spawning season, while males contribute more evenly. In general, eggs of a single female were
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fertilised by two or more males during a single spawning event. The extent of admixture between yellowtail kingfish and California yellowtail should be considered further, especially when considering the possible effects of outbreeding depression. Considering the relatively high rates of
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inbreeding, caused by the asymmetry of the genetic contributions, proper management of the
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broodstock contributions is required to increase the sustainability of yellowtail kingfish aquaculture. This study provides useful genetic information for the long-term development and management of the Chilean yellowtail kingfish industry, which involves a species of high importance for the diversification of Chilean aquaculture with native species. Acknowledgments The authors acknowledge the funding provided by PDACH-CORFO 09PDAC-7020, PTEC-CORFO 15PTEC-45861 and FONDEF IDEA en dos etapas ID14-10125. Some from Mexico were kindly supplied by F. Lafarga, from CICESE, Mexico and 64 samples of yellowtail kingfish from the Pacific coast of Australia (kindly supplied by Dr. G. Partridge, from Murdoch University).
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Journal Pre-proof Figure 1. Distribution of progeny samples within each spawning season obtained for each breeding unit (R1, R2, R3, and R4). Data are presented within breeding units in days from the first spawning day of the season (0).
Figure 2. Principal coordinates analysis based in Fst matrix between populations. Coordinate 1 and 2 represent the 90.38% and 4.99% of the total variation. BROD = broodstock; UFA = candidates broodstock; PUNCH = Punta Choros; GUAN = Guanaqueros; CALD = Caldera; AUS =
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Australia; MEX = Mexico.
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Figure 3. Delta K graph of structure analysis across all K values assumed.
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Figure 4. Cluster analysis from structure across different sample sets assuming K = 2.
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Representative graph across the 20 runs with K=2. Numbers indicate: 1 = Caldera; 2 = Guanaqueros; 3 = Punta Choros; 4 = Broodstock; 5 = broodstock candidates; 6 = Mexican
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population; 7 = Australian population.
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Figure 5. Progeny contributions of sires and dams across breeding units.
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1. References
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Abdelrahman, H., ElHady, M., Alcivar-Warren, A., Allen, S., Al-Tobasei, R., Bao, L., Beck, B., Blackburn, H., Bosworth, B., Buchanan, J., Chappell, J., Daniels, W., Dong, S., Dunham, R., Durland, E., Elaswad, A., Gomez-Chiarri, M., Gosh, K., Guo, X., Hackett, P., Hanson, T., Hedgecock, D., Howard, T., Holland, L., Jackson, M., Jin, Y., Kahlil, K., Kocher, T., Leeds, T., Li, N., Lindsey, L., Liu, S., Liu, Z., Martin, K., Novriadi, R., Odin, R., Palti, Y., Peatman, E., Proestou, D., Qin, G., Reading, B., Rexroad, C., Roberts, S., Salem, M., Severin, A., Shi, H., Shoemaker, C., Stiles, S., Tan, S., Tang, K.F., Thongda, W., Tiersch, T., Tomasso, J., Prabowo, W.T., Vallejo, R., van der Steen, H., Vo, K., Waldbieser, G., Wang, H., Wang, X., Xiang, J., Yang, Y., Yant, R., Yuan, Z., Zeng, Q., Zhou, T., 2017. Aquaculture genomics, genetics and breeding in the United States: current status, challenges, and priorities for future research. Bmc Genomics. 18, 191. An, H.S., Kang, H.W., Han, H.S., Park, J.Y., Hong, C.G., Park, J., Myeong, J.I., An, C.M., 2014. Genetic differences between the wild and hatchery-produced populations of Korean short barbeled grunter (Hapalogenys nitens) determined with microsatellite markers. Genet Mol Res. 13, 8901-8912. Blonk, R.J.W., Komen, J., Kamstra, A., Crooijmans, R.P.M.A., van Arendonk, J.A.M., 2009. Levels of inbreeding in group mating captive broodstock populations of Common sole, (Solea solea), inferred from parental relatedness and contribution. Aquaculture. 289, 26-31. Brown, R.C., Woolliams, J.A., McAndrew, B.J., 2005. Factors influencing effective population size in commercial populations of gilthead seabream, Sparus aurata. Aquaculture. 247, 219-225. de Oliveira, C.A., Ribeiro, R.P., Yoshida, G.M., Kunita, N.M., Rizzato, G.S., de Oliveira, S.N., Dos Santos, A.I., Nguyen, N.H., 2016. Correlated changes in body shape after five generations of selection to improve growth rate in a breeding program for Nile tilapia Oreochromis niloticus in Brazil. Journal of applied genetics. 57, 487-493. Dudgeon, C.L., Blower, D.C., Broderick, D., Giles, J.L., Holmes, B.J., Kashiwagi, T., Kruck , N.C., Morgan, J.A., Tillett, B.J., Ovenden, J.R., 2012. A review of the application of molecular genetics for fisheries management and conservation of sharks and rays. J Fish Biol. 80, 1789-1843. Evanno, G., Regnaut, S., Goudet, J., 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 14, 2611-2620. Fernandez, G., Cichero, D., Patel, A., Martinez, V., 2015. Genetic structure of Chilean populations of Seriola lalandi for the diversification of the national aquaculture in the north of Chile. Lat Am J Aquat Res. 43, 374-379. Gillanders, B.M., Ferrell, D.J., Andrew, N.L., 2001. Estimates of movement and life-history parameters of yellowtail kingfish (Seriola lalandi): how useful are data from a cooperative tagging programme? Mar Freshwater Res. 52, 179-192. Jones, O.R., Wang, J., 2010. COLONY: a program for paternity and sibship inference from multilocus genotype data. Molecular ecology resources. 10, 551-555. Martinez, V., Kause, A., Mantysaari, E., Maki-Tanila, A., 2006. The use of alternative breeding schemes to enhance genetic improvement in rainbow trout: II. Two-stage selection. Aquaculture. 254, 195-202. Miller, P.A., Fitch, A.J., Gardner, M., Hutson, K.S., Mair, G., 2011. Genetic population structure of Yellowtail Kingfish (Seriola lalandi) in temperate Australasian waters inferred from microsatellite markers and mitochondrial DNA. Aquaculture. 319, 328-336. 12
Journal Pre-proof
Jo
ur
na
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re
-p
ro
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Moran, D., Smith, C.K., Gara, B., Poortenaar, C.W., 2007. Reproductive behaviour and early development in yellowtail kingfish (Seriola lalandi Valenciennes 1833). Aquaculture. 262, 95-104. Nugroho, E., Ferrell, D.J., Smith, P., Taniguchi, N., 2001. Genetic divergence of kingfish from Japan, Australia and New Zealand inferred by microsatellite DNA and mitochondrial DNA control region markers. Fisheries Sci. 67, 843-850. Ottolenghi, F., Silvestri, C., Giordano, P., Lovatelli, A., New, M., 2004. CAPTURE-BASED AQUACULTURE THE FATTENING OF EELS, GROUPERS, TUNAS AND YELLOWTAILS. Food and Agriculture Organization of the United Nations, Rome. Palomino, J., Herrera, G., Dettleff, P., Martinez, V., 2014. Growth differentiation factor 9 and bone morphogenetic protein 15 expression in previtellogenic oocytes and during early embryonic development of Yellow-tail Kingfish Seriola lalandi. Biological research. 47, 60. Palumbi, S.R., 2003. Population genetics, demographic connectivity, and the design of marine reserves. Ecol Appl. 13, S146-S158. Patel, A., Dettleff, P., Hernandez, E., Martinez, V., 2016. A comprehensive transcriptome of early development in yellowtail kingfish (Seriola lalandi). Molecular ecology resources. 16, 364376. Peakall, R., Smouse, P.E., 2012. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research--an update. Bioinformatics. 28, 2537-2539. Porta, J.M., Novel, P., Martinez-Rodriguez, G., Alvarez, M.C., Porta, J., 2009. Isolation and characterization of microsatellites from Seriola dumerili (Risso 1810). Aquac Res. 40, 249251. Premachandra, H.K.A., Lafarga-De La Cruz, F., Takeuchi, Y., Miller, A., Fielder, S., O'Connor, W., Frere, C.H., Nguyen, N.H., Bar, I., Knibb, W., 2017. Genomic DNA variation confirmed Seriola lalandi comprises three different populations in the Pacific, but with recent divergence. Sci Rep-Uk. 7. Pritchard, J.K., Stephens, M., Donnelly, P., 2000. Inference of population structure using multilocus genotype data. Genetics. 155, 945-959. Purcell, C.M., Chabot, C.L., Craig, M.T., Martinez-Takeshita, N., Allen, L.G., Hyde, J.R., 2015. Developing a genetic baseline for the yellowtail amberjack species complex, Seriola lalandi sensu lato, to assess and preserve variation in wild populations of these globally important aquaculture species. Conserv Genet. 16, 1475-1488. Purcell, C.M., Seetharam, A.S., Snodgrass, O., Ortega-Garcia, S., Hyde, J.R., Severin, A.J., 2018. Insights into teleost sex determination from the Seriola dorsalis genome assembly. Bmc Genomics. 19, 31. Renshaw, M.A., Patton, J.C., Rexroad, C.E., Gold, J.R., 2007. Isolation and characterization of dinucleotide microsatellites in greater amberjack, Seriola dumerili. Conserv Genet. 8, 1009-1011. Rousset, F., 2008. genepop'007: a complete re-implementation of the genepop software for Windows and Linux. Molecular ecology resources. 8, 103-106. Sepulveda, F.A., Gonzalez, M.T., 2017. Spatio-temporal patterns of genetic variations in populations of yellowtail kingfish Seriola lalandi from the south-eastern Pacific Ocean and potential implications for its fishery management. J Fish Biol. 90, 249-264. Swart, B.L., von der Heyden, S., Bester-van der Merwe, A., Roodt-Wilding, R., 2015. Molecular systematics and biogeography of the circumglobally distributed genus Seriola (Pisces: Carangidae). Molecular phylogenetics and evolution. 93, 274-280. Yang, S.G., Ji, S.C., Lim, S.G., Hur, S.W., Jeong, M., Lee, C.H., Kim, B.S., Lee, Y.D., 2016. Management of Sexual Maturation and Natural Spawning of Captive - Reared Yellowtail 13
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Kingfish, Seriola lalandi, in an Indoor Rearing Tank. Development & reproduction. 20, 141147.
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Journal Pre-proof Tables
Table 1. Average Na, Ne, Ho and He in all sampled populations.
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Mean He 0.92 0.90 0.84 0.92 0.92 0.64 0.75 0.86 0.87 0.93 0.21 0.90
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Mean Ho 0.78 0.75 0.84 0.84 0.84 0.62 0.70 0.83 0.81 0.84 0.14 0.81
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Mean Ne 13.57 10.09 7.01 13.02 12.60 2.89 5.78 7.93 9.28 13.61 1.39 11.71
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Mean Na 22.57 17.71 11.29 21.43 19.14 6.14 11.29 13.43 16.00 23.14 3.86 17.71
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Locus Sequ_233 Sequ_114 Sequ_47 Sequ_216 Sequ_42 Sdu_46 Sequ_41 Sequ_230 Sequ_57 Sequ_77 Sequ_4 Sdu gA3D
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Journal Pre-proof Table 2. Fis and Fit statistics of the microsatellite set in all sampled populations.
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Fwc(it) 0.1762 0.3204 0.0731 0.1081 0.1263 0.2143 0.1942 0.0728 0.0704 0.1171 0.7133 0.109 0.1654
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Fwc(is) 0.1646 0.2974 0.0423 0.0983 0.108 0.0685 0.0864 0.0506 0.0529 0.1059 0.385 0.0968 0.1088
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Locus Sequ_233 Sequ_114 Sequ_47 Sequ_216 Sequ_42 Sdu_46 Sequ_41 Sequ_230 Sequ_57 Sequ_77 Sequ_4 Sdu gA3D All
Table 3. Number of broodstock contributing to each spawning per set and sex ratio. Set 1
Set 2
Set 3
Set 4
Total average
2 7 1
3 7 1
2 8 1
5 6 1
3 7 1
4 4.3
4 5.4
4 4.2
4 5.7
4 4.9
2.1
2.2
2.6
2.7
2.4
2.4±1.3
3.1±1.4
2.2±1.6
2.8±1.9
2.6±1.5
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Parameters
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Minimum number of males contributing per sampling Maximum number of males contributing per sampling Minimum number of females contributing per sampling Maximum number of females contributing per s sampling
Average number of males contributing in each sampling Average number of females contributing in each sampling
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Average number of males per female contributing in each sampling
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Table 4. Percentages of contribution of parents during the spawning season. Min % of Max % of Min % of Max % of Average % of Average % of Average n° Average n° Broodstock contribution contribution contribution contribution contribution contribution of spawning of spawning set of males of males of females of females of males of females per male per female 0
20.1
4.5
35.8
8.3
15.3
Set 2
0
11.9
0
36.9
6.3
10.2
Set 3
0
25.9
0
19.3
5.7
5.7
Set 4
0
15.2
0
37.9
3.1
total
0.0
18.3
1.1
32.5
5.9
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Set 1
9.4
4.0
2.5
3.1 2.3 0.6
Caldera
Guanaqueros
Guanaqueros Punta Choros Broodstock UFAs Mexico
-0.0013 0.0097 0.0103 0.0042 0.1382
0.0028 0.0033 -0.0029 0.1227
Australia
0.1135
Punta Choros
Broodstock
UFAs
0.0061 0.0025 0.1269
0.0038 0.1359
0.1244
0.1017
0.1237
0.1148
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Table 5. Fst values between populations.
3.8
6.4
6 5.4 3.5 1
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0.1037
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Mexico
0.1514
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Table 3. Number of broodstock contributing to each spawning per set and sex ratio. Set 1
Set 2
Set 3
Set 4
Minimum n° of males contributing per sampling Maximum n° of males contributing per sampling Minimum n° of females contributing per sampling Maximum n° of females contributing per sampling Average n° of males contributing in each sampling Average n° of females contributing in each sampling Average n° of males/female contributing in each sampling
2 7 1 4 4.3 2.1
3 7 1 4 5.4 2.2
2 8 1 4 4.2 2.6
5 6 1 4 5.7 2.7
Total average 3 7 1 4 4.9 2.4
2.4±1.3
3.1±1.4
2.2±1.6
2.8±1.9
2.6±1.5
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Parameter
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Journal Pre-proof Table 4. Percentages of contribution of parents during the spawning season.
Min % of contribution of males
Max % of contribution of males
Min % of contribution of females
Max % of contribution of females
Average % of Average % of Average n° of Averag contribution contribution spawning per spawni of males of females male fem
Set 1
0
20.1
4.5
35.8
8.3
15.3
6
3
Set 2
0
11.9
0
36.9
6.3
10.2
5.4
3
Set 3
0
25.9
0
19.3
5.7
5.7
3.5
2
Set 4
0
15.2
0
37.9
3.1
6.4
1
0
Average
0
18.3
1.1
32.5
9.4
4.0
2
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Broodstock set
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5.9
Journal Pre-proof Highlights.
- The parentage analysis showed that the average relation male/female contributing in a spawning event was 2.6, showing that female of S. lalandi require more than one male for each spawning event, confirming the spawning behavior observation of other studies.
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- Females tend to show pulses of egg production, while males show a more even contribution during the spawning season.
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- Wild and commercial populations evaluated of S. lalandi, are confirmed for at least two different populations, with Chilean populations for one side and Mexican in the other. The Australian population appear to be more admixed.
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Figure 1
Figure 2
Figure 3
Figure 4
Figure 5