# Is there a link between the acute stress response and the movement syndrome of a wild large herbivore?

Inès Khazar, Nicolas Morellet, A.J. Mark Hewison, Laura Gervais, Hélène Verheyden, Guillaume Le Loc'h, Arnaud Bonnet, Nicolas Cèbe, Yannick Chaval, Anne Geffré, et al.

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Inès Khazar, Nicolas Morellet, A.J. Mark Hewison, Laura Gervais, Hélène Verheyden, et al. Is there a link between the acute stress response and the movement syndrome of a wild large herbivore?. Animal Behaviour, 2025, 230 (123374), 10.1016/j.anbehav.2025.123374 hal-05441559

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# Is there a link between the acute stress response and the movement syndrome of a wild large herbivore?

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Inès Khazar<sup>a, b, * ID</sup>, Nicolas Morellet<sup>a, b ID</sup>, A. J. Mark Hewison<sup>a, b</sup>, Laura Gervais<sup>c, d ID</sup>, Hélène Verheyden<sup>a, b ID</sup>, Guillaume Le Loc'h<sup>e ID</sup>, Arnaud Bonnet<sup>a, b</sup>, Nicolas Cèbe<sup>a, b</sup>, Yannick Chaval<sup>a, b</sup>, Anne Geffré<sup>f</sup>, Bruno Lourtet<sup>a, b</sup>, Joël Merlet<sup>a, b</sup>, Nadège C. Bonnot<sup>a, b</sup>

## ARTICLE INFO

**Article history:**

Received 19 November 2024
Initial acceptance 10 February 2025
Final acceptance 1 August 2025
Available online 30 October 2025
MS. number: 24-00681R

**Keywords:**

activity
behavioural syndrome
boldness
coping style
docility
exploration
personality
physiological stress response
rectal temperature
roe deer

When faced with stressful situations, individuals differ in their behavioural and physiological responses depending on their behavioural type; that is, personality. For mobile organisms, movement is one of the primary responses to threats. Movement syndromes (i.e. suites of correlated movement traits) have been linked to individual differences in risk taking, resource acquisition and performance, and can influence the evolutionary trajectory of these behaviours. Often, standardized tests are used to identify behavioural types; however, behaviours evaluated in controlled settings might not accurately reflect behaviours expressed while free-ranging in the wild. Nonetheless, the acute stress response of an individual expressed during a test could be related to a stress-mitigating behaviour in natural conditions, such as movement. To address this question, we used a long-term monitored population of wild roe deer. *Capreolus capreolus*, to identify movement-related personality traits and investigate the existence of a movement syndrome. We then examined its link with among-individual differences in the acute stress response (rectal temperature, haematocrit, neutrophil/lymphocyte ratio and docility) expressed during capture and handling. Using GPS monitoring of up to 335 roe deer in a highly heterogenous landscape, we showed that risk-prone individuals (i.e. using riskier habitat and ranging closer to roads during daytime) were also more active, with a lower movement speed and larger home ranges. However, we only found weak support for a link at the among-individual level between an individual's movement syndrome and its stress response during controlled capture, although there was some indication that individuals with an attenuated physiological response to acute stress tended to take more risk when free-ranging. This lack of support might be explained by the fact that different traits reflect different dimensions of the stress response, or by the low number of replicates per individual for these traits. These findings highlight the complexity of interpreting stress responses in natural populations.

© 2025 The Author(s). Published by Elsevier Ltd on behalf of The Association for the Study of Animal Behaviour. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).

Due to widespread anthropogenic activities, modern landscapes have become more fragmented and urbanized (Hooke & Martín-Duque, 2012), generating novel and more numerous sources of stress on wildlife. Wild animals are, thus, increasingly confronted with human disturbance and risks that can alter their

behaviour (Gaynor et al., 2018; Tucker et al., 2018) and/or increase their chronic stress levels through repeated exposure to stressors (Romero & Wingfield, 2015). Chronic stress can have detrimental effects on the individual's ability to mount an acute stress response (Rich & Romero, 2005) or an effective immune response (Romero et al., 2009), ultimately impairing its capacity to cope with environmental change. For mobile species, movement is often one of the first behavioural responses to disturbance (Mitchell & Lima, 2002; Van Moorter et al., 2013), but all

<footnote><sup>a</sup> Université de Toulouse, INRAE, CEFS, Castanet-Tolosan, France</footnote>

<footnote><sup>b</sup> LTSER ZA PYRénées GARonne, Auzeville-Tolosane, France</footnote>

<footnote><sup>c</sup> CEFE, Université de Montpellier, CNRS, EPHE, IRD, Montpellier, France</footnote>

<footnote><sup>d</sup> Station d'Ecologie Théorique et Expérimentale du CNRS, Moulis, France</footnote>

<footnote><sup>e</sup> IHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France</footnote>

<footnote><sup>f</sup> CREFRE, INSERM, UPS, ENVT, Toulouse, France</footnote>

<footnote>* Corresponding author.
E-mail address: ines.khazar@inrae.fr (I. Khazar).</footnote>
individuals of a population do not respond in the same way, expressing contrasting tactics depending on their phenotype (Koolhaas et al., 1999). These consistent among-individual differences in the average expression of a behaviour are widely assumed to reflect personality traits (Réale et al., 2007). Although it is assumed that individuals of a population may exhibit contrasting movement tactics (Hertel et al., 2020; Spiegel et al., 2017), little is known about how these movement tactics correlate with individuals' responses to stress (Bonnot et al., 2015; Wesley et al., 2012) and whether they form a behavioural syndrome (i.e. a suite of correlated personality traits). Investigating such behavioural syndromes is particularly important as they can reshape the evolutionary trajectories of behaviours and their co-occurrence in wild populations (Sih et al., 2004), and can, thus, shape population responses to environmental challenges (Cockrem, 2022).

Historically, personality traits have been measured using standardized tests designed to describe the position of each individual along five axes of personality: boldness, exploration, activity, aggressiveness and sociability (Réale et al., 2007). These quasi-experimental approaches have the benefit of limiting the impact of noise linked to environmental variation on the expression of the target behaviour. However, behaviours evaluated in standardized settings might not accurately reflect behavioural patterns expressed in the wild (Archard & Braithwaite, 2010). This is likely the case for movement behaviours, which are often highly constrained under standardized test conditions. However, to date, only a few studies have explored the link between behaviour in controlled and in free-ranging contexts. For example, in European hares, *Lepus europaeus*, Stiegler et al. (2022) found that boldness evaluated by an emergence test in a controlled setting was correlated with larger home range size and higher activity levels in the wild; while in burrowing bettongs, *Bettongia lesueur*, less docile individuals during handling were also shyer in the wild, foraging closer to refuges, with longer flight initiation distances (West et al., 2019).

The capture and handling of wild animals provides an opportunity for a semiexperimental standardized setting during which behavioural and physiological traits can be measured to index individual differences in stress response. For example, the level of struggling of an animal during handling (i.e. docility) is a personality trait that appears to be related to boldness in the specific context of reaction to humans, and has been used to characterize behavioural types in several wild species; for example, bighorn sheep, *Ovis canadensis* (Réale et al., 2000) and wild eastern chipmunks, *Tamias striatus* (Careau et al., 2015). Docility has also been linked to vigilance levels (Wat et al., 2020) and risk avoidance in the wild (Bonnot et al., 2015). Furthermore, docility and various physiological and biochemical markers of stress measured during capture and handling have been found to be repeatable in wild populations (cortisol levels, Carbillet et al., 2022; heart rate, Müller et al., 2018; stress-induced hyperthermia, Careau et al., 2012) and to correlate with each other into a syndrome (or a coping style, sensu Koolhaas et al., 1999), notably on a proactive–reactive gradient (with proactive individuals being less docile and expressing lower physiological responses to stress). These among-individual differences in the stress response are presumed to correlate with other behavioural variations in the wild (Bonnot et al., 2015; Wesley et al., 2012). Notably, as movement is one of the first responses to avoid stressful situations, it is likely linked with behavioural and physiological responses expressed during an acute stress event.

One main difficulty of studying personality in the wild is the requirement for high quality, repeat observations to estimate the consistency (or repeatability) of behavioural traits (Réale et al., 2007), particularly for cryptic species or shy individuals (Brown et al., 2013;

Cagnacci et al., 2010). Observing and quantifying the behaviour of free-ranging animals can also be time and resource consuming. The advent of biologging technologies offers an efficient solution to these limitations (Kays et al., 2015). Indeed, the acquisition of large amounts of data on the spatial location of individuals (e.g. using a global positioning system, hereafter GPS) and their activity (e.g. using activity sensors) through time provides the raw material for generating repeat measures of movement-based metrics, such as turning angle correlation (Abrahms et al., 2017), habitat use (Bonnot et al., 2015), speed and daily distance travelled (Michelangeli et al., 2022) and overall dynamic body acceleration (ODBA, Stiegler et al., 2022). The time series of these metrics can then be used to explore the link between movement syndromes and environmental heterogeneity in wild populations (Spiegel et al., 2017; Stuber et al., 2022).

In this study, we used an extensive long-term monitoring program on a wild European roe deer, *Capreolus capreolus*, population in southern France to investigate the link between consistent among-individual differences in the behavioural and physiological responses to capture and handling in a standardized setting, and a panel of movement behaviours expressed while free-ranging in the wild. We evaluated consistent among-individual differences in the stress response by collecting repeat measures of four behavioural and physiological parameters during the initial capture event, which entails acute stress for a wild animal. Using subsequent GPS and activity monitoring of 335 individual roe deer over 1 year, we extracted six movement traits to describe individual movement tactics at the monthly scale. For each of these 10 traits (four behavioural and physiological responses and six movement traits), we first assessed among-individual variance and repeatability. Indeed, prior research indicated that roe deer exhibit consistent individual differences in several behavioural and physiological traits. For instance, docility has been shown to be repeatable (Bonnot et al., 2015; Debeffe et al., 2015; Monestier et al., 2016) and heritable (Gervais et al., 2020). To identify a general behavioural syndrome, we analysed trait covariations at the among-individual level with the variance–covariance partitioning method (Hertel et al., 2020), including both stress parameters measured during capture and movement traits measured in the wild as response variables in a single model. Previous work in this system has identified links between docility, markers of physiological stress (neutrophil to lymphocyte ratio, faecal cortisol metabolites) and habitat use (Bonnot et al., 2015; Carbillet et al., 2019, 2020). Hence, we expected that an individual's stress response during capture would correlate with its movement syndrome in the wild. Notably, we expected individuals that expressed a proactive stress response (lower corticosterone levels or heart rate variability, for example, and less docile; Koolhaas et al., 1999) during capture would also be bolder, more active and more exploratory when ranging in the wild (Bonnot et al., 2015; Sih et al., 2004; Spiegel et al., 2017). Finally, we evaluated the degree of phenotypic resemblance among individuals for both their stress syndrome and their movement syndrome to understand whether individuals that expressed a similar stress response also behaved similarly in terms of their movement tactics in the wild.

## METHODS

### *Study Site*

The long-term research site Vallées et Coteaux de Gascogne (Ouin et al., 2021) is a 19 000 ha rural area located in the southwest of France, in Aurignac canton (43.22°N, 0.88°E). This heterogenous landscape is dominated by open habitats, consisting of agricultural crops and meadows, which can be perceived as risky, notably
during daytime when human activities are more intense (Bjørneraas et al., 2011; Bonnot et al., 2013). There are two main forests and numerous smaller patches of woodland that provide refuge habitat from human-induced disturbance and risks. The rest of the landscape consists of hedgerows and human infrastructures, dispersed throughout the landscape and connected by a dense road network (for more details, see Hewison et al., 2009). A thorough description of the land use of the study site was obtained through aerial photographs (from the IGN's BD Ortho, https://geoservices.ign.fr/bdortho), which were manually digitized and verified in the field. For these analyses, we considered two types of habitats: forest versus open habitat, with the latter including all habitats other than woodland (i.e. crops, hedgerows, meadows). Roe deer are hunted for most of the year, with drive hunting permitted from the second Sunday of September to the end of February, involving both beaters and dogs. Hunting by stalking is also permitted from 1 June to the end of the general open season, and is presumed to generate less disturbance since it is more locally confined and does not involve dogs or beaters.

## Data Collection

The data used in this study come from the long-term monitoring of a population of free-ranging roe deer (Hewison et al., 2009). Since 2009, individuals were caught during annual winter captures from January to March (corresponding to official periods of authorized hunting in the area) using drives of between 30 and 100 beaters and up to 4 km of long falling nets positioned at one of the six capture sites (for more details on the capture procedure, see Morellet et al., 2009). Note that this study was conducted on a free-ranging and open population with high turnover due to hunting and road collisions, and a high natal dispersal rate, generating a low recapture rate (21%). Although individuals of a given personality type (e.g. bold animals) may be more susceptible to capture (Carter et al., 2012), this is difficult to quantify in our system. However, it seems likely that the drive-netting method generates an a priori random sample of personality types compared to other capture methods (box traps, for example), as capture probability is not affected by an animal's willingness to enter a trap. As soon as a roe deer is caught, the individual is tranquilized with an injection of acepromazine (Montané et al., 2003) and transferred to a wooden retention box (for more details on capture procedures, see Bonnot et al., 2017; Carbillet et al., 2019). Each individual is sexed and aged as a juvenile (less than 1 year old), yearling (between 1 and 2 years old) or adult (more than 2 years old; Hewison et al., 1999). Since 2009, we recorded rectal temperature every 2 min (with a Digitemp Color thermometer, Diaprax, www.diaprax.de, before 2015 and a YSI-402 rectal probe, Yellow Springs Instruments, www.ysi.com, from 2015 onwards, both with a precision of 0.1 °C), collected blood specimens to identify haematological analytes and recorded behaviour (see below). Finally, roe deer were equipped with a GPS collar (see below) and released on site.

## Acute stress-response parameters during the controlled setting

Capture and handling are stressful events for wild animals (Moe & Bakken, 1997). To quantify the individual stress response in such a controlled setting, we collected samples to evaluate three physiological stress parameters, namely the neutrophil to lymphocyte ratio (hereafter N:L ratio), the haematocrit (i.e. the volume percentage of red blood cells in blood, hereafter HCT) and the rectal temperature (T). Montané et al. (2007) found that, in roe deer, the N:L ratio increased around 2 h after a stressful stimulation, thus indexing a response to acute stress when

measured after this threshold. In our study, the time spent in the retention box varied greatly between individuals (from 36 to 466 min, median = 240 min), with most samples taken after 2 h. Additionally, the haematocrit has been shown to inform on the acute stress response in roe deer (Mentaberre et al., 2010; see Appendix for details of sample processing). As rectal temperature was measured multiple times (every 2 min) during handling (at least once, but up to eight times, mean ± standard deviation, SD = 2.04 ± 1.42), we calculated the mean of all rectal temperature measurements per individual (Monestier et al., 2015). Individuals in hypothermia ($T < 36.9$ °C; $N = 6$) were removed from the data set (Bonnot et al., 2015; Gervais et al., 2020). We also recorded six behaviours during handling and release to quantify the behavioural response during capture (see Table 1), and indexed an individual docility score by taking the mean of these six behavioural components, ranging from 0 to 1, with 0 characterizing very docile individuals and 1 very active, struggling and undocile ones (Koolhaas et al., 1999), with score values at intervals of $1/24 = 0.0417$ between 0 and 1. This docility score has been used to index boldness and correlates to habitat use while free-ranging and individual performance (Bonnot et al., 2015, 2018; Monestier et al., 2015). Moreover, in captive roe deer, the N:L ratio, HCT, rectal temperature and docility were reported to be significantly correlated (in Monestier et al., 2016, the N:L ratio was significantly negatively correlated to all other parameters, with Pearson correlations between -0.27 and -0.79, while rectal temperature, HCT and docility were positively correlated, with Pearson correlations ranging between 0.36 and 0.58, suggesting the existence of a stress syndrome; hence, we used these four traits to characterize the response of wild roe deer to acute stress in a controlled setting. More details about data collection and test protocols are given in Bonnot et al. (2015), Monestier et al. (2016) and Carbillet et al. (2019).

## Preprocessing of the telemetry data

Between 2009 and 2022, we equipped 539 roe deer (mean ± SD of 39 ± 7 individuals per year) with GPS collars (Lotek GPS 3300, Vectronic Plus-1C or Vectronic Vertex models, https://www.vectronic-aerospace.com) with an activity sensor that registered activity levels (value range 0–255) over 5 min periods along two axes (X and Y). Collars were programmed to record GPS locations following two schedules: one GPS fix every 6 h (four locations per day, at 0000, 0600, 1200 and 1800 hours) all year round, from capture to drop-off, between the end of November and mid-December; and one GPS fix every 10 min during a 24 h period from 1200 to 1200 hours (noon to noon), approximately once a month. Locations that were clearly aberrant were discarded, following Bjørneraas et al. (2010), resulting in the removal of 0.008% of fixes for the 6 h schedule and 0.05% of fixes for the 10 min schedule. We removed individuals monitored for less than a month or with less than 50 fixes recorded over the year due to mortality or equipment dysfunction ($N = 27$). We excluded juveniles ($N = 220$), as they have a different spatial behaviour during their first year of life, notably due to emancipation and natal dispersal (Debeffe et al., 2013). Finally, we removed locations recorded during the first 7 days after the capture event, because it could induce a temporary modification of spatial behaviour (Morellet et al., 2009). Thus, we obtained a total sample of 335 individuals, composed of 188 females and 147 males, with 103 yearlings and 232 adults for our analyses. Note that not all individuals had data available for all stress-response and movement traits, generating variable and somewhat lower sample size in certain subsequent analyses (Table 2).
<table><caption>**Table 1**
Behaviours recorded during handling and release</caption><thead><tr><td>Behaviour name</td><td>Description</td><td>Scores</td></tr></thead><tbody><tr><td>Upside down</td><td>In the wooden retention box, the individual is considered agitated and stressed if it turned upside down</td><td>0/1</td></tr><tr><td>Struggle on table</td><td>On the handling table, the individual struggled and/or panted</td><td>0/0.5/1</td></tr><tr><td>Remove collar</td><td>At release, the individual attempted to remove its collar</td><td>0/0.5/1</td></tr><tr><td>Jump and fall</td><td>At release, the individual jumped and/or fell, indicating a certain level of stress and struggling</td><td>0/0.5/1</td></tr><tr><td>Flight speed</td><td>At release, the individual either fled at high speed indicating stress, trotted or walked away calmly</td><td>0/0.5/1</td></tr><tr><td>Exhaustion</td><td>At release, the individual staggered and/or lay down, probably because of exhaustion due to acute stress</td><td>0/0.5/1</td></tr></tbody></table>

The behaviours were used to calculate the behavioural score, indexing individual docility in wild roe deer. Six behavioural indexes were obtained by combining different behaviours expressed by an individual during handling and at release. A score of 1 was assigned to each behaviour expressed by the animal (0 otherwise). The six indexes can thus have values of 0, 0.5 or 1, depending on the number of behaviours expressed for a given index. We then averaged these six indexes into a single behavioural score, ranging between 0 and 1, with a score of 0 indicating more docile individuals.

### Calculation of movement metrics in a free-ranging context

To characterize individual movement tactics in roe deer in a free-ranging context, we calculated six movement metrics based on GPS and activity data. Each metric was calculated over periods of 1 month, which provided a number of repetitions per individual ranging from 1 to 37. We obtained a mean of 1101 GPS fixes per individual (SD = 582, range 150–3733) for the 6 h schedule and 1626 GPS fixes per individual (SD = 882, range 151–4946) for the 10 min schedule. All computations and analyses were done using R version 4.3.1 (R Core Team, 2023) and RStudio version 2023.3.1.446 (Posit Team, 2023).

### The probability of using open habitats during daytime (P<sub>OH</sub>).
This trait has been shown to be repeatable within individuals and variable among individuals in wild roe deer, and related to docility (Bonnot et al., 2015). P<sub>OH</sub> has been used as an index of boldness in roe deer because, while open habitats provide richer feeding, they are perceived as riskier due to frequent disturbance. To quantify open habitat use during daytime, we considered fixes at 1200 hours (noon) only. For each individual-month (the movement data for an individual collected during a given month), we summed the number of fixes in open habitat at noon and divided by the total number of fixes recorded at noon irrespective of habitat type to obtain the probability of using open habitats during daytime per month, ranging between 0 and 1.

### The diurnality of the distance to roads (D<sub>road</sub>).
This index can also be considered as a measure of boldness for roe deer, as roads are perceived as a source of disturbance and danger (Carbillet et al., 2020; Padié et al., 2015). The proximity to roads during daytime (i.e. the riskier period of the day when traffic is higher) should therefore index their propensity to take risks. We used fixes at

1200 hours (noon) and at 0000 hours (midnight) to characterize the distance to roads during daytime and night-time, respectively. We calculated the distance of each location to the nearest road and averaged these values per month, while distinguishing between day and night. Following Hertel et al. (2020), we calculated the diurnality of the distance to roads for each month as:

$$ \frac{D_{\text{day}} - D_{\text{night}}}{D_{\text{day}} + D_{\text{night}}} $$

where D<sub>day</sub> is the mean distance to roads during daytime and D<sub>night</sub> the mean distance to roads at night. Values of D<sub>road</sub> close to -1 indicate that an individual is closer to roads during the day relative to their average distance to roads (risk prone), while values close to +1 indicate that an individual is relatively more distant from roads during daytime than average (risk shy).

### Home range size (HR size).
It represents the area used by an animal for its normal activities (Burt, 1943), and has previously been used to index personality traits, such as activity and exploration (Careau et al., 2008; Villegas-Ríos et al., 2017). We removed all individual-months with less than 10 locations (Börger et al., 2006), and estimated HR size with the kernel method at the 90% isopleth (Worton, 1989) using the hr_kde function within the amt package, with the reference smoothing parameter (implemented ad hoc method, Signer et al., 2019). To avoid including individuals with a nonstationary movement behaviour, for which the assumptions for estimating a home range are violated (Börger et al., 2008; Patin et al., 2020), we calculated the net squared displacement (NSD) for each individual-month. The NSD classifies movement trajectories into nonstationary modes (Börger & Fryxell, 2012; Edelhoff et al., 2016). We first used the mvtClass function (migrateR package,

<table><caption>**Table 2**
Summary of the characteristics and sample size for each trait</caption><thead><tr><td>Traits</td><td>Mean value</td><td>Range</td><td>N<sub>ind</sub></td><td>N<sub>obs</sub></td><td>N<sub>rep</sub> (range)</td><td>R<sub>adj</sub> (CrI)</td><td>I<sub>t adj</sub> (CrI)</td></tr></thead><tbody><tr><td>HR size (Ha)</td><td>65</td><td>2–507</td><td>242</td><td>2308</td><td>9.5 (1–32)</td><td>0.50 (0.44,0.57)</td><td>0.32 (0.26,0.38)</td></tr><tr><td>AVG<sub>speed</sub> (m/s)</td><td>0.2</td><td>0.02–0.87</td><td>239</td><td>2252</td><td>9.4 (1–31)</td><td>0.18 (0.13, 0.23)</td><td>0.04 (0.03, 0.06)</td></tr><tr><td>N<sub>exc</sub></td><td>0.20</td><td>0–5</td><td>242</td><td>2263</td><td>9.4 (1–30)</td><td>0.01 (0.00, 0.04)</td><td>1.32 (0.00, 6.01)</td></tr><tr><td>D<sub>road</sub></td><td>0.10</td><td>–0.7–0.8</td><td>243</td><td>2758</td><td>11.4 (1–37)</td><td>0.33 (0.28, 0.38)</td><td>1.23 (0.66, 2.45)</td></tr><tr><td>P<sub>OH</sub></td><td>0.46</td><td>0–1</td><td>243</td><td>2758</td><td>11.4 (1–37)</td><td>0.47 (0.42, 0.52)</td><td>2.41 (1.14, 4.58)</td></tr><tr><td>ODBA</td><td>13556</td><td>144–129888</td><td>222</td><td>2570</td><td>11.6 (3–37)</td><td>0.41 (0.29, 0.52)</td><td>0.11 (0.09, 0.14)</td></tr><tr><td>Docility score</td><td>0.16</td><td>0.00–0.83</td><td>335</td><td>459</td><td>1.4 (1–6)</td><td>0.19 (0.00, 0.33)</td><td>0.13 (0.04, 0.25)</td></tr><tr><td>N:L ratio</td><td>3.1</td><td>0.3–11.1</td><td>294</td><td>386</td><td>1.3 (1–4)</td><td>0.15 (0.00, 0.32)</td><td>0.05 (0.00, 0.19)</td></tr><tr><td>HCT (%)</td><td>45.7</td><td>26–65</td><td>289</td><td>372</td><td>1.3 (1–4)</td><td>0.27 (0.00, 0.45)</td><td>0.002 (0.000, 0.004)</td></tr><tr><td>Rectal T (°C)</td><td>38.8</td><td>37.0–41.1</td><td>310</td><td>412</td><td>1.3 (1–5)</td><td>0.33 (0.12, 0.53)</td><td>0.0008 (0.0003, 0.0014)</td></tr></tbody></table>

N<sub>ind</sub> is the number of individuals sampled for each trait, N<sub>obs</sub> the number of individual observations and N<sub>rep</sub> the number of repetitions per individual with the minimum and maximum number of observations per individual (range). Estimates of adjusted repeatability (R<sub>adj</sub>) and adjusted mean-scaled individual variation (I<sub>t adj</sub>) are also provided, with corresponding 95% credibility intervals (CrI) in brackets for each trait. Repeatability was computed on the latent scale for traits that were transformed or non-Gaussian. Values in italics of I<sub>t adj</sub> correspond to mean standardized among-individual variations to interpret with caution (for more details, see Appendix). See also Appendix for a complete description of the variance components.
Spitz et al., 2017) to classify each individual-month into four categories (sedentary, disperser, migrator, nomad), which generated 1777 individual-months that were classified as sedentary phases. We then visually checked the distribution of NSD for the remaining individual-months (N = 634) to identify months that were wrongly classified as nonsedentary behaviour (Bunnefeld et al., 2011). As a result, we removed 103 individual-months (corresponding to 49 unique individuals) for the repeatability analysis, which amounted to 47 individual-months for the behavioural syndrome analysis.

The number of excursions (Nexc). This metric has been used to index the degree of exploration of an individual (Debeffe et al., 2013; Van Moorter et al., 2008). We removed individual-months with less than 20 different days sampled. We then characterized each location as isolated or aggregated, following the method developed by Debeffe, Focardi, et al. (2014), based on the proximity of each location to its 20 closest neighbours. To qualify a location as isolated, we calculated for every individual the average distance between each GPS location and its 20 closest neighbours (d20loc). For each individual, we calculated the mean value of d20loc (hereafter D20ind) and the corresponding standard deviation (SD20ind). A location that satisfied the condition d20loc > D20ind + 3 × SD20ind was classified as isolated. Because some individuals express a multirange tactic (sensu Couriot et al., 2018), we first determined clusters of locations using the hclust function of the stats package (mean number of clusters = 1.03 ± 0.19, range 1–3). After removing the isolated locations, we determined the centre of gravity of each cluster (see Appendix). To identify a location as an excursion, we calculated the net squared displacement between each location and the centre of gravity of the corresponding cluster (NSDloc). For each individual and identified cluster, we calculated the mean NSDloc (hereafter NSDind) and the corresponding standard deviation (SDNSDind). All isolated locations that satisfied the condition NSDloc > NSDind + SDNSDind were categorized as potential excursions. Then, we recorded an excursion when at least two successive locations both classified as potential excursions were separated by no more than 24 h (see Appendix for example plots representing excursion locations). Series of more than 20 consecutive isolated locations (at least 120 h, i.e. 5 days) were not considered as excursions, but rather a cluster within a multirange. We computed the number of excursions per month for each individual.

The average movement speed (AVGspeed). This trait represents baseline movement activity and has also been used as a proxy for the general level of activity (Gervais et al., 2020). We used the 10 min schedule and the amt package (Signer et al., 2019) to calculate step length and duration between each pair of successive GPS locations. To obtain representative monthly values, we removed 24 h periods with less than 20 different hours of the day monitored and individual-months with less than 40 steps. Because the duration of a step may vary slightly due to the time needed for the collar to contact satellites, we only retained steps that were approximately 10 min apart (±2 min) and we divided each step length by the duration of the step (i.e. between 8 and 12 min) and averaged the resulting movement speed per month.

Mean daily overall dynamic body acceleration (ODBA). This trait was used as a proxy of the general level of activity for each individual (Benoit et al., 2023; Wilson et al., 2006). Using activity sensors, we summed the values of activity recorded on both the X and Y axes to obtain an activity level between 0 and 510 for each 5 m interval before and after the corresponding GPS location of the 6 h schedule. We then averaged these values over the month and

multiplied by 288 (i.e. the number of 5 min intervals in 24 h) to obtain a mean daily value (Benoit et al., 2020). Even though ODBA was calculated as a daily value, we averaged this trait per month to evaluate among-individual differences in activity. We thus obtained a monthly average of daily ODBA.

## Statistical Analyses

### Repeatability and variability

We assessed individual consistency for each of the 10 traits by estimating the degree of repeatability recorded either during the acute stress event of capture (docility score, N:L ratio, HCT, rectal temperature) or in the wild (P<sub>OH</sub>, D<sub>road</sub>, HR size, N<sub>exc</sub>, ODBA and AVG<sub>speed</sub>). We defined repeatability as the proportion of phenotypic variance that can be attributed to among-individual differences (Bell et al., 2009). This quantity is then a function of both within- and among-individual variation (Nakagawa & Schielzeth, 2010), so that low repeatability results from either high within-individual or low among-individual variation. Using univariate mixed models, we first calculated unadjusted repeatability (R) by including only the identity of the individual as a random effect (see Appendix for a comparison between unadjusted and adjusted repeatability estimates). We then calculated adjusted repeatability (R<sub>adj</sub>) by including potential confounding effects that could inflate the estimate of repeatability (Nakagawa & Schielzeth, 2010). Previous results on our study population have shown that the stress-response parameters can be influenced by the time elapsed between capture in the net and sampling (i.e. the time spent in the retention box, Carbillet et al., 2019) and the number of recaptures an individual experienced in its lifetime (i.e. habituation, Debeffe et al., 2015; Martin & Réale, 2008). Thus, for each of the four stress-response parameters, we included these measures as fixed effects, as well as the mean external temperature, to account for the influence of climatic conditions. We retrieved time series for temperature from the Météo France website (https://meteo.data.gouv.fr), using the closest weather station to Aurignac (Palaminy, 43.20°N, 1.05°E). For each of the six movement traits, we included mean day length (calculated with the geosphere R package; Hijmans et al., 2017) as a fixed effect to control for seasonal variations in climatic and environmental conditions, as well as the year of monitoring as a random effect to control for differences between years in environmental conditions. Finally, for the 10 traits, we included sex and age class (yearling or adult) as fixed effects, as well as individual identity as a random effect on the intercept. Note that we considered each individual to be an independent observation from a statistical point of view. Although pseudoreplication could in theory arise in gregarious systems due to constraints on movement imposed by group cohesion, the roe deer is not a gregarious species, with groups mainly composed of mothers and their dependent young for much of the year. Because we removed juveniles from the data set, and because adults of both sexes are largely solitary during most of the monitoring period (Hewison et al., 1998), we considered that the movement metrics were not influenced by group behaviour. All continuous fixed effects were scaled (i.e. standardized by removing the mean and dividing by the standard error) to facilitate model convergence. D<sub>road</sub>, ODBA, HR size, AVG<sub>speed</sub>, docility score, N:L ratio, HCT and rectal temperature were modelled with a Gaussian error distribution, with HR size, ODBA and N:L ratio log-transformed, while AVG<sub>speed</sub> and docility score were square-root transformed. N<sub>exc</sub> was modelled using a negative binomial distribution and a log link function, while P<sub>OH</sub> was modelled with a binomial distribution and a logit link function. Using a Bayesian framework, all models were run over four chains, for 130 000 iterations with a 100 000 warm-up, and a thinning parameter of 5 (brms R package, Bürkner, 2017).
For each trait, we ran a univariate model that included all the fixed effects and determined their certainty of effect existence using the probability of direction ($p_d$, i.e. the proportion of the posterior distribution that is of the same sign as the median; R package bayestestR, Makowski et al., 2019), which is strongly correlated to the frequentist P value, and the 95% high density posterior (HPD) credibility intervals. For all the following multivariate or univariate models, only the variables that had been shown to have an effect on a given trait (HPD not including zero) were then included as a fixed effect in the models. The resulting models used for each trait are given in the Appendix with the corresponding $p_d$ and credibility intervals (Table A3). Note that we included all individuals for repeatability calculations, including those sampled only once, following the recommendation of Martin et al. (2011), to improve model robustness because ‘these individuals do still provide information with respect to the model parameters to be estimated and thus may improve power to detect among-individual variance in reaction norms’ (Martin et al., 2011, p. 3) and because ‘these individuals may not be a random sample of the population (...), and excluding them may lead to biased results’ (Martin et al., 2011, p. 3).

Based on these models, the repeatability of each trait was calculated with the following equation:

$$R = \frac{\sigma_{\text{ind}}^2}{\sigma_{\text{ind}}^2 + \sigma_{\text{year}}^2 + \sigma_{\epsilon}^2}$$

where $\sigma_{\text{ind}}^2$ is the among-individual variance, $\sigma_{\text{year}}^2$ is the variance due to the year effect and $\sigma_{\epsilon}^2$ is the residual variance. Note that $\sigma_{\text{year}}^2$ is zero for the stress-response parameters and for unadjusted repeatability, since we did not include year as a random effect in the corresponding models. $\sigma_{\epsilon}^2$ was set to $\ln(1+\frac{1}{\lambda}+\frac{1}{\theta})$ for the negative binomial model (Nakagawa et al., 2017) where $\theta$ is the shape parameter of the distribution and $\lambda$ is the mean of the distribution obtained from the intercept of the model. $\sigma_{\epsilon}^2$ was set to $\pi^2/3$ for the logit binomial model and was calculated by the model for the response variables with Gaussian distributions (Nakagawa & Schielzeth, 2010). A high repeatability means that individuals differ among each other and are very consistent over time in their behaviour. For example, a repeatability close to 1 means that individuals have different home range sizes or activity levels but a given individual has almost the same home range size or activity level throughout the year.

Finally, to index the magnitude of among-individual variation for each trait, we also estimated the mean standardized individual variance (i.e. squared coefficient of variation), $I_l \text{ adj} = \frac{\sigma_{\text{ind}}^2}{\mu^2}$, where $\sigma_{\text{ind}}^2$ is the variance due to the identity of the individual, and $\mu$ is the mean value of the trait, extracted as the intercept from the univariate models corrected for the fixed effects. As highlighted by Pélabon et al. (2020), meaningful coefficients of variation cannot be calculated for every trait (see Table 2 and Appendix for more details). We report unstandardized mean variance components in the Appendix, as recommended in Dochtermann and Royauté (2019). A high coefficient of variation combined with a high value of repeatability indicates that the range of consistent behaviours expressed by individuals in a population is very large, pointing to the existence of distinct personality types.

## Behavioural syndrome

To characterize the overall behavioural syndrome, we used the covariance partitioning approach, which includes several repeated behavioural metrics to determine individual variation in different contexts, without the need to summarize the information by assigning each individual to a given behavioural type (Hertel et al.,

2020). This method allows the estimation of the covariance between multiple traits, while controlling for other biological effects. To do so, we used a multivariate linear mixed model to calculate among-individual variance and covariance across the 10 traits (movement and stress-response traits) with the brms R package (Bürkner, 2017). Unlike BLUPs (best linear unbiased predictors), which do not allow the propagation of uncertainties among models, the multivariate mixed model avoids detection of spurious correlations (Houslay & Wilson, 2017). For each response variable, we used the same explanatory variables, family distributions, link functions and transformations as for the univariate models (see Repeatability and variability, above). All response variables were scaled to facilitate model convergence. Note that we repeated the analysis of the syndrome without the trait $N_{exc}$, because of its lack of repeatability (see Table 2). The correlations among the other traits and all the other results did not differ with or without $N_{exc}$. Therefore, we decided to keep $N_{exc}$ for the subsequent analyses, as the number of excursions is an informative measure of exploration that could be considered in other species; hence, including it could be useful for comparison with future studies. We ran the models on four chains, with a warm-up of 100 000 and 400 000 iterations, a thinning parameter of 10 and uninformative priors. We checked the convergence of the models with the $R_{hat}$ parameter ($R_{hat} = 1$, Bürkner, 2017) and visually assessed the mixing of the chains through trace plots. We verified the bulk and tail effective sample sizes and posterior predictive checks for each response variable. We also performed a prior sensitivity analysis to examine the influence of prior specification on the results. Finally, we assessed the certainty of existence of the correlations by evaluating whether the 95% credibility interval (CrI) contained 0 and $p_d > 97.5\%$. We also characterized what we call in this study ‘observed trends’ where the correlations have a $p_d > 90\%$ (meaning that 90% of the posterior distribution is of the same sign as the median), but lower than the threshold for statistical significance of 97.5% (Kruschke, 2014; Makowski et al., 2019). This corresponds respectively to a threshold of 5% and 20% for the credibility intervals (95% and 80%).

To identify the main axes of (co)variation among traits, we extracted the variance-covariance matrix and performed its eigen decomposition (Houslay et al., 2018). This method is equivalent to a principal component analysis but considers the repeat measurements per individual and makes use of the among-individual covariation among metrics (Michelangeli et al., 2022). This method allows the identification of traits that have the most influence on the behavioural syndrome, as well as determining the structure of the syndrome. To obtain credibility intervals for eigen values and loadings, we performed the eigen decomposition on the entire posterior distribution of the covariance matrix.

## Relative positions of individuals on the movement and stress syndromes

To understand how the position of an individual on the stress syndrome correlates with its position on the movement syndrome, we ran two independent multivariate linear mixed models: one including the six movement traits as response variables to characterize the movement syndrome, and the other including the four stress-response traits as response variables to characterize the stress syndrome. Note that, for these two models, the sample sizes were larger (i.e. 218 individuals with 8.5 repetitions on average for the movement syndrome model and 288 individuals with 1.3 repetitions on average for the stress syndrome model; see Appendix for details). For both models, we retained the relevant fixed effects and the same number of iterations, warm-up and thinning parameter as for the general multivariate model (see Behavioural syndrome, above). To obtain the relative position of each
individual on the movement and stress syndromes, we extracted from both models the entire posterior distribution of the random intercepts for each trait and individual, and calculated the distance matrix between all pairs of individuals. For both models, we only kept the 177 individuals that were sampled for all 10 traits to compute and compare the distance matrices. We compared these distance matrices using a Mantel test (Mantel, 1967), which provides the correlation between two matrices, with the vegan package (Oksanen et al., 2007) and 1000 permutations. We performed the latter analysis on each draw of the posterior distributions of the two models, for a total of 120 000 draws. We computed the mean correlation, as well as the 95% HPD credibility interval over all the draws. We considered the rankings of individuals on the two syndromes to be significantly correlated if the HPD intervals did not contain 0. Finally, we also studied the relationship between the relative ranking of an individual on each stress trait separately and its relative ranking in terms of movement syndrome. To do so, we applied the same method as described above by computing the four distance matrices relative to each stress trait and compared each of them to the distance matrix extracted from the movement syndrome using a Mantel test.

### Ethical Note

All capture and marking protocols were approved by the French Ministry for Research and Higher Education and the ethics committee (Comité d'éthique en expérimentation animale, CEEA) no. 115 (project numbers Apafis 39320 and Apafis 7880). Animals were captured using drive netting including only human beaters (no dogs) to reduce the disturbance incurred to wildlife. Even though the presence of beaters created some disturbance, most wild animals (except roe deer) could escape quickly from the beaten area by crossing the nearest line of nets. If a nontarget animal was caught in the net, it was immediately removed and released on site. When caught, roe deer were immediately removed from the net by experienced handlers positioned regularly along the row of nets, and tranquilized with an intramuscular injection of acepromazine (Calmivet: 0.3 mL, or 0.6 mL if the individual was clearly distressed after 30 min of monitoring, N = 9 individuals between 2009 and 2022; Montané et al., 2003) to reduce stress while awaiting the marking procedure, and prevent potential postcapture myopathy (Delgiudice et al., 1990). Animals were then directly transferred to a wooden retention box (105 × 45 cm and 75 cm high), designed to limit the stress and minimize the risk of injuries, with holes for ventilation and lined inside with foam (Morellet et al., 2009). Boxes were distributed every 150–200 m in the field and so were carried directly to the capture site within 2–3 min maximum. Each year, depending on the sector of capture, around one third of the local population is caught. During the rest of the capture event, every individual was regularly checked by an experienced observer to ensure that it was calm and that stress was minimized. By keeping the individual in the retention box for the duration of the capture, we avoided recapturing the same individual several times during a single capture operation, even though, on rare occasions, a given individual was caught twice on different days within a given year, in different capture sites (i.e. N = 6 individuals between 2009 and 2022). At the end of the drive-netting event, every box was collected and each animal taken out, according to the order of capture. Individuals were then held onto a manipulation table by three experienced handlers, with the eyes covered to reduce stress. Every capture event was attended by a wildlife veterinarian to prevent injuries and to administer an intramuscular injection of selenium and vitamin E (Séléphérol) with a dosage adjusted to the individual's body mass, to avoid capture myopathy. Each animal was marked

with two plastic ear tags and a subcutaneous electronic chip, and several measurements were conducted (weight, antler size for males, hind foot length, body temperature) on the manipulation table. Blood (up to 25 mL from the jugular vein) and skin (ca. 10 mg from the ear) samples, ticks and faeces were collected. A GPS collar was fitted that did not exceed 3.5% of an individual's body mass. Rectal temperature was monitored during each marking procedure and animals were immediately released when the predefined endpoint (maximal temperature, panting, struggling behaviour) was attained. Individuals were released near their capture site and behaviour was recorded at the net, during handling and at release. Capture and marking procedures were performed by experienced and trained handlers. Each marking procedure lasted between 8 and 15 min. The capture and marking procedures, including the procedures made for minimizing stress, were similar for all roe deer caught, so that this should not impact among-individual differences in the behavioural and physiological parameters measured during handling. For more details of these procedures, see Morellet et al. (2009), Debeffe et al. (2013) and Bonnot et al. (2017, 2018). GPS collars were programmed to automatically drop off after 11 months of recording and were retrieved in the field. During this long-term roe deer monitoring programme, no adverse effects of GPS collars on behaviour or individual performance were recorded.

## RESULTS

### Trait Repeatability and Variability

For each stress and movement trait, we provide the population mean observed values and ranges, as well as the number of observations (i.e. repetitions), for each individual in Table 2. To calculate repeatability estimates, we used all the data available for each trait separately, which led to different sample sizes (see Table 2). Additionally, we give basic descriptive statistics in the Appendix (Tables A2 and A3) regarding how sex and age class influence the traits.

Adjusted repeatability ranged from 0.01 for $N_{exc}$ to 0.50 for HR size, with a mean value of 0.32 (SD = 0.19) for movement traits (0.38 when excluding $N_{exc}$ that was not repeatable) and 0.24 (SD = 0.08) for stress-response traits (Table 2, see Appendix and Table A1 for unadjusted repeatability). Stress-response traits had wider credibility intervals, probably due to a lower sampling effort. Indeed, almost 80% of individuals were captured only once (Table 2), which could make the estimation of within-individual variance imprecise. However, when performing the analysis with only individuals sampled at least twice, we obtained very similar results (Appendix, Fig. A3). Of the movement traits, HR size, $P_{OH}$ and ODBA were the most repeatable, while $N_{exc}$ was not repeatable, probably due to the large number of zeroes in the data, as most individuals did not make an excursion each month. Regarding mean-scaled individual variation ($I_l$ adj), movement metrics were generally more variable than stress-response traits, particularly HR size and ODBA. Both repeatability and variability varied greatly among traits, with some traits being repeatable, but exhibiting low mean-scaled variance (low $I_l$ adj, e.g. rectal temperature and average speed), while others were moderately variable as well as being repeatable (e.g. HR size and docility score).

### Behavioural Syndrome

Using the multivariate linear mixed model combining all traits on 177 roe deer (see Appendix, Table A6, for sample sizes), we found significant among-individual correlations (hereafter r) among movement traits forming a movement syndrome. More
<table><caption>**Table 3**
Correlations at the among-individual level between traits</caption><thead><tr><th>Traits</th><th>HR size</th><th>AVG<sub>speed</sub></th><th>N<sub>exc</sub></th><th>D<sub>road</sub></th><th>P<sub>OH</sub></th><th>ODBA</th><th>Docility score</th><th>N:L ratio</th><th>HCT</th><th>T</th></tr></thead><tbody><tr><td>HR size</td><td></td><td>76.5</td><td>86.0</td><td>61.8</td><td><strong>100</strong></td><td>50.1</td><td>78.0</td><td>84.2</td><td>70.8</td><td>64.9</td></tr><tr><td>AVG<sub>speed</sub></td><td>0.07 (-0.12, 0.25)</td><td></td><td>88.9</td><td><strong>98.0</strong></td><td><strong>98.9</strong></td><td><strong>99.9</strong></td><td>80.2</td><td><strong>96.4</strong></td><td><strong>94.2</strong></td><td>73.3</td></tr><tr><td>N<sub>exc</sub></td><td>0.27 (-0.28, 0.69)</td><td>-0.32 (-0.73, 0.26)</td><td></td><td>77.8</td><td>89.1</td><td>52.0</td><td>68.5</td><td>60.7</td><td>56.4</td><td>76.5</td></tr><tr><td>D<sub>road</sub></td><td>0.03 (-0.15, 0.20)</td><td><strong>0.20 (0.01, 0.39)</strong></td><td>-0.18 (-0.63, 0.34)</td><td></td><td>82.6</td><td>56.4</td><td>85.4</td><td>73.4</td><td>58.0</td><td>73.0</td></tr><tr><td>P<sub>OH</sub></td><td><strong>0.41 (0.27, 0.54)</strong></td><td><strong>-0.21 (-0.38, -0.03)</strong></td><td>0.32 (-0.25, 0.73)</td><td>-0.08 (-0.24, 0.09)</td><td></td><td>70.7</td><td>66.1</td><td>66.2</td><td>88.7</td><td><strong>90.2</strong></td></tr><tr><td>ODBA</td><td>-0.00 (-0.19, 0.19)</td><td><strong>-0.35 (-0.54, -0.13)</strong></td><td>-0.01 (-0.49, 0.48)</td><td>-0.01 (-0.19, 0.17)</td><td>-0.04 (-0.20, 0.12)</td><td></td><td>71.2</td><td>71.8</td><td>59.4</td><td>84.9</td></tr><tr><td>Docility</td><td>0.06 (-0.10, 0.22)</td><td>0.08 (-0.10, 0.25)</td><td>-0.11 (-0.55, 0.39)</td><td>-0.09 (-0.25, 0.08)</td><td>0.03 (-0.12, 0.18)</td><td>-0.04 (-0.19, 0.11)</td><td></td><td>84.6</td><td>79.7</td><td>69.8</td></tr><tr><td>score</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>N:L ratio</td><td>0.08 (-0.08, 0.24)</td><td><strong>0.16 (-0.02, 0.33)</strong></td><td>0.06 (-0.42, 0.51)</td><td>0.05 (-0.11, 0.21)</td><td>0.03 (-0.12, 0.18)</td><td>-0.05 (-0.20, 0.11)</td><td>0.08 (-0.07, 0.22)</td><td>0.05 (-0.10, 0.19)</td><td>73.9</td><td>73.1</td></tr><tr><td>HCT</td><td>-0.04 (-0.20, 0.11)</td><td><strong>0.14 (-0.04, 0.31)</strong></td><td>0.04 (-0.43, 0.50)</td><td>0.02 (-0.15, 0.18)</td><td>0.09 (-0.06, 0.24)</td><td>-0.02 (-0.17, 0.13)</td><td>0.06 (-0.09, 0.21)</td><td></td><td></td><td>75.7</td></tr><tr><td>T<sup>o</sup></td><td>-0.03 (-0.19, 0.13)</td><td>-0.06 (-0.23, 0.12)</td><td>0.17 (-0.34, 0.60)</td><td>-0.05 (-0.21, 0.11)</td><td><strong>0.10 (-0.05, 0.24)</strong></td><td>0.08 (-0.07, 0.23)</td><td>0.04 (-0.11, 0.19)</td><td>-0.05 (-0.19, 0.10)</td><td>-0.05 (-0.20, 0.09)</td><td></td></tr></tbody></table>

Correlation values were extracted from the general multivariate linear mixed model, with the corresponding 95% credibility intervals in brackets, and are given below the diagonal of the matrix. Above the diagonal are the corresponding probabilities of direction (p<sub>d</sub> ≠ 0%). Bold roman values indicated significant correlations (p<sub>d</sub> > 97.5%) while bold italic values indicated observed trends (p<sub>d</sub> > 90%). Variance and covariance components are reported in the Appendix (Table A7).

particularly, we found that individuals with a higher P<sub>OH</sub> had a lower AVG<sub>speed</sub> (r = -0.21, p<sub>d</sub> = 98.9%) and a larger HR size (r = 0.41, p<sub>d</sub> = 100%). Moreover, individuals with a lower AVG<sub>speed</sub> also had a higher ODBA (r = -0.35, p<sub>d</sub> = 99.9%) and a lower D<sub>road</sub> (r = 0.20, p<sub>d</sub> = 98.0%; Table 3). On the contrary, the existence of a stress syndrome was not supported, as no correlation was strictly different from zero among any of the four stress traits. Although the probability of existence was lower, some stress traits were weakly correlated with certain movement traits (Table 3). Notably, individuals with a lower AVG<sub>speed</sub> tended to have a lower N:L ratio (r = 0.16, p<sub>d</sub> = 96.4%) and a lower HCT (r = 0.14, p<sub>d</sub> = 94.2%). Similarly, individuals with a higher P<sub>OH</sub> tended to have a higher rectal temperature (r = 0.10, p<sub>d</sub> = 90.2%).

### Among-individual covariation of the overall behavioural syndrome

We identified two main orthogonal axes of among-individual covariation in the behavioural syndrome linking movement and stress-response traits. The first two axes of the decomposition of the variance-covariance matrix explained 33% (CrI: 29, 39%) and 16% (CrI: 13, 18%) of the (co)variance, respectively. As expected, individuals exhibited a combination of movement and stress behaviours that could not be measured by a single gradient; rather, the position of individuals relative to each other was described by two orthogonal gradients. P<sub>OH</sub> loaded significantly highly on the first axis, with HR size and N<sub>exc</sub> loading in the same direction and AVG<sub>speed</sub> and D<sub>road</sub> loading in the opposite direction (Fig. 1). Individuals that used open habitats more during daytime, thus, had a bigger home range and a lower speed of movement, and tended to be closer to roads during daytime and undergo more excursions. On the second axis, N:L ratio loaded significantly with AVG<sub>speed</sub> loading in the same direction. No other trait loaded significantly on this axis, although HCT and docility score both tended to load in the same direction, but with very large credibility intervals (Fig. 1).

### Individual relative positions along both the stress and movement syndromes

To characterize each of the stress and movement syndromes, we used two independent multivariate models and used individuals sampled for all relevant traits (N = 177). Based on the random intercepts from each model (Fig. 2a and b), we did not find any significant correlation (r = -0.002, CrI: -0.07, 0.07) between the ranking of an individual on the movement syndrome and its ranking on the stress syndrome (Fig. 2c). Furthermore, the ranking of individuals on the movement syndrome was not correlated with their relative rankings for each stress trait considered independently (docility score: r = 0.001, CI: -0.070, 0.073; N:L ratio: r = -0.004, CI: -0.073, 0.068; HCT: r = 0.012, CI: -0.055, 0.083; rectal temperature: r = -0.012, CI: -0.076, 0.054).

## DISCUSSION

An important consideration in the study of animal personality is the coherence between behavioural traits measured under artificial conditions with actual behaviour of wild individuals in their natural habitat (Archard & Braithwaite, 2010). Several previous studies have shown that individual differences in a standardized setting reliably reflected individual differences in behaviour in the wild (Stiegler et al., 2022; West et al., 2019). In this study, we could not find any clear relationship between an individual's acute stress response (i.e. behavioural and physiological responses to a stressful capture event) and its movement syndrome (i.e. movement patterns while free ranging) in roe deer, although there was some indication that the movement
<figure><img src="image_6.png" /><figcaption>**Figure 1.** Trait loadings on the first two eigenvectors of the variance-covariance matrix. Loadings were extracted from the general multivariate model and represent the two main axes of among-individual covariation of the general behavioural syndrome, with corresponding 95% HPD credibility intervals.</figcaption></figure>

syndrome was weakly related to certain stress-response traits evaluated independently. Nonetheless, we characterized individual differences in a panel of movement traits and showed they constituted a movement syndrome, highlighting individual differences in space use tactics in a large wild herbivore. These differences in movement tactics likely have significant consequences for individual performance (Santicchia et al., 2018), potentially driving nonrandom distributions of personalities in the landscape (Holtmann et al., 2017; Spiegel et al., 2017).

## Movement Traits are More Repeatable than Stress-Response Parameters

In their review across a wide range of behaviours and taxa, Bell et al. (2009) determined a mean repeatability value of 0.37. In our study, except for the number of excursions, the behavioural traits used to characterize a behavioural syndrome in wild roe deer were moderately repeatable (adjusted repeatability estimates $R_{adj}$ between 0.15 and 0.50; Table 2). On average, movement traits were more repeatable than stress-response traits (average $R_{adj}$ of 0.38 and 0.24, respectively), with values approaching the repeatability values between 0.55 and 0.76 for movement traits reviewed in Stuber et al. (2022). Still, stress-response traits were moderately repeatable (between 0.15 and 0.33; Table 2), suggesting that individuals do differ globally in a consistent way in their response to a capture event. Even though estimates of repeatability and within-individual variance might be imprecise when the majority of individuals included have a single measurement, we showed that values of within- and among-individual variances, as well as estimates of repeatability, did not differ greatly with or without these individuals (see Appendix, Table A1), thus indicating that our results are robust. Repeatability values for stress-response traits were also coherent with previous studies on the same roe deer population (Bonnot et al., 2015; Monestier et al., 2015), with similarly large credibility intervals depending on the set-up.

Movement traits were also more variable, in general, than stress-response traits. Furthermore, Gervais et al. (2020) showed that movement traits can be heritable, and so have the potential to respond to natural selection. If these movement traits are additionally linked to among-individual differences in survival or reproductive success, then they may participate in shaping the evolutionary response of populations to future environmental challenges (Cockrem, 2022).

## Movement Traits Measured in the Wild Form a Movement Syndrome in Roe Deer

By modifying the evolutionary trajectory of correlated traits, movement syndromes can have important consequences for population distribution, species interactions or population responses to human-induced rapid environmental change (Sih et al., 2012). In our study, we found that individual roe deer differed in their movement syndrome, with boldness, and notably the probability of using open habitats during daytime, being the strongest driver of among-individual variation in movement tactics. In particular, individuals that took more risk by using open habitats more and ranging closer to roads during daytime had a larger home range size, but a lower average movement speed, while being more active. These results are consistent with the literature showing that bold individuals take more risks, but exploit their habitat less thoroughly (e.g. in bank voles, *Myodes glareolus*, Schirmer et al., 2019). In our study system, roe deer face a trade-off between resource acquisition and risk avoidance and individuals differ consistently in solving this spatial trade-off: while shy individuals exploit safe woodland habitats for refuge, they must travel further to forage on widely dispersed items (e.g. buds and shoots of woody vegetation) that are less nutritive in certain seasons (Abbas et al., 2011; Morellet et al., 2011); in contrast, bolder individuals are able to exploit open habitats, which provide resources that are clumped in space
<figure><img src="image_7.png" /><figcaption>**Figure 2.** Relative positions of individuals in both their movement syndrome and their stress syndrome. (a, b) The positions of each individual (blue and orange dots) relative to each other on (a) the movement syndrome, using the mean random intercepts from the multivariate movement model, and (b) the stress-response syndrome, using the mean random intercepts from the multivariate stress model. (c) The shift in the relative position of an individual between the two syndromes. If the movement and the stress-response syndromes were correlated (i.e. if the ranking of an individual in terms of movement corresponded to its ranking in terms of stress response), the change in ranks between syndromes would either be negligible (short arrows) or in the same direction (parallel arrows). (d) The distances on the movement syndrome between pairs of individuals as a function of their distances on the stress syndrome, with each point representing a pair of individuals.</figcaption></figure>

and seasonally richer (e.g. maize, wheat or sorghum), but at the potential cost of increased antipredator behaviour (Benhaiem et al., 2008) and stress (Carbillet et al., 2020). The existence of such contrasting tactics might drive among-individual differences in survival, especially in hunted populations, potentially leading to a nonrandom selection of individuals in the population (Ciuti et al., 2012).

In the literature, bolder individuals are also generally more exploratory (e.g. Dammhahn & Almeling, 2012; Mazué et al., 2015), driving individual foraging tactics to solve the trade-off between exploitation of known resource patches and exploration to discover new resources (Patrick et al., 2017). Among-individual differences in exploration propensity impact the probability of dispersing (Debeffe, Morellet, et al., 2014), with explorers potentially disproportionately represented at the leading edge of colonization fronts (Brodin et al., 2013). Here, however, we did not find any support for an among-individual correlation between the propensity to explore (i.e. excursions outside of the home range) and boldness, nor with any other movement or stress response traits. Indeed, 24% of roe deer did not perform any excursion during the year of monitoring, while 32% performed only one. This is likely due to the fact that roe deer are highly sedentary, with stable home ranges across the adult

life stage (Lorenzini et al., 2022), so that excursions are rare events (but see Debeffe, Focardi, et al., 2014).

Finally, we found that individual roe deer that were more active also had a lower average movement speed, although movement speed has often been used as a proxy for activity (Hertel et al., 2019; Nakayama et al., 2016). This result suggests that, for certain species or individuals, movement speed might not be a good proxy of activity level. ODBA measures activity due to a variety of behaviours, including those that do not necessarily involve a change in location. Foraging, for instance, represents 40% of the activity budget in roe deer (Benoit et al., 2023), but does not necessarily entail moving long distances, especially when resources are clumped in space and locally superabundant, which is particularly true in agricultural habitats (Morellet et al., 2011). In roe deer, bolder individuals express higher levels of vigilance when foraging (Bonnot et al., 2015), scanning the environment more frequently when in risky open habitats. As a result, we suggest that bolder individuals are more active in terms of antipredator behaviour (Bonnot et al., 2017) but have a lower movement speed because of the clumped resource distribution in large agricultural fields, ultimately determining the energetic value of exploiting different types of resource.
## No Evidence for a Stress-Response Syndrome in Wild Roe Deer

When facing acutely stressful situations, the behavioural responses expressed by individuals are often related to their physiological responses, forming a stress syndrome, or coping style (Koolhaas et al., 1999). Contrary to the literature and our expectation, here we found no strong links among the four stress-response traits (i.e. docility score, N:L ratio, haematocrit and rectal temperature) at the among-individual level and, hence, no support for a stress syndrome. This was unexpected, as previous studies in captive roe deer found that less docile individuals have a higher rectal temperature and haematocrit, but a lower N:L ratio (Monestier et al., 2016). We presume that this was potentially due to the low number of replicates per individual in our study (80% of the individuals sampled once, Table 2). In their study, Monestier et al. (2016) caught captive roe deer individuals bimonthly over a year (six times), yielding a substantial number of replicates per individual. In support of this, phenotypic correlations (i.e. without distinguishing within- and among-individual variances, see Appendix, Table A8) indicated a weak but significant positive relationship between temperature, haematocrit and docility (respectively r = 0.11, Crl: 0.01, 0.21, and r = 0.12, Crl: 0.02, 0.22; Table A8), mirroring observations in captivity (Monestier et al., 2016). However, the fact that the majority of individuals in our data set had a single stress-response measurement might imply that we were not able to properly disentangle among- and within-individual (co)variance. We thus need to be cautious with respect to the 'individual gambit' (sensu Niemelä & Dingemanse, 2018), as the absence of a syndrome might also be due to the lack of repeat measurements for some individuals and traits, potentially generating a degree of imprecision when individuals express behavioural plasticity (Dingemanse et al., 2010). An alternative hypothesis would be that these traits reflect different dimensions of the stress response. Indeed, recent studies have highlighted the need to consider the multidimensionality of individual variation in the acute stress response rather than a single proactive-reactive gradient (Koolhaas et al., 2007). For instance, Koolhaas et al. (2010) suggested two orthogonal gradients: the coping style (proactive versus reactive) and the stress reactivity gradient (high versus low physiological response). Within this framework, animals that are proactive can exhibit low (bold) or high (panicky) stress reactivity, whereas reactive individuals may exhibit low (docile) or high (shy) reactivity to stress.

## No Evidence for a Link Between the Response to Acute Stress and Movement Types in the Wild

While behavioural and physiological differences in a standardized setting have previously been found to reflect behavioural differences in the wild (Dammhahn & Almeling, 2012; Stiegler et al., 2022), here we did not find strong support for a link between the response of an individual to acute stress in a standardized setting and their movement behaviour while free-ranging in the wild. Indeed, as bolder roe deer are routinely exposed to higher levels of perceived risk, we expected them to express higher levels of both chronic and acute stress. Although we found little support for such a relationship, there was some indication that bolder roe deer that used risky open habitats during daytime more had a higher rectal temperature, suggesting that bold individuals were more stressed during the acutely stressful event of capture (Table 3). However, individuals with a lower movement speed, which are also bolder (r = -0.21), tended to respond less strongly to stress (lower haematocrit and N:L ratio, Table 3). These a priori contradictory results might actually reflect two independent dimensions of the stress response: a higher

temperature might reflect more active defence through struggling (proactive coping style), while a lower physiological response suggests lower stress reactivity, characterizing a bold behavioural type (Koolhaas et al., 2010).

Because open habitats are perceived as riskier by roe deer due to human disturbances (Bonnot et al., 2013), they may generate higher chronic stress when used regularly. Individuals experiencing chronic stress have an attenuated physiological response when faced with acutely stressful situations (Romero et al., 2009), potentially leading to divergences in immunity. For example, Kortet et al. (2007) found that crickets, Gryllus integer, that invested more in antipredator behaviour had lower immune function (Niemelä et al., 2012). We could thus expect bolder risk-prone roe deer, which invest more in antipredator behaviours (Bonnot et al., 2015) but express a lower physiological response to an acutely stressful event, to have lower immune function, putting them at greater risk of contracting disease and pathogens.

## Conclusion and Perspectives

While we did not find a strong link between an individual response to acute stress in a controlled setting and movement types of free-ranging roe deer, our study identified a movement syndrome in which boldness, and particularly the tolerance of exposure to human disturbance, was the strongest driver of among-individual variation in movement tactics. A previous meta-analysis revealed that boldness was the personality trait with the highest impact on components of fitness (Smith & Blumstein, 2008). Indeed, individuals with different behavioural types may be well-adjusted to different habitats (Monestier et al., 2015), with differences in individual performance mediated by landscape structure (Brehm & Mortelliti, 2024; Santicchia et al., 2018), potentially driving personality-matching habitat choice (e.g. Holtmann et al., 2017). Altogether, these results suggest that, for wild populations living in human-dominated landscapes, boldness might be the main driver of space use and movement tactics in the wild, likely leading to a nonrandom distribution of personalities in the landscape (Spiegel et al., 2017), which has evolutionary consequences for the persistence of among-individual variations in wild hunted species. In this study, we focused on the relationship between the stress response and the mean expression of movement traits (behavioural types or personality). However, individuals consistently differ not only in their average behaviour, but also in the extent to which they adjust their behaviour to varying environmental conditions (Dingemanse & Wolf, 2013; Stamps & Biro, 2016). The relationship between the stress response and movement behaviour in roe deer could be more tightly linked to plasticity in risk taking and activity. Indeed, the European roe deer is a highly flexible species, originally forest dwelling, but now thriving in contrasting habitats, including human-dominated landscapes (Lorenzini et al., 2022). At the individual scale, roe deer differ in their movement tactics (Couriot et al., 2018; this study), but are highly plastic in their habitat and resource use (Abbas et al., 2011; Bonnot et al., 2018; Morellet et al., 2011). Previous studies have shown that bold, proactive individuals are generally less plastic (Cockrem, 2022; Villegas-Ríos et al., 2018), because they do not adjust their behaviour based on direct environmental feedback, but rather rely on preformed routines and memory (Jolles et al., 2019). Behavioural plasticity is a potentially powerful mechanism that allows wild animals to cope with the negative impacts of environmental changes to their habitat (Ghalambor et al., 2007). Future studies should evaluate how covariations between personality and plasticity constrain the resilience of animal populations in a rapidly changing world (Cockrem, 2022; Fox et al., 2019).
## Author Contributions

**Inès Khazar:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Nicolas Morellet:** Writing – review & editing, Writing – original draft, Supervision, Investigation, Funding acquisition, Data curation, Conceptualization. **A.J. Mark Hewison:** Writing – review & editing, Investigation, Funding acquisition, Data curation, Conceptualization. **Laura Gervais:** Writing – review & editing, Investigation, Data curation, Conceptualization. **Guillaume Le Loc'h:** Writing – review & editing, Resources, Investigation. **Arnaud Bonnet:** Investigation, Data curation. **Nicolas Cèbe:** Investigation. **Yannick Chaval:** Investigation, Data curation. **Anne Geffré:** Resources, Investigation. **Bruno Lourtet:** Visualization, Investigation, Data curation. **Joël Merlet:** Resources, Investigation. **Nadège C. Bonnot:** Writing – review & editing, Writing – original draft, Supervision, Investigation, Funding acquisition, Data curation, Conceptualization.

## Data Availability

The data for this study are available at https://anonymous.4ope.n.science/r/Movement_stress-B251.

## Declaration of Interest

The authors have no competing interests to declare.

## Acknowledgments

We thank the local hunting associations, the Fédération Départementale des Chasseurs de la Haute Garonne and numerous coworkers and volunteers for their assistance. We thank Srivats Chari and Simone Ciuti for preliminary discussions and considerations on the subject. We thank François-Xavier Dechaume-Moncharmont, Nathan Ranc and Raphaël Royauté for the interesting discussions surrounding the data and methods applied in this study. We also thank Tom Houslay and Alastair Wilson for the useful exchanges on methodological issues. Finally, our thoughts go to our late colleague, Lucie Debeffe, who participated in the first stages of this work, with her always insightful and joyful contributions. This project was supported by the 'Carry-Move' ANR grant no. ANR-23-CE02-0008 to N.M. and A.J.M.H. and the 'Anthropofear' Young researcher ANR grant no. ANR-23-CE02-0017 to N.C. B from the Agence Nationale de la Recherche.

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# Appendix

## Details on the Acute Stress-Response Parameters

### Neutrophil to lymphocyte ratio and haematocrit

The N:L ratio, a component of the leucocyte profile, has been shown to index the baseline level of stress in various species (Davis et al., 2008; Vleck et al., 2000). Measurements of haematological analytes (neutrophils and lymphocytes) were done on whole blood that was EDTA-preserved at 4 °C. Differential cell count was performed on the first 100 white blood cell counts on Wright-Giemsa-stained blood smears microscopically by experienced technicians at École Nationale Vétérinaire de Toulouse (ENVT). Blood samples were read manually by technicians from ENVT before 2019 and by INRAE lab technicians after 2019 to obtain the haematocrit value.

### Examples of the Movement Metric Number of Excursions ($N_{exc}$)

Figs A1 and A2 show the calculation of excursions.
<figure><img src="image_8.png" /><figcaption>**Figure A1.** Multirange tactic with two clusters. Locations from two individuals that exhibit a multirange tactic and identification of the two separate clusters of locations.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Figure A2.** Identification of excursions. Categorization of locations into normal (yellow), isolated (red) and excursions (blue) for two individuals.</figcaption></figure>
Unadjusted Versus Adjusted Repeatability Estimates

We calculated unadjusted repeatability estimates with univariate mixed models by including only the identity of the individual as a random effect. Unadjusted repeatabilities ranged from 0.01 for the number of excursion ($N_{exc}$) to 0.64 for ODBA, with a mean value (± SD) of 0.39 ± 0.21 for movement traits and 0.24 ± 0.07 for stress-response traits (see Table A1). Adjusted repeatability estimates were calculated using univariate mixed models as well but including all potential confounding effects that could artificially inflate the estimate of repeatability (see main text). Adjusted repeatabilities calculated in this Bayesian framework ranged from 0.01 for the number of excursion ($N_{exc}$) to 0.50 for HR size, with a mean value of 0.32 ± 0.19 for movement traits and 0.24 ± 0.08 for

stress-response traits (Fig. A3). For comparison with former studies, we also provided the adjusted repeatability estimates computed with a frequentist method $R_{ftpR}$ using the package rptR (Stoffel et al., 2017).

For most traits, unadjusted repeatability was higher than adjusted repeatability, and more substantially so for ODBA and AVGspeed (Table A1).

Based on Martin et al. (2011), we decided to retain individuals measured only once in our computation of repeatability, because this approach improves the estimate of among-individual variance. Yet, to verify that repeatability estimates were accurate and unbiased by this inclusion of singletons, we compared, for each trait, the repeatability estimate and its component (i.e. the within- and among-individual variance) obtained with or without individuals assayed only once.

<figure><img src="image_10.png" /><figcaption>**Figure A3.** Comparison between estimates of repeatability, derived from within- and among-individual variances for stress-response traits, calculated either with or without individuals assayed only once. For each of the four response traits, we show adjusted repeatability and both within- and among-individual variances, with credibility intervals using only individuals assayed at least twice (N = 82 with 206 observations for the behavioural score, N = 69 with 161 observations for N:L ratio, N = 64 with 147 observations for HCT and N = 74 with 176 observations for rectal temperature) in orange. These values of repeatability can be compared with $R_{adj}$ values obtained using all available individuals, including those assayed only once (in blue), and with values of $\sigma_{ind}^2$ and $\sigma_t^2$ in Table A5.</figcaption></figure>
<table><caption>**Table A1**
Summary of the estimates of repeatability with different methods</caption><thead><tr><th>Traits</th><th>R (95% CrI)</th><th>R<sub>adj</sub> (95% CrI)</th><th>R<sub>ftpR</sub> (95% CrI)</th></tr></thead><tbody><tr><td>HR size</td><td>0.47 (0.42, 0.53)</td><td>0.50 (0.44, 0.57)</td><td>0.51 (0.44, 0.56)</td></tr><tr><td>AVG<sub>speed</sub></td><td>0.41 (0.36, 0.47)</td><td>0.18 (0.13, 0.23)</td><td>0.18 (0.13, 0.24)</td></tr><tr><td>N<sub>exc</sub></td><td>0.01 (0.00, 0.04)</td><td>0.01 (0.00, 0.04)</td><td>0.0001 (0.00, 0.0002)</td></tr><tr><td>D<sub>road</sub></td><td>0.34 (0.29, 0.39)</td><td>0.33 (0.28, 0.38)</td><td>0.32 (0.27, 0.38)</td></tr><tr><td>P<sub>OH</sub></td><td>0.45 (0.40, 0.50)</td><td>0.47 (0.42, 0.52)</td><td>0.41 (0.36, 0.45)</td></tr><tr><td>ODBA</td><td>0.64 (0.59, 0.69)</td><td>0.41 (0.29, 0.52)</td><td>0.42 (0.33, 0.53)</td></tr><tr><td>Docility score</td><td>0.20 (0.04, 0.35)</td><td>0.19 (0.00, 0.33)</td><td>0.21 (0.05, 0.38)</td></tr><tr><td>N:L ratio</td><td>0.21 (0.00, 0.37)</td><td>0.15 (0.00, 0.32)</td><td>0.18 (0.00, 0.36)</td></tr><tr><td>HCT</td><td>0.21 (0.00, 0.40)</td><td>0.27 (0.00, 0.45)</td><td>0.33 (0.12, 0.53)</td></tr><tr><td>Rectal temperature</td><td>0.34 (0.12, 0.55)</td><td>0.33 (0.12, 0.53)</td><td>0.36 (0.20, 0.52)</td></tr></tbody></table>

R corresponds to unadjusted repeatability, R<sub>adj</sub> to adjusted repeatability computed with a Bayesian model and R<sub>ftpR</sub> to adjusted repeatability computed with a frequentist approach, with corresponding 95% credibility intervals in brackets calculated using variance components extracted from the univariate models.

## Descriptive Statistics for Each of the Traits Per Age and Sex Groups

For each of the 10 traits studied, we calculated the mean value and the standard deviation per age group (yearling and adult) and sex group (Table A2).

Repeatability and variability in the main text) and in the multi-variate models (see Behavioural syndrome in the main text). To do so, we ran full models including all the potential fixed effects for each trait. A fixed effect was considered significant when the probability of direction was ≥ 97.5% and the 95% credibility in-

<table><caption>**Table A2**
Mean value and standard deviation of movement and stress response traits per age group and sex</caption><thead><tr><th rowspan="2">Group</th><th colspan="10">Traits</th></tr><tr><th>HR size (ha)</th><th>AVG<sub>speed</sub> (m/s)</th><th>N<sub>exc</sub></th><th>D<sub>road</sub></th><th>P<sub>OH</sub></th><th>ODBA</th><th>Docility score</th><th>N:L ratio</th><th>HCT (%)</th><th>Rectal temperature (°C)</th></tr></thead><tbody><tr><td>Females</td><td>60.0 ± 57.5</td><td>176 ± 81</td><td>0.23 ± 0.53</td><td>0.09 ± 0.17</td><td>0.47 ± 0.34</td><td>13347 ± 5977</td><td>0.16 ± 0.14</td><td>2.84 ± 1.50</td><td>46.2 ± 4.39</td><td>38.8 ± 0.7</td></tr><tr><td>Males</td><td>73.5 ± 60.3</td><td>244 ± 113</td><td>0.17 ± 0.50</td><td>0.14 ± 0.19</td><td>0.45 ± 0.32</td><td>14001 ± 6236</td><td>0.16 ± 0.14</td><td>3.47 ± 1.77</td><td>45.1 ± 4.08</td><td>38.8 ± 0.7</td></tr><tr><td>Adults</td><td>64.3 ± 59.1</td><td>206 ± 101</td><td>0.20 ± 0.50</td><td>0.11 ± 0.18</td><td>0.47 ± 0.34</td><td>13497 ± 5947</td><td>0.16 ± 0.14</td><td>3.16 ± 1.69</td><td>45.9 ± 4.33</td><td>38.8 ± 0.7</td></tr><tr><td>Yearlings</td><td>65.9 ± 57.9</td><td>186 ± 92</td><td>0.24 ± 0.56</td><td>0.09 ± 0.17</td><td>0.44 ± 0.32</td><td>13776 ± 6412</td><td>0.16 ± 0.13</td><td>2.91 ± 1.47</td><td>44.9 ± 4.05</td><td>38.9 ± 0.6</td></tr></tbody></table>

## Explanatory Variables Used for Each Univariate and Multivariate Model

For each of the 10 traits studied, we determined which explanatory variables had a significant effect on the trait considered to include them as fixed effects in the univariate models (see

tervals (CrI) did not contain 0. Significant fixed effects were then included in the corresponding univariate and multivariate models (Table A3). Note that the individual identity was included as a random effect in each model; and the year of capture was also included as a random effect in each model with a movement trait as a response variable.
<table><caption>**Table A3**
Fixed effects included in the univariate models</caption><thead><tr><th rowspan="2">Effects</th><th colspan="10">Traits</th></tr><tr><th>HR size</th><th>AVG<sub>speed</sub></th><th>N<sub>exc</sub></th><th>D<sub>road</sub></th><th>P<sub>OH</sub></th><th>ODBA</th><th>Docility score</th><th>N:L ratio</th><th>HCT</th><th>Rectal temperature (°C)</th></tr></thead><tbody><tr><td>Sex</td><td><strong>99.99</strong><br><strong>0.29</strong><br>(<strong>0.14, 0.43</strong>)</td><td><strong>100</strong><br><strong>2.13</strong><br>(<strong>1.70, 2.56</strong>)</td><td><strong>99.79</strong><br><strong>-0.33</strong><br>(<strong>-0.56, -0.10</strong>)</td><td><strong>99.88</strong><br><strong>0.04</strong><br>(<strong>0.02, 0.07</strong>)</td><td>74.30<br>-0.15<br>(-0.62, 0.31)</td><td>85.75<br>0.06<br>(-0.05, 0.16)</td><td>88.17<br>-0.02<br>(-0.06, 0.01)</td><td><strong>99.98</strong><br><strong>0.20</strong><br>(<strong>0.10, 0.31</strong>)</td><td><strong>99.85</strong><br><strong>-1.40</strong><br>(-2.30, -0.49)</td><td>70.31<br>-0.03<br>(-0.17, 0.10)</td></tr><tr><td>Age class</td><td>88.14<br>0.06<br>(-0.04, 0.17)</td><td>93.27<br>-0.31<br>(-0.72, 0.09)</td><td>95.49<br>0.20<br>(-0.03, 0.43)</td><td>56.76<br>-0.00<br>(-0.03, 0.02)</td><td><strong>100</strong><br><strong>0.34</strong><br>(<strong>0.19, 0.48</strong>)</td><td>97.31<br>-0.07<br>(-0.14, 0.0001)</td><td>88.82<br>-0.03<br>(-0.07, 0.02)</td><td>55.96<br>-0.01<br>(-0.13, 0.11)</td><td><strong>99.92</strong><br><strong>-1.22</strong><br>(-2.25, -0.19)</td><td>58.63<br>0.02<br>(-0.13, 0.16)</td></tr><tr><td>Day length</td><td><strong>100</strong><br><strong>-0.32</strong><br>(<strong>-0.34, -0.30</strong>)</td><td><strong>100</strong><br><strong>-0.62</strong><br>(<strong>-0.73, -0.52</strong>)</td><td><strong>100</strong><br><strong>-0.43</strong><br>(<strong>-0.54, -0.31</strong>)</td><td><strong>100</strong><br><strong>-0.02</strong><br>(<strong>-0.03, -0.01</strong>)</td><td><strong>100</strong><br><strong>0.64</strong><br>(<strong>0.62, 0.66</strong>)</td><td><strong>99.96</strong><br><strong>0.02</strong><br>(<strong>0.01, 0.03</strong>)</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Number of captures</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td><strong>100</strong><br><strong>-0.05</strong><br>(<strong>-0.07, -0.02</strong>)</td><td><strong>99.38</strong><br><strong>0.08</strong><br>(<strong>0.02, 0.14</strong>)</td><td><strong>99.75</strong><br><strong>-0.76</strong><br>(-1.29, -0.22)</td><td><strong>99.94</strong><br><strong>-0.12</strong><br>(-0.19, -0.05)</td></tr><tr><td>Time in the box</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>96.32<br>0.02<br>(-0.0001, 0.03)</td><td><strong>99.99</strong><br><strong>0.10</strong><br>(<strong>0.05, 0.15</strong>)</td><td><strong>99.98</strong><br><strong>0.85</strong><br>(<strong>0.42, 1.27</strong>)</td><td><strong>99.98</strong><br><strong>-0.11</strong><br>(-0.17, -0.05)</td></tr><tr><td>External temperature</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>95.34<br>0.02<br>(-0.0001, 0.03)</td><td><strong>99.01</strong><br><strong>0.06</strong><br>(<strong>0.01, 0.11</strong>)</td><td>70.14<br>-0.15<br>(-0.58, 0.28)</td><td><strong>100</strong><br><strong>0.22</strong><br>(<strong>0.16, 0.28</strong>)</td></tr></tbody></table>

Probabilities of direction ($p_d$), mean effect size (in italic) and 95% credibility intervals (CrI, in brackets) for each fixed effect were extracted from full univariate models. For each trait studied, the significant effects that were included in the univariate and multivariate models are highlighted in bold.
<table><caption>**Table A4**
Among-individual variance, intercept and mean-standardized variance for each trait</caption><thead><tr><th colspan="4">Parameters (95% CrI)</th></tr><tr><th>Traits</th><th>σ<sup>2</sup><sub>ind</sub></th><th>β<sub>0</sub></th><th>I<sub>I adj</sub></th></tr></thead><tbody><tr><td>HR size</td><td>1337.1 (1087.4, 1598.3)</td><td>63.78 (56.97, 70.57)</td><td>0.32 (0.26, 0.38)</td></tr><tr><td>AVG<sub>speed</sub></td><td>1376.6 (1011.7, 1789.8)</td><td>185.5 (172.4, 198.8)</td><td>0.04 (0.03, 0.06)</td></tr><tr><td>N<sub>exc</sub></td><td>0.07 (0.00, 0.24)</td><td>0.23 (0.20, 0.26)</td><td>1.32 (0.00, 6.01)</td></tr><tr><td>D<sub>road</sub></td><td>0.010 (0.008, 0.012)</td><td>0.09 (0.07, 0.11)</td><td>1.23 (0.66, 2.45)</td></tr><tr><td>P<sub>OH</sub></td><td>0.33 (0.22, 0.44)</td><td>0.37 (0.31, 0.44)</td><td>2.41 (1.14, 4.58)</td></tr><tr><td>ODBA</td><td>20 182 412 (16 082 700, 24 607 767)</td><td>13 556 (13 308, 13 723)</td><td>0.11 (0.09, 0.14)</td></tr><tr><td>Docility score</td><td>0.005 (0.002, 0.008)</td><td>0.20 (0.18, 0.23)</td><td>0.13 (0.04, 0.25)</td></tr><tr><td>N:L ratio</td><td>0.295 (0.00, 0.804)</td><td>2.42 (2.07, 2.77)</td><td>0.05 (0.00, 0.19)</td></tr><tr><td>HCT</td><td>4.69 (0.00, 8.11)</td><td>47.56 (46.51, 48.61)</td><td>0.002 (0.00, 0.004)</td></tr><tr><td>Rectal temperature</td><td>0.13 (0.04, 0.21)</td><td>38.98 (38.87, 39.10)</td><td>0.0008 (0.0003, 0.0014)</td></tr></tbody></table>

Variance components and mean value used for each of the 10 response traits to calculate the mean standardized among-individual variation (I<sub>I adj</sub>) were extracted from univariate models including the fixed effects given in Table A3 and no variable transformation (a model without any variable transformation was run for each trait), with their corresponding 95% credibility intervals (CrI). σ<sup>2</sup><sub>ind</sub> is the variance due to the random effect of the identity of individuals, and β<sub>0</sub> is the intercept.

## Considerations About the Coefficients of Variation

Table A4 shows the values of variances and means used to compute mean standardized among-individual variation. No transformation was applied to Gaussian data and variance and population mean (intercepts) components were back-transformed onto the trait's original scale for non-Gaussian variables, respectively from the logit latent scale for P<sub>OH</sub> and the log latent scale for N<sub>exc</sub>. The mean was back-transformed using the inverse logit function for P<sub>OH</sub> and the exponential function for N<sub>exc</sub>. For the variance components, the following equation was applied for P<sub>OH</sub>:

$$ \text{Var}(Y) = \frac{e^{\mu_X}}{(1 + e^{\mu_X})^2} * \frac{\sigma^2 X}{(1 + e^{\mu_X})^2} $$

where μ<sub>X</sub> is the mean of the variable on the latent scale and σ<sup>2</sup><sub>X</sub> is the variance of the variable on the latent scale. For N<sub>exc</sub>, we used the following equation:

<table><caption>**Table A5**
Variance components for each of the 10 unscaled response traits</caption><thead><tr><th colspan="5">Variances (95% CrI)</th></tr><tr><th>Traits</th><th>σ<sup>2</sup><sub>ind</sub></th><th>σ<sup>2</sup><sub>year</sub></th><th>σ<sup>2</sup><sub>t</sub></th><th>V<sub>tot</sub></th></tr></thead><tbody><tr><td>HR size</td><td>0.26 (0.21, 0.32)</td><td>0.05 (0.04, 0.06)</td><td>0.22 (0.21, 0.24)</td><td>0.53 (0.46,0.60)</td></tr><tr><td>AVG<sub>speed</sub></td><td>1.80 (1.34, 2.30)</td><td>2.39 (1.96, 2.85)</td><td>6.01 (5.64, 6.38)</td><td>10.20 (8.48,12.42)</td></tr><tr><td>N<sub>exc</sub></td><td>0.03 (0.00, 0.09)</td><td>0.01 (0.00, 0.02)</td><td>2.01 (1.91, 2.12)</td><td>2.05 (1.92, 2.18)</td></tr><tr><td>D<sub>road</sub></td><td>0.010 (0.008, 0.012)</td><td>0.001 (0.00, 0.004)</td><td>0.020 (0.019, 0.021)</td><td>0.031 (0.028, 0.033)</td></tr><tr><td>P<sub>OH</sub></td><td>3.02 (2.44, 3.63)</td><td>0.11 (0.05, 0.18)</td><td>3.29 –</td><td>6.42 (5.84, 7.06)</td></tr><tr><td>ODBA</td><td>0.14 (0.11, 0.17)</td><td>0.10 (0.04, 0.15)</td><td>0.10 (0.09, 0.11)</td><td>0.34 (0.26, 0.44)</td></tr><tr><td>Docility score</td><td>0.007 (0.00, 0.012)</td><td>–</td><td>0.028 (0.022, 0.034)</td><td>0.034 (0.030, 0.039)</td></tr><tr><td>N:L ratio</td><td>0.04 (0.00, 0.08)</td><td>–</td><td>0.22 (0.16, 0.27)</td><td>0.26 (0.22, 0.29)</td></tr><tr><td>HCT</td><td>4.69 (0.00, 8.11)</td><td>–</td><td>12.43 (8.72, 16.75)</td><td>17.12 (14.67, 19.69)</td></tr><tr><td>Rectal temperature</td><td>0.13 (0.04, 0.21)</td><td>–</td><td>0.25 (0.18, 0.33)</td><td>0.38 (0.32, 0.43)</td></tr></tbody></table>

These values were extracted from univariate models including the fixed effects given in Table A3, and the corresponding credibility intervals in brackets. σ<sup>2</sup><sub>ind</sub> is the variance due to the random effect of the identity of individuals, σ<sup>2</sup><sub>year</sub> is the variance due to the random effect of year (when included) and σ<sup>2</sup><sub>t</sub> is the residual variance extracted from the model following Nakagawa & Schielzeth (2010) and Nakagawa et al. (2017) when the distribution was not Gaussian. Note that there are no credibility intervals for σ<sup>2</sup><sub>t</sub> of P<sub>OH</sub> because the residual variance of the overdispersion model is π<sup>2</sup>/3 for each draw. Finally, V<sub>tot</sub> is the total variance, i.e. the sum of all the variance components.

$$ \text{Var}(Y) = e^{2\mu_X + \sigma_X^2} (e^{\sigma_X^2} - 1) $$

As described in the main text, we estimated the mean standardized individual variance, I<sub>I adj</sub>, as follows:

$$ I_{I \text{ adj}} = CV^2 = \frac{\sigma_{\text{ind}}^2}{\beta_0^2} $$

Pélabon et al. (2020) discussed the use of the coefficient of variation (and by extension of the mean standardized individual variance) for different types of traits. Meaningful values can be calculated for variables that have a true zero or are on the log-interval or ratio scale. However, in our study, some traits do not meet these assumptions. This is the case for D<sub>road</sub>, P<sub>OH</sub> and N<sub>exc</sub>. For these traits, the mean standardized among-individual variation should be evaluated with caution.

## Variance Components for Each Trait
## Sample Sizes for Each of the Multivariate Models

To compute the movement syndrome, we used a multivariate model including the six movement traits based on 217 individuals. To compute the stress syndrome, we used a multivariate model including the four stress-response traits based on 288 individual roe deer. The sample sizes differed from the multivariate model used for the overall Behavioural syndrome as only individual-month pairs with data available for all the 10 traits were retained.

The data used for each of the three multivariate models is summarized in Table A6.

## Covariance and Variance Estimates for Each Movement and Stress-Response Trait Included in the Overall Behavioural Syndrome

Table A7 shows covariance estimates and Fig. A4 shows a syndrome figure representation.

<caption>**Table A6**</caption>

Sample sizes for each multivariate model.

<table><thead><tr><th rowspan="2">Model</th><th rowspan="2">N<sub>ind</sub></th><th colspan="2">N<sub>obs</sub></th><th colspan="2">N<sub>rep</sub> (range)</th></tr><tr><th>Stress</th><th>Movement</th><th>Stress</th><th>Movement</th></tr></thead><tbody><tr><td>Stress syndrome</td><td>288</td><td>371</td><td>—</td><td>1.3 (1–4)</td><td>—</td></tr><tr><td>Movement syndrome</td><td>217</td><td>—</td><td>1796</td><td>—</td><td>8.3 (1–25)</td></tr><tr><td>Overall behavioural syndrome</td><td>177</td><td>199</td><td>1433</td><td>1.1 (1–3)</td><td>8.1 (1–24)</td></tr></tbody></table>

N<sub>ind</sub> is the respective number of individuals sampled for each model, N<sub>obs</sub> the number of individual observations and N<sub>rep</sub> the number of repetitions per individual with the minimum and maximum number of observations per individual in the brackets (range).

<figure><img src="image_11.png" /><figcaption>**Figure A4.** Circular correlation diagram representing the links between the 10 movement and stress-response traits. Wider solid green lines indicate significant correlations ($p_d$ > 97.5%) while solid orange lines indicate observed trends ($p_d$ > 90%). Dashed grey lines represent correlations that were not significantly different from 0.</figcaption></figure>
<caption>**Table A7**</caption>

Correlations, variances, covariances and the corresponding credibility intervals for the 10 scaled response traits

<table><thead><tr><th>Trait</th><th>HR size</th><th>AVG<sub>speed</sub></th><th>N<sub>exc</sub></th><th>D<sub>road</sub></th><th>P<sub>OH</sub></th><th>ODBA</th><th>Docility score</th><th>N:L ratio</th><th>HCT</th><th>Rectal temperature</th></tr></thead><tbody><tr><td>HR size</td><td>0.44<br>(0.34, 0.56)</td><td>0.02<br>(-0.03, 0.08)</td><td>0.04<br>(-0.02, 0.13)</td><td>0.01<br>(-0.06, 0.08)</td><td><strong>0.46</strong><br>(<strong>0.28, 0.67</strong>)</td><td>-0.0001<br>(-0.12, 0.12)</td><td>0.04<br>(-0.06, 0.15)</td><td>0.06<br>(-0.06, 0.18)</td><td>-0.03<br>(-0.14, 0.08)</td><td>-0.02<br>(-0.12, 0.08)</td></tr><tr><td>AVG<sub>speed</sub></td><td>0.07<br>(-0.12, 0.25)</td><td>0.19<br>(0.14, 0.26)</td><td>-0.03<br>(-0.10, 0.01)</td><td><strong>0.05</strong><br>(<strong>0.002, 0.10</strong>)</td><td><strong>-0.15</strong><br>(-0.29, 0.02)</td><td><strong>-0.15</strong><br>(-0.26, -0.05)</td><td>0.03<br>(-0.04, 0.11)</td><td><strong>0.08</strong><br>(-0.01, 0.17)</td><td><strong>0.06</strong><br>(-0.02, 0.15)</td><td>-0.02<br>(-0.10, 0.05)</td></tr><tr><td>N<sub>exc</sub></td><td>0.27<br>(-0.28, 0.69)</td><td>-0.32<br>(-0.73, 0.26)</td><td>0.05<br>(0.001, 0.16)</td><td>-0.03<br>(0.10, 0.03)</td><td>0.12<br>(-0.04, 0.37)</td><td>-0.003<br>(-0.11, 0.10)</td><td>-0.02<br>(-0.13, 0.07)</td><td>0.02<br>(-0.09, 0.13)</td><td>0.01<br>(-0.09, 0.12)</td><td>0.04<br>(-0.05, 0.15)</td></tr><tr><td>D<sub>road</sub></td><td>0.03<br>(-0.15, 0.20)</td><td><strong>0.20</strong><br>(<strong>0.01, 0.39</strong>)</td><td>-0.18<br>(-0.63, 0.34)</td><td>0.38<br>(0.29, 0.49)</td><td>-0.08<br>(-0.26, 0.09)</td><td>-0.01<br>(-0.12, 0.10)</td><td>-0.05<br>(-0.15, 0.05)</td><td>0.04<br>(-0.08, 0.15)</td><td>0.01<br>(-0.10, 0.12)</td><td>-0.03<br>(-0.13, 0.07)</td></tr><tr><td>P<sub>OH</sub></td><td><strong>0.41</strong><br>(<strong>0.27, 0.54</strong>)</td><td><strong>-0.21</strong><br>(-0.38, -0.03)</td><td>0.32<br>(-0.25, 0.73)</td><td>-0.08<br>(-0.24, 0.09)</td><td>2.81<br>(2.22, 3.54)</td><td>-0.07<br>(-0.35, 0.19)</td><td>0.05<br>(-0.19, 0.30)</td><td>0.06<br>(-0.22, 0.34)</td><td>0.16<br>(-0.10, 0.44)</td><td><strong>0.15</strong><br>(-0.08, 0.40)</td></tr><tr><td>ODBA</td><td>-0.00<br>(-0.19, 0.19)</td><td><strong>-0.35</strong><br>(-0.54, -0.13)</td><td>-0.01<br>(-0.49, 0.48)</td><td>-0.01<br>(-0.19, 0.17)</td><td>-0.04<br>(-0.20, 0.12)</td><td>0.95<br>(0.68, 1.30)</td><td>-0.04<br>(-0.19, 0.10)</td><td>-0.05<br>(-0.22, 0.11)</td><td>-0.02<br>(-0.18, 0.14)</td><td>0.07<br>(-0.07, 0.22)</td></tr><tr><td>Docility score</td><td>0.06<br>(-0.10, 0.22)</td><td>0.08<br>(-0.10, 0.25)</td><td>-0.11<br>(-0.55, 0.39)</td><td>-0.09<br>(-0.25, 0.08)</td><td>0.03<br>(-0.12, 0.18)</td><td>-0.04<br>(-0.19, 0.11)</td><td>0.96<br>(0.77, 1.19)</td><td>0.08<br>(-0.08, 0.25)</td><td>0.06<br>(-0.09, 0.22)</td><td>0.04<br>(-0.10, 0.18)</td></tr><tr><td>N:L ratio</td><td>0.08<br>(-0.08, 0.24)</td><td><strong>0.16</strong><br>(-0.02, 0.33)</td><td>0.06<br>(-0.42, 0.51)</td><td>0.05<br>(-0.11, 0.21)</td><td>0.03<br>(-0.12, 0.18)</td><td>-0.05<br>(-0.20, 0.11)</td><td>0.08<br>(-0.07, 0.22)</td><td>1.21<br>(0.98, 1.51)</td><td>0.06<br>(-0.12, 0.23)</td><td>-0.05<br>(-0.21, 0.11)</td></tr><tr><td>HCT</td><td>-0.04<br>(-0.20, 0.11)</td><td><strong>0.14</strong><br>(-0.04, 0.31)</td><td>0.04<br>(-0.43, 0.50)</td><td>0.02<br>(-0.15, 0.18)</td><td>0.09<br>(-0.06, 0.24)</td><td>-0.02<br>(-0.17, 0.13)</td><td>0.06<br>(-0.09, 0.21)</td><td>0.05<br>(-0.10, 0.19)</td><td>1.10<br>(0.88, 1.37)</td><td>-0.05<br>(-0.20, 0.09)</td></tr><tr><td>Rectal temperature</td><td>-0.03<br>(-0.19, 0.13)</td><td>-0.06<br>(-0.23, 0.12)</td><td>0.17<br>(-0.34, 0.60)</td><td>-0.05<br>(-0.21, 0.11)</td><td><strong>0.10</strong><br>(-0.05, 0.24)</td><td>0.08<br>(-0.07, 0.23)</td><td>0.04<br>(-0.11, 0.19)</td><td>-0.05<br>(-0.19, 0.10)</td><td>-0.05<br>(-0.20, 0.09)</td><td>0.91<br>(0.73, 1.12)</td></tr></tbody></table>

Values were extracted from the multivariate model including only the significant fixed effects for each of the response variables (see Table A3). Covariances are given in the upper triangle of the table, correlations in the lower triangle and variances on the diagonal (italic). Bold roman values indicate significant correlations ($p_d > 97.5\%$) while bold italic values indicate observed trends ($p_d > 90\%$). This multivariate model included 177 individuals, with 199 observations for stress response and 1433 observations for movement traits, which amounts to a mean number of 1.1 observations per individual for the stress response and 8.1 observations per individual for the movement traits (see Table A6).
Phenotypic Correlations Between Stress-Response Traits

To estimate the phenotypic correlations between the four stress-response traits, we ran a Bayesian multivariate model without the random effect of individual identity and computed the residual correlations of the model (phenotypic correlation containing both the within- and the among-individual variance). Moreover, we also computed the Pearson correlation matrix between the four stress-response traits. The results showed that significant phenotypic correlations exist between the docility score at capture and the haematocrit, and the rectal temperature, respectively (Table A8). These correlations are coherent with previous results on tamed roe deer (Monestier et al., 2016). This suggests that phenotypic correlations between

stress traits exist, but they are driven by the within-individual (co)variation. This can mean that those traits respond similarly to changes in environmental conditions, for example, and thus the phenotypic correlations are artificially increased by these covariations.

To identify the main axes of (co)variation between the four stress-response traits, we also performed the eigen decomposition of the covariance matrix of the stress syndrome multivariate model. The first axis of variation explains 31% (CrI: 28, 34%) of the (co)variance observed, the second axis 26% (CrI: 24, 28%), the third axis 23% (CrI: 21, 25%) and the fourth axis 19% (CrI: 17, 22%). This means that no single component can summarize the (co)variation observed, even at the phenotypic level (Fig. A5), and that these four traits are mainly independent.

<figure><img src="image_12.png" /><figcaption>**Figure A5.** Loadings for the phenotypic covariance matrix of the stress response. Trait loadings on the four eigenvectors for the covariance matrix were extracted from the stress response multivariate model without random effects, with 95% credibility intervals (whiskers) calculated using the full posterior distributions of correlation and standard deviation parameters.</figcaption></figure>

<table><caption>**Table A8**
Correlation table at the phenotypic level for stress response traits</caption><thead><tr><th>Trait</th><th>Docility score</th><th>N:L ratio</th><th>HCT</th><th>Rectal temperature</th></tr></thead><tbody><tr><td>Docility score</td><td>0.99 (0.85, 1.14)</td><td>-0.004</td><td><strong>0.17</strong></td><td><strong>0.13</strong></td></tr><tr><td>N:L ratio</td><td>-0.03 (-0.13, 0.08)</td><td>0.92 (0.79, 1.06)</td><td>-0.04</td><td>-0.03</td></tr><tr><td>HCT</td><td><strong>0.12 (0.02, 0.22)</strong></td><td>-0.06 (-0.16, 0.04)</td><td>0.94 (0.82, 1.09)</td><td>-0.06</td></tr><tr><td>Rectal temperature</td><td><strong>0.11 (0.01, 0.21)</strong></td><td>-0.03 (-0.13, 0.07)</td><td>-0.03 (-0.13, 0.07)</td><td>0.84 (0.73, 0.97)</td></tr></tbody></table>

This table gives phenotypic correlations, Pearson correlations, variances and corresponding credibility intervals between stress response traits. Correlations from the multivariate model (with no random effect of individual identity) are given in the lower triangle of the table, Pearson correlations in the upper triangle and variances on the diagonal (italic). Significant correlations are highlighted in bold.