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. Author manuscript; available in PMC: 2023 Feb 15.
Published in final edited form as: Aquaculture. 2022 Nov 6;564:739032. doi: 10.1016/j.aquaculture.2022.739032

Survival and growth of triploid eastern oysters, Crassostrea virginica, produced from wild diploids collected from low-salinity areas

Sarah Bodenstein a, Brian R Callam b, William C Walton c, F Scott Rikard d, Terrence R Tiersch a, Jerome F La Peyre e,*
PMCID: PMC9910191  NIHMSID: NIHMS1863293  PMID: 36778722

Abstract

Triploid Eastern oysters have been reported to suffer greater mortalities than diploids when exposed to low-salinity (<5) conditions in the U.S. Gulf of Mexico and Atlantic estuaries. As such, the effect of broodstock parentage was investigated on the low-salinity tolerance of triploid progeny produced by mating diploid females (collected from three Louisiana estuaries differing in salinity regimes) with male tetraploids at two hatcheries. Diploid crosses were also produced using the wild broodstocks to verify expected differences in low-salinity tolerance among diploid progeny and between ploidy levels. All progeny were deployed at low and moderate-salinity (averages of 9.3 and 19.4) field sites to monitor monthly growth and mortality. Sex ratio, gametogenic stage, gonad-to-body ratio, condition index, and Perkinsus marinus infection were also measured periodically at both field sites Although high triploid mortality at the low-salinity site prevented complete analysis, results indicated that diploid parentage had little effect on triploid survival at low salinity. Broodstock parentage affected diploid mortality and growth, although results did not match with predictions made based on historical salinity at broodstock collection sites. Ploidy level had the largest effect on triploid survival and growth followed by the hatchery site where the oysters were produced.

Keywords: Crassostrea virginica, Ploidy, Mortality, Salinity, Temperature, Gametogenesis

1. Introduction

Eastern oysters (Crassostrea virginica) have been harvested from U.S. Gulf of Mexico (GoM) and Atlantic estuaries for centuries. Today, oyster farming is one of the highest valued sectors of aquaculture in the United States, with the Gulf region producing the most oysters (by volume) in 2017 (National Marine Fisheries Service, 2020). Crassostrea virginica has traditionally been grown sub-tidally on-bottom in GoM estuaries but production is being increasingly supplemented by off-bottom aquaculture (Walton et al., 2013; Walton and Swann, 2021). Triploid oysters (3 N), bred to have three sets of chromosomes instead of the normal two sets (2 N), have been adopted in off-bottom aquaculture partially to offset the higher initial investment (Petrolia et al., 2022). Triploids can grow faster and have better meat quality, especially in the summer months, than do diploids because of reduced gametogenesis of triploids (Allen Jr. and Downing, 1986; Dégremont et al., 2012). However, triploids suffer greater mortalities in low salinity conditions (<5) than do diploids, although the reasons remain unresolved (Callam et al., 2016; Matt et al., 2020; Wadsworth et al., 2019). Preventing high triploid mortality is an urgent goal considering that periods of low salinity are becoming more frequent and sustained in the most productive areas of the GoM (i.e., Louisiana). This is due to a combination of river and coastal management strategies and increased precipitation due to the changing climate (Powell and Keim, 2015; Soniat et al., 2013; Swam et al., 2022; Wang et al., 2017).

Although eastern oysters are tolerant to a wide range of salinity, long periods of low salinity (<5) can result in oyster mortality, especially in conjunction with high water temperatures (>28 °C) (La Peyre et al., 2013; Munroe et al., 2013; Rybovich et al., 2016). There is however increasing evidence of divergence in salinity tolerance among GoM oyster populations even across a narrow geographic range (Leonhardt et al., 2017; Marshall et al., 2021a; Swam et al., 2022). In Louisiana, diploid progeny of broodstock collected from a low-salinity site had higher survival when exposed to low salinity conditions (5.1 ± 3.0) than the progeny of broodstock from higher salinity regimes (Leonhardt et al., 2017). It remains to be determined whether breeding populations of diploid broodstock with higher tolerance to low salinity can enhance the tolerance of triploids to low salinity.

Using selected diploids to improve the performance of triploids has been demonstrated in past studies (Callam et al., 2016; Dégremont et al., 2010; Hand et al., 2004). As early as 2004, Hand et al. (2004) showed that the growth of chemically induced (cytochalasin B) triploid Sydney rock oysters (Saccostrea glomerata) was improved when using a line of diploids selected for enhanced growth compared to unselected diploids. In another study, the growth and survival of triploid Pacific oysters (Crassostrea gigas), produced by crossing diploid females selected for resistance to summer mortality with unselected tetraploid males, were shown to be greater than triploids produced from unselected diploid females (Dégremont et al., 2010). Additional support for additive gains in triploid C. virginica was recently provided by Callam et al. (2016) as diploid parental contributions to the performance of triploid offspring were found to be significant. This suggests that improved traits such as increased growth rate or disease resistance can be transferred from desirable diploids to triploids.

This study compared the performance of triploids produced by crossing wild diploid female oysters from three Louisiana estuaries differing in mean annual salinities with male tetraploid oysters. The diploid female oysters were also crossed with diploid males from the corresponding wild broodstock to verify expected differences in low salinity tolerance among diploid progeny and between ploidy levels. The broodstocks were spawned at two hatcheries, one in Louisiana and one in Alabama, to produce two cohorts of three triploid crosses and three diploid crosses. All progeny were deployed at low and moderate-salinity sites to monitor monthly growth and mortality. Sex ratio, gametogenic stage, gonad-to-body ratio, condition index, and infection prevalence and intensity of Perkinsus marinus, a parasitic protist causing the disease dermo, were also measured periodically at both field sites. Triploid and diploid crosses produced with broodstock collected from sites with lower mean salinities were predicted to have higher survival and growth in the low-salinity field site than crosses produced using broodstock from sites with higher mean annual salinity.

2. Materials and methods

2.1. Oysters

In January and February 2019, about 300 wild oysters were collected for broodstock from each of three Louisiana public oyster grounds that had different salinity regimes: Calcasieu Lake (CL, 29° 51′2.34”N, 93° 16′59.81”W) with an annual mean salinity of 16.2 ± 2.8 [± standard deviation (SD), n = 10, 2009–2018] (Louisiana Department of Wildlife and Fisheries [LDWF] hydrological data, Marshall et al., 2021b) Sister Lake (SL, 29°14′45.0”N, 90°54′35.0”W) with an annual mean salinity of 11.2 ± 5.5 [n = 10, 2009–2018] (United States Geological Survey [USGS] 07381349 water-quality monitoring station), and Vermilion Bay (VB, 29° 34′47”N, 92° 2′4”W) with an annual mean salinity of 7.4 ± 1.6 [n = 10, 2009–2018] (LDWF hydrological data, Marshall et al., 2021b) (Fig. 1). Oysters from each broodstock population were placed in baskets suspended on long lines (BST Oyster Co., Cowell, South Australia) for conditioning at the Louisiana Sea Grant Oyster Research Farm (LSURF) a moderate salinity site, adjacent to the Louisiana Sea Grant Oyster Research Laboratory and Mike C. Voisin Oyster Hatchery (LSUL) in Grand Isle, LA. In addition to the wild diploid broodstocks, the tetraploid broodstocks LSU 4DGNL17, maintained at LSURF, and AU 4MC18, maintained at the Grand Bay Oyster Park, Alabama, were used to produce the triploids. These tetraploid broodstocks were originally part of the 4MGNL13 line developed by LSUL, and both hatcheries had advanced the line two generations prior to their use in this study (Fig. 2).

Fig. 1.

Fig. 1.

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Map of Louisiana indicating the locations of the three broodstock collection sites (Calcasieu Lake, Vermilion Bay, and Sister Lake) and the two field sites (LUMCON and LSURF). LUMCON is an abbreviation for the Louisiana Universities Marine Consortium. LSURF is an abbreviation for the Louisiana Sea Grant Oyster Research Farm.

Fig. 2.

Fig. 2.

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Pedigree of tetraploid oysters used to produce F1 LSU and AU triploid oyster crosses for this study. The number “3” indicates triploid lines, “4” indicates tetraploid lines, “C” indicates chemically induced lines, “M” indicates mated lines, “GEN” refers to a generic or general lines, and the last two numbers indicate the spawning year. VIMS is the abbreviation for the Virginia Institute of Marine Science, Gloucester Point, Virginia. LSUL is the abbreviation for the Louisiana Sea Grant Oyster Research Laboratory, Grand Isle, Louisiana. AU is the abbreviation for the Auburn University Shellfish Laboratory., Dauphin Island, Alabama.

2.2. Spawning

Oysters were spawned at LSUL in June 2019 and at the Auburn University Shellfish Laboratory (AUSL) in Dauphin Island, AL immediately after transfer in July 2019. The F1 progeny were designated as LSU (Louisiana State University) or AU (Auburn) cohorts depending on which hatchery they were produced (Table 1). For spawning, the oysters were placed in individual containers and induced to release gametes via thermal shocking by alternating ambient and warm (5 °C above ambient) water flowing into each container (Wallace et al., 2008). Eggs collected from wild diploid females (~1 × 106 eggs per female) were pooled based on broodstock parentage and allocated based on intended ploidy breeding (i.e., based on cross). The eggs were aliquoted prior to fertilization with sperm from wild diploids or tetraploids. The number of aliquots was equal to the number of males used to fertilize female broodstock from each cross. Female and male oysters from each wild broodstock population (CL, SL, and VB) were used to produce three diploid F1 crosses at each hatchery (six crosses total): AU CL2 N, AU SL2 N, AU VB2 N, LSU CL2 N, LSU SL2 N, and LSU VB2 N (Table 1). In addition, female oysters from each wild broodstock were crossed with males from two tetraploid lines 4DGNL17 at the LSU hatchery and 4MC18 at the Auburn hatchery) to produce three triploid crosses at each hatchery (six total): AU CL3 N, AU SL3 N, AU VB3 N, LSU CL3 N, LSU SL3 N, and LSU VB3 N (Table 1). Sperm were verified to be from tetraploid males by flow cytometry prior to fertilization (Allen and Bushek, 1992). At the Auburn hatchery, diploid and triploid crosses were produced from the same females and were therefore half-siblings. At the LSU hatchery diploid and triploid crosses were not half-siblings and in some instances were produced on different days (Table 1). Larvae were reared and set on micro-cultch material to produce single oyster spat using standard hatchery techniques (Wallace et al., 2008). Spat were grown in upwelling nursery systems at each hatchery from July to September until oysters were large enough to be deployed in 6-mm mesh baskets at LSURF for further grow out before deployment at the study field sites. The F1 crosses produced at AU were transported to Grand Isle in September following authorization from the Louisiana Department of Wildlife and Fisheries. Ploidy verification by flow cytometry was performed on oysters from both cohorts at the LSUL as described by Callam et al. (2016).

Table 1.

Diploid broodstock collection site and number of diploid females and diploid or tetraploid males used to produce F1 oysters at the LSU and Auburn affiliated hatcheries (AU or LSU hatchery) in 2019. The Spawn Date for each cross is also provided. Tetraploid line name is listed next to the number of tetraploid males used. Full F1 Cross Name (used to identify the different crosses in each cohort) are listed in addition to their abbreviations (F1 Stock Abbr.).

Collection Site No. 2 N Females No. 2 N Males No. 4 N Males Tetraploid Lines Hatchery/Cohort Spawn Date F1 Cross Name F1 Cross Abbr.
Calcasieu Lake 14 7 AU 07/18/20 Auburn Diploid Calcasieu Lake AU CL2 N
Calcasieu Lake 14 10 4MC18 AU 07/18/20 Auburn Triploid Calcasieu Lake AU CL3 N
Calcasieu Lake 5 3 LSU 06/04/20 LSU Diploid Calcasieu Lake LSU CL2 N
Calcasieu Lake 15 3 4DGNL17 LSU 06/04/20 LSU Triploid Calcasieu Lake LSU CL3 N
Sister Lake 51 33 AU 07/23/20 Auburn Diploid Sister Lake AU SL2 N
Sister Lake 51 8 4MC18 AU 07/23/20 Auburn Triploid Sister Lake AU SL3 N
Sister Lake 3 4 LSU 06/13/20 LSU Diploid Sister Lake LSU SL2 N
Sister Lake 6 2 4DGNL17 LSU 06/27/20 LSU Triploid Sister Lake LSU SL3 N
Vermilion Bay 40 46 AU 07/25/20 Auburn Diploid Vermilion Bay AU VB2 N
Vermilion Bay 40 8 4MC18 AU 07/25/20 Auburn Triploid Vermilion Bay AU VB3 N
Vermilion Bay 14 19 LSU 06/21/20 LSU Diploid Vermilion Bay LSU VB2 N
Vermilion Bay 7 2 4DGNL17 LSU 06/27/20 LSU Triploid Vermilion Bay LSU VB3 N
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2.3. Field sites and experimental design

Oysters from both cohorts were deployed at two study sites in Louisiana with different salinity regimes: a low-salinity site, near the Louisiana Universities Marine Consortium (LUMCON) in Cocodrie, LA, with an annual mean salinity of 9.3 ± 5.0, [n = 11, 2010–2020] (LUMCON, 2021), and LSURF a moderate salinity site, with an annual mean salinity of 19.4 ± 6.7 [n = 11, 2010–2020] (USGS 073802516 water-quality monitoring station). Oysters from the Auburn cohort were deployed in November 2019 at both sites while oysters from the LSU cohort were deployed at LSURF in December 2019 and at LUMCON in January 2020. Four replicate baskets containing 80 oysters each were deployed at each site for each of the six crosses of each ploidy in the two cohorts for a total of 48 baskets per site by January 2020. Eighty oysters (~40 mm shell height) filled less than one third of the total basket volume and was below the maximum stocking density of a long-line basket (Comeau et al., 2011; Davis, 2013), eliminating overcrowding as a potential stressor. The baskets were suspended on long-lines beneath the water surface and off-bottom to reduce the chance of predation. Growth and mortality were assessed monthly until November 2020, with the exception of May due to Covid-19 restrictions accessing the field site. Because of COVID-19, there were six weeks (instead of four weeks) between sampling in mid-March to the end of April and the end of April to mid-June (Appendix, Table A1).

2.4. Data collection

Mortality was evaluated by counting the number of live and dead oysters in each basket. Dead oysters were discarded after each sampling. For each basket at both sites, interval, adjusted interval, and cumulative mortality of oysters were calculated following procedures from Ragone-Calvo et al. (2003).

Growth was evaluated by measuring the shell height (from umbo to furthest shell edge) of 25 oysters from each basket by use of digital calipers (Mitutoyo 500–171-30, Mitutoyo Corp., Japan). Overall growth rates were calculated for each basket at both sites by subtracting the mean starting shell height from the mean final shell height and dividing that by the number of days the bags had been deployed.

During June 2020 sampling, ten oysters were also collected from each cross (2–3 per basket) to be cross-sectioned and processed by standard histological technique (Howard et al., 2004) to determine sex and gametogenic stage and to measure gonad-to-body ratio. The stages of gametogenic development were determined using methods described in Matt and Allen (2021) as Inactive (I), Very Early Active (VEA), Early Active (EA), Active (A), Late Active (LA), Ripe (R), Spawning (S), Advanced Spawning (AS), and Spawned Out (SO). The Inactive stage had the least amount of gonad development (follicles) continuing to increase until the Ripe stage (≥ 80% follicle coverage) at which point follicle coverage began to decrease when spawning occurred. Gonad-to-body ratio was measured using the image analysis software ImageJ (Quintana et al., 2011; Schneider et al., 2012).

Additionally, in June and September 2020, 20 oysters from each cross (5 per basket) at each site were collected and processed to analyze condition index and P. marinus prevalence and infection intensity. Condition index [whole oyster dry wt * 100 / (whole oyster wt - wet shell wt)] and infection intensity (parasite number per g wet tissue) were assessed using standard methods (La Peyre et al., 2019). Perkinsus marinus prevalence was calculated by dividing the number of infected oysters by the number of oysters sampled multiplied by 100 and the mean infection intensity of infected oysters was determined. Oysters were further classified as uninfected, lightly infected (<1 × 104 parasites g−1 wet tissue), moderately infected (1 × 104–5 × 105 parasites g−1 wet tissue) or heavily infected (> 5 × 105 g−1 wet tissue) (Bushek et al., 1994; La Peyre et al., 2019).

2.5. Statistical analysis

All statistical analyses were performed in RStudio (R Core Team, 2020). General linear models were used to assess water temperature and salinity differences between the two field sites. The Shapiro Wilkes test was used to test normality of interval and cumulative mortality, overall growth rate, gonad-to-body ratio, condition index and P. marinus infection intensity. Log transformations were used to restore normality to interval and cumulative mortality, gonad-to-body ratio, and infection intensity. General linear models were used to analyze the effects of ploidy, cohort, and broodstock parentage (called stock) on each measurement (interval and cumulative mortality, overall growth rate, gonad-to-body ratio, condition index and infection intensity). The data for each measurement were organized by site. This allowed focus to be directed on the potential differences between ploidy and cohorts, and among stocks. In addition, for interval mortality, a posteriori tests were conducted by selecting intervals at each study site (LUMCON June–August; LSURF: April–July) that showed the most obvious peaks in mortality. Finally, Chi-squared analysis was used to assess potential differences in the stages of gametogenic development and prevalence of P. marinus infection level among oyster stocks and between cohorts.

3. Results

3.1. Environmental data

Mean salinity at LUMCON over the year-long study period was lower than at LSURF (general linear model, t = −22.7, P < 0.01) (Fig. 3A). The mean daily salinity at LUMCON was 9.0 ± 4.5 while the mean daily salinity at LSURF was 17.4 ± 5.8 from mid-November 2019 to mid-November 2020. A long period of low salinity (2.4 ± 1.2) at LUMCON was recorded between the mid-June and mid-July samplings. The lowest salinity at LSURF (10.4 ± 3.5) was recorded between the mid-March and end of April samplings. The salinity difference between the two sites was smallest between the mid-March and end of April samplings. Starting toward the end of April, the salinity at LUMCON started to fall below that of LSURF.

Fig. 3.

Fig. 3.

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Daily salinity (A) and water temperatures (B) at LUMCON, black triangles, and LSURF, grey circles, from November 2019 to November 2020. Dates indicate sampling dates of the study.

Water temperatures between the two sites were similar (general linear model, t = 0.35, P = 0.73) (Fig. 3 B). LUMCON had a mean water temperature of 23.1 ± 5.9 °C while LSURF had a mean water temperature of 23.0 ± 5.6 °C from mid-November 2019 to mid-November 2020. Water temperatures at LUMCON and LSURF were highest between the mid-July and mid-August samplings (general linear models, P ≤ 0.05 for all cases). Water temperature had a daily mean of 30.3 ± 1.6 °C at LUMCON and 30.2 ± 1.6 °C at LSURF during this interval. From May to the end of September at both sites, water temperatures trended above 28 °C, averaging 28.7 ± 2.4 °C.

3.2. Interval and cumulative mortality

Cumulative mortality of triploids was higher than diploids at LUMCON, the low-salinity site (general linear model, t = 11.89, P < 0.01). Stock did not affect triploid cumulative mortality; triploid crosses across cohort (CL3 N, SL3 N, and VB3 N) at LUMCON had similarly high cumulative mortalities (general linear model, t < 1.94, P > 0.07 for all cases). However, there was a cohort effect; cumulative mortality of LSU triploids was slightly higher (4%, 95% CI 0–10%) than AU triploids (general linear model, t = 2.35, P = 0.04) (Fig. 4 A). Cumulative mortality was 89% in LSU triploids (95% CI 86–93%) and 85% in AU triploids over the course of the study (95% CI 81–88%) at LUMCON. Mortalities for triploids at LUMCON peaked from mid-June to mid-August (the selected interval for the a posteriori test) (Fig. 4A).

Fig. 4.

Fig. 4.

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Cumulative mortality for triploids (A) and diploids (B) at LUMCON from Nov 2019 to November 2020. Error bars denote 2 standard errors. The inset box in panel 4B highlights interval mortality from the period selected for a posteriori analysis. Dates indicate sampling dates of the study. Letters indicate significant differences (p < 0.05) between groups.

Stock affected the cumulative mortality of diploids at LUMCON (Fig. 4 B). At the end of the study, cumulative mortality was higher in the SL2 N, 61% (95% CI 55–67%), and VB2 N, 48% (95% CI 4–54%), crosses than in the CL2 N cross, 40% (95% CI 35–46%) (general linear model, t ≥ 2.12, P ≤ 0.04, for all cases). Peak mortalities for LSU and AU diploids were from mid-June to mid-August, similar to triploids. The SL2 N cross exhibited higher interval mortality during this period than the CL2 N cross (general linear model, t = 2.88, P < 0.01) (box in Fig. 4 B). No differences in interval mortality were observed between the CL2 N or SL2 N crosses and the VB2 N cross. Additionally, no cohort effect was observed for diploids at LUMCON.

At LSURF (the moderate salinity site), triploids again had higher cumulative mortality than diploids (general linear model, t = 5.37, P < 0.01). The cumulative mortality of all triploids at LSURF, across cohort and stock, were similar (general linear model, t ≤ 1.35, P ≥ 0.20 for all cases) (Fig. 5 A). However, from the end of April to mid-July, LSU triploids had higher interval mortality than AU triploids, across stock (general linear model, t = 3.32, P < 0.01) (box in Fig. 5 A). After this interval, mortality of triploids was less severe but continued to gradually increase until the end of the study.

Fig. 5.

Fig. 5.

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Cumulative mortality for triploids (A) and diploids (B) at LSURF from November 2019 to November 2020. Error bars denote 2 standard errors. The inset box in panel 5A highlights the interval mortality from the period selected for a posteriori analysis. Dates indicate sampling dates of the study. Letters indicate significant differences (p < 0.05) between groups.

Stock and cohort interacted to affect diploid cumulative mortality (Fig. 5 B). The CL2 N and VB2 N crosses in the AU cohort had 14% (95% CI 1–27%) higher cumulative mortality than the AU SL2 N cross (general linear model, t ≤ −2.32, P ≤ 0.03 for all cases) at the end of the study. However, all crosses in the LSU cohort (CL2 N, SL2 N, and VB2 N) had similar cumulative mortalities to each other and to the SL2 N cross in the AU cohort (general linear model, t ≥ −2.40, P ≤ 0.03 for all cases). Diploid cumulative mortalities at LSURF started to rise at the end of April and increased largely between mid-June and mid-July. Additionally, unlike at LUMCON, from mid-October to mid-November 2 crosses (CL2 N and VB2 N) in the AU cohort experienced another marked increase in mortality (Fig. 5 B).

3.3. Overall growth rate

At LUMCON, triploids did not exhibit faster growth rates than diploids over the study (general linear model, t = 1.67, P = 0.10). Oysters grew throughout the study period except from mid-June to mid-August for triploids and mid-June to mid-July for diploids. During these periods, the mean shell heights of diploids and triploids decreased (Fig. 6 A & B) presumably due to differential mortality of larger oysters. Stock and cohort interacted to affect triploid growth rates at LUMCON. During the study, the SL3 N cross of the AU cohort grew faster than any other cross and grew 60% (95% CI 30–80%) faster than the next fastest growing cross, LSU CL3 N (general linear model, t ≤ −2.42, P ≤ 0.03 for all cases). Among diploids, overall growth rate was affected by cohort. Although diploids from both cohorts had similar shell heights at the end of the study (general linear model, t = −0.43, P = 0.66), AU diploids, which were smaller at the time of deployment, grew 23% (95% CI 4–49%) faster than LSU diploids over the study (general linear model, t = −2.41, P = 0.03).

Fig. 6.

Fig. 6.

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Shell heights (in mm) of triploids (A) and diploids (B) at LUMCON from November 2019 to November 2020. Error bars denote 2 standard errors.

Triploids and diploids at LSURF had faster growth rates than oysters at LUMCON over the course of the study (general linear model, t = −36.67, P < 0.01). Additionally, triploids exhibited faster growth than diploids at LSURF (general linear model, t = 4.99, P < 0.01). Triploids at LSURF grew on average 4.1 ± 0.2 mm every 30 days and were on average 10.5 ± 0.5 mm longer than diploids at the end of the study (Fig. 7 A). Stock and cohort affected triploid growth rate. The CL3 N cross had 6.1% (95% CI 0.6–11%) faster growth than the SL3 N and VB3 N crosses, across cohort (general linear model, t ≥ 2.59, P ≤ 0.02 for all cases). Additionally, triploids of the AU cohort had 6.4% (95% CI 2.1–11.2%) faster growth rate than triploids of the LSU cohort (general linear model, t = 3.69, P < 0.01). Diploids at LSURF grew an average of 3.7 ± 0.3 mm every 30 d throughout the course of the experiment (Fig. 7 B). Stock affected the overall growth rate of diploids at LSURF. The SL2 N and VB2 N crosses had averaged 10% (95% CI 2–18%) faster growth than the CL2 N cross, across cohort (general linear model, t ≥ 2.59, P ≤ 0.02 for all cases).

Fig. 7.

Fig. 7.

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Shell heights (in mm) of triploids (A) and diploids (B) at LSURF from November 2019 to November 2020. Error bars denote 2 standard errors.

3.4. Sex, gametogenic stage, and gonad-to-body ratio

Of the 240 oysters sampled in June, 58.8% were male and 41.2% were female. There was a difference in the sex ratio between sites (Pearson’s chi-squared, P = 0.02). At LUMCON the sex ratio was 79:41 (M:F) and at LSURF the sex ratio was 62:58 (M:F). Diploids and triploids were at different stages of gonadal development (Pearson’s chi-square, P < 0.1). Diploids had more advanced gonadal development (95% in the LA, R, and S stages) than triploids (79% in the VEA, EA, and A stages). Furthermore, diploids from the two cohorts were at different stages of gonadal development (Pearson’s chi-square, P < 0.1) as more LSU diploids were at later stages of development (63% in S stage) than AU diploids (76% in LA and R stages, 15% in S stage). While cohort did not affect the gametogenic stage of triploids, stock did have an effect (Pearson’s chi-square, P = 0.01). The oysters in the VB3 N cross were in earlier gametogenic stages (47.5% in the VEA stage) than oysters in the CL3 N or SL3 N crosses (65% in EA and A stages). Triploids had lower gonad-to-body ratios across site, cohort, and stock than did diploids (general linear model, t = 7.5, P < 0.01). At LUMCON in June, Sister Lake triploids at LUMCON had lower gonad-to-body ratios than CL and VB triploids (general linear model, t ≥ 2.94, P ≤ 0.01 for all cases). There was no effect of stock or cohort on gonad-to-body ratio for diploids (general linear model, t ≥ −1.63, P ≥ 0.11 for all cases). At LSURF in June, there was no effect of stock or cohort on gonad-to-body ratios for triploids or diploids (general linear model, t ≥ −1.59, P ≥ 0.12 for all cases).

3.5. Condition index

Condition index of triploids was only affected by stock in June at LUMCON. The SL3 N and VB3 N crosses had lower condition indices than the CL3 N cross (general linear model, t < −2.08, P < 0.04 for all cases, Table A1). The SL3 N oysters had 12% (95% CI 1–28%) lower condition indices than did CL3 N oysters, and VB3 N oysters had 18% (95% CI 5–35%) lower condition indices than did CL3 N oysters. Diploids of the LSU cohort had 16% (95% CI 6–30%) lower condition indices than those of the AU cohort (general linear model, t = −3.52, P < 0.01). Additionally, SL2 N and VB2 N crosses had lower condition indices than the CL2 N cross (SL: 16%, 95% CI 4–32%; VB: 14%, 95% CI 2–29%), across cohort (general linear model, t ≤ −2.40, P ≤ 0.01 for all cases). At LSURF in June, only cohort affected the condition indices of diploids (general linear model, t = −5.77, P < 0.01). Diploids in the LSU cohort had 36% (95% CI 6–30%) lower condition indices that diploids in the AU cohort.

In September, condition index was affected by site and ploidy only. Oysters at LSURF had 21% (95% CI 11–29%) lower condition indices across cohort and stock than did oysters at LUMCON (general linear model, t = 3.54, P < 0.01) (Table A1). In addition, diploids across cohort and stock had 40% (95% CI 32–45%) lower condition indices than did triploids (general linear model, t = 6.61, P < 0.01).

3.6. P. marinus prevalence and infection intensity

Average P. marinus prevalence (% infected oysters) was higher at LSURF (47%) than at LUMCON in June (22%, Pearson’s chi-squared, P < 0.01). The prevalence of P. marinus was higher in oysters in the LSU cohort than AU cohort across ploidy and stock in September (AU average: 7%, LSU average: 97%, Pearson’s chi-squared, P < 0.01).

The majority of P. marinus infection intensities were light (< 104 parasites g−1 wet tissue) for all oysters at both sites (Table A1). Across both sites in June, oysters had similar mean infection intensities regardless of ploidy, cohort, and stock (general linear model, t ≤ 1.84, P ≥ 0.07, for all cases). Across ploidy, oysters of the AU cohort had higher mean infection intensities than did oysters of the LSU cohort (intensities: general linear model, t = −2.38, P = 0.02). At LSURF, triploids had higher mean infection intensities than did diploids across stock and cohort (general linear model, t = 3.14, P < 0.01). In September, across both sites, 16% of all oysters in the LSU cohort were heavily infected (> 5 × 105 parasites g−1 wet tissue), while 26% were moderately infected (104 - 5 × 105 parasites g−1 wet tissue). No oysters in the AUSL cohort collected for P. marinus infection measurements in September had heavy infection intensities.

4. Discussion

The main objective of this study was to examine the effect of wild broodstock parentage on the tolerance of triploid progeny to low salinity. Triploid crosses produced with broodstock from lower-salinity estuaries (SL and VB) were predicted to have higher survival and growth in a low-salinity field site than progeny produced with brood-stock from a higher-salinity estuary (CL). Diploid crosses were also produced using wild broodstock parents to verify expected differences among diploid progeny and between ploidy levels (i.e., higher triploid mortality at low salinity). Overall, diploid parentage (stock) had the smallest effect on the performance (growth and survival) of triploid progeny, while ploidy level, followed by cohort, had the largest effect. The near 100% cumulative mortality of all triploid crosses at the low-salinity site precluded analysis of the effect of broodstock parentage on triploid low-salinity tolerance, and this underscores that ploidy drove this result. Broodstock parentage affected diploid field performance, although the field performance of diploids did not match with predictions based on broodstock origin; for example, CL diploids had the lowest mortality at LUMCON. Ploidy had the largest effect on oyster performance, with triploids having higher cumulative mortality rates than diploids at both field sites and faster growth rates than diploids only at the high-salinity field site. Finally, hatchery cohort affected oyster performance at both sites.

Triploids experienced higher cumulative mortality than diploids at the low-salinity site in agreement with results from previous studies (Callam et al., 2016; Matt et al., 2020; Wadsworth et al., 2019). Interval mortalities peaked during an extended period of low salinity (2.4 ± 1.2) from mid-June to mid-July. During this period water temperatures were high, averaging 30 ± 1.6 °C. Previous studies in the GoM have also observed disproportionately high triploid mortality when compared to diploid mortality (Wadsworth et al., 2019), and high diploid oyster mortality has been associated with periods of low salinity (<5) and high water temperatures (> 28 °C) (La Peyre et al., 2009, 2013; Rybovich et al., 2016). In addition to increased mortality, triploids in this study did not have the expected faster growth rates than diploids at the low-salinity field site. Other studies have also observed that the triploid growth advantage (over diploids) is “site-dependent” and triploids in unfavorable growing conditions, such as low salinity, did not exhibit faster growth rates than diploids (Callam et al., 2016; Davis, 1994; Wadsworth et al., 2019). Higher triploid mortality and slower growth at the low-salinity site may have been caused by polyploid gigantism, an aspect of triploid cellular architecture.

In polyploid gigantism, polyploid animals, which acquire one or more additional sets of chromosomes, have a greater amount of cellular DNA contained within a larger cell nucleus, and larger cell volumes (Cavalier-Smith, 1982; Child and Watkins, 1994; Guo and Allen Jr, 1994). The increase in cell volume of a triploid animal without a reduction in cell number may partly explain why triploids are larger than diploids of the same age (Guo et al., 1996; Guo and Allen Jr, 1994; Piferrer et al., 2009; Wang et al., 2002). An increase in cell volume, however, lowers the cell surface-to-volume ratio of triploids compared to diploids, and likely slows the cellular response to changing environmental conditions in osmo-conforming and poikilothermic organisms. As oysters depend on cell volume regulation to deal with fluctuating salinity (Shumway et al., 1996), triploids may be at a disadvantage especially at low salinity when cellular metabolism, as well as intra-cellular ion and acid-base regulation, are being negatively affected (Ballantyne and Berges, 1991; Paparo and Dean, 1984; van Winkle, 1972). Further studies are needed to compare the ability of triploids relative to diploids to osmoconform and regulate the volume of their cells when exposed to low salinity.

At the moderate-salinity site, triploids also experienced higher cumulative mortalities than did diploids. Interval triploid mortalities increased in late spring while an uptick of diploid mortalities was most noticeable in early summer. The causes of the triploid mortalities are unknown and none of the environmental conditions nor P. marinus infection intensities (which were light, <104 parasites per g−1 wet tissue and similar in triploids and diploids) would be considered lethal (Bushek et al., 1994; La Peyre et al., 2019). Diploid mortalities were slightly higher than expected as monthly summer mortalities in GoM estuaries are generally not excessive (<5%) unless associated with low salinity (<5) or heavy P. marinus infection intensity (La Peyre et al., 1995, 2018, 2019; La Peyre et al., 2013; Wadsworth et al., 2019). Mortalities of diploids are generally attributed to stress from the intense physiological changes associated with gonad development and spawning (Huvet et al., 2010; Samain et al., 2007).

Interestingly, a positive correlation between reproductive effort and summer mortality in Pacific oysters (Crassostrea gigas) has been repeatedly reported (Cotter et al., 2010; Huvet et al., 2010; Koganezawa, 1974; Samain et al., 2007). Moreover, Pacific oysters which only partially spawned and retained unspawned gametes displayed greater mortality (Samain et al., 2007). The triploid mortalities in our study may be linked to advanced gametogenesis and unspawned gametes as almost 35% of triploids had gonads in an advanced stage of development (≥ 50% follicle coverage, Matt and Allen, 2021) while their condition index remained high indicating no spawning. It is possible that elevated metabolism for developing and maintaining gonadal tissues over an extended period at elevated temperature in triploids lead to oxidative stress and eventually cell death (Lesser, 2006); higher respiration rates have been observed in oysters with higher investment in gametogenesis (Bayne and Widdows, 1978; Casas et al., 2018) and could cause an imbalance between reactive oxygen species production and elimination, resulting in oxidative stress. Higher energy expenditure has long been observed to be predictive of natural mortality with the oxidative stress theory the most generally accepted explanation (Speakman et al., 2002; Hulbert et al., 2007). Differences or changes in the fatty acid composition of cell membranes which underlies variation in metabolic activity have also been linked to natural mortality including of oysters (Hulbert et al., 2007; Pernet et al., 2010). A third possible explanation could include a disruption of cellular energy homeostasis leading to death of the oysters (Sokolova, 2013; Sokolova et al., 2012). The processes of energy storage and release are dependent on highly integrated systems that may be dysregulated or insufficient to provide the energy and nutrients required by cells to support their high metabolism even in the presence of adequate stored energy. The bioenergetics of diploid and triploids at the organismal and cellular levels (such as oxygen consumption, mitochondrial function, fatty acids composition) will need to be compared in future studies to test these hypotheses.

Triploid mortalities may also be the result of a lack of genetic diversity. It is possible that some inbreeding of the tetraploid line could have occurred since its creation because of the high genetic load of oysters (Plough, 2016). The loss of genetic diversity in the tetraploids would be especially detrimental because they contribute a greater amount of genetic material than diploid parents to the triploid progeny and could explain the greater mortality of triploids compared to diploids in this and other studies that used the same line of commercial tetraploid oysters (Wadsworth et al., 2019). Moreover, the combination of reproductive effort and high temperature may act as a tipping point triggering mortality as environmental stress has generally been shown to increase the expression and magnitude of inbreeding depression (Fox and Reed, 2011; Plough, 2012). The genetic diversity and performance of the tetraploid line used in our study will need to be evaluated. More emphasis on monitoring genetic diversity in selected hatchery stocks could help in avoiding reduced allelic diversity and lowered heterozygozity and accompanying inbreeding depression (Boudry et al., 2002; Varney and Wilbur, 2020).

Triploids of the LSU cohort had higher peak interval mortality at the moderate salinity site, higher cumulative mortality at the low-salinity site, and lower growth rates at both sites than did AU triploids. Cohort differences observed in triploids could be due to genetic differences resulting from different levels of heterozygosity or from selection events caused by hatchery conditions where each cohort was produced. Associations have been made between higher heterozygosity and increased weight, growth rate, and survival in eastern oysters (Britten, 1996; Rodhouse and Gaffney, 1984; Zouros and Foltz, 1983). Auburn triploids could have had higher heterozygosity because a larger number of parents were used to produce AU triploids than LSU triploids (Aho et al., 2006; Hughes et al., 2019; Lind et al., 2010). Another mechanism for generating cohort genetic differences could be hatchery conditions. Oysters raised in hatcheries are exposed to stressors constantly present at the hatchery, such as water quality. Differential survival may occur due to stressors acting on larvae or spat, causing selection events that would occur before oysters are placed in the field, and could influence field performance (Nascimento-Schulze et al., 2021).

While high triploid mortality at the low-salinity site impeded analysis of the effect of broodstock parentage on triploid low-salinity tolerance, there were no differences in mortality rates among triploid crosses during the die-off between June and August. This suggests that brood-stock parentage had little effect on triploid survival at low salinity. The field performance of diploids at the low-salinity site was however affected by broodstock parentage. The CL diploids at the low-salinity site had lower cumulative and interval mortalities than did SL and VB diploids. Additionally, CL diploids, across cohort, had the slowest overall growth rate at the moderate salinity site. In a related study, CL diploids (produced at the Auburn hatchery) also displayed lower peak interval mortality rates than did SL diploids at a low salinity field site (Eastburn, 2021). Furthermore, Calcasieu Lake diploids in Eastburn (2021) had slower growth than other stocks (SL) at a higher salinity site (19.2 ± 5.42 g/L Oct. 2019 – Sept. 2020). All of these results were unexpected because crosses produced with broodstock from the historically higher salinity estuary (CL) were predicted to perform poorly at lower salinity field sites (Casas et al., 2017; Leonhardt et al., 2017). However, mean salinity in Calcasieu Lake has decreased in recent years from 19.5 for the 2009 to 2014 period to 15.2 for the 2015 to 2019 period (Swam et al., 2022). The recent increase in freshwater entering the estuary combined with overfishing has contributed to a 90% loss of the CL oyster population (LDWF, 2020). Therefore, it is possible that the genetic structure of CL broodstock has changed, altering the survival of CL crosses in low-salinity conditions and illustrating how natural and anthropogenic variability can shift the multidirectional selection pressure oysters face routinely in estuarine environments.

5. Conclusions

The effect of parentage on the salinity tolerance of triploid progeny was predicted at the onset of the study. However, prolonged low-salinity conditions at LUMCON caused high mortality levels in triploids and prevented proper analysis of broodstock parentage on triploid low-salinity tolerance. The influence of broodstock parentage was observed in diploids, although results in this study did not always align with predicted growth and mortality based on broodstock origin. However, other studies in the region have observed progeny survival that matched predictions based on broodstock origin although estuaries had more extreme regimes (high or low) than those used in the current study (Marshall et al., 2021a; Swam et al., 2022). In future work, wild diploid broodstock should be collected from estuaries with salinity regimes that are historically and currently more extreme and different from one another. Alternatively, diploid broodstock that have been selectively bred to be more tolerant to low salinity could be crossed with unselected tetraploids to test whether triploid progeny would inherit low salinity tolerance. Survival at low salinity has recently been shown to be a trait with moderate heritability (h2 = 0.4) (McCarty et al., 2020). While there is some evidence that traits from diploid broodstocks can be passed to triploid progeny (Dégremont et al., 2010), improvement of triploid survival and growth may also require selectively breeding a line of low-salinity tolerant tetraploids as proposed by Callam et al. (2016).

Acknowledgments

We thank Nicholas Coxe and Lauren Swam for field and laboratory help, and Lauren Swam for assistance in creating Fig. 1. We also thank Emily Craft, Erin Olson, Emily Baukema, Olivia Maggiacomo, and Virginia Morejon of the Michael Voisin Oyster Research Lab and Hatchery, and Glen Chaplin, Sara Betbeze Spellman, Caitlin Henning, and Mary Collier Eastburn of the Auburn University Research Lab for producing the oysters used in this study. This work was supported by Sea Grant Marine Aquaculture Grant Program (NA18OAR4170350), Department of Commerce, National Institutes of Health, Office of Research Infra-structure Programs (R24-OD028443), with additional support provided by National Institute of Food and Agriculture, United States Department of Agriculture (Hatch projects LAB94420, LAB94509).

Appendix A. Appendix

Table A1.

Condition index, P. marinus prevalence (% infected), infection intensity (mean ± standard deviation, count g−1 wet tissue) in June and September for oysters from both sites (LSURF and LUMCON), both cohorts (AU and LSU), both ploidies, (2 N and 3 N), and all stocks (CL, SL, and VB). Crosses with “NA” had no infected individuals; crosses with “NA” listed for standard deviation only had one infected oyster. The number of oysters under each category of infection are listed: uninfected (U), light (L, NA or < 104 parasites g−1 wet tissue), moderate (M, 104–5 × 105 parasites g−1 wet tissue), or heavy (H,〉 5 × 105 g−1 wet tissue).

Month Site Cohort Ploidy Stock Condition Index Prevalence (%) U L M H Infection Intensity (count g−1)
June LSURF AU 2 N CL 15.8 ± 3.2 20 16 4 0 0 38 ± 53
SL 15.4 ± 3.1 65 7 13 0 0 38 ± 32
VB 14.3 ± 3.2 45 11 8 1 0 222 ± 661 (× 102)
3 N CL 16 ± 1.95 35 13 7 0 0 87 ± 193
SL 16.4 ± 1.47 95 1 18 1 0 575 ± 2360
VB 14.3 ± 2.6 60 8 11 1 0 120 ± 300 (× 10)
LSU 2 N CL 12.1 ± 1.66 45 11 9 0 0 12 ± 6
SL 8.5 ± 3.6 45 11 9 0 0 51 ± 113
VB 9.7 ± 9.5 45 11 9 0 0 28 ± 58
3 N CL 15.5 ± 2.4 30 14 5 1 0 320 ± 680 (× 10)
SL 15.4 ± 9.2 40 12 7 1 0 160 ± 440 (× 10)
VB 15.7 ± 9.6 35 13 7 0 0 246 ± 489
LUMCON AU 2 N CL 15.5 ± 4.3 15 17 3 0 0 17 ± 3
SL 12.5 ± 4.4 15 17 3 0 0 21 ± 9
VB 12.6 ± 3.1 25 15 5 0 0 15 ± 3
3 N CL 16.9 ± 2.4 15 17 3 0 0 15 ± 4
SL 14.5 ± 2.5 15 17 3 0 0 13 ± 2
VB 14.7 ± 3.5 10 18 2 0 0 8 ± 2
2 N CL 12 ± 3.9 35 13 7 0 0 18 ± 14
LSU SL 10.6 ± 1.5 30 14 6 0 0 14 ± 11
VB 11.2 ± 3.1 25 15 5 0 0 21 ± 12
3 N CL 15.7 ± 7.7 30 14 6 0 0 26 ± 31
SL 14.1 ± 4.0 25 15 5 0 0 9 ± 5
VB 12.4 ± 2.8 25 15 5 0 0 11 ± 6
Sept LSURF AU 2 N CL 6.4 ± 1.9 0 20 0 0 0 NA
SL 5.9 ± 2.4 10 18 2 0 0 21 ± 1
VB 6.7 ± 1.4 15 17 3 0 0 8 ± 3
3 N CL 8.4 ± 1.8 5 19 1 0 0 320 ± NA (× 10)
SL 7.3 ± 2.1 0 20 0 0 0 NA
VB 8.3 ± 1.9 20 16 4 0 0 298 ± 505
LSU 2 N CL 5.2 ± 3.9 100 0 13 6 1 112 ± 241 (× 103)
SL 5.7 ± 2.1 100 0 10 7 3 261 ± 443 (× 103)
VB 6.5 ± 3.6 100 0 9 5 6 770 ± 1437 (× 103)
3 N CL 9.6 ± 2.7 100 0 9 2 9 163 ± 336 (× 104)
SL 9.2 ± 1.7 95 1 7 7 5 411 ± 768 (× 103)
VB 9.4 ± 9.6 100 0 2 11 7 937 ± 1403 (× 103)
LUMCON AU 2 N CL 7.6 ± 2.6 5 19 1 0 0 7 ± NA
SL 7.4 ± 2.6 5 19 1 0 0 10 ± NA
VB 6.4 ± 2.0 0 20 0 0 0 NA
3 N CL 10.1 ± 2.6 0 20 0 0 0 NA
SL 10.7 ± 2.0 5 19 1 0 0 13 ± NA
VB 8.8 ± 2.1 15 17 3 0 0 12 ± 3
LSU 2 N CL 6.6 ± 2.1 90 2 17 1 0 130 ± 5210 (× 102)
SL 10.9 ± 10.6 90 2 10 8 0 610 ± 960 (× 102)
VB 7.5 ± 1.3 100 0 16 1 3 492 ± 1582 (× 103)
3 N CL 8.9 ± 3.6 95 1 14 4 1 643 ± 137 (× 103)
SL 11.4 ± 2.6 95 1 13 6 0 360 ± 690 (× 102)
VB 8.2 ± 3.1 95 1 11 4 4 520 ± 1139 (× 103)
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Footnotes

CRediT authorship contribution statement

Sarah Bodenstein: Investigation, Formal analysis, Visualization, Data curation, Writing – original draft. Brian R. Callam: Conceptualization, Funding acquisition, Resources, Writing – review & editing. William C. Walton: Conceptualization, Funding acquisition, Resources, Writing – review & editing. F. Scott Rikard: Conceptualization, Funding acquisition, Resources, Writing – review & editing. Terrence R. Tiersch: Conceptualization, Funding acquisition, Writing – review & editing. Jerome F. La Peyre: Conceptualization, Funding acquisition, Methodology, Investigation, Supervision, Writing – review & editing.

Declaration of Competing Interest

The authors have no conflicts of interest to declare.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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