Stress responses comprise suites of physiological and behavioural traits that enable individuals to cope with adverse conditions (Romero, 2004; Overli et al., 2007; McEwen and Wingfield, 2010; Taborsky et al., 2021). Some individuals are likely better at coping with adverse conditions than others, and understanding the role played by underlying genetic variation could have important implications for managing stress-related disease in captive populations and predicting the evolutionary responses of free-living populations to both natural and anthropogenic stressors (Barton and Iwama, 1991; Koolhaas et al., 1999; McEwen and Wingfield, 2003; Romero, 2004; Koolhaas, 2008). For instance, if among-individual differences in stress response traits are a product of genetic variation (Koolhaas et al., 1999; Koolhaas et al., 2007) then they may be a viable target for artificial selection strategies (Mignon-Grasteau et al., 2005). This could be used to reduce stress-related welfare issues in captive populations (e.g. in livestock; Broom and Johnson, 1993; von Borell, 1995; Möstl and Palme, 2002; Kasper et al., 2020). In free-living populations, variation in stress response traits is expected to cause fitness variation under stressful conditions (Wingfield, 2003; Koolhaas, 2008). Thus, exposure to stressors could lead to evolutionary changes in the distributions of traits that contribute to a population’s long-term resilience in the face of natural (e.g. predation risk; Clinchy et al., 2013) and/or anthropogenic (Tarlow and Blumstein, 2007; Busch and Hayward, 2009; Angelier and Wingfield, 2013; Sadoul et al., 2021) challenges. However, as the evolutionary response of any trait to selection is determined in large part by its genetic variation, our ability to predict – and potentially harness – evolutionary changes in the stress response is currently hampered by limited understanding of the underlying genetics.

Natural selection does not act on single traits in isolation, but rather on multivariate phenotypes (Lande and Arnold, 1983; Blows, 2007). This is likely to be an important consideration for understanding the evolution of the stress response. For instance, while glucocorticoid (GC) levels are frequently used to measure the stress response (McEwen and Wingfield, 2003; Korte et al., 2005), an individual’s first line of defence against acute environmental challenges will typically be behavioural (Moberg, 2000). This may include risk avoidance strategies as well as the widely known ‘fight-or-flight’ responses. Subsequent GC release then serves to mediate physiological (and further behavioural) responses (Wingfield et al., 1998; Wingfield and Kitaysky, 2002). Natural selection is therefore expected to favour combinations of behavioural and physiological stress response traits that act synergistically to maintain fitness under stressful conditions (Koolhaas et al., 1999; Overli et al., 2007). Evolutionary theory predicts that correlational selection will shape the structure of multivariate quantitative genetic variance (as represented by the genetic covariance matrix G; Cheverud, 1982; Blows, 2007; Roff and Fairbairn, 2012), contingent on the distribution of new pleiotropic mutations that generate multivariate genetic variance as well as the selection that depletes it (Blows and Walsh, 2009; Walsh and Blows, 2009). In general, correlational selection should (under certain assumptions) have direct and strong effects on genetic covariances (Lande, 1980; Jones et al., 2003; Arnold et al., 2008). In the context of the stress response, we should therefore expect genetic – as well as phenotypic – integration of behavioural and physiological traits (McGlothlin and Ketterson, 2008; Ketterson et al., 2009; Cox et al., 2016). By genetic integration, we mean genetic correlation structure among suites of traits. Genetic integration is hypothesised to underpin adaptive variation in multivariate phenotypes in many fields of evolutionary biology (e.g. life history [Stearns, 1989; Roff, 1992], behavioural ecology [Sih and Bell, 2008]), but has not been tested for explicitly among stress response components using quantitative genetic approaches.

The most compelling evidence for genetic integration of behavioural and physiological stress response traits to date comes from artificial selection experiments on domestic animal populations (e.g. rainbow trout Oncorhynchus mykiss (Pottinger and Carrick, 1999), Japanese quail Coturnix coturnix japonicus (Jones et al., 1994), house mice Mus musculus domesticus Veenema et al., 2003). For example, lines of rainbow trout selected for stress-induced plasma cortisol levels (Pottinger and Carrick, 1999) experienced correlated evolutionary changes in behaviour (Overli et al., 2005). In a rare study of a wild-type population (albeit under captive conditions), cortisol levels were found to evolve in response to selection on behavioural ‘personality’ in great tits (Parus major; Carere et al., 2003). In these examples, the correlated responses of behavioural and physiological stress response traits to selection are consistent with some degree of genetic integration of these behavioural and physiological traits. However, the extent and ‘structure’ of this integration remains unclear, and some results were inconsistent with a hypothesised simple axis of genetic (co)variation among behavioural and physiological components of the stress response. For example, in the trout study, the ‘low-cortisol response’ selected lines actually showed a higher metabolic stress response under confinement (suggestive of opposing responses to selection by different physiological components of the stress response; Trenzado et al., 2003). In the same selection lines, there was also no link between ‘boldness’ (an aspect of personality related to risk-taking behaviour) and cortisol response under standardised testing (Thomson et al., 2011).

While selection experiments illustrate that genetic integration of behaviour and physiology can occur, estimation of the genetic variance-covariance matrix (G) through quantitative genetic modelling provides a complementary strategy that also allows investigation of exactly how multivariate genetic variation is structured within populations. In the context of the stress response, this should provide insights into both how selection has acted in the past (Ketterson et al., 2009), and whether responses to future selection are likely to be constrained (Blows and Walsh, 2009; Walsh and Blows, 2009). Insight into past selection follows from the fact that strong correlational selection should lead to integration of traits in G over the long term, a phenomenon explored most commonly for suites of morphological traits (following, e.g. Cheverud, 1982), but that is equally applicable to any aspect of the phenotype (see, e.g. Hine et al., 2004; Hunt et al., 2007; Oswald et al., 2013 for examples pertaining to behavioural evolution and mate choice). Insight into future responses to selection follows from the fact that the direction (in multivariate trait space) and magnitude of a response to selection is limited by the amount of variance in G that is in alignment with the vector of (directional) selection β (Blows and Walsh, 2009; Walsh and Blows, 2009). Integration in G is manifest as genetic correlations between trait pairs, and also at the level of the whole matrix by an overdispersion of eigenvalues—indicating that there are fewer effective dimensions of genetic variation than there are traits measured (Blows, 2007).

Here, we estimate G for behavioural and physiological components of the acute stress response in Trinidadian guppies (Poecilia reticulata). This enables us to determine not only (i) whether these components are genetically integrated on the major axis of genetic (co)variation (i.e. the first eigen vector of G, g_max), but also (ii) whether the structure and orientation of this axis suggests variation in overall stress responsiveness and/or ‘coping style’ (explained further below; Koolhaas et al., 2010; Boulton et al., 2015). We use fish from a captive colony of guppies derived from wild ancestors sampled from the Aripo River, Trinidad in 2008 and subsequently maintained at high population size (with no deliberate inbreeding or selection). We have validated the use of standardised ‘open field trials’ (OFTs) for testing (acute) behavioural stress responses in this species (Houslay et al., 2018), and demonstrated significant additive genetic (co)variance underpinning variation in risk-taking, exploratory, and ‘flight’ type components of the behavioural stress response using this testing paradigm (White et al., 2018; White and Wilson, 2019). We have also demonstrated, using a non-invasive waterborne hormone sampling method, that individuals differ significantly in their GC (specifically, free circulating cortisol) response to an acute stressor (handling, coupled with short-term isolation and confinement; Houslay et al., 2019) and that, on average, this physiological response declines with repeated stressor exposure (consistent with habituation). Nothing is known about the genetic basis of variation in these physiological traits, or about their integration (phenotypically or genetically) with behavioural components of the stress response.

First, we combine OFT results with complementary ‘emergence trials’ (ET) and ‘shoaling trials’ (ST) to characterise among-individual and genetic variation in the behavioural stress response. Second, we characterise the physiological stress response and its rate of habituation by assaying GC levels following first and third exposure to a handling and confinement stressor (see Materials and methods). Utilising repeated behavioural and physiological testing of individual fish within a known pedigree structure, we are able to estimate the repeatable (among-individual) component of phenotypic (co)variance in these stress response traits, and then determine the additive genetic contribution to this (G; the genetic variance-covariance matrix for this suite of stress response traits). We predicted that individual traits will be heritable and that G will contain strong genetic correlation structure between behavioural and physiological components of the stress response consistent with genetic integration. We also predicted that both behavioural and endocrine components of the stress response would load on the major axis of genetic variation in multivariate trait space, g_max. The ‘stress coping style’ model (Koolhaas et al., 1999) predicts variation in the type of response to stress. Simplistically, as originally proposed this verbal model posits that individuals (or genotypes) perceive equal degrees of stress but differ in how this manifests phenotypically: genotypes at one end of the axis having ‘reactive’ behavioural phenotype (e.g. freezing behaviour) coupled to lower GC levels, while the ‘proactive’ end is characterised by more active ‘fight or flight’ behaviour coupled to higher GC levels. However, previous analyses of this population suggest variation may be more in terms of ‘stress responsiveness’ than stress coping style (Houslay et al., 2018; Prentice et al., 2020; White et al., 2020). That is to say, some individuals (or genotypes) perceive the trial as a more severe stimulus and exhibit more characteristic stress behaviours (e.g. flight and/or freezing, thigmotaxis) while others show more typical ‘unstressed’ behavioural profiles (e.g. exploration of the arena). In this scenario, we predicted high GC levels to co-occur with characteristic stress behaviours, and low GC levels with ‘unstressed’ behavioural profiles.

In total, we obtained multivariate behavioural data from 5966 trials (3379 OFTs, 1548 ETs and 1039 STs) on 1384 individual fish. The number of individuals phenotyped (OFTs = 1365, ETs = 806, STs = 532) and the mean number of observations per fish (OFTs = 2.5, ETs = 1.9, STs = 2.0) varied across the behavioural data types. All fish were contained within a genetic pedigree structure comprising maternal full-sibships nested within paternal half-sibships. This structure was produced via multiple rounds of breeding work and has a maximum depth of five ‘generations’. Some of the OFT data have already been used in studies of the evolutionary genetics of personality (White et al., 2018; White and Wilson, 2019), but here we extend that dataset and use it in conjunction with other behavioural and physiological measures for different purposes. We also obtained 1,238 waterborne assays of cortisol levels for 629 fish (almost all from the final generation). The handling and confinement stressor applied for this assay was performed three times (at 48 hr intervals) for all fish tested, but the holding water sample was only processed for GC content at two time points (the first and last confinement, subsequently Cortisol₁ and Cortisol₃). Full details of husbandry, phenotyping and analysis are provided in Materials and methods.

Genetic variance in behavioural components of the stress response

Behavioural data were extracted from OFTs, ETs, and STs using video tracking of fish (as described in White et al., 2016; Houslay et al., 2018). Time to emerge from the shelter (‘emergence time’) was extracted from ETs and natural log (ln) transformed for analysis, while shoaling tendency was calculated from STs as the time spent in the third of the tank closest to a same-sex shoal (which was visible but physically separated) minus the time spent in the third of the tank farthest from the shoal. The OFT, ET, and ST testing paradigms are all considered to assay behavioural components of the stress response in the broad sense, as each test starts with the capture and transfer of the focal fish into a novel and brightly lit arena away from their home tank and familiar tank mates.

Four traits were defined from the OFT and measured by videotracking for 4 m 30 s after an initial 30 second acclimation period upon transfer into the arena: track length (distance swum), area covered (as a proportion of the arena floor area), the number of freezings (the number of times a fish’s speed dropped below the minimum velocity threshold of 4 cm/s for at least 2.5 s, Houslay et al., 2018; this trait was square root transformed for analysis) and time in the middle (i.e. in the central area of the open field arena away from the tank walls, which is assumed to be perceived as riskier, e.g. Houslay et al., 2018). Note low values of time in the middle imply thigmotaxis (i.e. tendency to avoid exposure to potential threats by hugging walls), and were very highly correlated with a measure of average distance from the wall at the observation level (r = 0.94, t_1,3368 = 159.2, p < 0.0001; see Appendix 1 for further discussion of the selection of OFT behavioural traits). All these OFT traits are repeatable and heritable in this population (White et al., 2016; White et al., 2018; Houslay et al., 2018White and Wilson, 2019).

The absence of a strong positive correlation between track length and area covered (Figure 1A) is notable and potentially biologically informative; if fish moved randomly with respect to direction in the arena then area covered would increase monotonically (to an asymptote at 100%) with track length. A possible explanation is that a long track length arises sometimes from a (putatively) less stressed fish exploring the arena (fish one in Figure 1B) and sometimes from a (putatively) more stressed fish exhibiting a typical ‘flight’ response (fish four in Figure 1B). These two types of response can be discriminated based on whether, in a given trial, higher track length is associated with higher area covered and time in the middle (exploration) or the converse (flight response).

Figure 1

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To quantitatively discriminate between exploratory behaviour and flight responses we derived a new trait, ‘relative area covered’. We used a simple simulation procedure (see Materials and methods) to predict expected area covered for any given track length under a null ‘random swim’ within the arena (Figure 1C). Relative area covered is then calculated as observed area covered – expected area covered given the track length (Figure 1D) and will be high for fish engaging in exploration, and low for an obvious ‘flight’ response manifest as rapid swimming around the tank walls. Our subsequent analyses then used this derived relative area covered trait in place of area covered.

Pedigree-based ‘animal models’ (Wilson et al., 2010) were used to test for and estimate additive genetic variation in each of the six behavioural traits (emergence time, shoaling tendency, and the four OFT traits) while controlling statistically for social housing group and non-genetic sources of among-individual variance (as well as several fixed effects; see Materials and methods for full details). These confirmed the presence of significant additive genetic variation for the relative area covered trait, as well as for track length, time in the middle (as expected from previous findings; White et al., 2018; White and Wilson, 2019), √freezings and ln emergence time (Table 1). With the exception of shoaling tendency, heritabilities (conditional on fixed effects; see Materials and methods) are low to moderate (range of 9–20%; Table 1) and within the expected range for behaviours (Stirling et al., 2002). We detected no additive genetic variance for shoaling tendency (Table 1), despite there being repeatable differences among individuals (R = 0.19 ± 0.04; χ²_0,1 = 20.01, p < 0.001).

Table 1

Trait	Va	Vpe	Vgroup	Vresidual	h2	χ20,1	p
Relative area covered	42.87 ± 12.89	56.16 ± 10.92	19.17 ± 4.92	203.17 ± 6.26	0.13 ± 0.04	31.2	< 0.001
Time in the middle	554.78 ± 144.18	473.24 ± 114.77	196.94 ± 53.54	2002.06 ± 62.38	0.17 ± 0.04	47.2	< 0.001
Track length	23584.54 ± 5534.90	26587.74 ± 4556.87	3173.34 ± 1396.84	76140.49 ± 2376.32	0.18 ± 0.04	88.7	< 0.001
√Freezings	0.34 ± 0.08	0.18 ± 0.06	0.08 ± 0.02	1.13 ± 0.04	0.20 ± 0.05	48.2	< 0.001
ln Emergence time	0.12 ± 0.05	0.06 ± 0.06	0.05 ± 0.02	1.07 ± 0.05	0.09 ± 0.02	22.5	< 0.001
Shoaling tendency	0 ± 0	2457.36 ± 570.96	708.87 ± 316.30	9900.95 ± 622.10	0 ± 0	0	0.5
ln Cortisol	0.08 ± 0.02	0.02 ± 0.02	0.01 ± 0.01	0.15 ± 0.01	0.31 ± 0.09	24.4	< 0.001

Genetic variance in physiological components of the stress response

Using a series of nested bivariate animal models, we tested for additive genetic variance in cortisol levels (ln-transformed) following stressor exposure (handling and confinement) and for genotype-by-environment interaction (GxE). In this context, the environment (E) is the trial number in each fish’s stress trial series (i.e., cortisol level after the first or third trial). Any GxE present can therefore be interpreted as genetic variance for habituation to the stressor, given that the average cortisol level was lower following exposure to the third stressor than the first (ln transformed ng/hr, mean ± SE; Cortisol₁ = 8.47 ± 0.05, Cortisol₃ = 8.02 ± 0.05, Wald F_1,38.8 = 110.0, p < 0.001; see Materials and methods for explanation of units). We first modelled Cortisol₁ and Cortisol₃ as distinct response variables in a bivariate framework assuming no GxE (such that we constrain V_A-Cortisol1 = V_A-Cortisol3 and the cross context additive genetic correlation r_{A-Cortisol1,Cortisol3} = 1). This model revealed a significant additive genetic component to variation among individuals in their cortisol levels following stressor exposure (χ²_0,1 = 18.2, p < 0.001).

Expanding the model to allow GxE by estimating separate genetic variances for Cortisol₁ and Cortisol₃ provides a significantly better fit to the data (χ²_0,1 = 3.8, p = 0.03), meaning GxE is present. This can be viewed as genetic variance for habituation to repeated stressor exposure, or as a change in genetic variance for cortisol from the first to the third sampling (Figure 2). These are two perspectives of the same phenomenon; a reduction in additive genetic variance between the first stressor (V_A-Cortisol1 = 0.081 ± 0.029) and the third (V_A-Cortisol3 = 0.041 ± 0.025) arises because genotypes with higher-than-average levels for Cortisol₁ habituate more rapidly (i.e. have more negative reaction norm slopes). Note however that allowing the cross-context genetic correlation to deviate from +1 does not significantly improve the model fit (χ²₁ = 0.0, p = 1): thus the rank order of the genotypes does not appreciably change across the two contexts (i.e. genetic reaction norms show little crossing; Figure 2), so there is a strong positive cross-context genetic correlation (r_{A-Cortisol1,Cortisol3}± SE = 0.83 ± 0.21).

Figure 2

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In this model we also find that variance in cortisol explained by housing group effects is similar across contexts (V_{Group-Cortisol1} = 0.038 ± 0.014, V_{Group-Cortisol3} = 0.032 ± 0.012), but that residual (unexplained) variance is greater after the third stressor exposure (V_R-Cortisol1 = 0.164 ± 0.022, V_R-Cortisol3 = 0.237 ± 0.023). In combination, the changes in both additive genetic and residual variance between the two contexts lead to appreciably higher heritability for cortisol levels following the first stressor exposure relative to the third (h²_Cortisol1 = 0.285 ± 0.094, h²_Cortisol3 = 0.131 ± 0.078).

Testing for genetic integration and identifying the major axis of genetic (co)variance

There is strong evidence for phenotypic integration of Cortisol with behaviour at the among-individual levels (see Supplementary file 5). To test for and characterise the hypothesised genetic integration between behavioural and physiological components of the stress response, we built a multivariate animal model to estimate G. We excluded shoaling tendency given the absence of detectable genetic variance in the univariate model. We also elected to treat cortisol as a single trait (allowing for a fixed effect of stressor exposure number [1 vs 3] on the mean). Although the above analysis demonstrates GxE for cortisol, the strong positive cross-context genetic correlation justifies collapsing Cortisol₁ and Cortisol₃ into a single trait to maximise statistical power to detect any genetic covariance with behaviour.

Our final model contained six response traits: relative area covered, time in the middle, track length, (square root-transformed) freezings, (ln-transformed) emergence time, and (ln-transformed) Cortisol (now treated as two repeats of a single trait). We standardised all traits to standard deviation units (after transformation where applicable) to assist multivariate model fitting and to prevent eigenvectors of G (see below) being dominated by traits with higher variance in observed units. To simplify interpretation of G, we also multiplied emergence time by –1 after transformation. Thus, high values denote rapid emergence from the shelter.

The resultant estimate of G (Table 2) contains significant additive genetic covariance structure overall (Likelihood Ratio Test of the full model vs. a reduced model G that contains variances but not covariances: χ²₁₅ = 91.06, p < 0.001). We found a large number of strong genetic covariance/correlation estimates between trait pairs that were deemed statistically significant (based on the bootstrapped 95% confidence intervals not crossing zero). The 4 OFT traits (relative area covered, time in the middle, track length, and √freezings) show strong patterns of pairwise genetic correlation. Track length shows significant negative genetic correlation with relative area covered, time in the middle, and √freezings (such that genotypes predisposed to higher values of track length are also predisposed to lower values of relative area covered, time in the middle, and √freezings, and vice versa). Relative area covered and time in the middle show significant positive genetic correlation with one another, and √freezings shows significant positive correlation with time in the middle. The only genetic correlation among OFT traits that is not significant is between √freezings and relative area covered. The speed at which individuals emerge from the shelter during the emergence trials, -ln emergence time, shows significant positive genetic correlation with track length (i.e., genotypes predisposed to higher values of track length in the OFT are also predisposed to faster emergence in the ET). There are no significant pairwise genetic correlations between –ln emergence time and the other OFT traits, but all correlation estimates are negative. Pairwise genetic correlations between ln cortisol and all behavioural traits are illustrated in Figure 3. Ln-transformed cortisol shows significant negative genetic correlations with both time in the middle and √freezings. Genetic correlations between ln cortisol and other OFT traits were not significantly different from zero (based on 95% confidence intervals), with correlations estimated as negative with relative area covered and positive with track length. The genetic correlation between –ln emergence time and ln cortisol is also not significant, and is estimated at very close to zero.

Figure 3

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Table 2

	Relative area covered	Time in the middle	Track length	√Freezings	-ln Emergence time	Ln Cortisol
Relative area covered	0.115 (0.050,0.182)	0.795 (0.601,0.952)	–0.549 (-0.789,–0.239)	0.139 (-0.260,0.511)	–0.438 (-1.305,0.239)	–0.376 (-0.959,0.272)
Time in the middle	0.103 (0.044,0.165)	0.145 (0.070,0.215)	–0.658 (-0.86,–0.414)	0.363 (0.024,0.659)	–0.153 (-0.815,0.538)	–0.617 (-1.139,–0.155)
Track length	–0.071 (-0.127,–0.017)	–0.096 (-0.154,–0.035)	0.147 (0.080,0.219)	–0.801 (-0.931,–0.647)	0.61 (0.064,1.328)	0.425 (-0.027,0.968)
√Freezings	0.020 (-0.034,0.076)	0.059 (-0.001,0.120)	–0.132 (-0.200,–0.060)	0.185 (0.091,0.269)	–0.483 (-1.103,0.146)	–0.556 (-1.069,–0.067)
-ln Emergence time	–0.041 (-0.103,0.012)	–0.016 (-0.073,0.041)	0.065 (0.008,0.121)	–0.057 (-0.126,0.009)	0.076 (0.009,0.148)	–0.020 (-0.807,0.790)
ln Cortisol	–0.044 (-0.105,0.023)	–0.082 (-0.145,–0.018)	0.057 (-0.002,0.119)	–0.083 (-0.155,–0.01)	–0.002 (-0.058,0.053)	0.121 (0.036,0.206)

Eigen decomposition of G provides a more holistic view of the genetic covariance structure and the level of integration among traits. Here, the major axis g_max (first principal component, PC1, with 95% confidence intervals from 5000 bootstrap replicates) explains 59.5% (47.5%, 75.7%) of the genetic variance in multivariate phenotype. Subsequent axes necessarily capture declining proportions of the multivariate genetic variance (PC2 = 20.2% [14.8, 32.1]; PC3 = 14.1% [5.6, 19.6]; PC4 = 4.5% [0.9, 8.2]; PC5 = 1.0% [0, 2.8], PC6 = 0.7% [0,0.1]; see Supplementary file 6, Figure 4—figure supplement 1). All traits except -ln emergence time load significantly on g_max (Figure 4). Relative area covered, time in the middle and √freezings load in one direction, while track length and ln Cortisol load in the other direction. This structure indicates a major axis of genetic variation in integrated stress response (Figure 4—figure supplement 2), where genotypes at one end of this axis can be considered to have a ‘freeze’ (or possibly ‘weaker’; discussed later) type of behavioural stress response to the OFT assay (i.e. freezing more frequently, swimming shorter distances, spending more time in the central area of the tank, and exhibiting exploratory swimming patterns that cover greater areas relative to their distance swum). This behavioural profile is associated with ‘weaker’ physiological responses to stress (i.e. producing lower cortisol levels in response to the stressor). Meanwhile, genotypes at the other end of g_max have a more ‘flight’ (or arguably ‘stronger’) type of behavioural stress response to the OFT assay (i.e. freezing less frequently, swimming further, spending more time close to the tank edges, and covering less area relative to their distance travelled in OFTs). This behavioural profile is associated with ‘stronger’ physiological responses to stress (i.e. producing higher cortisol levels in response to the stressor).

Figure 4 with 2 supplements see all

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In this study, we sought to determine whether – and to what extent – there exists genetic variation for, and integration between, behavioural and physiological (endocrine) components of the stress response. Our results provide three main novel insights. First, we find that genetic variation does underpin individual differences in both behavioural and physiological components of the stress response. Second, we find genetic covariance structure among these behavioural and physiological traits, indicating that they are indeed genetically integrated. Thirdly, having identified the structure of the major axis g_max of the genetic variance-covariance matrix G we suggest that it is more readily interpreted as an axis of genetic variation in stress coping style than in stress responsiveness, although we acknowledge that any distinction may be somewhat subjective. Overall, by estimating the genetic covariance structure among traits we find the first quantitative genetic support to date for the hypothesis of evolutionary integration between behavioural and endocrine components of the stress response.

We find heritable variation in, and covariation among, behaviours assayed in the open field trial (OFT), including the derived trait relative area covered. The latter trait, derived by considering an appropriate biological null model of the relationship between track length and (absolute) area covered, serves as a useful proxy for exploratory behaviour. Here, we demonstrate an axis of repeatable and heritable variation that spans from ‘freeze’ type responses characterised by less active but more exploratory swimming patterns (more freezings and lower track length, coupled with higher relative area covered and time in the middle) through to a ‘flight’ type response characterised by higher activity coupled with thigmotaxis and low exploration (fewer freezings and higher track length, coupled with lower relative area covered and time in the middle). Given the wide use of OFTs in biomedical research (e.g., Rex et al., 1998; Karl et al., 2003) as well as in animal behaviour, our phenotyping approach (including the derivation of relative area) may have broad applicability for discriminating between exploration and stress/anxiety-related patterns of behaviour. The OFT paradigm is also widely applied to studies of ‘shy—bold’ type personality variation in fishes (Toms et al., 2010) and other vertebrates (Carter et al., 2013; Perals et al., 2017). The extent to which behavioural differences deemed characteristic of a ‘shy—bold’ personality axis (commonly, if not universally, defined as repeatable variation in response to perceived risk; Sloan Wilson et al., 1994) should be viewed as equivalent to variance in ‘stress coping style’ and/or ‘stress responsiveness’, is a matter of debate (see Boulton et al., 2015). While we view these as overlapping concepts, our findings do highlight reasons for caution in considering them interchangeable. For example, while the structure of the major axis of G, g_max, is broadly consistent with it reflecting an axis of integrated genetic variation in ‘stress coping style’ (see below), emergence time, an OFT trait that is commonly considered to reflect ‘boldness’ (Burns 2008), was found to be heritable but did not load significantly on g_max (see Figure 4). This could indicate that the shy—bold continuum does not align with the stress coping style continuum in our model organism. However, it could also reflect complexities with interpreting emergence time. For example, at least some genotypes (and individuals) could perceive the shelter area as less safe than the open arena (i.e. counter to the assumption that fast emergence reflects a lack of fear of the open arena, and thus greater boldness, e.g. Burns 2008). Indeed, in an earlier study some guppies decreased (rather than increased) shelter use following simulated predation events (Houslay et al., 2018).

Tendency to shoal varies among individuals but is not detectably heritable. Although not generally considered a stress-response trait per se, shoaling is an anti-predator behaviour in guppies (Herbert-Read et al., 2017). We had therefore predicted that heightened perception of risk in the open field might also be associated with increased shoaling tendency. This was not the case at the among-individual level (Supplementary file 5), while the absence of detectable genetic variance meant that we could not test this prediction in G.

We find strong evidence of significant additive genetic variance in a key physiological component of the stress response: waterborne cortisol concentrations following exposure of the fish to a handling stressor. Our findings suggest that previously detected differences among individuals in their cortisol response to a stressor (Houslay et al., 2019) are primarily attributable to genetic effects, with the estimated heritability (h² = 0.31) constituting over 80% of the individual–level repeatability (R = 0.37) for ln-transformed Cortisol. In addition, by adopting a reaction norm approach to modelling stress physiology, as recently advocated by ourselves (Houslay et al., 2019) and others (e.g. Fürtbauer et al., 2015; Hau and Goymann, 2015; Taff and Vitousek, 2016; Guindre-Parker, 2020; Malkoc et al., 2021), we detect GxE reflecting genetic differences in the extent of habituation to the stressor over repeated exposures. This result is potentially important because poor habituation of the hypothalamic-pituitary-adrenal/interrenal (HPA/I) response to repeated or ongoing stressors can lead to well documented health problems in human and animal populations (Segerstrom and Miller, 2004; Koolhaas, 2008; Romero et al., 2009; Mason, 2010). Our detection of heritable variation in the degree of habituation to stressors raises the possibility of developing targeted selection strategies to improve welfare in captive populations (e.g. Frankham et al., 1986; Muir and Craig, 1998; Oltenacu and Algers, 2005).

Our findings also highlight that there is greater additive genetic variance (and heritability) for cortisol levels following the first exposure to the stressor than following the third. This pattern, which occurs because genotypes that produce the highest cortisol response at first exposure also show the most marked habituation, is consistent with the idea of cryptic genetic variance being ‘released’ by exposure to novel, and so potentially stressful, environments (Ledón-Rettig et al., 2010; Ledón-Rettig et al., 2014; Berger et al., 2011; Paaby and Rockman, 2014). All else being equal, it also means that selection on cortisol levels following stressor exposure should induce a stronger evolutionary response in naïve relative to habituated fish. However, the strong positive cross-environment correlation means that the ranking of genotypes with regard to their cortisol responses is consistent across repeated stressor exposures. Thus selection on the (average) GC response would result in a correlated evolutionary response of habituation rate, and vice versa. With regard to drawing inferences from these GC levels about the extent to which an organism is in a ‘stressed’ state, it is worth noting the potential complexity that may arise from GCs serving numerous functions. For example, in addition to mobilising energy and down-regulating non-essential processes in order to facilitate coping with an acute stressor (Romero and Beattie, 2022), GCs such as cortisol are also thought to play a key role in reproductive processes (Wingfield and Sapolsky, 2003; Breuner et al., 2008; Bonier et al., 2009b; Ouyang et al., 2011; but see also Bonier et al., 2009a). Thus, there is a need for some caution when equating a high GC level with a highly stressed ‘state’. Notably, studies of GC levels alone will naturally not capture a complete picture of among-individual variation in stress physiology. The HPA/I axis (which culminates in GC secretion) reflects just one of several important neuro-endocrine stress response pathways (see MacDougall-Shackleton et al., 2019) whose wider integration, while logistically challenging to study at present, strongly merits attention going forward (Gormally et al., 2020).

Considering all traits together, G shows evidence of genetic integration between behavioural and endocrine components of the stress response. The ends of the major axis of G (g_max) are largely consistent with expectations given genetic variance in ‘stress coping styles’ (Koolhaas et al., 1999). Genotypes showing (putatively) more proactive ‘flight’ type behaviour in the OFT (i.e. thigmotaxis, high track length, low relative area covered, few freezings) produce higher levels of cortisol following the handling and confinement stressor, while genotypes showing (putatively) more reactive ‘freezing’ type behaviour in the OFT (i.e. low track length and many freezings, in addition to higher relative area covered and more time in the middle) produce lower levels of cortisol following the same stressor. Were one to accept this view, the trait loadings on g_max would suggest that thigmotaxis and freezing behaviour are strong indicators of stress coping style. Notably, here we find continuous variation along this major axis of genetic variation (see Figure 4—figure supplement 2), rather than the bimodal distribution suggested by some of the coping styles literature (Koolhaas et al., 1999; Koolhaas et al., 2007; Koolhaas et al., 2010). An alternative interpretation, however, is that g_max represents variance in stress response ‘magnitude’ rather than coping style per se. This is because the putatively ‘reactive’ end of g_max might simply reflect a low magnitude stress response rather than a particular style of ‘coping’ with a stressor. Such an interpretation would help explain why fish (or genotypes) that produce lower levels of cortisol following handling and confinement also have more ‘exploratory’ movement (higher relative area covered) and reduced thigmotaxis (i.e. increased time in the middle) in the OFT. We note that the distinction between ‘style’ and ‘responsiveness’ may be rather moot if, for example, fish with proactive styles are also more responsive.

In fact, a subsequent ‘two-tier’ iteration of the coping style model proposed that variation in the magnitude of the stress response (termed ‘stress reactivity’) could be viewed as a second dimension of variation, distinct from differences in the ‘type’ (or style) of response (Koolhaas et al., 2010). It is not clear to us that these two dimensions, if both present, can be tested for and disentangled empirically in the current data—for example, there is no a priori expectation that style and magnitude should manifest as orthogonal axes in G among traits analysed here (and so both could conceivably align on g_max). In principle, disentangling these dimensions should be possible by characterising stress response through within-subject comparison of behaviour and endocrine state between stressed and ‘baseline’ (unstressed) states. In practice, we found here that genetic variation in GC responsiveness (i.e. the slope across states, accepting that habituation represents a form of this) is very strongly correlated with the average (or intercept). We found similar results for behavioural responses to increased stressor severity in another study of this population, where there exist strong among-individual correlations between the intercept and slope of the plastic response (Houslay et al., 2018). While we cannot claim a true ‘baseline’ state in either case, these results suggest that stress coping style and the magnitude of response do both exist as axes of variation, but may also be tightly correlated.

The genetic integration of behaviour and physiology detected here is consistent with (but not proof of) the hypothesis that correlational selection in the past has led to the coevolution of these components of the stress response. This hypothesis assumes that correlational selection favours any combination of trait values that yield higher fitness, creating a genetic correlation as mutations that generate such combinations will be recruited into the population while those that do not will be selected out (Lande, 1980; Roff and Fairbairn, 2012). While reflections on the role of past correlational selection are necessarily speculative, we can say that the structure of G should shape (and potentially constrain) future evolutionary responses to selection—whether natural or artificial. Here we have no direct knowledge of how contemporary selection is acting in the wild or whether it might be changing as a consequence of anthropogenic stressors. Nor do we know exactly how well our estimate of G will match that which may be found in the wild (although our study animals are all recent descendants of wild-collected animals). Thus, we cannot comment directly on how G will shape future evolution of the guppy stress response beyond noting that selection on behaviour will cause correlated evolution of endocrine physiology (and vice versa). Nonetheless, while it seems reasonable to expect that current integration of stress response in natural populations should be broadly adaptive, this seems less likely in captive populations (at least for species without a long history of domestication and opportunity for adaptation to artificial environments). We know that prolonged, chronic activation of stress response pathways (notably the HPA/I axis) frequently disrupts health and survival in captive animals (Huether, 1996; Boonstra and Fox, 2013). It may be that more stress-responsive genotypes are disadvantaged in novel artificial conditions (e.g., if acute stress responsiveness positively predicts susceptibility to chronic stress). However, even if true this would not imply high (acute) stress-responsiveness was also disadvantageous in the wild. Since natural selection should purge alleles that are universally detrimental, it seems more plausible that genetic variation along the major axis described here is maintained by some form of selective trade-off (as widely hypothesised for maintenance of personality variation; e.g., Stamps, 2007; Wolf et al., 2007; Réale et al., 2010). For instance, genotypes susceptible to harm under chronic stressor exposure will likely persist in populations if they also confer advantages under an acute stress challenge. In natural populations not only is exposure to acute stressors more common than to chronic stressors, but also selection through chronic stress exposure may be conditional on (and subsequent to) surviving acute challenges (such as predator attacks).

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Author details

1. Thomas M Houslay

   Centre for Ecology and Conservation, University of Exeter (Penryn Campus), Penryn, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing - original draft, Writing – review and editing

   For correspondence

   houslay@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5592-9034

2. Ryan L Earley

   Department of Biological Sciences, University of Alabama, Tuscaloosa, United States

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – review and editing

   Competing interests

   No competing interests declared

3. Stephen J White

   Centre for Ecology and Conservation, University of Exeter (Penryn Campus), Penryn, United Kingdom

   Contribution

   Data curation, Investigation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8538-6066

4. Wiebke Lammers

   Centre for Ecology and Conservation, University of Exeter (Penryn Campus), Penryn, United Kingdom

   Contribution

   Investigation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

5. Andrew J Grimmer

   Centre for Ecology and Conservation, University of Exeter (Penryn Campus), Penryn, United Kingdom

   Contribution

   Investigation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

6. Laura M Travers

   Centre for Ecology and Conservation, University of Exeter (Penryn Campus), Penryn, United Kingdom

   Present address

   School of Biological Sciences, University of East Anglia, Norwich, United Kingdom

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Elizabeth L Johnson

   Department of Biological Sciences, University of Alabama, Tuscaloosa, United States

   Present address

   Southern Research, Birmingham, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2009-7685

8. Andrew J Young

   Centre for Ecology and Conservation, University of Exeter (Penryn Campus), Penryn, United Kingdom

   Contribution

   Conceptualization, Funding acquisition, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

9. Alastair Wilson

   Centre for Ecology and Conservation, University of Exeter (Penryn Campus), Penryn, United Kingdom

   Contribution

   Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

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