INTRODUCTION
⌅Changes in spawning dynamics, size or age at maturity, size structure and poor condition can increase the variability of recruitment (Marteinsdottir and Thorarinsson 1998Marteinsdottir G., Thorarinsson K. 1998. Improving the stock-recruitment relationship in Icelandic cod (Gadus morhua) by including age diversity of spawners. Can. J. Fish. Aquat. Sci. 55: 1372-1377. https://doi.org/10.1139/f98-035 , Blanchard et al. 2003Blanchard J.L., Frank K.T., Simon J.E. 2003. Effects of condition on fecundity and total egg production of eastern Scotian Shelf haddock (Melanogrammus aeglefinus). Can. J. Fish. Aquat. Sci. 60: 321-332. https://doi.org/10.1139/f03-024 , Anderson et al. 2008Anderson C.N.K., Hsieh C.H., Sandin S.A. et al. 2008. Why fishing magnifies fluctuations in fish abundance. Nature 452: 835-839. https://doi.org/10.1038/nature06851 ), reduce the resilience and capacity of populations to dampen environmental changes (Hsieh et al. 2006Hsieh C.H, Reiss C.S, Hunter J.R, et al. 2006. Fishing elevates variability in the abundance of exploited species. Nature 443: 859-862. https://doi.org/10.1038/nature05232 ) and increase the impact of climate change (Cheung et al. 2009Cheung W. Lam W. L., Sarmiento V.W.Y., et al. 2009. Projecting global marine biodiversity impacts under climate Change scenarios. Fish Fish. 10: 235-251. https://doi.org/10.1111/j.1467-2979.2008.00315.x ). Fisheries management will considerably benefit from a better understanding of how maternal features affect offspring phenotypes (the so-called maternal effect) and hence of how stock reproductive potential determines population productivity and recruitment.
Consequently, fecundity studies are critical for understanding the reproductive potential of fish populations (Tomkiewicz et al. 2003Tomkiewicz J., Morgan M.J., Burnett J. Saborido-Rey F. 2003. Available information for estimating reproductive potential of northwest Atlantic groundfish stocks. J. Northwest. Atl. Fish. Soc. 33: 1-21 https://doi.org/10.2960/J.v33.a1 , Lambert et al. 2003Lambert Y., Yaragina N. A., Kraus G., et al. 2003. Using environmental and biological indices as proxies for egg and larval production of marine fish. J. Northwest. Atl. Fish. Soc. 33: 115-159. https://doi.org/10.2960/J.v33.a7 , Saborido-Rey and Trippel 2013Saborido-Rey F., Trippel E.A. 2013. Fish reproduction and fisheries. Fish. Res. 138: 1-4. https://doi.org/10.1016/j.fishres.2012.11.003 ) and how maternal effects can interact with fecundity (Thorsen and Kjesbu 2006). Fecundity is a highly temporal and geographically sensitive variable that changes drastically with attributes of the individual spawners, including length, age and condition factor (Murua and Saborido-Rey 2003Murua H., Saborido-Rey F. 2003. Female Reproductive Strategies of Marine Fish Species of the North Atlantic. J. Northwest. Atl. Fish. Soc. 33: 23-31. https://doi.org/10.2960/J.v33.a2 , Rideout and Morgan 2010Rideout R.M, Morgan M.J. 2010. Relationships between maternal body size, condition and potential fecundity of four northwest Atlantic demersal fishes. J. Fish. Biol. 76: 1379-1395. https://doi.org/10.1111/j.1095-8649.2010.02570.x ). In consequence, the population’s egg production is highly dependent on adult stock demography and factors affecting demography, such as growth, maturation schedules, fishing pressure, environmental conditions and disease (McElroy et al. 2013McElroy W.D., Wuenschel M.J., Press Y.K., et al. 2013. Differences in female individual reproductive potential among three stocks of winter flounder, Pseudopleuronectes americanus. J. Sea. Res. 75: 52-61. https://doi.org/10.1016/j.seares.2012.05.018 , Chang et al. 2021Chang H. Y., Richards R. A., Chen Y. 2021. Effects of environmental factors on reproductive potential of the Gulf of Maine northern shrimp (Pandalus borealis). GECCO 30: e01774. https://doi.org/10.1016/j.gecco.2021.e01774 ). Moreover, in many teleosts, significant differences have revealed disproportionally positive relationships between potential fecundity and fish length (Stafford et al. 2014Stafford D.M., Sogard S.M., Berkeley S.A. 2014. Maternal influence on timing of parturition, fecundity, and larval quality in three shelf rockfishes (Sebastes spp.). Aquat. Biol. 21: 11-24. https://doi.org/10.3354/ab00564 , Love et al. 2002), age and condition (Thorsen et al. 2006Thorsen A., Marshall C.T., Kjesbu O.S. 2006. Comparison of various potential fecundity models for north-east Arctic cod Gadus morhua, L. using oocyte diameter as a standardizing factor. J. Sea. Res. 69: 1709-1730. https://doi.org/10.1111/j.1095-8649.2006.01239.x , Lambert 2008Lambert Y. 2008). Why should we closely monitor fecundity in marine fish populations? J. Northwest. Atl. Fish. Soc: 41: 93-106. https://doi.org/10.2960/J.v41.m628 ), highlighting the importance of maternal effects. Several studies have shown that in Pacific rockfish species maternal effects are determined by release offspring date and seasonal changes in the productivity of the California current, so offspring quality is directly affected (Fisher et al. 2007Fisher R, Sogard S.M, Berkeley S.A. 2007 Trade-offs between size and energy reserves reflect alternative strategies for optimizing larval survival potential in rockfish. Mar. Ecol. Prog. Ser. 344: 257-270. https://doi.org/10.3354/meps06927 ).
Monitoring of fecundity, as reported in the literature, can be used in stock assessment and fisheries management (Yoneda and Wright 2004Yoneda M., Wright P.J. 2004. Temporal and spatial variation in reproductive investment of Atlantic cod Gadus morhua in the northern North Sea and Scottish west coast. Mar. Ecol. Prog. Ser. 276: 237-248. https://doi.org/10.3354/meps276237 , Lambert 2008Lambert Y. 2008). Why should we closely monitor fecundity in marine fish populations? J. Northwest. Atl. Fish. Soc: 41: 93-106. https://doi.org/10.2960/J.v41.m628 , McElroy et al. 2013McElroy W.D., Wuenschel M.J., Press Y.K., et al. 2013. Differences in female individual reproductive potential among three stocks of winter flounder, Pseudopleuronectes americanus. J. Sea. Res. 75: 52-61. https://doi.org/10.1016/j.seares.2012.05.018 ), especially under the climate change scenario in species with a strong maternal influence, such as those showing viviparity. However, long time series of fecundity are usually not available, as reported by Tomkiewicz et al. (2003)Tomkiewicz J., Morgan M.J., Burnett J. Saborido-Rey F. 2003. Available information for estimating reproductive potential of northwest Atlantic groundfish stocks. J. Northwest. Atl. Fish. Soc. 33: 1-21 https://doi.org/10.2960/J.v33.a1 , and the situation has not improved over time. The difficulty of estimating fecundity is likely the main hindrance to regular and routine estimation. In this regard, the autodiametric method developed by Thorsen and Kjesbu (2001)Thorsen A., Kjesbu O.S. 2001. A rapid method for estimation of oocyte size and potential fecundity in Atlantic cod using a computer-aided particle analysis system. J. Sea. Res. 46: 295-308. https://doi.org/10.1016/S1385-1101(01)00090-9 must facilitate fecundity estimations.
In this study, for the first time we applied the autodiametric method to estimate fecundity in S. fasciatus on the Flemish Cap bank to build a unique long time series of fecundity data of 20 years from 1996 to 2020. We analysed the maternal influence on several reproductive traits and tested whether water temperature influences fecundity. Our overall aim was to improve our understanding of the effects of maternal influence and climate variability on the productivity of S. fasciatus, following the hypothesis that spawning stock biomass (SSB) and other stock indexes do not represent well the variation in stock reproductive potential, often leading to impaired stock-recruitment relationships. Our results highlight the importance of building time series of reproductive potential variables other than SSB, such as fecundity.
MATERIALS AND METHODS
⌅Study area
⌅The study was carried out on the Flemish Cap in the northwest Atlantic, between 46°N and 49°N and 44°W and 46°W (Fig. 1). It is separated from the Newfoundland shelf by the Flemish Pass, a channel with depths in excess of 1100 m, which hinders the migration to and from the Grand Bank for most of the fish species inhabiting the Flemish Cap, including S. fasciatus. The Flemish Cap is a dome-shaped, deep-water mountain, with a total area of 17.000 square miles up to 1460 m and 10.555 square miles up to 730 m, with the shallowest part of the bank (120 m depth) located in the southeastern quadrant.
Data collection, histology and ovarian processing
⌅Ovaries of S. fasciatus were collected from the EU Flemish Cap survey conducted annually in June/July since 1988 as part of the European Union sampling programme with the participation of Spain and Portugal. For each fish, fork length (FK), total weight (TW), gutted weight (GW) and maturity stage were recorded on board. Otoliths were removed for further age determination.
Ovaries were preserved in 4% buffered formaldehyde and then weighed in the laboratory. Ovary sections of 0.5 cm thickness from the central portion of the gonad were embedded in paraffin based on conventional histological processing. Sections of 3 µm were stained with haematoxylin and eosin protocol. The ovarian developmental phase, as described in Brown-Peterson et al. (2011)Brown-Peterson N.J., Wyanski D.M., Saborido-Rey F., et al. 2011. A standardized terminology for describing reproductive development in fishes. Mar. Coast. Fish. 3: 52-70. https://doi.org/10.1080/19425120.2011.555724 , was determined under a microscope.
Bottom temperature was obtained from the Copernicus Marine Environment Monitoring service (https://doi.org/10.48670/moi-00021) for the Flemish Cap area (between 45°N to 49°N and 47°W to 43°W) for July within the period 1996-2020 and for sampling depths between 300 and 600 m.
Fecundity estimation and image analyses
⌅Fecundity was estimated in ovaries with the presence of advanced vitellogenic oocytes and no signs of postovulatory follicles and/or fertilization. Because Sebastes species are group-synchronous with a determinate oocyte recruitment mode, this single leading cohort of oocytes is considered representative of the potential fecundity (Murua and Saborido-Rey 2003Murua H., Saborido-Rey F. 2003. Female Reproductive Strategies of Marine Fish Species of the North Atlantic. J. Northwest. Atl. Fish. Soc. 33: 23-31. https://doi.org/10.2960/J.v33.a2 ). A total of 281 ovaries were selected according to the above criteria between 1996 and 2020 (Table 1). Ovary weight was recorded and subsamples of about 0.15 g were taken from the central ovary section. Several studies have shown no significant differences in mean follicle diameter and abundance between and within ovaries (Nichol and Acuna 2001Nichol D.G., Acuna E.I. 2001. Annual and batch fecundities of yellow fin sole, Limanda aspera, in the Eastern Bering Sea. Fish. Bull. U.S. 99: 108-122., Kennedy et al. 2007Kennedy J., Witthames P.R., Nash R.D.M. 2007. The concept of fecundity regulation in plaice (Pleuronectes platessa) tested on three Irish Sea spawning populations. Can. J. Fish. Aquat. Sci. 64: 587-601. https://doi.org/10.1139/f07-034 , McElroy et al. 2013McElroy W.D., Wuenschel M.J., Press Y.K., et al. 2013. Differences in female individual reproductive potential among three stocks of winter flounder, Pseudopleuronectes americanus. J. Sea. Res. 75: 52-61. https://doi.org/10.1016/j.seares.2012.05.018 ). Then, oocytes of each subsample were washed and separated from the connective tissue throughout sieving (150 µm, 300 µm and 600 µm).
Year | n | Age (years) | Length (cm) | GW (g) | Condition factor |
---|---|---|---|---|---|
1996 | 14 | 8.43±2.53 | 29.29±2.53 | 381.54±169.99 | 1.43±0.08 |
(6-14) | (25-39) | (240-820) | (1.3-1.57) | ||
1997 | 12 | 11.36±3.93 | 30±3.3 | 428.75±133.04 | 1.53±0.06 |
(7-21) | (25-35) | (220-640) | (1.41-1.61) | ||
1998 | 5 | 8.4 | 25.8 | 269 | 1.56 |
(7-10) | (24-28) | (235-340) | (1.45-1.7) | ||
1999 | 10 | 11.7±4 | 30.6±4.97 | 436.1±168.62 | 1.45±0.18 |
(6-16) | (21-36) | (140-659) | (1.27-1.78) | ||
2000 | 16 | 12.88±2.22 | 32.44±2.9 | 503.44±112.68 | 1.46±0.1 |
(10-17) | (27-37) | (320-720) | (1.28-1.63) | ||
2001 | 29 | 11.23±3.28 | 28.9±2.81 | 351.14±97.17 | 1.42±0.11 |
(6-16) | (22-35) | (140-610) | (1.23-1.74) | ||
2002 | 4 | 11.75±3.86 | 28.25±3.1 | 328.25±109.59 | 1.41±0.06 |
(6-14) | (24-31) | (188-445) | (1.36-1.49) | ||
2004 (2003-2004)* | 13 | 12.44±2.92 | 30.31±2.14 | 399.08±77.77 | 1.43±0.14 |
(7-17) | (27-33) | (291-580) | (1.22-1.61) | ||
2005 | 32 | 12.31±3.45 | 29.28±2.45 | 364.12±91.35 | 1.43±0.12 |
(6-19) | (24-34) | (230-530) | (1.07-1.67) | ||
2006 | 12 | 11.08±3.48 | 28.83±2.37 | 354.58±95.38 | 1.44±0.11 |
(7-16) | (24-32) | (180-500) | (1.3-1.63) | ||
2008 | 5 | 12±4.47 | 30.8±3.7 | 384±110.59 | 1.3±0.14 |
(6-18) | (26-36) | (240-510) | (1.09-1.48) | ||
2010 (2009-2010)* | 7 | 10.86±2.79 | 31.14±2.97 | 431.29±116.45 | 1.4±0.13 |
(7-16) | (26-34) | (260-580) | (1.25-1.61) | ||
2011 | 3 | 12.33±3.79 | 32.67±4.51 | 523±213.19 | 1.44±0.12 |
(8-15) | (28-37) | (293-714) | (1.33-1.56) | ||
2013 | 13 | 14.85±6.03 | 32.77±4.9 | 565.08±219.82 | 1.55±0.16 |
(7-25) | (26-40) | (272-975) | (1.32-1.81) | ||
2014 | 12 | 13.92±3.12 | 32.58±2.84 | 508.92±117.7 | 1.45±0.14 |
(9-20) | (28-36) | (325-653) | (1.25-1.66) | ||
2015 | 8 | 15.25±7.15 | 35±6.85 | 672±352.8 | 1.46±0.2 |
(6-24) | (22-44) | (198-1190) | (1.13-1.86) | ||
2016 | 21 | 14.13±4.12 | 31.75±1.96 | 432.43±85.97 | 1.3±0.09 |
(9-25) | (29-35) | (270-560) | (1.11-1.44) | ||
2018 (2017-2018)* | 35 | 14.76±4.02 | 33.06±3.34 | 518.09±148.81 | 1.41±0.16 |
(8-28) | (27-41) | (316-950) | (1.22-2.05) | ||
2019 | 15 | 16.73±3.2 | 35.27±3.53 | 675.13±172.18 | 1.52±0.13 |
(10-22) | (28-40) | (360-940) | (1.3-1.83) | ||
2020 | 15 | 14.4±4 | 34.07±4.77 | 630.47±259.34 | 1.53±0.13 |
(8-20) | (27-44) | (323-1230) | (1.25-1.81) | ||
Total | 281 | 271 | 280 | 280 | 280 |
Potential fecundity was estimated using the autodiametric method (Thorsen and Kjesbu 2001Thorsen A., Kjesbu O.S. 2001. A rapid method for estimation of oocyte size and potential fecundity in Atlantic cod using a computer-aided particle analysis system. J. Sea. Res. 46: 295-308. https://doi.org/10.1016/S1385-1101(01)00090-9 ). This method relies on a relationship between mean vitellogenic oocyte diameter (OD) and oocyte packing density (OPD). Once this relationship is attained, fecundity is obtained by estimating the mean OD of an ovarian subsample and then converted to OPD to scale up to the weight of the ovary.
To build the autodiametric calibration curve, 115 ovaries were used. Oocyte counts and measurements were carried out using the software Leica LAS and the images were taken with a Leica Z6 APOA macroscope using a Leica DFC 490 camera. Each subsample was divided into 2 to 3 portions, and each one was analysed separately to facilitate the image analysis. The oocytes were counted and measured using a macro developed by Lucia Sánchez-Ruiloba (IIM-CSIC) in Image J software (https://imagej.nih.gov/ij/). Oocytes departing from sphericity were not considered for estimating average OD in each ovary, but they were counted to determine the final oocyte density (number of oocytes/g of ovary tissue) in each ovary.
The autodiametric calibration curve is based on the principle that OPD is inversely proportional to OD with a power relationship (Thorsen and Kjesbu 2001Thorsen A., Kjesbu O.S. 2001. A rapid method for estimation of oocyte size and potential fecundity in Atlantic cod using a computer-aided particle analysis system. J. Sea. Res. 46: 295-308. https://doi.org/10.1016/S1385-1101(01)00090-9 ):
where a and b are equation constants.
To improve the fit of the autodiametric calibration curve, OPD and OD data of S. mentella and S. norvegicus collected in the Irminger Sea and Iceland (Witthames et al. 2009Witthames P.R., Thorsen A., Murua H., et al. 2009. Advances in methods for determining fecundity: application of the new methods to some marine fishes. Fish. Bull. 107: 148-164., Saborido-Rey et al. 2015Saborido-Rey F., Domínguez-Petit R., Garabana D., Sigurðsson Þ. 2015. Fecundity of Sebastes mentella and Sebastes norvegicus in the Irminger Sea and Icelandic waters. Sci. Mar., 41: 107-124. https://doi.org/10.7773/cm.v41i2.2500 ) were also used, and results among species were compared before the data were merged. Finally, the potential fecundity of 281 S. fasciatus females on the Flemish Cap was estimated by obtaining the OD of each ovary using the image analysis described above and applying the autodiametric calibration curve:
where OW () is the ovary weight (g) of each female analysed.
Maternal traits
⌅The Fulton condition index (K) and the gonadosomatic index (GSI) were calculated as follows:
where GW represents gutted weight (g) and FL is fork length (cm) recorded for each female. Age (yr) and GW (g) were also recorded for each female for further analyses as an explanatory variable in the models and to estimate relative fecundity.
Statistical analysis
⌅Generalized linear models (GLM) were fitted to examine the relationships between the reproductive investment (absolute and relative potential fecundity) and maternal traits (length, age and fish condition).
When bottom water temperature was included, generalized linear mixed models (GLMM) were used to analyse the effect of female traits on their reproductive output. Models were fitted using length, age, K and GSI as fixed effects, haul as a random effect and water temperature as a random slope to allow the relationship with bottom temperature to differ by year. Water temperature data were unavailable for numerous coordinate-year combinations, hindering possible water bottom temperature effects on potential fecundity relationships. However, a dataset covering 15 years was obtained. The reproductive output was analysed as follows:
where reproductive output is the absolute and relative potential fecundity in year t and haul i, α is the intercept, FL is the fork length, A is age, K is Fulton’s condition factor, GSI is the gonadosomatic index, TBTM is the bottom temperature at deep habitat range (300-600 m), ai is the random intercept allowing for variation between years, and bi is the random intercept allowing for variation between hauls. The residuals ɛt,i are a normally distributed random error with mean 0 representing the within-year and haul variation.
To avoid collinearity due to fish length and age correlation, they were used in separate models: the models were fitted for absolute and relative fecundity using age and length separately. Haul and Year were included as random effects to correct for the non-independence of reproductive output from the same year and haul. Thus, we evaluated the effects of how these maternal traits and water bottom temperature affect potential fecundity. GLMM were fitted using negative binomial mean variance with a “log” link function. Diagnostic plots testing residual homogeneity, independence and normality and the Akaike information criterion (AIC) were used for model validation (Supplementary material, Tables S1, S2). We avoided transforming the response variable as long as possible using a negative binomial distribution. First, Poisson distribution was used in all the models because of the nature of the response variable (count data). However, high overdispersion values were obtained, so negative binomial distribution was used to avoid overdispersion problems (Zuur and Ieno 2013Zuur A.F., Ieno E.N. 2013. Mixed effects Models and Extensions in Ecology with R. In Journal of Chemical Information and Modeling. 53(9).). Variance inflation factor was calculated in each model to test for collinearity between independent covariates. All statistical analyses were performed with the statistical software R4.0.1 (R Core Team 2020R Core Team. 2020 R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.) and using the glmmTMB package (Brooks et al. 2017Brooks M. E., Kristensen K., van Benthem K. J., et al. 2017. glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling. The R Journal 9: 378-400. https://doi.org/10.32614/RJ-2017-066 ).
RESULTS
⌅The autodiametric method
⌅The estimated autodiametric relationship between S. fasciatus oocyte density (n/g) and mean OD was significant (p<0.001, r2=0.80, n=108). No significant difference was detected (df=255, P=0.133) between the autodiametric curves of S. fasciatus on the Flemish Cap, S. norvegicus in Iceland and S. mentella in Iceland and the Irminger Sea (Fig. 2). The autodiametric curve with all species combined (p<0.001, r2=0.88, n=256) was the following:
We then used this curve to estimate the potential fecundity from OD and ovary weight for S. fasciatus on the Flemish Cap.
Potential fecundity
⌅Influence of female traits on fecundity
⌅Four maternal traits (fork length, age, GSI and K) were used to study their influence on fecundity. To avoid using age and length together, two separate models were built. The resulting two GLM models explained 75% and 62% of potential fecundity (Table 2) using length and age, respectively. In the case of relative fecundity, the two models explained 47% and 50% of the variation (Table 2). Our results show that potential fecundity increased significantly with age and size. Interestingly, relative fecundity also increased with those female traits, indicating a disproportionally higher fecundity at larger sizes and older ages (Fig. 3).
Models | Response variable | n | R2 | Variable | Coeffs | SE | z value | Pr(>|z|) |
---|---|---|---|---|---|---|---|---|
1 | Potential fecundity | 252 | 0.75 | α | 4.862 | 0.278 | 17.491 | <0.001 |
Length | 0.113 | 0.005 | 22.216 | <0.001 | ||||
K | 0.858 | 0.142 | 6.028 | <0.001 | ||||
GSI | 0.445 | 0.033 | 13.654 | <0.001 | ||||
2 | Potential fecundity | 252 | 0.62 | α | 7.917 | 0.276 | 28.732 | <0.001 |
Age | 0.093 | 0.006 | 14.852 | <0.001 | ||||
K | 0.424 | 0.175 | 2.421 | <0.05 | ||||
GSI | 0.405 | 0.042 | 9.767 | <0.001 | ||||
3 | Relative fecundity | 252 | 0.47 | α | 2.905 | 0.251 | 11.588 | <0.001 |
Length | 0.016 | 0.005 | 3.367 | <0.001 | ||||
K | 0.096 | 0.112 | 0.857 | 0.39138 | ||||
GSI | 0.436 | 0.031 | 14.187 | <0.001 | ||||
4 | Relative fecundity | 244 | 0.50 | α | 3.257 | 0.188 | 17.301 | <0.001 |
Age | 0.023 | 0.005 | 4.685 | <0.001 | ||||
K | 0.042 | 0.108 | 0.391 | 0.696 | ||||
GSI | 0.413 | 0.038 | 13.437 | <0.001 |
Females with higher K and constant GSI (1.58, the average value of the time series) had a higher potential fecundity than fish with lower K (Fig. 3). For example, for a length of 34 cm (the average of the mature stock), the potential fecundity varied between 33707 oocytes with a K=1.2 compared with 47512 oocytes with a K=1.6 and 56409 oocytes with a K=1.8, i.e. an increase of 41% and 67%, respectively (Fig. 3A). Similarly, for a female at 15 years old, the predicted potential fecundity for all three scenarios of K was 34544 oocytes for K=1.2, 40739 oocytes for K=1.6 and 44242 oocytes for K=1.8 (Fig. 3B). Thus, potential fecundity of females in poorer condition was notably lower.
However, relative fecundity did not increase significantly with condition. For a fixed length of 34 cm, the relative fecundity was 71 oocytes g-1 body weight for K=1.2, 76 oocytes g-1 body weight for K=1.6 and 79 oocytes g-1 body weight for K=1.8, i.e. a difference of 7% and 11%, respectively. Moreover, for a fixed age of 15 years, relative fecundity was 72 oocytes g-1 body weight for K=1.2, 76 oocytes g-1 body weight for K=1.6 and 78 oocytes g-1 body weight for K=1.8, i.e. a difference of barely 5% and 8% (Fig. 3C and D).
Interannual variation of fecundity
⌅Interannual variation in potential fecundity was examined by comparing potential relationships between fecundity and maternal traits between 1996 and 2020 (Fig. 4). Fork length and age showed a significant effect on potential fecundity in all years analysed (p<0.001), and the optimal model showed a significant year effect (p<0.001). However, the post hoc Tukey test showed that the fecundity variation between years was caused by only a few years (Tables S3, S4), mostly 2010, a year with a low sample size.
Figure 5 shows the fecundity variation for a 34 cm female. Potential fecundity showed generally higher values at the beginning of the time series, an average of 48500 oocytes between 1996 and 2001 and four years with fecundity above 50 thousand oocytes. Later, fecundity decreased to an average of 42000 oocytes for the rest of the times series (except 2010). During this period, fecundity was below 45000 oocytes in all years except 2010, with particularly low values in the latest years (2015-2019). The year with the highest (2000) fecundity for a fixed size of 34 cm and age of 15 years showed 1.8-fold greater fecundity rates on average than the year with the lowest fecundity (2015) (not considering 2010).
The analyses with relative fecundity yielded similar results to those with absolute potential fecundity. Optimal models included length, age and year, which explained 22% and 34%, respectively (Supplementary Table 5 and Supplementary Table 6). For a 34 cm female, relative fecundity ranged between 62 and 139 oocytes g-1, showing a very similar pattern to potential fecundity, with higher values before 2002 (mostly above 90 oocytes g-1) and lower values thereafter (mostly below 80 oocytes g-1).
The role of bottom water temperature in fecundity
⌅The range of bottom temperature in which females were sampled varied between 3°C and 5°C, with the highest frequencies in a narrow range between 3.5°C and 4°C, i.e. 70% of females were sampled in a range of 0.5°C (Fig. 6). Because samples were randomly taken during the survey, this result likely reflects the distribution of females in advanced stage of vitellogenesis.
The potential and relative fecundity increased per degree of bottom temperature water (Fig. 6 A, B). The median of potential fecundity increased from 30743 oocytes in 3°C to 45000 oocytes in 4.5°C, i.e. by 31%. Similarly, relative fecundity increased from 64 oocytes g-1 body weight at 3ºC to 85 oocytes g-1 body weight at 4°C, i.e. an increase of 24%. The two GLMMs fitted (using length and age) showed that fecundity-at-length and at-age increased with temperature (Fig. 6C, D). However, only the model using age showed a significant positive relationship between bottom temperature and potential fecundity in the age model (Table 3 and Table 4).
Fixed effects | Parameter estimate | SE | z value | Pr(>|z|) |
---|---|---|---|---|
Intercept | 3.764875 | 0.490177 | 7.68 | <0.001 |
Length | 0.125494 | 0.007794 | 16.10 | <0.001 |
K | 0.971214 | 0.188958 | 5.14 | <0.001 |
GSI | 0.526113 | 0.05054 | 10.41 | <0.001 |
Sea bottom temperature | 0.107033 | 0.07218 | 1.48 | 0.138 |
Random effects (SD) | ||||
Year | 0.00971 | |||
Haul | 0.01133 | |||
Metric | ||||
R2 LMM(m) | 0.813 | |||
R2 LMM(c) | 0.858 |
Fixed effects | Parameter estimate | SE | z value | Pr(>|z|) |
---|---|---|---|---|
Intercept | 7.413802 | 0.439226 | 16.879 | <0.001 |
Age | 0.090452 | 0.007516 | 12.035 | <0.001 |
K | 0.334705 | 0.200544 | 1.669 | 0.135 |
GSI | 0.418751 | 0.057942 | 7.227 | <0.001 |
Sea bottom temperature | 0.166456 | 0.082827 | 2.010 | <0.05 |
Tandom effects (SD) | ||||
Year | 0.009952 | |||
Haul | 0.026120 | |||
Metric | ||||
R2 LMM(m) | 0.677 | |||
R2 LMM(c) | 0.778 |
DISCUSSION
⌅Our findings provide empirical evidence of autodiametric curve stability, indicating that the autodiametric method for estimating fecundity originally developed in cod (Thorsen and Kjesbu 2001Thorsen A., Kjesbu O.S. 2001. A rapid method for estimation of oocyte size and potential fecundity in Atlantic cod using a computer-aided particle analysis system. J. Sea. Res. 46: 295-308. https://doi.org/10.1016/S1385-1101(01)00090-9 ) can be applied in North Atlantic Sebastes species.
This study demonstrated no significant differences in autodiametric curves between three species of Sebastes on the Flemish Cap, in Iceland and in the Irminger Sea. Likewise, no significant differences were obtained between autodiametric curves from different stocks in the northeast Arctic, the northern Gulf of St. Lawrence and Georges Bank (Thorsen and Kjesbu 2001Thorsen A., Kjesbu O.S. 2001. A rapid method for estimation of oocyte size and potential fecundity in Atlantic cod using a computer-aided particle analysis system. J. Sea. Res. 46: 295-308. https://doi.org/10.1016/S1385-1101(01)00090-9 , Lambert 2008Lambert Y. 2008). Why should we closely monitor fecundity in marine fish populations? J. Northwest. Atl. Fish. Soc: 41: 93-106. https://doi.org/10.2960/J.v41.m628 , Alonso-Fernández et al. 2009Alonso-Fernández, A., Vallejo, A. C., Saborido-Rey, F., et al. 2009. Fecundity estimation of Atlantic cod (Gadus morhua) and haddock (Melanogrammus aeglefinus) of Georges Bank: Application of the autodiametric method. Fish. Res. 99: 47-54. https://doi.org/10.1016/j.fishres.2009.04.011 ). For example, for a fixed diameter size of 800 µm, the oocyte density varied between 3174 oocytes in the Flemish Cap autodiametric curve and 3303 oocytes in the Irminger Sea autodiametric curve, a difference of 4%. Lambert (2008)Lambert Y. 2008). Why should we closely monitor fecundity in marine fish populations? J. Northwest. Atl. Fish. Soc: 41: 93-106. https://doi.org/10.2960/J.v41.m628 found a similar difference of less than 6.5% in oocyte density estimated with different calibration curves for two cod stocks, concluding that they were essentially the same curve.
The use of OPD and the success of the autodiametric method could vary between areas, stocks and species (Dominguez-Petit et al. 2018Dominguez-Petit R., Rideout R.M., Garabana D., et al. 2018. Evaluating the use of the autodiametric method for estimating fecundity of Reinhardtius hippoglossoides, a species with an unusual oocyte development strategy. ICES J. Mar. Sci. 75: 831-839. https://doi.org/10.1093/icesjms/fsx162 ) spatial differences in the autodiametric calibration curve were observed in the Northwest Atlantic, but did not translate into differences in fecundity at length. This is the first time that spatial differences between ACCs of the same species have been reported, what could be the result of (i, for reasons such as energy allocation and preservation techniques (Friedland et al. 2005Friedland K.D., Ama-Abasi D., Manning M., et al. 2005. Automated egg counting and sizing from scanned images: Rapid sample processing and large data volumes for fecundity estimates. J. Sea. Res. 54: 307-316. https://doi.org/10.1016/j.seares.2005.06.002 ).Thus, fecundity estimations could be inaccurate when published calibration curves not estimated for the species or a stock of interest are used (Witthames et al. 2009Witthames P.R., Thorsen A., Murua H., et al. 2009. Advances in methods for determining fecundity: application of the new methods to some marine fishes. Fish. Bull. 107: 148-164.).
In this paper, we have studied the fecundity of S. fasciatus on the Flemish Cap for the first time, building a twenty-year time series between1996 and 2020; such long time series in fecundity are rarely seen in the literature. Mean potential fecundity and mean relative fecundity were 36000 oocytes per female and 78.17 oocytes/gram female respectively. These results are in accordance with the fecundity reported for S. mentella in the Irminger Sea (Saborido-Rey et al. 2015Saborido-Rey F., Domínguez-Petit R., Garabana D., Sigurðsson Þ. 2015. Fecundity of Sebastes mentella and Sebastes norvegicus in the Irminger Sea and Icelandic waters. Sci. Mar., 41: 107-124. https://doi.org/10.7773/cm.v41i2.2500 ). Our study shows annual changes in potential fecundity between several years of the time series, as other reported in species of the genus Sebastes (Beyer et al. 2015Beyer S.G., Sogard S.M., Harvey C. J., Field J.C. 2015. Variability in rockfish (Sebastes spp.) fecundity: species contrasts, maternal size effects, and spatial differences. Environ. Biol. Fish. 98: 81-100. https://doi.org/10.1007/s10641-014-0238-7 ).
We have shown that larger, older and better-conditioned fish produced more offspring in both absolute and relative terms than smaller individuals. Therefore, SSB may not be an accurate metric for the reproductive potential of stocks with a different demographic composition. The relative fecundity-age relationship suggests that there is a significant effect of repeat spawners in S. fasciatus stocks and highlights the importance of maintaining a strong length/age population structure. Similar results have been reported in several species, such as cod (Blanchard et al. 2003Blanchard J.L., Frank K.T., Simon J.E. 2003. Effects of condition on fecundity and total egg production of eastern Scotian Shelf haddock (Melanogrammus aeglefinus). Can. J. Fish. Aquat. Sci. 60: 321-332. https://doi.org/10.1139/f03-024 , Yoneda and Wrigth. 2004Yoneda M., Wright P.J. 2004. Temporal and spatial variation in reproductive investment of Atlantic cod Gadus morhua in the northern North Sea and Scottish west coast. Mar. Ecol. Prog. Ser. 276: 237-248. https://doi.org/10.3354/meps276237 , Mion et al. 2018Mion M., Thorsen A., Vitale F., et al. 2018. Effect of fish length and nutritional condition on the fecundity of distressed Atlantic cod Gadus morhua from the Baltic Sea. J. Fish. Biol. 92: 1016-1034. https://doi.org/10.1111/jfb.13563 ). We have also shown significant variation in fecundity between years. It is well known that fecundity, like many other life-history traits, is highly variable between stocks, geographic areas and/or years (Kraus et al. 2000Kraus G., Müller A., Trella K., Köster F.W. 2000. Fecundity of Baltic cod: Temporal and spatial variation. J. Fish. Biol. 56: 1327-1341. https://doi.org/10.1111/j.1095-8649.2000.tb02146.x , Marteinsdottir and Begg 2002Marteinsdottir G., Begg G. A. 2002. Essential relationships incorporating the influence of age, size and condition on variables required for estimation of reproductive potential in Atlantic cod Gadus morhua. Mar. Ecol. Prog. Ser. 235: 235-256. https://doi.org/10.3354/meps235235 , McElroy et al. 2013McElroy W.D., Wuenschel M.J., Press Y.K., et al. 2013. Differences in female individual reproductive potential among three stocks of winter flounder, Pseudopleuronectes americanus. J. Sea. Res. 75: 52-61. https://doi.org/10.1016/j.seares.2012.05.018 ). Nevertheless, fecundity is still mostly ignored in the monitoring programmes. As a consequence, population egg production is rarely estimated for assessment purposes, or if estimated a constant fecundity-at-length or at-age relationship is used.
An increase in potential fecundity with female size was observed in other Sebastes species, including Sebastes melanops, S. goodei, S. entomelas, S. flavidus and S. atrovirens (Berkeley et al. 2004Berkeley S.A., Bobko S.J. 2004. Maturity, ovarian cycle, fecundity, and age-specific parturition of black rockfish (Sebastes melanops). Fish. Bull. 102: 418-429., Sogard et al. 2008Sogard S. M., Berkeley S.A., Fisher R. 2008. Maternal effects in rockfishes Sebastes spp.: A comparison among species. Mar. Ecol. Prog. Ser. 360: 227-236. https://doi.org/10.3354/meps07468 , Dick 2009Dick E. J. 2009. University of California Santa Cruz modeling the reproductive potential of rockfishes, 299 pp.). However, our results show an increase in reproductive potential with size and age and the importance of using indexes other than SSB to measure stock reproductive potential. This finding has ben reported in S. mentella and S. norvegicus (Saborido-Rey et al 2015Saborido-Rey F., Domínguez-Petit R., Garabana D., Sigurðsson Þ. 2015. Fecundity of Sebastes mentella and Sebastes norvegicus in the Irminger Sea and Icelandic waters. Sci. Mar., 41: 107-124. https://doi.org/10.7773/cm.v41i2.2500 ), where the exponent of the fecundity-length power function differed significantly from 3. It is important to highlight that we used females with ovaries showing advanced vitellogenic stages, as down-regulation of fecundity has been shown to drastically modify fecundity during the course of vitellogenesis (Saborido-Rey et al 2015Saborido-Rey F., Domínguez-Petit R., Garabana D., Sigurðsson Þ. 2015. Fecundity of Sebastes mentella and Sebastes norvegicus in the Irminger Sea and Icelandic waters. Sci. Mar., 41: 107-124. https://doi.org/10.7773/cm.v41i2.2500 ). This process is likely driven by fish condition and environment factors (Murua et al. 2003Murua H., Saborido-Rey F. 2003. Female Reproductive Strategies of Marine Fish Species of the North Atlantic. J. Northwest. Atl. Fish. Soc. 33: 23-31. https://doi.org/10.2960/J.v33.a2 , Armstrong and Witthames 2012Armstrong M.J, Witthames P.R. 2012. Developments in understanding of fecundity of fish stocks in relation to egg production methods for estimating spawning stock biomass. Fish. Res. 117/118: 35-47. https://doi.org/10.1016/j.fishres.2010.12.028 ).
In line with length and age, Fulton’s condition factor and the GSI were only significantly related to potential but not to relative fecundity in our study. In addition, other studies have demonstrated that fish condition has a high influence on potential fecundity, with the result that fish in better nutritional status had a higher fecundity than fish in poorer conditions (Thorsen et al. 2006Thorsen A., Marshall C.T., Kjesbu O.S. 2006. Comparison of various potential fecundity models for north-east Arctic cod Gadus morhua, L. using oocyte diameter as a standardizing factor. J. Sea. Res. 69: 1709-1730. https://doi.org/10.1111/j.1095-8649.2006.01239.x , Kennedy et al. 2007Kennedy J., Witthames P.R., Nash R.D.M. 2007. The concept of fecundity regulation in plaice (Pleuronectes platessa) tested on three Irish Sea spawning populations. Can. J. Fish. Aquat. Sci. 64: 587-601. https://doi.org/10.1139/f07-034 , Lambert 2008Lambert Y. 2008). Why should we closely monitor fecundity in marine fish populations? J. Northwest. Atl. Fish. Soc: 41: 93-106. https://doi.org/10.2960/J.v41.m628 ).
In this paper, explained variance of fecundity was high when K and GSI were included. The GLM model using fish length, condition factor and GSI as a dependent variable explained 75% of the variability in fecundity, in agreement with an earlier study carried out in cod (Lambert et al. 2008Lambert Y. 2008). Why should we closely monitor fecundity in marine fish populations? J. Northwest. Atl. Fish. Soc: 41: 93-106. https://doi.org/10.2960/J.v41.m628 ). Considering that the effect of the condition factor can be related to the fact that it intervenes in the final part of oocyte recruitment, i.e. during this phase fish will feed and therefore the condition factor will be a key maternal trait determining fish fecundity. However, a recent study (Beyer et al. 2015Beyer S.G., Sogard S.M., Harvey C. J., Field J.C. 2015. Variability in rockfish (Sebastes spp.) fecundity: species contrasts, maternal size effects, and spatial differences. Environ. Biol. Fish. 98: 81-100. https://doi.org/10.1007/s10641-014-0238-7 ) showed that the hepatosomatic index (HSI) was significantly related in four studied species, whereas K was significant in one species. This finding suggests that a more accurate index of fish condition, such as HSI, lipid concentration or muscle water content and prey availability index (Kraus et al. 2002Kraus G., Tomkiewicz J., Köster F.W. 2002. Egg production of Baltic cod (Gadus morhua) in relation to variable sex ratio, maturity, and fecundity. Can. J. Fish. Aquat. Sci. 59:1908-1920. https://doi.org/10.1139/f02-159 ), should be included in future research into maternal effects on fecundity.
In this study, we found a positive relation between potential fecundity and bottom water temperature. Several studies have described water temperature as an important factor that can play a direct or indirect key role in fecundity variation in fish (Kjesbu et al. 1998, Kraus et al. 2000Kraus G., Müller A., Trella K., Köster F.W. 2000. Fecundity of Baltic cod: Temporal and spatial variation. J. Fish. Biol. 56: 1327-1341. https://doi.org/10.1111/j.1095-8649.2000.tb02146.x , Lambert et al. 2008Lambert Y. 2008). Why should we closely monitor fecundity in marine fish populations? J. Northwest. Atl. Fish. Soc: 41: 93-106. https://doi.org/10.2960/J.v41.m628 ). Moreover, bottom temperature, which has been increasing on the Flemish Cap since the 1990s (Colbourne et al. 2018Colbourne E., Perez-Rodriguez A., Cabrero A., Gonzalez-Nuevo G. 2018. Ocean Climate Variability on the Flemish Cap in NAFO Subdivision 3M during 2017 June.), could generate changes in the way in which S. fasciatus allocates energy to reproduction during the whole time series. For example, Yoneda and Wright (2004)Yoneda M., Wright P.J. 2004. Temporal and spatial variation in reproductive investment of Atlantic cod Gadus morhua in the northern North Sea and Scottish west coast. Mar. Ecol. Prog. Ser. 276: 237-248. https://doi.org/10.3354/meps276237 describe spatial and temporal fecundity variation as changes in energy allocation that influence maternal condition. The increasing temperature reported on the Flemish Cap may be one of the causes of the sharp increase in S. fasciatus abundance after several strong year-classes in 2002-2006 (González-Troncoso et al. 2022González-Troncoso D., Garrido I. Rábade S., et al. 2022. Results from Bottom Trawl Survey on Flemish Cap of June-July 2021. NAFO SCR Doc. 22/004.). Although recruitment was poor thereafter, it produced a shift in dominance on the Flemish Cap, where the traditionally more abundant S. mentella declined in favour of S. fasciatus, traditionally considerably less abundant. It is important to highlight that S. mentella has a distribution towards more northern and colder waters than S. fasciatus. Reproduction of other aquatic species can also be affected by variability of environmental factors such as sea surface temperature, which plays an important role in regulating brooding activity in crustaceans (Chang et al. 2021Chang H. Y., Richards R. A., Chen Y. 2021. Effects of environmental factors on reproductive potential of the Gulf of Maine northern shrimp (Pandalus borealis). GECCO 30: e01774. https://doi.org/10.1016/j.gecco.2021.e01774 ) and barnacles (Román et al. 2022Román S., Weidberg N., Muñiz C., et al. 2022. Mesoscale patterns in barnacle reproduction are mediated by upwelling-driven thermal variability. Mar. Ecol. Prog. Ser. 685: 153-170. https://doi.org/10.3354/meps13992 ) through the primary productivity.
Potential implications and future directions
⌅Firstly, our findings provide for the first time an autodiametric calibration curve between oocyte mean diameter and ovarian oocyte density in S. fasciatus, which can be applied to estimate potential fecundity in North Atlantic for species of the genus Sebastes. Secondly, our study shows that potential fecundity varies interannually in S. fasciatus, probably a response of maternal effects of individual females to varying combinations of biological and environmental factors. Because maternal effects have been reported in a number of exploited species, we suggest that annual variations in fecundity should be monitored regularly. This would improve stock reproductive indexes and increase our understanding of the processes affecting reproductive success. Our results suggest that developing a better understanding of how maternal effects impact on offspring quality may help to understand recruitment processes, enhance stock assessment models, and ultimately improve our capacity to achieve a sustainable fisheries management.