Reactive stress-coping styles show more variable reproductive expenditure and fitness outcomes

Stress-coping styles dictate how individuals react to stimuli and can be measured by the integrative physiological parameter of resting heart-rate variability (HRV); low resting HRV indicating proactive coping styles, while high resting HRV typifies reactive individuals. Over 5 successive breeding seasons we measured resting HRV of 57 lactating grey seals. Mothers showed consistent individual differences in resting HRV across years. We asked whether proactive and reactive mothers differed in their patterns of maternal expenditure and short-term fitness outcomes within seasons, using maternal daily mass loss rate to indicate expenditure, and pup daily mass gain to indicate within season fitness outcomes. We found no difference in average rates of maternal daily mass loss or pup daily mass gain between proactive and reactive mothers. However, reactive mothers deviated more from the sample mean for maternal daily mass and pup daily mass gain than proactive mothers. Thus, while proactive mothers exhibit average expenditure strategies with average outcomes, expenditure varies much more among reactive mothers with more variable outcomes. Overall, however, mean fitness was equal across coping styles, providing a mechanism for maintaining coping style diversity within populations. Variability in reactive mothers’ expenditures and success is likely a product of their attempts to match phenotype to prevailing environmental conditions, achieved with varying degrees of success.

accounted for by ID). As expected, post-partum mass was found to have a strong effect on both maternal daily mass loss rate and pup daily mass gain rate, where heavier mothers were found to have higher rates of mass loss, but also higher rates of pup mass gain (Table 2). However, for a given maternal post-partum mass, those that pupped later in the season tended to exhibit higher rates of maternal daily mass loss and pup daily mass gain ( Table 2). Year also had a strong effect on the mass change parameters. In 2015 there were significantly higher rates overall for maternal daily mass loss (Kruskal-Wallis rank sum test by Year: Χ 2 = 13.97, df = 4, p = 0.0074) and pup daily mass gain (Kruskal-Wallis rank sum test: Χ 2 = 12.82, df = 4, p = 0.0123) relative to the other breeding seasons. However, the putative interaction between resting HRV and year was not retained in any of the confidence sets for any of the tested response variables. None of the parameters tested, however, appeared to explain observed differences in mass transfer efficiency as the null model was the only model remaining after model selection (Table 1). Device type (see methods) did not feature in any of the confidence sets for any of the tested response variables. HRV and deviance from seasonal means. Resting HRV appears to be influential in the degree of individuals' deviation from annual mean rates of maternal mass loss and pup mass gain, though not for deviance in maternal post-partum mass or mass transfer efficiency. Females who gave birth later in the season were found to deviate more from the annual mean with respect to post-partum mass (Tables 1 and 2), which was again strongly influenced by individual ID (accounting for 63% of the model variation). While resting HRV was also retained in the best model, with more reactive individuals having more variable post-partum masses, the effect was non-significant. However, these results should be considered with caution as the null model was also retained in the confidence set (Table 1, ΔAIC Null = 3.33). By contrast, resting HRV was retained as a significant effect in our models of the degree of deviation from annual mean rates for maternal mass loss and pup mass gain (Fig. 1a,b). More reactive individuals appeared to exhibit greater variation in maternal daily mass loss rates as well as greater variation in pup daily mass gain rates than proactive individuals (Tables 1 and 2, Fig. 1a,b). Birth date was also retained, with mothers pupping later in the season exhibiting greater variation in rates of maternal mass loss and pup mass gain. In contrast to maternal post-partum mass, individual ID appeared to account for little variation in the deviance of maternal daily mass loss rate (37% of model variation) and pup daily mass gain rate (7% of model variation). Only the null model was retained in the confidence set for deviance in mass transfer efficiency model ( Table 1). None of the confidence sets retained the putative interaction between Resting HRV and year and device type did not feature in any of the confidence sets for any of the tested response variables.

Discussion
In our study we use a physiological measure of coping style, resting HRV, which is based on a physiological system that is highly conserved across vertebrates, and underpins behavioural patterns 29,30,34 . We show high inter-annual repeatability in resting HRV among female grey seals in an entirely natural setting, demonstrating across individual differences and long-term within individual consistency in stress-coping style on the pro-reactive spectrum. In addition, these different coping styles exhibit subtle differences in patterns of reproductive performance that  93,96 . The table also provides R 2 (with 95% confidence intervals) for fixed effects within the best models (derived using r2beta 96 ). Abbreviations: HRV = Resting heart rate variability, MPPM = maternal post-partum mass, MDML = maternal daily mass loss rate, PDMG = pup daily mass gain rate, MTE = mass transfer efficiency. www.nature.com/scientificreports www.nature.com/scientificreports/ suggest a mechanism by which inter-individual variation in coping style can be maintained within a wild population subject to natural environmental conditions.
Coping style did not influence real values of maternal size (post-partum mass), maternal performance (maternal daily mass loss rate, mass transfer efficiency), or short-term reproductive outcomes in terms of pup growth rates (pup daily mass gain rate). Therefore, across the sampled population, pro-and reactive mothers did not differ significantly in their initial state on arrival at the colony, their in-year expenditure, or their reproductive outcome. It is notable that we found no effect of coping style on the deviance from population mean values of maternal post-partum mass. Therefore, reactive mothers are not necessarily returning to the breeding colony in more variable states, suggesting that both pro-and reactive mothers are faring equally well overall in terms of net mass gain during the at-sea foraging phases of their lifecycle. It seems therefore that the mass change consequences of stress-coping style reveal themselves more on the breeding colony. Individual ID accounted for a large proportion of the variation in both absolute measures of maternal post-partum mass (86%) and deviance from mean post-partum mass (63%). This is indicative of individual differences in overall body size (as opposed to mass) and likely foraging success prior to the breeding season that are independent of coping style; a larger mother will always be larger. Whether pro-and reactive individuals adopt different, but equally successful, foraging strategies (e.g. specialist vs. generalist) while at sea is an area that requires further research.
By contrast, coping style appears to strongly influence the extent of inter-individual variability in our reproductive performance measures. Reactive mothers deviated more from the annual population mean in terms of their daily mass loss and in the daily mass gain of their pups. Proactive mothers exhibited a more consistent mean expenditure and outcome. This coping style effect was independent of pupping date and maternal post-partum masses which are known to influence maternal expenditure and consequent pup growth outcomes in grey seals 41 . These results demonstrate a reproductive pattern that can maintain inter-individual variation in stress-coping styles within a natural population. Overall, there was no net difference in fitness proxies across the spectrum of coping styles in terms of individual expenditure or outcomes. Therefore, there is likely no net selective advantage to being either proactive or reactive in terms of within season reproductive performance, at least under the environmental conditions that seals experienced during this study (indicated by the lack of evidence for an interaction between coping style and year in our models). There are very few empirical studies that directly show a link between coping style and fitness outcomes in natural environments, and the results of these efforts are mixed 12,40 . Kontiainen et al. 40 found evidence that in conditions of temporally variable food availability proactive Ural owl mothers were more successful overall than reactive mothers. Monestier et al. 's 12 study of coping-style in roe deer, showed that pro-and reactive mothers fare better in different habitats, providing a spatially dependent advantage to each coping style. Studies of personality (as opposed to coping style) in non-human animals suggest similar differential trade-offs for individuals across a personality spectrum. For example, the differential success of fast and slow exploring great tits (Parus major) across breeding seasons depending upon habitat resource quality 10 . A similar pattern of changing costs and benefits of boldness dependent upon spatio-temporal changes in resources was also found in Eurasian red squirrels (Sciurus vulgaris 42 ). Such processes represent potential solutions to the conundrum of the maintenance of inter-individual variation in natural populations through differential costs and benefits dependent upon either spatial or temporal variation in environment 4 . In contrast, our results from studying female grey seals in their natural habitat suggested proactive mothers were not more successful, but instead were more uniform in their reproductive performance than reactive mothers, maintaining expenditure and outcomes closer to the mean values for the cohort.
Why should reactive mothers show greater variability in their reproductive expenditure (and consequently pup growth rates) than proactive mothers? Reactive individuals tend to be more responsive to environmental stimuli and consequently exhibit greater behavioural flexibility. Proactive individuals are less responsive to environmental stimuli, express little behavioural flexibility and form routines more readily 29,30,34,43 . Therefore, it is likely that reactive grey seal mothers tend to invest more or less than average in their current reproductive episode dependent upon the conditions prevailing at the time. A range of local, fine-scale spatial and temporal environmental factors have already been shown to strongly influence the behaviour of grey seal mothers on breeding colonies [44][45][46][47][48][49][50] . Grey seals are typically highly site-faithful to breeding colonies in the UK and tend to occupy similar parts of a colony year after year 51 . Furthermore, once mothers have pupped, they tend to be 'tied' to a small area of the colony due to the limited mobility of the pup 51 . However, conditions at each locality vary within and between breeding seasons [46][47][48] , and a grey seal is clearly unlikely to be able to predict future conditions prior to pupping. Therefore, we might expect reactive individuals to modify their behaviour and/or physiology to attempt to match the prevailing environmental conditions (phenotype-environment matching 4,43,52 ), whereas proactive mothers will adopt an average 'one size fits all' approach. The reactive strategy is essentially a high reward but high-risk strategy. If a mother's phenotype successfully matches to the prevailing conditions, then she is likely to be able to focus her investment in her current pup. If, however, she fails to appropriately match her phenotype to the conditions then she is likely to have to expend energy on other potentially costly activities (e.g. aggression, locomotion), reducing expenditure on her pup. There are various reasons why phenotypically plastic individuals may fail to successfully match their phenotype to the environment, including energetic and physical costs of, and limits to, plasticity but also where the environment changes too rapidly or unpredictably for an individual to make the appropriate changes in time 53 . Individuals with a reactive coping style may experience higher rates of mass loss due to the action of catabolic hormones (corticosteroids) which are in integral part of the suite of correlated traits defining coping styles 29,30,34,54 . Future studies could seek to measure not only coping style (using resting HRV), but also individual reactivity 30 using stress hormones such as cortisol. Pro-and reactive individuals will likely respond differently to handling and sampling, which may provide a useful indicator of stress-reactivity if appropriately standardised.
Our results and their implications are particularly pertinent in the context of current rapid environmental change. Climate driven local weather patterns are becoming more variable and unpredictable 55 . Previous work (2020) 10:9550 | https://doi.org/10.1038/s41598-020-66597-3 www.nature.com/scientificreports www.nature.com/scientificreports/ has shown that grey seal behaviour and success on the breeding colony are weather dependent, particularly with respect to temperature and rainfall and their influence on thermoregulation and consequently behaviour [46][47][48]50 . However, grey seals are not alone in being subjected to ever more variable and unpredictable conditions during key phases of their lifecycle that are tied to specific seasons (e.g. 56 ). Such changing environmental patterns will inevitably impact differentially upon proactive and reactive individuals within populations 27,57,58 . Therefore, assessing the extent of variation in coping styles within wild populations and their responses to changing environmental conditions is a vital step in understanding species resilience to rapid environmental change. Species able to cope with anthropogenic disturbance are likely to be those that contain some portion of behaviourally flexible individuals, rather than being species that are tolerant of human activities per se 59 . Although it remains unclear how directly linked the physiological underpinnings of coping styles and behavioural aspects of personality are 38,60 , coping styles are linked to the degree of behavioural flexibility that individuals may be able to expr ess 29,30,34,38,39,43,61 . Given that the basic structure of the mammalian autonomic nervous system and the interplay of sympathetic and parasympathetic branches are highly conserved, it is probable that the behavioural and physiological distinctions between pro-and reactive types represent a fundamental biological pattern that can be observed in many mammalian species and indeed vertebrates more generally 29,30,34,43 . Consequently, findings from this study will have general applicability and broad relevance across vertebrate species.

Methods
Study site and years. The study was conducted during the annual grey seal breeding season at the Isle of  41 . The Isle of May colony was part of a long-term study of grey seal reproductive energetics and behaviour by the Sea Mammal Research Unit, SMRU 41,63 . Known individuals can be identified using pre-existing brands, flipper tags and/or pelage patterns 41,64 . A subset of known breeding females are routinely handled each year by SMRU to obtain morphometric data, with individual females being captured and measured twice during their stay on the colony; once early in lactation (within 5 days of parturition) and once late in lactation (approx. day 14-16 after parturition). We used these handling events to deploy heart rate monitors on known females during the first capture, and to recover them at the second capture. Our focus was on re-deploying devices on the same individuals over multiple-years where possible to gain data on inter-annual consistency of resting HRV. Where individuals did not return or were not available to capture in a particular year, we then completed the annual sample size by including other mothers from the group of known individuals within the long-term study of grey seals on the Isle of May. Details of the standard capture procedure are published elsewhere 41,65,66 . Derivation of resting HRV estimates. We instrumented focal seals with externally mounted heart rate monitors that provide millisecond precision measurements of IBIs through accurate detection of RR peaks 32,67 and transmit these data to remote receivers. In the 2013 and 2014 seasons we used modified Polar H2/H3 monitors (Polar Electro Oy, Kempele, Finland) which transmitted IBI data to Polar RS800CX heart rate receivers. Polar devices have been validated for the recording of HRV in a range of domesticated mammalian species [31][32][33][67][68][69][70][71] . In 2013 the Polar monitors were attached to Polar soft strap electrodes extending 14 cm either side of the midline of the monitor. These electrode straps were mounted dorsally, just behind the right shoulder blade, extending laterally to the left and right. The entire unit was covered with a neoprene patch to provide protection and retain electrode gel ( Fig. 2a; see 72 for full design details). In 2014 this design was improved by replacement of the short Polar soft strap electrodes with two 50 cm protected cables leading to silver chloride electrodes located immediately posterior of the left and right fore-flippers providing more optimal electrode placement (Fig. 2b). The Polar system requires each monitor to be paired to a single receiver located within approximately 20 m 72 , which can constrain data collection especially in wild, free ranging animals. Therefore, in 2015-2017, we switched to using Firstbeat heart rate monitors (https://international-shop.firstbeat.com/product/team-pack/) as these provided a much increased signal transmission range (up to 200 m line of sight), and the capability to record multiple monitors (up to 80) at a single receiving station. These monitors were again modified with the extended electrodes (Fig. 2b). In all seasons, IBI data were collected during daylight hours only.
Artefacts in IBI data can lead to significant biases in estimates of HRV 32,67,73 . Sources of artefacts in IBI data can be intrinsic (e.g. arrhythmias, noise from muscle action potentials), or extrinsic (e.g. equipment malfunction, electromagnetic potentials), causing beats to be either missed or spuriously generated, leading to erroneously long or short IBI values 32,67,72 . We examined our raw IBI data for potential artefacts using Firstbeat Sports software (v.4.5.0.2 74 ) and RHRV 75 which detect extreme values and make corrections by deletion of spurious extra beats (extreme short IBIs) or interpolation for missing beats (extreme long IBIs). Artefacts that involve invariable sequences of IBIs (flats) or sequences of monotonically increasing or decreasing IBIs (stairs) cannot be corrected 33 , therefore these were identified using bespoke R scripts written by the authors (NB, AMB, SDT) to permit subsequent filtering of IBI traces with excessive flats and stairs 72 . IBI traces were then segmented into non-overlapping 300-second periods 32 and HRV was computed as the root mean square of successive differences (RMSSD) between IBIs using RHRV. Although there are several measures of sympathovagal balance that can be computed from IBI data 32 , RMSSD is a preferred metric for use under free-running conditions because it is affected less by respiratory cycles 76,77 and provides a measure that is relatively easy to interpret 32,77 . Any 300-second traces that had >5% flats or stairs were removed from subsequent analyses 32,33,72 .
Each IBI in the remaining traces was matched with a behavioural state derived from in-field video footage recorded concurrently with the IBI data collection. Video footage was decoded by experienced observers (n = 4) post-field season using a focal sampling protocol 78 and a bespoke Visual Basic for Applications Macro in Microsoft Excel to record behaviours based on a pre-established ethogram 37,46,72,79 . In order to define resting HRV, the behavioural state of interest for this study was Resting (defined as the female remaining largely motionless, lying prone or supine with head in contact with the ground, eyes open or closed). All observers were trained to the same ethogram by SDT, and inter-observer reliability was tested on two hours of example video footage, showing high levels of agreement (ICC2 = 0.96, F 16,48 = 109, p < 0.0001, 95% C.I. = 0.93-0.99; comparison across four independent observers based on 17 behavioural categories, of which Resting is one). Resting behaviour is relatively easy to identify reliably and tends to comprise the bulk of a breeding grey seal's activity budget (typically over 60% 37,45,46,[79][80][81]. Resting HRV was computed only from 300-second periods where the seal was at rest for ≥95% of the IBIs (and where the remaining time did not involve high energy behaviours such as locomotion or aggression). As our measure of resting HRV we computed the median RMSSD from all 300-second traces for each seal in each breeding season (number of 300 second traces per female per year ranged from three to 333, median = 32).
Mass and mass change proxies of maternal expenditure and short-term fitness outcomes. Females and their pups were weighed at each capture as described in Pomeroy et al. 41 . Mass data were used to calculate three measures of maternal reproductive performance 41 ; maternal post-partum mass (kg), maternal daily mass loss rate (kg/day), and pup daily mass gain rate (kg/day). Maternal post-partum mass was estimated using maternal daily mass loss rate to extrapolate a mother's mass on the date of first capture to her parturition date as determined through in-field observations of the known females. Maternal post-partum mass provides a measure of maternal size and condition at a standardised time point (immediately after parturition) and is an index of realised somatic growth and foraging success prior to the breeding season 41 . As pup daily mass gain rate is influenced by pup physiology and behaviour as well as the maternal expenditure 41,65,[80][81][82][83][84] we also computed the ratio of daily rates of maternal mass loss: pup mass gain as a measure of maternal mass transfer efficiency.
We used our real values of maternal post-partum mass, maternal daily mass loss rate, pup daily mass gain rate, and mass transfer efficiency to examine whether proactive and reactive mothers differed in any of these proxies of maternal reproductive performance. However, as previous studies argue that reactive individuals are more likely to exhibit flexibility, either in terms of behaviour or physiology 29,30,34 , we also examined whether reactive individuals tended to deviate more from annual means of these mass metrics compared to more proactive individuals. Therefore, for every individual, we computed the modulus (the absolute value) of the deviance from the sample mean within each year for each of these metrics. We chose to use deviance from the annual mean (as opposed to a grand mean across all five years) to account for any interannual differences in seal behaviour and energetics which may have been driven by prevailing weather conditions at the breeding colony and consequent access to water across the population 37,46-50 .
Statistical analysis. All analyses were conducted in R version 3.5.0 85 . For females with measures of resting HRV for more than one season (Table 3) we determined population level repeatability (R 86,87 ) using the 'rptR' package 88 with individual as a random effect and the number of measures per individuals as a fixed effect, with parametric bootstrapped 95% confidence intervals computed from 1,000 simulations. We also derived repeatability estimates for every individual (Ri) by dividing the between-individual variance (σ2α) by the sum of between-individual variance and the residual variance for each individual 20 .
We examined the effect of resting HRV on mass and mass change proxies of fitness by fitting generalized linear mixed-effects models (GLMM) within the R package lme4 89 . Female ID was included as a random effect to account for pseudoreplication of individuals across years 90,91 . We constructed separate models for each of the response variables; maternal post-partum mass, maternal daily mass loss rate, pup daily mass gain rate, and mass transfer efficiency, and for the deviance versions of these variables. In all models, response variables were www.nature.com/scientificreports www.nature.com/scientificreports/ standardised by z-transformation. The deviance measures were also log-transformed to meet heteroscedasticity assumptions and address overdispersion 92 . All response variables were checked for normality using Q-Q plots.
Our independent variables included our estimate of resting HRV for every individual. As individuals exhibited highly repeatable resting HRV across years, we used the median RMSSD across all years present for every individual as our estimate of resting HRV in these models. This was to avoid confounding the random effect of ID, included to account for pseudoreplication, with the highly individual measures of resting HRV. Global models for each response variable also included the date on which the female gave birth (birthdate, as number of days from 1 st January) as a covariate, with the sex of the pup, year and device type (Polar or Firstbeat) as factors. For all models (apart from those with maternal post-partum mass as the response variable), we included maternal post-partum mass as a covariate to account for the known positive relationship between maternal mass and daily rates of maternal mass loss and pup-growth 41 . All continuous covariates (resting HRV, birthdate and maternal post-partum mass) were standardised by z-transformation. Resting HRV and year were included in models as an interaction term to allow for an examination of whether different coping style fared better under different annual conditions [46][47][48]50 . For each mass response variable, the modelling procedure began by fitting the full (global) model using the Gaussian family and identity link. For model inference, we examined all plausible alternate models with reduced combinations of explanatory variables using the R function 'dredge' from the Package 'MuMIn' 93 . The 'best' model was defined as the model with the lowest corrected Akaike's information criterion (AICc). However, we also retained and examined all models within a confidence set, defined according to criteria established by Richards 94 . All models within a ∆AICc ≤ 6 of the 'best' model were retained within a preliminary confidence set. This initial confidence set was then subsetted, retaining only models with a ∆AICc value lower than more complex models within which they were nested. This approach avoids retaining overly complex models but also acknowledges that the model with the lowest AICc score is not necessarily the most parsimonious model 94,95 . For each response variable we also provide the output from the null model for comparison (models with no fixed effects and only the random effect).
We included no other interaction terms in the models reported here. However, where resting HRV and birthdate both featured in a confidence set, we did test alternative models with an interaction between resting HRV and birthdate, on the basis that seals with different coping styles might occupy the island at different stages of the breeding season. All these models performed worse (based on AICc) than the corresponding models lacking the interaction term, and therefore this interaction was not considered further. In all models the total number of observations was 95, and the total number of individuals was 57.
For each response variable we examined the relative contribution of ID as a random effect within the best model using r.squaredGLMM from the MuMIn package 93,96 to provide conditional and marginal coefficients of determination. We also used r2beta 96 to compute R 2 (with 95% confidence intervals) for fixed effects within the best models.

Data availability
Data are available in the electronic supplementary material.  Table 3. Summary of number of seals for which resting HRV estimates were obtained and the number of individuals with repeat measures across years.