Study system and species

As with many freshwater and marine systems globally, the Laurentian Great Lakes already have begun to warm43. Over Lake Erie, average winter air temperature (mean daily temperatures from Toledo, OH [NOAA-NCDC station #94830] and Cleveland, OH [NOAA-NCDC station #14820]) averaged across both sites and smoothed using a 3d moving average) during 1956–2012 increased by 2.4 °C (Pearson correlation coefficient: r=0.39, P=0.003, N=57) and the annual average number of days with air temperatures below freezing decreased by 16 days (r=−0.34, P=0.01, N=56). Average air temperatures also are expected to continue to rise in the Great Lakes basin, with predicted increases of 2–5 °C during winter by 2050, depending on emission scenarios44. Further, in at least one of the Great Lakes, water temperature has been increasing twice as fast as air temperature45, largely due to a reduction in winter ice cover, which has decreased 72% over the past four decades46.

Yellow perch is a common, coolwater iteroparous fish that is widespread across the Atlantic, Great Lakes and Mississippi River basins of North America. This species is particularly important in the Laurentian Great Lakes basin, where it serves as an important consumer in the middle of the food web47 and supports valuable commercial and recreational fisheries. Yellow perch supports Lake Erie’s largest commercial fishery and second most valuable recreational fishery48. Recruitment to the fishery has been quite variable in Lake Erie over the past several decades48, a period of time during which variability in winter ice cover also has been increasing49.

In Lake Erie, as across much of its range, yellow perch develop ovaries during winter months49 and spawn during spring (mid-April through May18) across the lake, with different local spawning stocks that mix in the open lake as adults50. Embryos hatch and develop into pelagic larvae during May through June, depending on lake basin, with larvae feeding solely on zooplankton before becoming demersal omnivores after about 25–35 days of age51. Juveniles recruit to agency (fishery-independent) assessment gear by August of their first year of life and eventually become reproductively mature and enter the fishery at age-2. Juvenile abundance is a strong predictor of recruitment to the fishery at age-2. In turn, because strong recruitment events to the fishery at age-2 support the fishery for many years afterwards, factors such as winter duration, which influence juvenile abundance, leave a long legacy that is evident in fishery harvest.

Historical analysis of recruitment in relation to ice cover

Juvenile abundance indices. Analysis of Lake Erie yellow perch population dynamics (1973–2010) used juvenile (age-0) catch rate data (number of juveniles caught per min of bottom trawling) generated annually during an October fishery-independent assessment survey conducted by the Ohio Department of Natural Resources-Ohio Division of Wildlife (ODNR-DOW), as coordinated by the Great Lakes Fishery Commission’s Lake Erie Yellow Perch Task Group48. As sampling was conducted by multiple vessels during these years, we applied vessel-specific fishing power corrections to juvenile abundances from 1982 to 2010 to standardize catches52. Juvenile abundances from 1973 to 1981 had no fishing power corrections applied, due to a lack of studies comparing older and modern research vessels. However, sampling biases do not appear to underlie observed relationships (see abundance-ice cover analysis).

Because the ODNR-DOW survey design changed during the past 35+ years, we used yellow perch data only from 12 fixed, historical sites, sampled consistently during this time. Historical sites were spread across the Ohio waters of western (N=4) and central (N=8) Lake Erie23. Annual juvenile abundances from these historical sites were strongly correlated with overall annual abundances calculated using all sites in both western (Pearson correlation: r=0.92, N=24; ∼80 sites per year since 1987) and central Lake Erie (Pearson correlation: r=0.94, N=21; ∼40 sites per year since 1990), indicating that historical sites closely track population-level variation in juvenile yellow perch abundance.

Ice cover. We used ice cover trends from 1973 to 2010 for Lake Erie as a proxy for winter water temperature because no continuous data set of water temperature exists for our entire study period for either lake basin. Ice cover data were compiled from ice charts generated at approximately weekly intervals by the Canadian Ice Service and NOAA National Ice Center, which were subsequently summarized and interpolated into daily values of lake-area ice cover46. Because ice cover reaches its peak on Lake Erie during February through March46, we selected this timeframe to distinguish between years of low and high ice cover. Importantly, late winter (February-March) ice cover was highly correlated (Pearson correlation: r=−0.86, P<0.0001, N=37) with an independent index of mean winter air temperature (January–March; 1973–2010) developed for Lake Erie (see Study system and species section above), indicating that ice cover is a valid indicator of winter severity. This notion is further supported by analyses that related our basin-specific indices of yellow perch juvenile abundance to the most complete data sets (1969–1992) of daily water temperature for western (Put-it-Bay, OH) and central (Erie, PA) Lake Erie43. These analyses produced similar results to those obtained with our ice cover analyses in that we detected significant thresholds in mean winter water temperature below which recruitment could be either high or low, but above which, recruitment was always low (Supplementary Fig. 1).

Abundance-ice cover analysis. We used a two-dimensional Kolmogorov–Smirnov test53 to determine whether changes in the variance of juvenile abundance were related to our index of winter duration (that is, ice cover). P values from this distribution-free test indicate whether the variance of the dependent variable (that is, juvenile abundance) significantly differs between the two sides of threshold values in the independent variable (that is, ice cover). This analysis, spanning 1973–2010, included data collected from older research vessels (1973–1981) for which no fishing power corrections could be applied. Therefore, we repeated this analysis using only data from 1982 to 2010. Using only these recent data, with fishing power corrections applied, we obtained significant two-dimensional thresholds53 similar to those found using 1973–2010 data. Thus, we feel confident that sampling biases do not underlie our observed relationships.

Stock size. While yellow perch stock size (that is, number of age-3 and older mature yellow perch) varied markedly in both Lake Erie basins during 1973–2010, we do not feel that it is responsible for observed recruitment variation. In support of this notion, previous research showed that stock size explained <1% of the variation in yellow perch recruitment in both the western and central basins during 1973–2010 (ref. 54). Further, yellow perch spawning stock size generally declined in the west basin during this time period, whereas it increased in the central basin during this same period48,54. These opposing trends in stock size between adjacent lake basins also suggest that stock size was relatively unimportant to driving recruitment variation, given that we documented near identical responses of recruitment to variation in winter thermal regime.

Timing of spawning in Lake Erie

Spawner classifications. To determine the timing of spawning in the wild, we sampled yellow perch weekly in central Lake Erie during spring (April–May) 2010–2012. Individuals were collected near Sandusky, OH (41° 30’ N, 82° 37’ W) by bottom trawling two nearshore-to-offshore transects. Transects were divided into five 1.5-m depth contours (5, 7.5, 9, 10.5 and >12 m) with two trawls conducted per depth contour. All female yellow perch collected during 2010 (N=551), 2011 (N=429) and 2012 (N=279) were euthanized, dissected and classified as either immature, mature (gravid but not spawning), spawning or spent, based on macroscopic inspection of gonads55: (1) immature females had a small thread-like transparent ovary; (2) mature females had clearly visible eggs with the ovary filling about two-thirds of the body cavity; (3) spawning females expressed egg ribbons with gentle pressure; and (4) spent females had empty, flaccid ovaries that were reddish grey.

Spring temperatures. While our study spanned only 3 years, these years varied greatly in spring (March–May) temperatures. Air temperatures varied greatly during the spawning period, with 2012 being the warmest year on record for OH (as well as for the contiguous United States). By contrast, 2011 and 2010 were ranked as the 92nd and 115th warmest spring on record, out of 119 annual observations (1895–2013)56. These differences were generally reflected in terms of water temperatures in Lake Erie during spring, which was measured before each trawl during 2011–2012 and for each depth contour during 2010. While spring water warming rates during 2010–2012 were similar among the 3 years (ANCOVA: P=0.67, N=15), spring water temperatures during 2010 and 2012 were warmer than 2011 (one-way ANOVA, Tukey’s HSD post hoc test: 2010 versus 2011, P=0.01, N=11; 2012 versus 2011, P<0.001, N=10).

Probability of spawning. We used logistic regression to relate the number of mature females (that is, females in classification criteria 2–4 per above) to water temperatures. By including only mature females in our analyses, we were able to quantify how the transition of females from a gravid (that is, egg bearing) to spent condition varied in relation to spring water temperature. We created a binomial variable (0=gravid; 1=spawning or spent) to indicate the spawning status of each individual and used logistic regression (PROC GENMOD, SAS v. 9.3) to identify significant relationships within years (Supplementary Table 2). Confidence intervals (95%) generated for 8, 10 and 12 °C were used to determine whether the timing of spawning in each year differed in response to annual variation in temperature.

Laboratory experiment

We conducted a controlled laboratory experiment with wild yellow perch to quantify the effects of winter duration on the timing of reproduction, fecundity, egg quality (that is, egg size, energetic and lipid content), embryo hatching success and larval-size-at-hatching. Below, we provide details regarding the fish used in the experiment, the experimental design and the treatment of the data.

Collection and rearing of wild fish. The central basin yellow perch population is the largest in Lake Erie, representing 66 and 70% of the population in terms of lake-wide abundance and biomass during 1987–2010 (ref. 48). For this reason, we collected male and female yellow perch for our experiment from central Lake Erie near Fairport Harbor, Ohio, USA (41° 46′ N, 81° 21′ W) during April–May 2011 (when sex could be determined by external examination). We collected all individuals via bottom trawling conducted aboard the ODNR-DOW’s RV Grandon. Immediately upon collection, we placed all individuals into live wells and ‘fizzed’57 each fish with a hypodermic needle to prevent overinflation of the gas bladder57, a common phenomenon that can cause mortality in fish rapidly brought to the surface from depth. Fizzing reduced post-capture mortality and did not adversely affect long-term survival, as previously reported57.

After collection, all individuals were transported to the Aquatic Ecology Laboratory (AEL) at The Ohio State University (Columbus, OH, USA), where they were held until October 2011 in 2,500 l circular tanks in the AEL’s outdoor pool facility, which provided constant aeration and flow-through, dechlorinated city water. After 2 weeks of acclimation at the AEL, all individuals were injected with a unique passive integrated transponder (PIT) tag (Biomark, Boise, ID, USA), which allowed us to monitor individual growth and maturation. We fed all individuals live fathead minnows (Pimephales promelas) during the pre-experiment holding period (April/May–October 2011). Water temperatures (measured daily with a YSI 550a, YSI Incorporated, Yellow Springs, OH, USA) varied from 7 to 8 °C during April, when the first yellow perch arrived in the facility, to a maximum of 23 °C during July, before falling to 18 °C by the first week of October. During this time, we made no effort to control water temperature so that it could closely mimic the seasonal variation observed in Lake Erie43. Water quality parameters (that is, ammonia, nitrite, nitrate and pH) were measured weekly and levels were always within acceptable ranges (that is, unionized ammonia<0.1 p.p.m.; nitrates<40 p.p.m.) during the pre-experiment holding period.

Experimental design. Our experiment was designed to quantify the effects of winter duration (number of days below 5 °C, treatment levels=52 and 107 days) on the timing of reproduction, fecundity, egg quality (that is, egg size, energetic and lipid content), embryo hatching success and larval size-at-hatching. Winter-duration treatments and the rates of fall cooling and spring warming (both 0.25 °C day−1) used in our experiment were based on historical (1994–2010) field measurements of water temperature from a central Lake Erie water intake located near Cleveland, OH, USA (41° 32′ 53′′ N, 81° 44′ 60′′ W). Our two winter-duration treatment levels were intended to simulate historical (107 days) and future (52 days) conditions for Lake Erie. Our short winter duration (52 days) was similar to number of days below 5 °C recorded during winter 1999 (N=59 days below 5 °C), which was, until 2012, the warmest winter on record for Ohio (1895–2013)56. Our long winter duration (107 days) was equal to the mean number of days below 5 °C observed during 1994–2010. After our simulated short winter, we halted spring warming when temperatures reached 15 °C because 1) yellow perch spawning in Lake Erie is typically completed by the time temperatures exceed 15 °C (ref. 18) and (2) previous laboratory work has documented reduced egg viability at temperatures>15 °C (ref. 58). As our objective was to assess the effects of winter duration on reproductive success, we view this approach as conservative. Continued warming above 15 °C would have likely caused reduced egg viability due to elevated spawning temperatures58.

Our experiment was conducted during October 2011 through June 2012 in two walk-in environmental-control chambers (located at the AEL) in which all physico-chemical conditions were controlled. Each chamber represented a single winter-duration treatment and contained a recirculating system with six 189 l tanks. Each recirculating system was supplied with continuous aeration along with physical and biological filtration to maintain water quality. Ammonia, nitrite, nitrate and pH were measured daily and water changes were conducted as needed to maintain high levels of water quality (that is, unionized ammonia<0.1 p.p.m.; nitrates<40 p.p.m.). Hobo loggers (Onset, Bourne, MA, USA) recorded water temperature (nearest 0.1 °C) every 2 h in each recirculating system, and temperature and dissolved oxygen were measured daily with a YSI 550a handheld meter (YSI Incorporated, Yellow Springs, OH, USA). Lighting in all rooms was provided by incandescent lights and controlled by a digital system that simulated daily reductions (during the fall) and increases (during winter and spring) in photoperiod so as to mimic the photoperiod at Cleveland, OH, USA (located at the south shore of the central basin). In addition, at dawn and dusk, 1 h of increasing or decreasing light intensity preceded day and night periods.

During the first week of October 2011, all fish were removed from outdoor holding tanks, briefly anaesthetised in buffered MS-222, measured (nearest 1-mm total length, TL), weighed (nearest 1-g wet mass) and scanned for PIT tags. A subset of nine females was euthanized with an overdose of MS-222, dissected and gonads weighed to quantify reproductive development at the initiation of our experiment. We assigned all remaining individuals to a winter-duration treatment and tank, using a random number generator. Each individual tank contained 12–15 individuals (8–9 females and 3–7 males; numbers varied to standardize initial tank biomass), for a total of 49 females and 28 males and 49 females and 26 males in the long and short winter-duration treatments, respectively. During the experiment, we fed all yellow perch daily maintenance rations of live fathead minnows. We determined maintenance rations from an existing bioenergetics model59, based on daily temperatures and the mass of individuals in each tank.

During the experiment, some females died (short winter N=19; long winter N=15). The majority (56%) of mortalities occurred during the first 3 weeks of October 2011 at the start of the experiment, likely due to transfer stress and failure to acclimate to indoor laboratory conditions. No mortalities occurred once spawning began in the spring (April–June 2012). Once spawning began, we euthanized another group of randomly selected female yellow perch in the short (N=14) and long (N=14) winter-duration treatments to assess reproductive development (results not presented). The remaining females in both the short and long winter-duration treatments all spawned, with some being hand-stripped (short winter N=7; long winter N=10) and some spawning in tanks (short winter N=9; long winter N=10). Thus, our sample sizes for fecundity, egg quality, embryo hatching success and larval-size-at-hatching for each treatment were based on the number of yellow perch that were able to be successfully hand-stripped of eggs.

To simulate the end of winter and onset of spring, we increased water temperatures based on historical Lake Erie water temperature data (see above). For our short winter-duration treatment, spring warming began at the end of February, whereas in our long winter-duration treatment, spring warming began during mid-April. During the spring warming period, we monitored females during the day and hand-stripped females of eggs once signs of ovulation were present. Although six tanks (that is, replicates) were used in each winter-duration treatment, we were able to hand-strip females from only four of the tanks in each treatment.

Reproduction metrics. We used a variety of metrics to assess the impact of winter duration on maternal investment in reproduction, considering both the number of potential offspring produced (that is, fecundity) and investment per offspring (that is, egg size, energetic and lipid content). We fertilized eggs using the dry method60 with composite milt samples from males in the same treatment (see Spawning and fertilization section). We also collected unfertilized egg samples to quantify fecundity, egg size and energetic and lipid content (see Fecundity and Egg quality sections). Fecundity gave us a measure of overall reproductive output, whereas egg size and energetic and lipid content served as measures of egg quality, as these metrics have been shown to be positively related to responses such as embryo hatching success61.

To assess the impact of egg quality on hatching success and larval quality, we incubated fertilized eggs and hatched them under controlled conditions. Embryo hatching success was calculated as the number of hatched larvae per number of fertilized eggs per sample (see Embryo hatching success section). All hatched larvae were preserved (3% buffered glutaraldehyde) for counts and measurements of larval size (see Larval size-at-hatching section).

Blinding of treatments from investigators during the experiment was not possible due to logistical constraints (that is, we were required to enter walk-in environmental chambers to conduct the experiment and differences in temperature between treatments were obvious). However, by labelling egg and larval samples with non-descript letter and number combinations upon collection, we were able to remain blinded to treatment assignments during the processing of samples for fecundity, egg quality, embryo hatching success and larval-size-at-hatching analyses.

Spawning and fertilization. To identify spawning activity as water temperatures increased at the conclusion of each winter-duration treatment, we monitored females every 30 min from first light until 3 h after last light for external signs of ovulation. Once females showed signs of ovulation (that is, swollen and slightly reddish genital papilla, bulging of the ovary towards the exterior), they were removed from tanks, briefly anaesthetised in MS-222, scanned for PIT tags, and dried with a cloth before applying gentle pressure to the abdomen to strip ovulated egg ribbons. Eggs were expressed into a dry pan and their mass recorded (nearest 0.1 g).

Subsamples from each individual’s egg mass were fertilized, with a third subsample used to determine fecundity and quantify egg quality (that is, egg size, energetic and lipid content). For each fertilization event, two 2 g egg masses were fertilized using the dry method60 with a ‘fresh’ composite of milt (that is, collected within 10 min of stripping eggs from each female) from three haphazardly selected males within the same winter-duration treatment as the stripped female. In this way, eggs from each female were fertilized with a new, unique composite milt sample. Milt from the three males was composited into Moore’s extender62, where it was diluted 20-fold. Each 2 g egg mass was subsequently fertilized with a concentration of 100,000 spermatozoa per egg. We also analysed both individual and composite milt samples from each fertilization event for per cent sperm motility, duration of sperm motility and sperm density62. Results showed that none of these milt quality metrics were related to embryo hatching success (Pearson correlation: all P>0.05)54.

Some females released egg ribbons spontaneously in tanks, between our monitoring activities. We did not use these spontaneously released eggs to assess fertilization or embryo hatching success, as in-tank egg ribbon release can result in highly variable male fertilization success rates (that is, 40–85%)63. However, the timing of these spontaneous spawning events was recorded and used in our determination of spawning time.

Timing of spawning. The date and water temperature at which each female spawned was recorded, allowing us to determine spawning time differences between treatments. Following hand-stripping of eggs, each female was euthanized in an overdose of buffered MS-222, measured for TL and wet mass, and dissected to remove the spent ovary, which was subsequently weighed (nearest 0.1 g) for determination of somatic growth during the experiment (see Over-winter growth section). The sagittal otoliths of each female also were removed for age determination. Otoliths were cracked and sanded to the origin and briefly burned with an alcohol burner to aid in distinguishing annuli. Annuli were counted by three independent readers to determine age, following established guidelines64.

Embryo hatching success. To measure embryo hatching success, we first placed fertilized eggs into mesh-covered jars in upwelling California-style tray incubators. The incubators were supplied with water from a partially recirculating system equipped with a chiller to maintain water temperatures at optimal levels for yellow perch egg incubation (10–18 °C (ref. 65); see Supplementary Table 3) during the course of the experiment. Temperature and dissolved oxygen were recorded daily and eggs were monitored carefully for the first presence of eyed embryos. When fertilized eggs reached the eyed-embryo stage (8–10 days, depending on water temperature), the samples were moved into clear, plastic 500 ml jars that were filled with water and sealed to quantify embryo hatching success. An air stone in each jar provided vigorous aeration to assist with hatching. Hatching jars also were held in a bath of flow-through water to maintain stable temperatures. Incubation temperature for each egg mass was calculated as the mean daily temperature measured during incubation in both California-style tray incubators and plastic hatching jars.

Eggs were checked every 12–24 h for hatching. Once hatched larvae were visible, a 1,800 μm sieve was used to separate hatched larvae from un-hatched eggs. All hatched larvae were immediately euthanized and preserved in 3% buffered glutaraldehyde. After collection of hatched larvae, all unhatched eggs were returned to the hatching jar, with fresh water. The embryo hatching success of each fertilized egg sample was determined by dividing the total number of hatched larvae collected by the total number of eggs in each sample (determined following methods for fecundity estimation described below).

Fecundity. Fecundity was determined from egg ribbons of females that were hand-stripped and measured as the number of eggs per gram of ribbon. In brief, three subsamples of each egg ribbon (∼0.5 g each) were collected and weighed, and the number of eggs in each subsample was counted under a dissecting microscope. The total number of eggs produced by each female was estimated by multiplying the number of eggs per gram of ribbon by the overall ribbon mass18. While fecundity does not necessarily indicate fertility, which was tested with hatching tests, it can provide an objective measure of reproductive output.

Egg quality. In addition to using embryo hatching success as a measure of egg quality, we quantified egg size and energetic and total lipid content. These additional metrics of egg quality were intended to assist in our investigation of possible mechanisms underlying variation in embryo hatching success and also to determine whether other, more easily collected measures of egg quality could be used in future studies to accurately predict embryo hatching success.

Because larger eggs generally produce larger offspring that have higher rates of survival than small eggs35, we used egg size as a proxy of egg quality. To do so, we estimated the mass of an individual egg (nearest 0.01 mg) by taking the inverse of our eggs per gram of ribbon metric (see Fecundity section above). Because estimated individual egg mass is prone to possible overestimation, as it depends on ribbon mass, we also used independent measurements of individual egg diameters in a subsample of each female’s egg mass that was preserved in 3% buffered glutaraldehyde immediately after spawning (N=20 eggs per female) as a way to check for this bias54. Both proxies of egg size showed similar significant differences between our short and long winter-duration treatments54, thus providing strong evidence that egg size differed between our treatments and was not due to differences in ribbon mass among females or treatments.

We measured the energetic content of ovaries in two ways. First, we quantified the total energetic content of egg ribbon subsamples using bomb calorimetry66. In brief, ovaries were dehydrated in a drying oven (65–70 °C; 48–72 h), homogenized into a fine powder, compressed into small pellets (N=2–3 replicates per ovary) and combusted in a Parr oxygen bomb calorimeter (Parr Instrument Company, Moline, IL). Second, we quantified the percentage of total lipids in each hand-stripped egg mass. Total lipids were extracted from ovaries after homogenization in chloroform-methanol according to refs 67, 68. The organic solvent was evaporated under a stream of nitrogen and the lipid content determined gravimetrically. To quantify energetic and lipid content on a per egg basis, we divided the total energetic and lipid content of each sample by the wet mass of the sample (expressed as calories or lipids (mg) per gram of wet mass). We then multiplied this wet mass concentration by the total ovary mass and divided by the fecundity, previously determined for each female, to convert wet mass concentrations into calories or lipids (μg) per egg, respectively.

Larval size-at-hatching. To determine whether larval size-at-hatching differed between winter-duration treatments, we measured three metrics of size and quality (that is, energy reserves) in preserved larvae: total length (nearest 0.1 mm); body depth at the insertion of the anal fin (nearest 0.1 mm); and yolk-sac volume (that is, a measure of energetic reserves; nearest 0.01 mm3). To quantify yolk-sac volume, we measured the length and width of the yolk-sac and converted these measurements into volume using the standard equation for a prorate spheroid, which closely approximates the shape of the yolk-sac in many larval fishes69. Measurements of preserved larvae were made using an image analysis system that consisted of a Nikon SMZ800, Digital Sight DS-U3 dissecting microscope and Nikon NIS-Elements BR v.4.00.07 software.

Over-winter growth. To quantify female somatic (that is, non-reproductive) growth during our winter experiment, we used data from two sources: (1) females euthanized during the first week of October 2011(the start of our winter experiment); and (2) data collected from PIT-tagged females during the course of the experiment. Females euthanized in October (N=9) were weighed (nearest 0.1 g wet mass) and gonads dissected to determine gonad mass (nearest 0.1 g). For these euthanized females, gonad mass ranged from 1 to 4% of total female mass (nearest 0.1 g) and was positively related to total female mass (linear regression: gonad mass=0.036 × female mass−0.798; R2=0.59, P=0.015, N=9). Using this relationship, we estimated the gonad mass for each female at the start of our winter experiment in early October. By then subtracting this estimated gonad mass from the total mass of each female, we obtained an estimate of each female’s somatic mass at the start of the experiment. After each female had spawned (that is, lost mass associated with eggs) at the conclusion of the experiment, we subtracted the spent ovary mass from the total mass of each female to quantify somatic mass at the end of the experiment. We used the difference between somatic mass at the start and end of our winter-duration experiment as our measure of over-winter growth for female yellow perch that were successfully hand-stripped of eggs.

Using this approach, we found that all females lost somatic mass during the experiment, with the amount of mass lost (g) being negatively related to the somatic mass of females (g) measured during the first week of October 2011 (linear regression: over-winter growth=−0.25 × female mass+13.7; R2=0.58, P<0.001, N=16). To determine whether over-winter growth differed between our experimental treatments, we used residuals from this relationship as our response variable.

Data analysis. Before testing whether fecundity, egg quality, embryo hatching success, larval size-at-hatching and over-winter growth differed between winter-duration treatments, we tested whether each response variable was related to female size (TL), age and condition (residual mass from a log 10 (wet mass)−log 10 (TL) relationship, calculated separately for both spring [at time of spawning] and fall [when winter-duration treatments began]); previous research has shown that egg and larval traits are often correlated with female spawner size and age70. In addition, we tested whether embryo hatching success and larval-size-at-hatching were related to incubation temperature (calculated as the mean of mean daily temperature measured during the incubation period for each egg mass). When significant relationships were identified, we used residuals from these relationships as our response variables in our final analyses, which allowed us to remove the effect of female size, age, condition or incubation temperature (see Supplementary Table 3 for raw data).

Once the appropriate response variables had been identified and calculated, we used generalized linear mixed models (GLMMs; PROC MIXED SAS v.9.3) to test for tank effects. Tanks were included in GLMMs as random categorical effects (that is, replicates nested within winter-duration treatment, which was considered a fixed categorical effect in our models). Finding no tank effects for any of our response variables (all P>0.05), we calculated means of each response variable by tank (our experimental unit) and proceeded with testing for treatment effects using generalized linear models (PROC GLM, SAS v.9.3). Finally, we used regression techniques (linear and non-linear) to evaluate the relationship between embryo hatching success and our metrics of egg quality (that is, egg size, energetic and lipid content). Before conducting statistical tests (α=0.05), we verified that each response variable was normally distributed. We also analysed residuals from each model to verify that assumptions of normality, constant variance, independence and (when appropriate) linearity were met.

All field collections and laboratory experiments were conducted according to animal use guidelines outlined in IACUC protocol # 2009A0073 at The Ohio State University.