Influence of maternal effects and temperature on fecundity of Sebastes fasciatus on the Flemish Cap

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

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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.

Fig. 1.  Map of the location of Flemish Cap in the Northwest Atlantic. Lines indicated isobath depth. The inset shows in detail the area of the red square: straight lines and codes indicate Northwest Atlantic Fisheries Organization (NAFO) management divisions around the Flemish Cap (3M). Red polygons indicate sponge closure areas.

RESULTS

The autodiametric method

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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:

$\mathrm{O}\mathrm{o}\mathrm{c}\mathrm{y}\mathrm{t}\mathrm{e}\mathrm{ }\mathrm{d}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{y}\mathrm{ }(\mathrm{n}/\mathrm{g})\mathrm{ }=\mathrm{ }\mathrm{e}\mathrm{x}\mathrm{p}\mathrm{ }(1.068\times 10\mathrm{ }-\mathrm{ }(3.234\mathrm{ }{\mathrm{10}}^{\mathrm{-}\mathrm{3}})\times \mathrm{O}\mathrm{D}\mathrm{ }(\mathrm{\mu }\mathrm{m})$  (5)

Fig. 2.  Relationship between oocyte diameter and oocyte density (number of oocytes/g) for species of the genus Sebastes sampled on the Flemish Cap bank (S. fasciatus with green dots) and in the Irminger Sea and Iceland (S. norvegicus and S. mentella with coral and blue dots, respectively). No significant differences were observed between areas.

We then used this curve to estimate the potential fecundity from OD and ovary weight for S. fasciatus on the Flemish Cap.

Potential fecundity

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Influence of female traits on fecundity

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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).

Table 2.  - Summary of GLM negative binomial models fitted to estimate the effect on potential and relative fecundity of the maternal traits fork length, age, condition factor (K) and gonadosomatic index (GSI) of S. fasciatus on the Flemish Cap.

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

Fig. 3.  - Relationship and fitted curves between potential fecundity by length (A) and age (B), and between relative fecundity by length (C) and age (D) of S. fasciatus on the Flemish Cap between 1996 and 2020. Data were fitted for three different values of condition factor (K=1.2, K=1.6 and K=1.8)

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

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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.

Fig. 4.  - Interannual variation of the relationships between potential fecundity by length (A) and age (B), and between relative fecundity by length (C) and age (D) of S. fasciatus on the Flemish Cap between 1996 and 2020.

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).

Fig. 5.  - Temporal variation in potential fecundity (A) and relative fecundity (B) between 1996 and 2020 for a 34 cm S. fasciatus female.

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

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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.

Fig. 6.  - Boxplots displaying the relationship of sea bottom temperature with potential fecundity (A) and relative fecundity (B) of S. fasciatus on the Flemish Cap. The relationships of potential fecundity at four different temperatures are shown in C) for length and in D) for age.

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).

Table 3.  Parameters of the optimal GLMM using potential fecundity as the response variable and including length, condition factor (K), gonadosomatic index (GSI) and bottom temperature from 21 years (N=280 observations) as explanatory variables. SD, standard deviation; SE, standard error. R2LMM(m) describes the proportion of variance explained by fixed effects alone and R2LMM(c) describes the proportion of variance explained by fixed and random effects combined.

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

Table 4.  Parameters of the optimal GLMM using potential fecundity as the response variable and including age, condition factor (K), gonadosomatic index (GSI) and bottom temperature from 21 years (n=280 observations) as explanatory variables. SD, standard deviation; SE, standard error. R2LMM(m) describes the proportion of variance explained by fixed effects alone and R2LMM(c) describes the proportion of variance explained by fixed and random effects combined.

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.