Higher proactivity in later-borns: effects of birth date on personality in a small mammal

Higher proactivity in later-borns: effects of birth date on personality in a small mammal | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Higher proactivity in later-borns: effects of birth date on personality in a small mammal Jingyu QIU, Neville Pillay, Carsten Schradin, Lindelani Makuya, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4905612/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2024 Read the published version in Behavioral Ecology and Sociobiology → Version 1 posted 5 You are reading this latest preprint version Abstract In short-lived animals, individuals born earlier in the breeding season frequently reproduce within the season of birth. Consequently, it has been proposed that those born early benefit from a more proactive behavioral type to compete for reproductive resources whereas later-borns adopt a more reactive personality to conserve energy to survive through the non-breeding season and reproduce in the following year. However, being proactive could also benefit later-borns in acquiring decreasing resources in the late breeding season. We investigated personality differences depending on the date of birth in relation to resource variation in a free-living population of the bush Karoo rat ( Otomys unisulcatus ). This species constructs stick lodges, a critical resource protecting the rats from the harsh semi-desert environments, but the availability of vacant lodges decreases with increasing population density during the breeding season. We predicted an increased occurrence of proactive phenotypes during the later breeding season, contrasting with the commonly assumed decrease in proactive phenotypes in late season due to lack of reproductive opportunity. We behaviorally phenotyped n = 99 individuals through repeated behavioral tests and found consistent individual differences along a proactive-reactive gradient. Most importantly, later-borns showed greater activity, boldness and exploration tendencies, indicating a more proactive personality. In addition, among early-born females, individuals which reproduced showed no differences in personality compared to those which did not reproduce. Our results indicate that seasonal differences in personality types in the bush Karoo rat may be driven by resource constraints in the late season rather than by differences in reproduction opportunities. behavioral phenotype consistent individual differences Otomys pace-of-life syndrome Figures Figure 1 Figure 2 Figure 3 Significance statement In short-lived animals, the birth date during the breeding season can lead to differences in life history and corresponding behavioral traits. We studied the link between birth date and personality in the bush Karoo rat, a seasonally-breeding and short-lived rodent which relies on a limited resource, stick lodges, essential to survive in a harsh semi-desert environment. Individuals born later in the season were more active, bolder and more explorative; these traits likely help them to successfully compete for stick lodges during the late breeding season when population density and thus competition is high. Early-born females, which can potentially start reproducing within their season of birth, showed no personality differences regardless of their reproductive status, suggesting that resource availability rather than reproduction opportunity may underly the evolution of seasonal personality differences in this species. Introduction For animals living in seasonal environments, reproduction typically coincides with periods of high resource abundance (Whittier and Crews 1987 ). The timing of birth during such highly concentrated reproductive periods can lead to different life history trade-offs and thus to alternative phenotypic developmental trajectories (Roff 1993 ; Varpe 2017 ). In many short-lived species, such as rodents, individuals born early during the breeding season can reach sexual maturity and reproduce within the same season (Tkadlec and Zejda 1995 ; Montiglio et al. 2014 ). In contrast, individuals born later experience a shorter resource-rich period and are less likely to reproduce within the season of their birth. Thus, they need to survive through the non-productive period to reproduce in the following year (Lambin and Yoccoz 2001 ). Consequently, the timing of birth within the season may shape distinct behavioral traits in early and late born offspring to adaptively cope with their different survival and reproduction challenges. Over the past decade, behavioral studies have gained a deeper understanding of consistent individual differences (animal personality), how these are maintained and their association with life history (Réale et al. 2007 ; Biro and Stamps 2008 ). Pace-of-life syndrome proposes that the trade-off between current versus future reproduction leads to differences in behavioral traits (Réale et al. 2010; Dammhahn et al. 2018 ): in seasonal breeding rodents, individuals born early which have an opportunity to reproduce within the season of birth, should benefit from an active and risk-taking personality that could be advantageous when acquiring resources needed for reproduction. In contrast, being less bold and less risk-taking may be more adaptive for later-borns because such a personality type will contribute to saving energy, increasing the probability of surviving through the non-breeding season until they can reproduce in the following year (Gracceva et al. 2014 ). Such an association between personality and the timing of birth has been reported, for example, in eastern chipmunks ( Tamias striatus ), in which birth cohorts with early reproductive opportunities were faster explorers than those reproducing at a later age (Montiglio et al. 2014 ). Similarly, in European shags ( Phalacrocorax aristotelis ), chicks hatched early in the breeding season had higher social ranks and showed higher levels of aggression compared to those hatched later (Velando 2000 ). However, this hypothesis does not appear to apply to a within-population study: in short-lived common voles ( Microtus arvalis ); individuals captured in spring (mostly late born cohort from the previous year) were bolder than those captured at other times of the year (Eccard and Herde 2013 ). Apart from reproductive trade-offs that underly the pace-of-life continuum, ecological conditions can also be important drivers of personality (Dammhahn et al. 2018 ; Jablonszky et al. 2018 ; Montiglio et al. 2018 ). Individuals with proactive phenotypes are usually more successful in competing for resources (Sih et al. 2004 ; Smith and Blumstein 2008 ). During the late breeding season, resource availability typically declines, while population density increases at the same time. Being proactive can be beneficial for individuals born late in the season in competition for limited resources. Therefore, two alternative hypotheses exist: (1) early-borns are more proactive to acquire enough resources for reproduction versus (2) late-borns are more proactive to acquire enough resources for survival. Therefore, to understand the association between personality and the birth timing, further investigations, preferably under natural conditions are necessary. The seasonal breeding bush Karoo rat ( Otomys unisulcatus ), a short-lived small mammal living in arid environments of South Africa, is an appropriate model to study the association between birth timing and personality. This species shows a distinct ecological feature that can result in intense resource competition during the breeding season. Bush Karoo rats construct “stick lodges” from dry plant material as refugia (Vermeulen 1988 ; Pillay 2001 ), providing a favorable micro-climate that protects the rat from the harsh ambient environment (Vermeulen 1988 ; Brown and Willan 1991 ; Du Plessis et al. 1992 ). A stick lodge is costly to build and is commonly used by only one adult individual in this solitary species (Makuya et al. 2024 ), although it can be reused by others after the builder disappears. Thus, vacant stick lodges represent a limited survival resource, and the availability decreases in the late breeding season when subadult individuals start to occupy stick lodges of their own. As more lodges become occupied, individuals born later face increasing difficulties in finding unoccupied lodges or will even need to build new ones. In this situation, having a proactive personality becomes adaptive as it could lead to finding and competing for unoccupied lodges, or in competing for building materials for constructing new lodges (Vermeulen 1988 ). Our aim was to investigate the effect of the date of birth on personality in adult bush Karoo rats. First, we studied whether free-living bush Karoo rats show consistent personality traits in activity, boldness and exploration behavior. Next, considering the potential effects of increasing population density on the availability of stick lodges, we predicted that such personality traits would be associated with birth timing, as later-borns would adopt a more proactive (active, bold and/or explorative) behavioral type. Finally, we studied whether there was a seasonal change in behaviour, specifically whether the proactive response decreased in the food restricted dry season when compared to the food rich moist season. We conducted a field study over two years (2022–2023) and repeatedly quantified six behavioral parameters related to three different behavioral personality traits, activity, boldness and exploration (sensu Réale et al. 2007 ), which was compared between individuals born earlier and later during the breeding season. Materials and Methods Study site and study population Our study population occurred in the arid Succulent Karoo, a biodiversity hotspot in South Africa, characterized by variable climate and low precipitation. Bush Karoo rat breeding activity coincides with season (Wolhuter et al. 2022 ). Reproduction is concentrated in the moist period from July to November, followed by the hot and dry non-breeding period from December to June. Bush Karoo rats have a relatively short life span (1–2 years). The earliest age at sexual maturity is between 5–6 weeks, and the reproductive period spans over 4–5 months per year (Vermeulen 1988 ; Wolhuter et al. 2022 ). Offspring born early in the breeding season can reach sexual maturity within the season of their birth (Wolhuter et al. 2022 ). The competition for reproductive resources mostly concerns individuals born early in the season, while the availability of stick lodges is relevant for the survival of all individuals and may be especially limiting for those born late in the season. The study was conducted in the Goegap Nature Reserve, Northern Cape Province, South Africa. The field site in the semi-arid Succulent Karoo (Cowling et al. 1999 ) is characterized by an annual rainfall of 160 mm/year on average, and by temperatures varying from − 1.5 to 24°C during winter and from 4 to 42°C during summer (weather station at the field site). Most of the rainfall occurs in winter, creating abundant vegetation that supports the onset of reproductive activity in our study population (Wolhuter et al. 2021). The field site for the study is approximately 4.5 ha. Stick lodge surveys Stick lodge surveys were conducted twice a year, at the beginning of the breeding season in July and after the breeding season in January. We classified the stick lodges, which built within shrubs, into three size categories: (i) small: lodges with a height below 20 cm; (ii) medium: lodges with a height from 21–50 cm; and (iii) large: lodges that almost covered the entire shrub with a height above 50 cm (Schradin 2005 ). For every lodge, we recorded whether it was old (several years old) or whether it was newly built within the past few months, based our field records. For each season, we calculated the total number of stick lodges on the field site as the total of old lodges surveyed in January (e.g. lodge survey in January 2023 for the breeding season that started in July 2022). Because we wanted to have a measure of available old lodges, we recorded these separately from lodges that were built within prevailing season (the new lodges). Trapping and individual tagging In the field site, trapping was carried out at all occupied stick lodges throughout the year as part of a long-term data collection. The field site was split into 6 trapping areas, with trapping being carried out at two areas simultaneously by two people for three days, before switching to two other areas. Additional trapping was done at lodges with previously unmarked juveniles and focal individuals for behavioral tests. Trapping was done 5 days a week, and occurred before sunrise. The traps were set at lodge entrances and checked every 30 min. All traps were closed within two hours after sunrise to avoid overheating. We used Sherman traps and locally produced metal (Sherman-like) live traps (26 × 9 × 9 cm), which had small holes in the sides to allow circulation of air. At first capture, bush Karoo rats were individually marked with aluminum band ear tags in both ears (0.25 g per tag) with a unique individual number (National Band and Tag Co., Newport, KY, USA). During re-trapping, we always checked for infections at the ear tags, which would have resulted in the removal of the tag on the affected ear; such cases never occurred during the study period. Birth date was estimated from the animals’ body mass, based on the linear association between age and body mass in the bush Karoo rat as published in Pillay ( 2001 ). Trapping data used for our study spanned from 1st January 2022 to 14th December 2023. When a female born within the season showed signs of pregnancy/lactation (palpable embryos at late pregnancy stage; lactation as evident by the developmental stage of the mammary glands), or when a female had dependent young (as evident by juveniles trapped at the same stick lodge prior to the next breeding season), this female was considered as having reproduced during the season of birth (‘precocious reproduction’). Assessment of changes in population density Based on trapping data, we recorded the population density of adult (older than 5 weeks) bush Karoo rats ( n /ha) at the beginning of the breeding season in July and again after the breeding season in January for both seasons. Therefore, for each month, we counted the total number of trapped adult bush Karoo rats and divided this number by the size of the field site. We were able to mark and monitor the population through observations because the field site is an open terrain (dispersed shrubs with sandy areas in between), the bush Karoo rat is diurnal, and occupied stick lodges showed clear signs of occupancy. At lodges with signs of occupancy where we did not trap an individual within 3 days, we continued with additional trapping. We also conducted behavioral observations (described in Makuya et al. 2024 ), enabling us to identify occupied lodges and unmarked juveniles. Experimental procedure Trapping of focal individuals was conducted using the same method as described above. Individuals were transported to a field laboratory situated next to the field site (less than 10 min walking distance) for behavioral testing. Except when checking ear tags and performing behavioral tests, the rats remained in their traps during the whole time. After the test procedure, individuals were released next to their stick lodge, i.e., at the site where they had been trapped. Focal individuals underwent up to four repetitions of behavioral tests. Because the field site changed from the moist to dry season with a decrease in food abundance over time, we measured behavioral repeatability at short term (two week interval) and long term (16 week interval, in the moist and dry seasons) to account for the seasonal variation of food abundance. The test schedule was determined by age; the first and second tests were conducted at early adult stage (age class “young”), with the first age at approximately 6 weeks when they reach sexual maturity (Pillay 2001 ). The second test was scheduled two weeks later. The third and fourth tests were conducted at fully adult stage (age class “old”), with the third test scheduled when individuals were approximately 20 weeks old, and the fourth test two weeks later. As was evident from our trapping data, females usually disperse for shorter distances and therefore were more likely be continually caught using our trapping protocol. Thus, in the first year, the selection of focal individuals was limited to females and included four behavioral test replicates. In the second year, both males and females were selected as focal individuals. However, due to time constraints, the rats underwent only the first and second behavioral tests in the second year. Due to unpredictable field conditions, trapping of focal individuals was not always successful, which led to delays for repeated behavioral tests in some cases. The average age for “young” adults (at the 1st and 2nd test) was 54 days, and the average age for “old” adults (at the 3rd and 4th test) was 151 days. Because our study involved individually-marked focal animals, the experimenter(s) were not naive to individual identities during testing. Population density of small mammals in our field site typically decreases dramatically during the dry season to the onset of the next breeding season, when it is only approximately a quarter of the density at the end of the breeding season (Nater et al. 2018 ). As expected, many individuals disappeared from the field site throughout the study, mainly due to predation. We could not predict which individuals would disappear, so we tested as many individuals as possible at the onset of the breeding season (1st test: n = 99; 2nd test: n = 78 individuals), and we still had a sample size providing acceptable statistical power at the end of the dry season (3rd test: n = 30; 4th test: n = 19). Behavioral tests One to a maximum of four individuals were trapped for behavioral tests per day. A white chamber (100 cm long, 85 cm wide and 65 cm high) made of melamine panels was used as a test arena (Fig. 1 ). Before introducing a new individual into the arena, the arena was always thoroughly cleaned using 95% alcohol and air dried. All tests were video-recorded and later analyzed using the software BORIS (Friad and Gamba 2016) and Single Mouse Tracker (Icy software, De Chaumont 2012). Focal individuals underwent three successive behavioral tests directly after the morning trapping. Starting box – This part of the apparatus (the starting box) consisted of a 10 cm 3 black acrylic and opaque square box with one side that could be opened (door) and a lid at the other side. The open side was directly attached to the test arena and separated by a closed door (Fig. 1 a). After individuals were placed into the starting box via the lid, they were allowed to calm down for 3 min in the closed box. Then the door was opened and we recorded whether the animal entered the test arena in the following 10 min. If the animal did not enter the arena within this time, it was gently nudged in using a plastic ruler (2 × 30 cm) by reaching into the box through the lid. Once the individual had entered the arena, the door was closed to prevent its return into the starting box. Open field test - Once the rat had entered the arena, the open field test began (Fig. 1 a). During the following 5 min, the individual could freely explore the arena. We recorded three behavioral parameters: (1) the distance travelled, defined as the total distance of locomotion measured in cm; (2) the % time the individual was active in the arena, defined as the total time minus the time being immobile (i.e. no obvious movements for more than 10 seconds); and (3) the % time the animal spent exploring the walls and corners of the arena, defined as sniffing or putting the front paws against the walls. Novel odor test - During our preliminary tests, bush Karoo rats did not show notable interactions with plastic toys presented as visual novel objects. However, interactions were observed when objects were applied with novel odors (e.g., orange peel). We therefore did not apply classical novel object tests (Denninger et al. 2018 ) but instead measured the animals’ exploration behavior towards objects carrying novel odors in a standardized setting. After the open field test was completed, the animal remained in the arena and was confined again by the experimenter using the same black acrylic square box. The box was attached to a fixed pulley system so it could be lifted to release the individual with minimum disturbance. The focal individual was first placed in the center of the arena while covered by the box. Then, four identical hollow metal, egg-shaped sieve balls (4.5 × 3.9 cm, steel) providing the same novel odors were placed inside the arena, 10 cm away on the extended diagonal of the black box (see Fig. 1 b). The source of odor used in the 1st test was orange peel, tomato sauce was in the 2nd test (two weeks later), peanut butter was used in the 3rd test and strawberry jam in the 4th test. After setting up the arena, the rat was allowed to calm down for 5 min inside the box, then the box was lifted and the individual was given 5 min to explore the four metal balls. We recorded two behavioral parameters: (1) the time the animal spent exploring the objects, defined as the total (summed-up) duration the individual sniffed or touched one of the four tea balls; and (2) the number of objects explored, defined as the total number of tea balls, which were sniffed or touched by the individual during the test (range from 0 − 4). Quantification of seasonal food abundance Because the repeated behavioral tests were conducted in two seasons with variable food availability, we quantified seasonal changes in the abundance of food plants and considered the potential effects on individual behavioral performance during testing. Food plant abundance was measured monthly using the Braun-Blanquet method as part of the long-term data collection on the field site, assessed by the average number of food plants from eight 2 m × 2 m plots randomly located in the field site (Werger 1974 ; Schradin and Pillay 2006 ). The resulting index was used in the statistical analysis (see details below) as an estimate of the food plant availability at the time of the different test sessions. Statistical analysis and sample sizes In total, 99 individuals ( n males = 15, n females = 84) were tested in 226 behavioral tests. We considered potential effect of multiple test replicates, as such repeated testing may lead to habituation effects (Salomons et al. 2010 ). The sample sizes available during the different test replicates decreased over time due to individuals disappearing from the field site, with n = 99 during the 1st tests, n = 78 during the 2nd tests, n = 30 during the 3rd tests, and n = 19 during the 4th tests. Statistical analyses were carried out in R, version 4.3.0 (R Core Team 2023 ). In the first step, we checked for repeatabilities of the six behavioral variables quantified in the starting box test, open field test and in the novel odor test, across the four different test sessions, as well as for associations between these behavioral variables. All six response variables (behavioral variables, given in Table 1 ) were scaled for analysis. This analysis was done using a single multivariate linear mixed-effects model (i.e., with six response variables) based on the R package MCMCglmm (Hadfield 2010 ). The predictors were year and sex (2 levels each) and test sequence (number of tests the individual has done before, 4 levels), with random effects for individual ID. We applied a weakly informative prior, allowing the data to primarily inform the posterior distributions. One chain was run with 100,000 iterations. The first 5,000 iterations were discarded as burn-in, and every 100th iteration was retained (thinning interval = 100), resulting in 950 samples per chain. The response variables followed appropriate distributions: a categorical distribution for the latency to leave shelter, Gaussian distribution for the continuous behavioral variables, and Poisson distribution for the number of objects explored. Table 1 Repeatability ( R , including its 95% credible interval CI ) of behavior parameters measured in repeated starting box (SB), open field (OF) and novel odor (NO) tests of 99 individuals. Analysis by a multivariate LMM including individual identity as a random factor, year, sex and test sequence (4 levels) as fixed variance. (a) Long-term repeatability was based on all (up to) four behavioral tests of all age classes ( n = 226 measurements for each behavioral variable), (b) Short-term repeatability was based on (up to) two behavioral tests during young adult stage ( n = 145 measurements for each behavioral variable) (a) Long-term repeatability (b) Short-term repeatability R CI 95% R CI 95% SB - Probability to enter arena 0.699 [0.483, 0.872] 0.477 [0.050, 0.860] OF - Distance travelled 1 0.364 [0.204, 0.521] 0.384 [0.141, 0.610] OF - % Time active 0.167 [0.025, 0.308] 0.219 [< 0.001, 0.417] OF - % Time exploring walls and corners of arena 1 0.253 [0.093, 0.427] 0.317 [0.039, 0.536] NO - Time exploring object 2 0.119 [< 0.001, 0.325] 0.038 [< 0.001, 0.158] NO - Number of objects explored 0.127 [< 0.001, 0.364] 0.051 [< 0.001, 0.244] 1 square-root transformation of dependent variable 2 log [x + 1] transformation of dependent variable Repeatabilities across the (up to) four repeated tests per behavioral parameter (see Table 1 ) and pair-wise correlation coefficients between the different behaviors (see Table 3 ) were calculated based on the within-individual and among-individual variance matrices provided by this model. Associations between the different behavioral variables were considered statistically significant ( P < 0.05) when the 95% Bayesian credible intervals of the correlation coefficients ( R ) did not overlap zero (Houslay and Wilson 2017 ). However, in case of repeatabilities over time, as per definition only non-negative values can be obtained and thus the 95% credible intervals cannot overlap zero, P -values could not be calculated based on the above-mentioned method (Houslay and Wilson 2017 ). Consequently, our inference on repeatabilities was only based on interpretation of the 95% credible intervals. We calculated the overall (long-term) repeatability, based on all (up to) four behavioral tests of our 99 focal individuals over all age classes (young adult at first and second tests, old adult at third and fourth tests; n = 226 measurements for each behavioral variable), as well as short-term repeatability, based on the first two behavioral tests for 99 young adults with a total of n = 145 measurements for each behavioral variable. In the second step, we tested the effects of the date of birth (covariate, 1st July as baseline; see the rather consistent distribution of birth dates along the season in Fig. 2 ) on the six different behavioral variables (see Table 3 ). Therefore, using the R package lme4 (Bates et al. 2015 ), we applied separate models - a generalized linear mixed-effects model (GLMM) for binomial data with a logit link for the probability to enter the test arena (see Table 3 a), and linear mixed-effects models (LMM) for all remaining (continuous) behavioral variables (see Table 3 b-f). To obtain a normal distribution of model residuals (verified by visually checking normal probability plots) and homogeneity of variances (by plotting residuals versus fitted values) for LMMs, we square-root transformed the distance travelled and the % time the animals spent exploring the walls and corners of the arena, and log [x + 1] transformed the time the animal spent exploring the object in the novel odor test. Individual identity was always included as a random (intercept) factor. All models included the age class at testing (“young adult” at the 1st and 2nd tests or “old adult” at the 3rd and 4th tests; 2 levels), the sex of the focal animals and the year of testing (all factors with 2 levels), the test sequence (factor with 4 levels) and the food plant abundance at testing (covariate). Because we were interested in whether possible differences between earlier- and later-born individuals were only apparent in young adults or in old adult individuals, we also tested the 2-way interaction between date of birth and age class. When non-significant, this interaction was removed from the models and these were recalculated (Engqvist 2005 ). P -values were calculated by type-3 Wald chi-square tests (Bolker et al. 2009 ). We also tested whether the behavioral responses of early-born females (i.e., females born until/including the 5th week of the breeding season, when the last reproducing female was born) in the different tests were associated with their actual reproductive activity during their season of birth. Using the R package lme4 (Bates et al. 2015 ), we applied GLMMs for binomial data with a logit link using the same transformations for some of the behavioral variables (now used as predictors in our model) as described above. Each model included one behavioral variable, and all included year of testing (2 levels) as fixed variance. Individual identity was always included as a random (intercept) factor. Also, P -values were calculated by type-3 Wald chi-square tests. Results Seasonal differences in population density and stick lodge availability In both years of the study, the adult population density showed dramatic variation between the breeding/non-breeding seasons. Population density increased during the breeding season from July to January by 120.7% on average. Specifically, during the first year of study (Jul 2022 – Jan 2023), the adult density increased by 81.4% from 12.6 to 23.4 individuals/ha, and during the second year (Jul 2023 – Jan 2024), it increased by 160% from 6.5 to 16.9 individuals/ha. However, the population also dramatically decreased during the non-breeding season by 72.0% between January to July 2023. In the breeding season starting in July 2022, 172 old lodges (i.e. existing ones) were available and 26 new lodges were built. The availability of old lodges decreased during the breeding season, from 3.1 to 1.6 lodges/adults from July 2022 to January 2023. In the breeding season starting in July 2023, 194 old lodges were available and 9 new lodges were built. The availability of old lodges decreased from 6.9 to 1.8 lodges/adults from July 2023 to January 2024. When only considering the large lodges with a height above 50 cm, which can be assumed to be the most valuable resource, this seasonal difference was more pronounced. The availability of such large lodges decreased from 0.8 per adult individual to 0.4 in 2022/2023, and from 1.7 to 0.4 in 2023/2024. Pattern of seasonal reproduction We quantified the temporal distribution of reproductive events during the breeding season using the estimated dates of birth of the juveniles trapped, based on a sample of n = 227 juveniles (130 born in 2022 and 97 born in 2023, Fig. 2 ). The start of the reproductive season was determined by the occurrence of at least three juveniles born on different dates within a week (i.e., apparently from different litters). The time of reproduction season was highly similar between the two years of study (2022: 19th Jul – 1th Nov; 2023: 14th Jul – 5th Nov). Consistent individual differences in behavior We analyzed the consistency of six behavioral variables recorded in up to four repeated starting box tests, open field tests and novel odor tests. Overall, we found notable long-term consistencies across time (i.e., repeatability) and thus across different age classes (young to old adult), with respect to three of the four behavioral variables recorded in the starting box and open field tests. These variables were the probability to enter the arena within 10 min, the distance travelled, and the % time the individual spent exploring the walls and corners of the arena (Table 1 a). In contrast, the % time the rats were active in the open field arena as well as both behavioral variables measured during the novel odor tests showed very low repeatabilities with large credible interval closely approaching zero (Table 1 a). Regarding short-term consistency, when only considering the first two tests during which individuals could be considered as young adults, we found repeatabilities similar to long-term consistency in the starting box and open field test, but again no noticeable repeatability in the novel odor test (Table 1 b). We also found intra-individual associations among the parameters recorded in the starting box test and open field test. Specifically, the probability to enter the arena, the distance travelled, the % time the animals showed activity and the % time the animals spent exploring the walls and corners of the open field arena were positively and significantly correlated (Table 2 ). Table 2 Associations between the different behavioral parameters, based on measurements taken from 99 individuals of different age classes. Analysis by a multivariate LMM including individual identity as a random factor, and year, sex and test sequence (4 levels) as fixed variance. Note that results stem from the same model as used for the calculation of Table 1 a, more details, including the credible intervals, are given in Table A in Suppl. Materials. Correlation coefficients (among individual-level) are given, significant effects ( P < 0.050) are given in brackets PEA DT %TA %TEA TEO NOE PEA 0.763 0.840 0.780 (0.549) (0.454) DT 0.786 0.620 (0.484) (0.429) %TA 0.848 (0.605) (0.470) %TEA (0.494) (0.372) TEO (0.614) NOE PEA: Probability to enter arena; DT: Distance travelled (square-root transformed); %TA: % Time active; %TEA: % Time exploring walls and corners of arena (square-root transformed); TEO: Time exploring object (log [x + 1] transformed); NOE: Number of objects explored Effects of birth date on personality traits All behavioral parameters recorded in the starting box and open field tests were significantly associated with the individual date of birth (Table 3 ). The probability to enter the arena (Table 3 a, Fig. 3 a), the distance travelled (Table 3 b, Fig. 3 b), the % time active (Table 3 c, Fig. 3 c) and the % time exploring the walls and corners of the arena (Table 3 d, Fig. 3 d) were all significantly higher in individuals born later in the season. The interaction between the date of birth and age class at testing was never statistically significant, indicating that the significant effects of date of birth on behavior (Table 3 a-d) were independent of age class. In contrast, the two parameters recorded during the novel odor test were not significantly associated with date of birth (Table 3 e, f). We found a significant and positive effect of the current food plant abundance only for the total time spent exploring the objects carrying the novel odors (Table 3 e). Specifically, the higher the food availability around the time of testing, the longer the individuals explored the novel odor. Table 3 Effects of different predictor variables on behavioral traits (a-g) of 99 individuals, repeatedly measured up to 4 times in starting box (SB), open field (OF) and novel odor (NO) tests. Analysis by a multifactorial LMM including individual identity as a random factor. The 2-way interaction between age class at testing and the date of birth during the reproductive season was tested in all models but was never statistically significant ( P > 0.05). Significant effects are given in bold Dependent variable Predictors χ 2 df β ± SE P (a) SB - Probability to enter arena Date of birth within season 6.065 1 0.042 ± 0.017 0.014 Age class at testing [old] 0.001 1 –0.036 ± 0.999 0.971 Sex [m] 3.489 1 2.431 ± 1.302 0.062 Food plant abundance at testing 0.631 1 0.381 ± 0.480 0.427 Test sequence [2nd ] 2.546 3 –0.801 ± 0.523 0.467 [3rd ] –0.529 ± 0.989 [4th ] –0.135 ± 1.117 Year [2nd ] 3.251 1 –1.543 ± 0.856 0.071 (b) OF - Distance travelled 1 Date of birth within season 3.909 1 2.879 ± 1.456 0.048 Age class at testing [old] 0.887 1 3.193 ± 3.391 0.346 Sex [m] 5.507 1 9.160 ± 3.904 0.019 Food plant abundance at testing 0.773 1 1.411 ± 1.606 0.379 Test sequence [2nd ] 91.866 3 –15.800 ± 1.703 < 0.001 [3rd ] –16.284 ± 3.464 [4th ] –21.509 ± 3.983 Year [2nd ] 0.067 1 0.755 ± 2.911 0.795 (c) OF: % Time active Date of birth within season 8.561 1 0.306 ± 0.105 0.003 Age class at testing [old] 1.099 1 7.514 ± 7.166 0.294 Sex [m] 3.252 1 13.182 ± 7.309 0.071 Food plant abundance at testing 0.921 1 3.399 ± 3.541 0.337 Test sequence [2nd ] 51.091 3 –25.280 ± 3.942 < 0.001 [3rd ] –22.142 ± 7.242 [4th ] –40.723 ± 8.330 Year [2nd ] 1.338 1 –6.244 ± 5.397 0.247 (d) OF: % Time exploring walls and corners of arena 1 Date of birth within season 10.626 1 0.021 ± 0.006 0.001 Age class at testing [old] 4.515 1 0.876 ± 0.412 0.034 Sex [m] 0.475 1 0.307 ± 0.446 0.491 Food plant abundance at testing 1.985 1 0.281 ± 0.200 0.159 Test sequence [2nd ] 41.002 3 –1.197 ± 0.216 < 0.001 [3rd ] –1.358 ± 0.419 [4th ] –2.306 ± 0.482 Year [2nd ] 0.692 1 0.275 ± 0.331 0.405 (e) NO: Time exploring object 2 Date of birth within season 2.361 1 0.191 ± 0.125 0.124 Age class at testing [old] 2.962 1 0.514 ± 0.298 0.085 Sex [m] 0.026 1 0.053 ± 0.330 0.873 Food plant abundance at testing 6.517 1 0.366 ± 0.143 0.011 Test sequence [2nd ] 7.727 3 –0.324 ± 0.154 0.052 [3rd ] 0.008 ± 0.304 [4th ] 0.351 ± 0.349 Year [2nd ] 4.694 1 –0.533 ± 0.246 0.030 (f) NO: Number of objects explored Date of birth within season 1.199 1 0.087 ± 0.079 0.274 Age class at testing [old] 3.079 1 0.351 ± 0.200 0.079 Sex [m] 0.437 1 0.143 ± 0.216 0.509 Food plant abundance at testing 3.089 1 0.168 ± 0.095 0.079 Test sequence [2nd ] 2.036 3 –0.146 ± 0.113 0.565 [3rd ] –0.132 ± 0.199 [4th ] –0.017 ± 0.222 Year [2nd ] 2.110 1 –0.234 ± 0.161 0.146 1 square-root transformation of dependent variable 2 log [x + 1] transformation of dependent variable In two parameters measured in the open field, we found significant sex differences; males travelled a longer distance in the arena (Table 3 b) and spent a higher % time being active (Table 3 c). We also found highly significant effects of the test sequence (number of tests the individuals had experienced, range from 0–3) regarding the distance travelled, the % time active, and the % time of exploring walls and corners in the arena. For these three parameters, the values were significantly higher during the first test compared to all subsequent test sessions (Tables 3 b-d; Fig. A in Suppl. Material). Personality-specific reproduction of early-born females Out of 84 females tested, 10 (11.9%) had already reproduced in the breeding season of their birth. All reproducing females were born early in the breeding season, during in the first 3 weeks of the season in 2022 and during the first 5 weeks of the season in 2023. There were no significant associations between any of the six behavioral variables in early-born females (birth dates during the first 5 weeks of the breeding season, n = 27), and the probability of reproduction during the same season (GLMM for binomial data, all P > 0.50; see details in Table B in Suppl. Materials). Discussion We studied whether the date of birth during the breeding season affects personality in a seasonally breeding rodent, the bush Karoo rat. We hypothesized that individuals born later in the season should adopt a more proactive personality because such a behavioral type would be advantageous for finding/building their own stick lodges. This was confirmed by our findings: later-born individuals were bolder and more active and explorative in behavioral tests, suggesting a more proactive personality in these rats than in those born earlier. We did not find support that proactivity has evolved to support reproduction in early-born females because females that reproduced within the season of their birth did not show higher proactivity compared to females that did not reproduce within the same season. Finally, while food abundance decreased from the moist breeding season to dry non-breeding season, this did not affect the proactive responses. Individuals displayed higher levels of the behaviors during the first test compared to all subsequent tests, even in behavioral parameters which were notably repeatable over time (Suppl. Material, Fig. A). This indicates patterns of learning or habituation in response to the test procedure, which, however, does not call into question the existence of individual differences in personality. Similar findings have been reported in other studies using personality phenotyping in small mammals, frequently showing notably higher or lower responses (dependent on the kind of test) during the first test repeat test compared to subsequent ones (Matsunaga and Watanabe 2010 ; Lewejohann et al. 2011 ). Despite such changes over time in the absolute levels of some of the behaviors considered, we found consistent individual differences in measurements of activity, boldness and exploration as well as significant associations between these different variables, confirming findings obtained in other small mammals under field conditions (e.g., Lantová et al. 2011 ; Eccard and Herde 2013 ). Individuals in our study that were more active were also bolder and more explorative, suggesting a behavioral syndrome which we describe as “proactivity” (Koolhaas et al. 1999 ). Similar associations have been reported in other small mammals under field conditions; for example, more exploratory European rabbits ( Oryctolagus cuniculus ) were bolder during early age, and were less sociable and tended to be more aggressive as subadults (Rödel et al. 2015 ). Furthermore, bent-wing bats ( Miniopterus fuliginosus ) showed positive associations between traits reflecting boldness, activity and exploration, described by the authors as ‘proactiveness’ (Kuo et al. 2024 ). The main hypothesis of our study, that later-borns are more proactive, was based on the assumption that such individuals face more intense competition for stick lodges, a critical and limiting resource for survival in their harsh ambient environment. As it is typical for a short-lived seasonal breeder, population density of bush Karoo rat peaks during the late breeding season, and such a high population density has the potential to negatively affect resource availability (White 2008 ). Accordingly, in our study, we found a sharp rise in population density during the course of the breeding season and an associated and notable decrease in stick lodge availability. Although there was always more than one lodge available per adult, lodges differed in their quality, such that the availability of large high-quality lodges decreased to below one lodge per adult by the end of the breeding season. The availability of smaller lodges was higher but they were usually less steady and in poor condition, thus needing a higher investment in building and repairing. The emergence of new lodges during both seasons suggests that the existing lodges were not able to meet the demand of the increasing population. Thus, stick lodges clearly represented a limited resource at the end but not at the start of the breeding season. Individuals born at high population density during the late breeding season can be expected to be at a disadvantage at locating and occupying vacant stick lodges. Due to their young age and relatively small body size, they can be expected to be less competitive than older and larger individuals born earlier. As a result, they would need to invest in either finding unoccupied lodges or building their own ones, and both would require travelling and exploring a broader range of their habitat. In support, a link between increased space use and a more proactive personality has been found in North American red squirrels ( Tamiasciurus hudsonicus ) in which individuals with higher activity levels in standardized tests were re-trapped over a larger range of the study site (Boon et al. 2008 ). Under field conditions, the trait combination of being more exploratory and more active, as found in our study (see also Perals et al. 2017 ), could contribute to an increased efficiency in searching for stick lodges or of building materials. Thus, being more proactive could be beneficial in later-born individuals in response to the increasing difficulty of acquiring stick lodges. The life history of bush Karoo rats allows individuals born early to have the chance for precocious reproduction within the same season. In our study, precocious reproduction occurred in 11.9% ( n = 10) of the females, which is lower than in some other seasonally breeding rodents or lagomorphs (greater Guinea pig Cavia magna : 18.8%, Kraus et al. 2005 ; European rabbits in a Mediterranean habitat: 18.6%, Soriguer 1981 ). In our study, females reproducing during their season of birth were born relatively early, all before the middle of the breeding season. However, when only considering early born females, we did not find support for a higher proactivity associated with precocious reproduction. In addition, females born early, i.e. the ones that had an opportunity to reproduce, were generally less proactive than those born later during the season (see Fig. 3 ). We suggest that the emergence of a lower proactivity in earlier-born bush Karoo rats has possibly evolved due to ecological constraints. Our study population experiences a short breeding season and individuals have to survive a long dry season thereafter. Such a harsh environment could limit the benefits of precocious reproduction. Reproducing at young age can even have negative fitness consequences on both the first litters and the mothers (Lambin and Yoccoz 2001 ; Rödel et al. 2023 ), and furthermore a more proactive personality has negative consequences on survival (Smith and Blumstein 2008 ; Cole and Quinn 2011 ; Luna et al. 2020), for example through increased predation risk (Rödel et al. 2015 ; Denoël et al. 2019 ). Even though earlier-born females may benefit from being proactive in competition for reproductive resources, the survival impact might decrease the general fitness of both the young females and their litters to below the threshold for surviving the extremely harsh non-productive period. As a result, the benefit of being proactive may depend on the time of birth during the breeding season: for earlier-born individuals, being a proactive breeder may not be adaptive due to the harsh ecological environments, while for later-borns, the scarcity of life-critical resources (stick lodges) makes the risk of being proactive worthwhile in exchange of better chances in acquiring stick lodges. In conclusion, our study presents an example of personality differences in association with a key life history trait, the timing of birth within the season. Bush Karoo rats born later in the season showed higher proactivity and we suggest that such an association has evolved in response to the availability of survival resources, particularly the reduced availability of vacant stick lodges. Being more active, bolder, and more exploratory could be adaptive under such conditions because it could help later-borns explore more habitat to find unoccupied lodges or building materials. Our study highlights the importance of investigating not only reproduction opportunity, but also potential survival challenges to better understand the diverse mechanisms underlying the integration between life history and behavioral traits. Declarations Ethics approval The study was conducted according to accepted international standards regarding the guidelines for the use of animals in behavioral research (Vitale et al. 2018 ), and the legal requirements (section 20 permit) of South Africa, where the study was carried out. Ethical clearance (2022/05/02B) was provided by the Animal Research and Ethics Committee of the University of the Witwatersrand, Johannesburg, South Africa. No injuries or mortalities occurred in any study animals; all individuals were successfully released at the exact sites from where they were trapped. Competing interests The authors declare no conflicts of interest. Funding This study was supported by a Wits-CNRS joint PhD fellowship and is part of the long-term Studies in Ecology and Evolution (SEE-Life) program of the CNRS. Author contributions Jingyu Qiu and Carsten Schradin conceived the study. Data collection was performed by Jingyu Qiu and Lindelani Makuya, data analysis was performed by Jingyu Qiu and Heiko G. Rödel. The first draft of the manuscript was written by Jingyu Qiu and Heiko G. Rödel, all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgements This study is made possible by the administrative and technical support of the Succulent Karoo Research Station (registered South African NPO 122–134). We are thankful to Siya Sangweni and several research assistants for managing the field site, marking, trapping and monitoring the bush Karoo rats. Data All data generated or analysed during this study are included in this article and the supplementary information files “ESM_2.zip”. References Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48 Biro PA, Stamps JA (2008) Are animal personality traits linked to life-history productivity? Trends Ecol Evol 23(7):361–368 Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White JSS (2009) Generalized linear mixed models: a practical guide for ecology and evolution. 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Mammal Res 67(1):73–81 Supplementary Files ESM1supplementaryfiguresandtables.docx ESM2BKRpersonalitydataset.csv ESM3reproductiveactivityforFig.2.csv ESM4codeMainmodel.r Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2024 Read the published version in Behavioral Ecology and Sociobiology → Version 1 posted Editorial decision: Minor Revisions Needed 30 Sep, 2024 Reviewers agreed at journal 08 Sep, 2024 Reviewers invited by journal 04 Sep, 2024 Editor assigned by journal 18 Aug, 2024 First submitted to journal 13 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4905612","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":349509998,"identity":"4704dbb4-0a72-4b2c-b8b8-2a056b185feb","order_by":0,"name":"Jingyu QIU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBADHgb2BiBlYEGUasYGsBaeAyAtEsRrYWCQSACThNXLt/cef/CBwVrG4Obzqxt+FEgw8Ld3J+C3oudcYuMMhnQeg9s5ZTd7gA6TOHN2A14tzBI5hs08DIdBWtJu8AC1GEjk4tfCJv8GquXmmbSbf4jRwiPBA9Vyg/3YbaJskeDJS5w5wyCdR/JMDtttGQMJHoJ+kW8/e+DDhwpre77jx5/dfPPHRo6/vRe/FlAkAmOQGcQwgHEJAbAakBb2B0SoHgWjYBSMgpEIABCpQk1trwrmAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-6993-2549","institution":"IPHC DEPE: Institut Pluridisciplinaire Hubert Curien Departement Ecologie Physiologie et Ethologie","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jingyu","middleName":"","lastName":"QIU","suffix":""},{"id":349509999,"identity":"2c59e06f-7984-4fe4-b02b-f5035964322c","order_by":1,"name":"Neville Pillay","email":"","orcid":"","institution":"University of the Witwatersrand Johannesburg","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Neville","middleName":"","lastName":"Pillay","suffix":""},{"id":349510000,"identity":"09672e35-8c3b-41c6-90d3-3ece7799a31e","order_by":2,"name":"Carsten Schradin","email":"","orcid":"","institution":"IPHC DEPE: Institut Pluridisciplinaire Hubert Curien Departement Ecologie Physiologie et Ethologie","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Carsten","middleName":"","lastName":"Schradin","suffix":""},{"id":349510001,"identity":"fa50dcfc-a3c0-4b8b-90fa-3f051b1965ee","order_by":3,"name":"Lindelani Makuya","email":"","orcid":"","institution":"University of the Witwatersrand Johannesburg","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lindelani","middleName":"","lastName":"Makuya","suffix":""},{"id":349510002,"identity":"b65b3717-43ca-4bc0-a536-4bdaecc78ef0","order_by":4,"name":"Heiko G. 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Note that the starting box inside the arena in (b) was lifted by a pulley system once the experiment started\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4905612/v1/0f4f6f64dd62f688b0046729.png"},{"id":66078378,"identity":"665ea85f-a92c-4c49-89b2-b714ce128a19","added_by":"auto","created_at":"2024-10-07 13:34:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40233,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of reproductive activity (assessed by the frequency of birth events) during the breeding season in the bush Karoo rat, given as density probability (red line)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4905612/v1/b70400ebb4c151ced4af0c13.png"},{"id":66076825,"identity":"f6ed974a-9ec1-49f5-9605-6f8bb1486ea1","added_by":"auto","created_at":"2024-10-07 13:18:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109570,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of birth date within the breeding season on different behavioral traits in the bush Karoo rat. All effects presented here are statistically significant. Regression lines (including 95% confidence intervals given as grey shadings) are based on parameter estimates as given in Table 3\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4905612/v1/e819130f70b0fce90fbfd413.png"},{"id":70382681,"identity":"abce5d4f-b65d-469b-aee9-7136f9621d8b","added_by":"auto","created_at":"2024-12-02 16:29:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1082572,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4905612/v1/722b0d45-f194-46ba-8f90-1795a6bced45.pdf"},{"id":66076826,"identity":"1702f239-125c-4672-a203-815608a6e5f6","added_by":"auto","created_at":"2024-10-07 13:18:43","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":153207,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1supplementaryfiguresandtables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4905612/v1/42bbc5c80f344d0a1c246464.docx"},{"id":66076708,"identity":"16a4a24d-f472-4b27-bade-3ad759ed3925","added_by":"auto","created_at":"2024-10-07 13:10:43","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":31143,"visible":true,"origin":"","legend":"","description":"","filename":"ESM2BKRpersonalitydataset.csv","url":"https://assets-eu.researchsquare.com/files/rs-4905612/v1/36cd210dab9338f46970b77c.csv"},{"id":66077926,"identity":"133c0172-87b8-4c04-ab04-d62bf59d7bfc","added_by":"auto","created_at":"2024-10-07 13:26:43","extension":"csv","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2300,"visible":true,"origin":"","legend":"","description":"","filename":"ESM3reproductiveactivityforFig.2.csv","url":"https://assets-eu.researchsquare.com/files/rs-4905612/v1/4d13213d98e85f10654a1b08.csv"},{"id":66076703,"identity":"5ab806e3-6efc-4182-b909-a4c6cf818669","added_by":"auto","created_at":"2024-10-07 13:10:43","extension":"r","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":45886,"visible":true,"origin":"","legend":"","description":"","filename":"ESM4codeMainmodel.r","url":"https://assets-eu.researchsquare.com/files/rs-4905612/v1/385e0ea5cea5f2c0bde6e5b8.r"}],"financialInterests":"","formattedTitle":"Higher proactivity in later-borns: effects of birth date on personality in a small mammal","fulltext":[{"header":"Significance statement","content":"\u003cp\u003eIn short-lived animals, the birth date during the breeding season can lead to differences in life history and corresponding behavioral traits. We studied the link between birth date and personality in the bush Karoo rat, a seasonally-breeding and short-lived rodent which relies on a limited resource, stick lodges, essential to survive in a harsh semi-desert environment. Individuals born later in the season were more active, bolder and more explorative; these traits likely help them to successfully compete for stick lodges during the late breeding season when population density and thus competition is high. Early-born females, which can potentially start reproducing within their season of birth, showed no personality differences regardless of their reproductive status, suggesting that resource availability rather than reproduction opportunity may underly the evolution of seasonal personality differences in this species.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eFor animals living in seasonal environments, reproduction typically coincides with periods of high resource abundance (Whittier and Crews \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). The timing of birth during such highly concentrated reproductive periods can lead to different life history trade-offs and thus to alternative phenotypic developmental trajectories (Roff \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Varpe \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In many short-lived species, such as rodents, individuals born early during the breeding season can reach sexual maturity and reproduce within the same season (Tkadlec and Zejda \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Montiglio et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In contrast, individuals born later experience a shorter resource-rich period and are less likely to reproduce within the season of their birth. Thus, they need to survive through the non-productive period to reproduce in the following year (Lambin and Yoccoz \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Consequently, the timing of birth within the season may shape distinct behavioral traits in early and late born offspring to adaptively cope with their different survival and reproduction challenges.\u003c/p\u003e \u003cp\u003eOver the past decade, behavioral studies have gained a deeper understanding of consistent individual differences (animal personality), how these are maintained and their association with life history (R\u0026eacute;ale et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Biro and Stamps \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Pace-of-life syndrome proposes that the trade-off between current versus future reproduction leads to differences in behavioral traits (R\u0026eacute;ale et al. 2010; Dammhahn et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e): in seasonal breeding rodents, individuals born early which have an opportunity to reproduce within the season of birth, should benefit from an active and risk-taking personality that could be advantageous when acquiring resources needed for reproduction. In contrast, being less bold and less risk-taking may be more adaptive for later-borns because such a personality type will contribute to saving energy, increasing the probability of surviving through the non-breeding season until they can reproduce in the following year (Gracceva et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Such an association between personality and the timing of birth has been reported, for example, in eastern chipmunks (\u003cem\u003eTamias striatus\u003c/em\u003e), in which birth cohorts with early reproductive opportunities were faster explorers than those reproducing at a later age (Montiglio et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Similarly, in European shags (\u003cem\u003ePhalacrocorax aristotelis\u003c/em\u003e), chicks hatched early in the breeding season had higher social ranks and showed higher levels of aggression compared to those hatched later (Velando \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). However, this hypothesis does not appear to apply to a within-population study: in short-lived common voles (\u003cem\u003eMicrotus arvalis\u003c/em\u003e); individuals captured in spring (mostly late born cohort from the previous year) were bolder than those captured at other times of the year (Eccard and Herde \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eApart from reproductive trade-offs that underly the pace-of-life continuum, ecological conditions can also be important drivers of personality (Dammhahn et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jablonszky et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Montiglio et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Individuals with proactive phenotypes are usually more successful in competing for resources (Sih et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Smith and Blumstein \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). During the late breeding season, resource availability typically declines, while population density increases at the same time. Being proactive can be beneficial for individuals born late in the season in competition for limited resources. Therefore, two alternative hypotheses exist: (1) early-borns are more proactive to acquire enough resources for reproduction \u003cem\u003eversus\u003c/em\u003e (2) late-borns are more proactive to acquire enough resources for survival. Therefore, to understand the association between personality and the birth timing, further investigations, preferably under natural conditions are necessary.\u003c/p\u003e \u003cp\u003eThe seasonal breeding bush Karoo rat (\u003cem\u003eOtomys unisulcatus\u003c/em\u003e), a short-lived small mammal living in arid environments of South Africa, is an appropriate model to study the association between birth timing and personality. This species shows a distinct ecological feature that can result in intense resource competition during the breeding season. Bush Karoo rats construct \u0026ldquo;stick lodges\u0026rdquo; from dry plant material as refugia (Vermeulen \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Pillay \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), providing a favorable micro-climate that protects the rat from the harsh ambient environment (Vermeulen \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Brown and Willan \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Du Plessis et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). A stick lodge is costly to build and is commonly used by only one adult individual in this solitary species (Makuya et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), although it can be reused by others after the builder disappears. Thus, vacant stick lodges represent a limited survival resource, and the availability decreases in the late breeding season when subadult individuals start to occupy stick lodges of their own. As more lodges become occupied, individuals born later face increasing difficulties in finding unoccupied lodges or will even need to build new ones. In this situation, having a proactive personality becomes adaptive as it could lead to finding and competing for unoccupied lodges, or in competing for building materials for constructing new lodges (Vermeulen \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1988\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur aim was to investigate the effect of the date of birth on personality in adult bush Karoo rats. First, we studied whether free-living bush Karoo rats show consistent personality traits in activity, boldness and exploration behavior. Next, considering the potential effects of increasing population density on the availability of stick lodges, we predicted that such personality traits would be associated with birth timing, as later-borns would adopt a more proactive (active, bold and/or explorative) behavioral type. Finally, we studied whether there was a seasonal change in behaviour, specifically whether the proactive response decreased in the food restricted dry season when compared to the food rich moist season. We conducted a field study over two years (2022\u0026ndash;2023) and repeatedly quantified six behavioral parameters related to three different behavioral personality traits, activity, boldness and exploration (sensu R\u0026eacute;ale et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), which was compared between individuals born earlier and later during the breeding season.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy site and study population\u003c/h2\u003e \u003cp\u003eOur study population occurred in the arid Succulent Karoo, a biodiversity hotspot in South Africa, characterized by variable climate and low precipitation. Bush Karoo rat breeding activity coincides with season (Wolhuter et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Reproduction is concentrated in the moist period from July to November, followed by the hot and dry non-breeding period from December to June. Bush Karoo rats have a relatively short life span (1\u0026ndash;2 years). The earliest age at sexual maturity is between 5\u0026ndash;6 weeks, and the reproductive period spans over 4\u0026ndash;5 months per year (Vermeulen \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Wolhuter et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Offspring born early in the breeding season can reach sexual maturity within the season of their birth (Wolhuter et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The competition for reproductive resources mostly concerns individuals born early in the season, while the availability of stick lodges is relevant for the survival of all individuals and may be especially limiting for those born late in the season.\u003c/p\u003e \u003cp\u003eThe study was conducted in the Goegap Nature Reserve, Northern Cape Province, South Africa. The field site in the semi-arid Succulent Karoo (Cowling et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) is characterized by an annual rainfall of 160 mm/year on average, and by temperatures varying from \u0026minus;\u0026thinsp;1.5 to 24\u0026deg;C during winter and from 4 to 42\u0026deg;C during summer (weather station at the field site). Most of the rainfall occurs in winter, creating abundant vegetation that supports the onset of reproductive activity in our study population (Wolhuter et al. 2021). The field site for the study is approximately 4.5 ha.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStick lodge surveys\u003c/h2\u003e \u003cp\u003eStick lodge surveys were conducted twice a year, at the beginning of the breeding season in July and after the breeding season in January. We classified the stick lodges, which built within shrubs, into three size categories: (i) small: lodges with a height below 20 cm; (ii) medium: lodges with a height from 21\u0026ndash;50 cm; and (iii) large: lodges that almost covered the entire shrub with a height above 50 cm (Schradin \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). For every lodge, we recorded whether it was old (several years old) or whether it was newly built within the past few months, based our field records.\u003c/p\u003e \u003cp\u003eFor each season, we calculated the total number of stick lodges on the field site as the total of old lodges surveyed in January (e.g. lodge survey in January 2023 for the breeding season that started in July 2022). Because we wanted to have a measure of available old lodges, we recorded these separately from lodges that were built within prevailing season (the new lodges).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eTrapping and individual tagging\u003c/h2\u003e \u003cp\u003eIn the field site, trapping was carried out at all occupied stick lodges throughout the year as part of a long-term data collection. The field site was split into 6 trapping areas, with trapping being carried out at two areas simultaneously by two people for three days, before switching to two other areas. Additional trapping was done at lodges with previously unmarked juveniles and focal individuals for behavioral tests. Trapping was done 5 days a week, and occurred before sunrise. The traps were set at lodge entrances and checked every 30 min. All traps were closed within two hours after sunrise to avoid overheating. We used Sherman traps and locally produced metal (Sherman-like) live traps (26 \u0026times; 9 \u0026times; 9 cm), which had small holes in the sides to allow circulation of air.\u003c/p\u003e \u003cp\u003eAt first capture, bush Karoo rats were individually marked with aluminum band ear tags in both ears (0.25 g per tag) with a unique individual number (National Band and Tag Co., Newport, KY, USA). During re-trapping, we always checked for infections at the ear tags, which would have resulted in the removal of the tag on the affected ear; such cases never occurred during the study period. Birth date was estimated from the animals\u0026rsquo; body mass, based on the linear association between age and body mass in the bush Karoo rat as published in Pillay (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Trapping data used for our study spanned from 1st January 2022 to 14th December 2023. When a female born within the season showed signs of pregnancy/lactation (palpable embryos at late pregnancy stage; lactation as evident by the developmental stage of the mammary glands), or when a female had dependent young (as evident by juveniles trapped at the same stick lodge prior to the next breeding season), this female was considered as having reproduced during the season of birth (\u0026lsquo;precocious reproduction\u0026rsquo;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of changes in population density\u003c/h2\u003e \u003cp\u003eBased on trapping data, we recorded the population density of adult (older than 5 weeks) bush Karoo rats (\u003cem\u003en\u003c/em\u003e/ha) at the beginning of the breeding season in July and again after the breeding season in January for both seasons. Therefore, for each month, we counted the total number of trapped adult bush Karoo rats and divided this number by the size of the field site. We were able to mark and monitor the population through observations because the field site is an open terrain (dispersed shrubs with sandy areas in between), the bush Karoo rat is diurnal, and occupied stick lodges showed clear signs of occupancy. At lodges with signs of occupancy where we did not trap an individual within 3 days, we continued with additional trapping. We also conducted behavioral observations (described in Makuya et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), enabling us to identify occupied lodges and unmarked juveniles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eExperimental procedure\u003c/h2\u003e \u003cp\u003eTrapping of focal individuals was conducted using the same method as described above. Individuals were transported to a field laboratory situated next to the field site (less than 10 min walking distance) for behavioral testing. Except when checking ear tags and performing behavioral tests, the rats remained in their traps during the whole time. After the test procedure, individuals were released next to their stick lodge, i.e., at the site where they had been trapped.\u003c/p\u003e \u003cp\u003eFocal individuals underwent up to four repetitions of behavioral tests. Because the field site changed from the moist to dry season with a decrease in food abundance over time, we measured behavioral repeatability at short term (two week interval) and long term (16 week interval, in the moist and dry seasons) to account for the seasonal variation of food abundance. The test schedule was determined by age; the first and second tests were conducted at early adult stage (age class \u0026ldquo;young\u0026rdquo;), with the first age at approximately 6 weeks when they reach sexual maturity (Pillay \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The second test was scheduled two weeks later. The third and fourth tests were conducted at fully adult stage (age class \u0026ldquo;old\u0026rdquo;), with the third test scheduled when individuals were approximately 20 weeks old, and the fourth test two weeks later.\u003c/p\u003e \u003cp\u003eAs was evident from our trapping data, females usually disperse for shorter distances and therefore were more likely be continually caught using our trapping protocol. Thus, in the first year, the selection of focal individuals was limited to females and included four behavioral test replicates. In the second year, both males and females were selected as focal individuals. However, due to time constraints, the rats underwent only the first and second behavioral tests in the second year. Due to unpredictable field conditions, trapping of focal individuals was not always successful, which led to delays for repeated behavioral tests in some cases. The average age for \u0026ldquo;young\u0026rdquo; adults (at the 1st and 2nd test) was 54 days, and the average age for \u0026ldquo;old\u0026rdquo; adults (at the 3rd and 4th test) was 151 days. Because our study involved individually-marked focal animals, the experimenter(s) were not naive to individual identities during testing.\u003c/p\u003e \u003cp\u003ePopulation density of small mammals in our field site typically decreases dramatically during the dry season to the onset of the next breeding season, when it is only approximately a quarter of the density at the end of the breeding season (Nater et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As expected, many individuals disappeared from the field site throughout the study, mainly due to predation. We could not predict which individuals would disappear, so we tested as many individuals as possible at the onset of the breeding season (1st test: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;99; 2nd test: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;78 individuals), and we still had a sample size providing acceptable statistical power at the end of the dry season (3rd test: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30; 4th test: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBehavioral tests\u003c/h2\u003e \u003cp\u003eOne to a maximum of four individuals were trapped for behavioral tests per day. A white chamber (100 cm long, 85 cm wide and 65 cm high) made of melamine panels was used as a test arena (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Before introducing a new individual into the arena, the arena was always thoroughly cleaned using 95% alcohol and air dried. All tests were video-recorded and later analyzed using the software BORIS (Friad and Gamba 2016) and Single Mouse Tracker (Icy software, De Chaumont 2012). Focal individuals underwent three successive behavioral tests directly after the morning trapping.\u003c/p\u003e \u003cp\u003e \u003cem\u003eStarting box\u003c/em\u003e\u0026ndash; This part of the apparatus (the starting box) consisted of a 10 cm\u003csup\u003e3\u003c/sup\u003e black acrylic and opaque square box with one side that could be opened (door) and a lid at the other side. The open side was directly attached to the test arena and separated by a closed door (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). After individuals were placed into the starting box via the lid, they were allowed to calm down for 3 min in the closed box. Then the door was opened and we recorded whether the animal entered the test arena in the following 10 min. If the animal did not enter the arena within this time, it was gently nudged in using a plastic ruler (2 \u0026times; 30 cm) by reaching into the box through the lid. Once the individual had entered the arena, the door was closed to prevent its return into the starting box.\u003c/p\u003e \u003cp\u003e \u003cem\u003eOpen field test -\u003c/em\u003e Once the rat had entered the arena, the open field test began (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). During the following 5 min, the individual could freely explore the arena. We recorded three behavioral parameters: (1) the distance travelled, defined as the total distance of locomotion measured in cm; (2) the % time the individual was active in the arena, defined as the total time minus the time being immobile (i.e. no obvious movements for more than 10 seconds); and (3) the % time the animal spent exploring the walls and corners of the arena, defined as sniffing or putting the front paws against the walls.\u003c/p\u003e \u003cp\u003e \u003cem\u003eNovel odor test -\u003c/em\u003e During our preliminary tests, bush Karoo rats did not show notable interactions with plastic toys presented as visual novel objects. However, interactions were observed when objects were applied with novel odors (e.g., orange peel). We therefore did not apply classical novel object tests (Denninger et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) but instead measured the animals\u0026rsquo; exploration behavior towards objects carrying novel odors in a standardized setting.\u003c/p\u003e \u003cp\u003eAfter the open field test was completed, the animal remained in the arena and was confined again by the experimenter using the same black acrylic square box. The box was attached to a fixed pulley system so it could be lifted to release the individual with minimum disturbance. The focal individual was first placed in the center of the arena while covered by the box. Then, four identical hollow metal, egg-shaped sieve balls (4.5 \u0026times; 3.9 cm, steel) providing the same novel odors were placed inside the arena, 10 cm away on the extended diagonal of the black box (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The source of odor used in the 1st test was orange peel, tomato sauce was in the 2nd test (two weeks later), peanut butter was used in the 3rd test and strawberry jam in the 4th test. After setting up the arena, the rat was allowed to calm down for 5 min inside the box, then the box was lifted and the individual was given 5 min to explore the four metal balls. We recorded two behavioral parameters: (1) the time the animal spent exploring the objects, defined as the total (summed-up) duration the individual sniffed or touched one of the four tea balls; and (2) the number of objects explored, defined as the total number of tea balls, which were sniffed or touched by the individual during the test (range from 0\u0026thinsp;\u0026minus;\u0026thinsp;4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eQuantification of seasonal food abundance\u003c/h2\u003e \u003cp\u003eBecause the repeated behavioral tests were conducted in two seasons with variable food availability, we quantified seasonal changes in the abundance of food plants and considered the potential effects on individual behavioral performance during testing. Food plant abundance was measured monthly using the Braun-Blanquet method as part of the long-term data collection on the field site, assessed by the average number of food plants from eight 2 m \u0026times; 2 m plots randomly located in the field site (Werger \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Schradin and Pillay \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The resulting index was used in the statistical analysis (see details below) as an estimate of the food plant availability at the time of the different test sessions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis and sample sizes\u003c/h2\u003e \u003cp\u003eIn total, 99 individuals (\u003cem\u003en\u003c/em\u003e\u003csub\u003emales\u003c/sub\u003e = 15, \u003cem\u003en\u003c/em\u003e\u003csub\u003efemales\u003c/sub\u003e = 84) were tested in 226 behavioral tests. We considered potential effect of multiple test replicates, as such repeated testing may lead to habituation effects (Salomons et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The sample sizes available during the different test replicates decreased over time due to individuals disappearing from the field site, with \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;99 during the 1st tests, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;78 during the 2nd tests, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30 during the 3rd tests, and \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19 during the 4th tests.\u003c/p\u003e \u003cp\u003eStatistical analyses were carried out in R, version 4.3.0 (R Core Team \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the first step, we checked for repeatabilities of the six behavioral variables quantified in the starting box test, open field test and in the novel odor test, across the four different test sessions, as well as for associations between these behavioral variables. All six response variables (behavioral variables, given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were scaled for analysis. This analysis was done using a single multivariate linear mixed-effects model (i.e., with six response variables) based on the R package \u003cem\u003eMCMCglmm\u003c/em\u003e (Hadfield \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The predictors were year and sex (2 levels each) and test sequence (number of tests the individual has done before, 4 levels), with random effects for individual ID. We applied a weakly informative prior, allowing the data to primarily inform the posterior distributions. One chain was run with 100,000 iterations. The first 5,000 iterations were discarded as burn-in, and every 100th iteration was retained (thinning interval\u0026thinsp;=\u0026thinsp;100), resulting in 950 samples per chain. The response variables followed appropriate distributions: a categorical distribution for the latency to leave shelter, Gaussian distribution for the continuous behavioral variables, and Poisson distribution for the number of objects explored.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRepeatability (\u003cem\u003eR\u003c/em\u003e, including its 95% credible interval \u003cem\u003eCI\u003c/em\u003e) of behavior parameters measured in repeated starting box (SB), open field (OF) and novel odor (NO) tests of 99 individuals. Analysis by a multivariate LMM including individual identity as a random factor, year, sex and test sequence (4 levels) as fixed variance. (a) Long-term repeatability was based on all (up to) four behavioral tests of all age classes (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;226 measurements for each behavioral variable), (b) Short-term repeatability was based on (up to) two behavioral tests during young adult stage (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;145 measurements for each behavioral variable)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(a) Long-term repeatability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(b) Short-term repeatability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003csub\u003e95%\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003csub\u003e95%\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSB - Probability to enter arena\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.483, 0.872]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.050, 0.860]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOF - Distance travelled\u003c/b\u003e \u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.204, 0.521]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.141, 0.610]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOF - % Time active\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.025, 0.308]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[\u0026lt;\u0026thinsp;0.001, 0.417]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOF - % Time exploring\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ewalls and corners of arena\u003c/b\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.093, 0.427]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[0.039, 0.536]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNO - Time exploring object\u003c/b\u003e \u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[\u0026lt;\u0026thinsp;0.001, 0.325]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[\u0026lt;\u0026thinsp;0.001, 0.158]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNO - Number of objects explored\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[\u0026lt;\u0026thinsp;0.001, 0.364]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e[\u0026lt;\u0026thinsp;0.001, 0.244]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e1\u003c/sup\u003e square-root transformation of dependent variable\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e2\u003c/sup\u003e log [x\u0026thinsp;+\u0026thinsp;1] transformation of dependent variable\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRepeatabilities across the (up to) four repeated tests per behavioral parameter (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and pair-wise correlation coefficients between the different behaviors (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e) were calculated based on the within-individual and among-individual variance matrices provided by this model. Associations between the different behavioral variables were considered statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) when the 95% Bayesian credible intervals of the correlation coefficients (\u003cem\u003eR\u003c/em\u003e) did not overlap zero (Houslay and Wilson \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, in case of repeatabilities over time, as per definition only non-negative values can be obtained and thus the 95% credible intervals cannot overlap zero, \u003cem\u003eP\u003c/em\u003e-values could not be calculated based on the above-mentioned method (Houslay and Wilson \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Consequently, our inference on repeatabilities was only based on interpretation of the 95% credible intervals. We calculated the overall (long-term) repeatability, based on all (up to) four behavioral tests of our 99 focal individuals over all age classes (young adult at first and second tests, old adult at third and fourth tests; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;226 measurements for each behavioral variable), as well as short-term repeatability, based on the first two behavioral tests for 99 young adults with a total of \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;145 measurements for each behavioral variable.\u003c/p\u003e \u003cp\u003eIn the second step, we tested the effects of the date of birth (covariate, 1st July as baseline; see the rather consistent distribution of birth dates along the season in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) on the six different behavioral variables (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Therefore, using the R package \u003cem\u003elme4\u003c/em\u003e (Bates et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), we applied separate models - a generalized linear mixed-effects model (GLMM) for binomial data with a logit link for the probability to enter the test arena (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), and linear mixed-effects models (LMM) for all remaining (continuous) behavioral variables (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-f). To obtain a normal distribution of model residuals (verified by visually checking normal probability plots) and homogeneity of variances (by plotting residuals versus fitted values) for LMMs, we square-root transformed the distance travelled and the % time the animals spent exploring the walls and corners of the arena, and log [x\u0026thinsp;+\u0026thinsp;1] transformed the time the animal spent exploring the object in the novel odor test. Individual identity was always included as a random (intercept) factor. All models included the age class at testing (\u0026ldquo;young adult\u0026rdquo; at the 1st and 2nd tests or \u0026ldquo;old adult\u0026rdquo; at the 3rd and 4th tests; 2 levels), the sex of the focal animals and the year of testing (all factors with 2 levels), the test sequence (factor with 4 levels) and the food plant abundance at testing (covariate). Because we were interested in whether possible differences between earlier- and later-born individuals were only apparent in young adults or in old adult individuals, we also tested the 2-way interaction between date of birth and age class. When non-significant, this interaction was removed from the models and these were recalculated (Engqvist \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). \u003cem\u003eP\u003c/em\u003e-values were calculated by type-3 Wald chi-square tests (Bolker et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe also tested whether the behavioral responses of early-born females (i.e., females born until/including the 5th week of the breeding season, when the last reproducing female was born) in the different tests were associated with their actual reproductive activity during their season of birth. Using the R package \u003cem\u003elme4\u003c/em\u003e (Bates et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), we applied GLMMs for binomial data with a logit link using the same transformations for some of the behavioral variables (now used as predictors in our model) as described above. Each model included one behavioral variable, and all included year of testing (2 levels) as fixed variance. Individual identity was always included as a random (intercept) factor. Also, \u003cem\u003eP\u003c/em\u003e-values were calculated by type-3 Wald chi-square tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSeasonal differences in population density and stick lodge availability\u003c/h2\u003e \u003cp\u003eIn both years of the study, the adult population density showed dramatic variation between the breeding/non-breeding seasons. Population density increased during the breeding season from July to January by 120.7% on average. Specifically, during the first year of study (Jul 2022 \u0026ndash; Jan 2023), the adult density increased by 81.4% from 12.6 to 23.4 individuals/ha, and during the second year (Jul 2023 \u0026ndash; Jan 2024), it increased by 160% from 6.5 to 16.9 individuals/ha. However, the population also dramatically decreased during the non-breeding season by 72.0% between January to July 2023.\u003c/p\u003e \u003cp\u003eIn the breeding season starting in July 2022, 172 old lodges (i.e. existing ones) were available and 26 new lodges were built. The availability of old lodges decreased during the breeding season, from 3.1 to 1.6 lodges/adults from July 2022 to January 2023. In the breeding season starting in July 2023, 194 old lodges were available and 9 new lodges were built. The availability of old lodges decreased from 6.9 to 1.8 lodges/adults from July 2023 to January 2024. When only considering the large lodges with a height above 50 cm, which can be assumed to be the most valuable resource, this seasonal difference was more pronounced. The availability of such large lodges decreased from 0.8 per adult individual to 0.4 in 2022/2023, and from 1.7 to 0.4 in 2023/2024.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePattern of seasonal reproduction\u003c/h2\u003e \u003cp\u003eWe quantified the temporal distribution of reproductive events during the breeding season using the estimated dates of birth of the juveniles trapped, based on a sample of \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;227 juveniles (130 born in 2022 and 97 born in 2023, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The start of the reproductive season was determined by the occurrence of at least three juveniles born on different dates within a week (i.e., apparently from different litters). The time of reproduction season was highly similar between the two years of study (2022: 19th Jul \u0026ndash; 1th Nov; 2023: 14th Jul \u0026ndash; 5th Nov).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eConsistent individual differences in behavior\u003c/h2\u003e \u003cp\u003eWe analyzed the consistency of six behavioral variables recorded in up to four repeated starting box tests, open field tests and novel odor tests. Overall, we found notable long-term consistencies across time (i.e., repeatability) and thus across different age classes (young to old adult), with respect to three of the four behavioral variables recorded in the starting box and open field tests. These variables were the probability to enter the arena within 10 min, the distance travelled, and the % time the individual spent exploring the walls and corners of the arena (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). In contrast, the % time the rats were active in the open field arena as well as both behavioral variables measured during the novel odor tests showed very low repeatabilities with large credible interval closely approaching zero (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eRegarding short-term consistency, when only considering the first two tests during which individuals could be considered as young adults, we found repeatabilities similar to long-term consistency in the starting box and open field test, but again no noticeable repeatability in the novel odor test (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eWe also found intra-individual associations among the parameters recorded in the starting box test and open field test. Specifically, the probability to enter the arena, the distance travelled, the % time the animals showed activity and the % time the animals spent exploring the walls and corners of the open field arena were positively and significantly correlated (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between the different behavioral parameters, based on measurements taken from 99 individuals of different age classes. Analysis by a multivariate LMM including individual identity as a random factor, and year, sex and test sequence (4 levels) as fixed variance. Note that results stem from the same model as used for the calculation of Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, more details, including the credible intervals, are given in Table A in Suppl. Materials. Correlation coefficients (among individual-level) are given, significant effects (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050) are given in brackets\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%TA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%TEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTEO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNOE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePEA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.763\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.840\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.780\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.549)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.454)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.786\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.620\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.484)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.429)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e%TA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.848\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.605)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.470)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e%TEA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.494)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.372)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTEO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(0.614)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNOE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ePEA: Probability to enter arena; DT: Distance travelled (square-root transformed); %TA: % Time active; %TEA: % Time exploring walls and corners of arena (square-root transformed); TEO: Time exploring object (log [x\u0026thinsp;+\u0026thinsp;1] transformed); NOE: Number of objects explored\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEffects of birth date on personality traits\u003c/h2\u003e \u003cp\u003eAll behavioral parameters recorded in the starting box and open field tests were significantly associated with the individual date of birth (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The probability to enter the arena (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), the distance travelled (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), the % time active (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) and the % time exploring the walls and corners of the arena (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003ed, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed) were all significantly higher in individuals born later in the season. The interaction between the date of birth and age class at testing was never statistically significant, indicating that the significant effects of date of birth on behavior (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-d) were independent of age class. In contrast, the two parameters recorded during the novel odor test were not significantly associated with date of birth (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003ee, f). We found a significant and positive effect of the current food plant abundance only for the total time spent exploring the objects carrying the novel odors (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Specifically, the higher the food availability around the time of testing, the longer the individuals explored the novel odor.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffects of different predictor variables on behavioral traits (a-g) of 99 individuals, repeatedly measured up to 4 times in starting box (SB), open field (OF) and novel odor (NO) tests. Analysis by a multifactorial LMM including individual identity as a random factor. The 2-way interaction between age class at testing and the date of birth during the reproductive season was tested in all models but was never statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Significant effects are given in bold\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(a) SB - Probability to enter\u003c/p\u003e \u003cp\u003e arena\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDate of birth within season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.042\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge class at testing [old]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.036\u0026thinsp;\u0026plusmn;\u0026thinsp;0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex [m]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.431\u0026thinsp;\u0026plusmn;\u0026thinsp;1.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFood plant abundance at testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.381\u0026thinsp;\u0026plusmn;\u0026thinsp;0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest sequence [2nd ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.801\u0026thinsp;\u0026plusmn;\u0026thinsp;0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[3rd ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.529\u0026thinsp;\u0026plusmn;\u0026thinsp;0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[4th ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.135\u0026thinsp;\u0026plusmn;\u0026thinsp;1.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear [2nd ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;1.543\u0026thinsp;\u0026plusmn;\u0026thinsp;0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(b) OF - Distance travelled \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDate of birth within season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.879\u0026thinsp;\u0026plusmn;\u0026thinsp;1.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge class at testing [old]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.193\u0026thinsp;\u0026plusmn;\u0026thinsp;3.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex [m]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e9.160\u0026thinsp;\u0026plusmn;\u0026thinsp;3.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFood plant abundance at testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.411\u0026thinsp;\u0026plusmn;\u0026thinsp;1.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest sequence [2nd ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;15.800\u0026thinsp;\u0026plusmn;\u0026thinsp;1.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[3rd ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;16.284\u0026thinsp;\u0026plusmn;\u0026thinsp;3.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[4th ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;21.509\u0026thinsp;\u0026plusmn;\u0026thinsp;3.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear [2nd ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.755\u0026thinsp;\u0026plusmn;\u0026thinsp;2.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(c) OF: % Time active\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDate of birth within season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.306\u0026thinsp;\u0026plusmn;\u0026thinsp;0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge class at testing [old]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e7.514\u0026thinsp;\u0026plusmn;\u0026thinsp;7.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex [m]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e13.182\u0026thinsp;\u0026plusmn;\u0026thinsp;7.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFood plant abundance at testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.399\u0026thinsp;\u0026plusmn;\u0026thinsp;3.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest sequence [2nd ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;25.280\u0026thinsp;\u0026plusmn;\u0026thinsp;3.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[3rd ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;22.142\u0026thinsp;\u0026plusmn;\u0026thinsp;7.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[4th ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;40.723\u0026thinsp;\u0026plusmn;\u0026thinsp;8.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear [2nd ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;6.244\u0026thinsp;\u0026plusmn;\u0026thinsp;5.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(d) OF: % Time exploring \u003c/p\u003e \u003cp\u003e walls and corners of arena \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDate of birth within season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.021\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge class at testing [old]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.876\u0026thinsp;\u0026plusmn;\u0026thinsp;0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex [m]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.307\u0026thinsp;\u0026plusmn;\u0026thinsp;0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFood plant abundance at testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.281\u0026thinsp;\u0026plusmn;\u0026thinsp;0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest sequence [2nd ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;1.197\u0026thinsp;\u0026plusmn;\u0026thinsp;0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[3rd ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;1.358\u0026thinsp;\u0026plusmn;\u0026thinsp;0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[4th ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;2.306\u0026thinsp;\u0026plusmn;\u0026thinsp;0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear [2nd ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.275\u0026thinsp;\u0026plusmn;\u0026thinsp;0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(e) NO: Time exploring \u003c/p\u003e \u003cp\u003e object \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDate of birth within season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.191\u0026thinsp;\u0026plusmn;\u0026thinsp;0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge class at testing [old]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.514\u0026thinsp;\u0026plusmn;\u0026thinsp;0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex [m]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.053\u0026thinsp;\u0026plusmn;\u0026thinsp;0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFood plant abundance at testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.366\u0026thinsp;\u0026plusmn;\u0026thinsp;0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest sequence [2nd ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.324\u0026thinsp;\u0026plusmn;\u0026thinsp;0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[3rd ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.008\u0026thinsp;\u0026plusmn;\u0026thinsp;0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[4th ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.351\u0026thinsp;\u0026plusmn;\u0026thinsp;0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear [2nd ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.533\u0026thinsp;\u0026plusmn;\u0026thinsp;0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(f) NO: Number of objects \u003c/p\u003e \u003cp\u003e explored\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDate of birth within season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.087\u0026thinsp;\u0026plusmn;\u0026thinsp;0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge class at testing [old]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.351\u0026thinsp;\u0026plusmn;\u0026thinsp;0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex [m]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.143\u0026thinsp;\u0026plusmn;\u0026thinsp;0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFood plant abundance at testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.168\u0026thinsp;\u0026plusmn;\u0026thinsp;0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest sequence [2nd ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.146\u0026thinsp;\u0026plusmn;\u0026thinsp;0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[3rd ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.132\u0026thinsp;\u0026plusmn;\u0026thinsp;0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[4th ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.017\u0026thinsp;\u0026plusmn;\u0026thinsp;0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear [2nd ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.234\u0026thinsp;\u0026plusmn;\u0026thinsp;0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e1\u003c/sup\u003e square-root transformation of dependent variable\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e2\u003c/sup\u003e log [x\u0026thinsp;+\u0026thinsp;1] transformation of dependent variable\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn two parameters measured in the open field, we found significant sex differences; males travelled a longer distance in the arena (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) and spent a higher % time being active (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). We also found highly significant effects of the test sequence (number of tests the individuals had experienced, range from 0\u0026ndash;3) regarding the distance travelled, the % time active, and the % time of exploring walls and corners in the arena. For these three parameters, the values were significantly higher during the first test compared to all subsequent test sessions (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-d; Fig. A in Suppl. Material).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePersonality-specific reproduction of early-born females\u003c/h2\u003e \u003cp\u003eOut of 84 females tested, 10 (11.9%) had already reproduced in the breeding season of their birth. All reproducing females were born early in the breeding season, during in the first 3 weeks of the season in 2022 and during the first 5 weeks of the season in 2023.\u003c/p\u003e \u003cp\u003eThere were no significant associations between any of the six behavioral variables in early-born females (birth dates during the first 5 weeks of the breeding season, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27), and the probability of reproduction during the same season (GLMM for binomial data, all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.50; see details in Table B in Suppl. Materials).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe studied whether the date of birth during the breeding season affects personality in a seasonally breeding rodent, the bush Karoo rat. We hypothesized that individuals born later in the season should adopt a more proactive personality because such a behavioral type would be advantageous for finding/building their own stick lodges. This was confirmed by our findings: later-born individuals were bolder and more active and explorative in behavioral tests, suggesting a more proactive personality in these rats than in those born earlier. We did not find support that proactivity has evolved to support reproduction in early-born females because females that reproduced within the season of their birth did not show higher proactivity compared to females that did not reproduce within the same season. Finally, while food abundance decreased from the moist breeding season to dry non-breeding season, this did not affect the proactive responses.\u003c/p\u003e \u003cp\u003eIndividuals displayed higher levels of the behaviors during the first test compared to all subsequent tests, even in behavioral parameters which were notably repeatable over time (Suppl. Material, Fig. A). This indicates patterns of learning or habituation in response to the test procedure, which, however, does not call into question the existence of individual differences in personality. Similar findings have been reported in other studies using personality phenotyping in small mammals, frequently showing notably higher or lower responses (dependent on the kind of test) during the first test repeat test compared to subsequent ones (Matsunaga and Watanabe \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lewejohann et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite such changes over time in the absolute levels of some of the behaviors considered, we found consistent individual differences in measurements of activity, boldness and exploration as well as significant associations between these different variables, confirming findings obtained in other small mammals under field conditions (e.g., Lantov\u0026aacute; et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Eccard and Herde \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Individuals in our study that were more active were also bolder and more explorative, suggesting a behavioral syndrome which we describe as \u0026ldquo;proactivity\u0026rdquo; (Koolhaas et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Similar associations have been reported in other small mammals under field conditions; for example, more exploratory European rabbits (\u003cem\u003eOryctolagus cuniculus\u003c/em\u003e) were bolder during early age, and were less sociable and tended to be more aggressive as subadults (R\u0026ouml;del et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, bent-wing bats (\u003cem\u003eMiniopterus fuliginosus\u003c/em\u003e) showed positive associations between traits reflecting boldness, activity and exploration, described by the authors as \u0026lsquo;proactiveness\u0026rsquo; (Kuo et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe main hypothesis of our study, that later-borns are more proactive, was based on the assumption that such individuals face more intense competition for stick lodges, a critical and limiting resource for survival in their harsh ambient environment. As it is typical for a short-lived seasonal breeder, population density of bush Karoo rat peaks during the late breeding season, and such a high population density has the potential to negatively affect resource availability (White \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Accordingly, in our study, we found a sharp rise in population density during the course of the breeding season and an associated and notable decrease in stick lodge availability. Although there was always more than one lodge available per adult, lodges differed in their quality, such that the availability of large high-quality lodges decreased to below one lodge per adult by the end of the breeding season. The availability of smaller lodges was higher but they were usually less steady and in poor condition, thus needing a higher investment in building and repairing. The emergence of new lodges during both seasons suggests that the existing lodges were not able to meet the demand of the increasing population. Thus, stick lodges clearly represented a limited resource at the end but not at the start of the breeding season.\u003c/p\u003e \u003cp\u003eIndividuals born at high population density during the late breeding season can be expected to be at a disadvantage at locating and occupying vacant stick lodges. Due to their young age and relatively small body size, they can be expected to be less competitive than older and larger individuals born earlier. As a result, they would need to invest in either finding unoccupied lodges or building their own ones, and both would require travelling and exploring a broader range of their habitat. In support, a link between increased space use and a more proactive personality has been found in North American red squirrels (\u003cem\u003eTamiasciurus hudsonicus\u003c/em\u003e) in which individuals with higher activity levels in standardized tests were re-trapped over a larger range of the study site (Boon et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Under field conditions, the trait combination of being more exploratory and more active, as found in our study (see also Perals et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), could contribute to an increased efficiency in searching for stick lodges or of building materials. Thus, being more proactive could be beneficial in later-born individuals in response to the increasing difficulty of acquiring stick lodges.\u003c/p\u003e \u003cp\u003eThe life history of bush Karoo rats allows individuals born early to have the chance for precocious reproduction within the same season. In our study, precocious reproduction occurred in 11.9% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10) of the females, which is lower than in some other seasonally breeding rodents or lagomorphs (greater Guinea pig \u003cem\u003eCavia magna\u003c/em\u003e: 18.8%, Kraus et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; European rabbits in a Mediterranean habitat: 18.6%, Soriguer \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). In our study, females reproducing during their season of birth were born relatively early, all before the middle of the breeding season. However, when only considering early born females, we did not find support for a higher proactivity associated with precocious reproduction. In addition, females born early, i.e. the ones that had an opportunity to reproduce, were generally less proactive than those born later during the season (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe suggest that the emergence of a lower proactivity in earlier-born bush Karoo rats has possibly evolved due to ecological constraints. Our study population experiences a short breeding season and individuals have to survive a long dry season thereafter. Such a harsh environment could limit the benefits of precocious reproduction. Reproducing at young age can even have negative fitness consequences on both the first litters and the mothers (Lambin and Yoccoz \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; R\u0026ouml;del et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and furthermore a more proactive personality has negative consequences on survival (Smith and Blumstein \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Cole and Quinn \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Luna et al. 2020), for example through increased predation risk (R\u0026ouml;del et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Deno\u0026euml;l et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Even though earlier-born females may benefit from being proactive in competition for reproductive resources, the survival impact might decrease the general fitness of both the young females and their litters to below the threshold for surviving the extremely harsh non-productive period. As a result, the benefit of being proactive may depend on the time of birth during the breeding season: for earlier-born individuals, being a proactive breeder may not be adaptive due to the harsh ecological environments, while for later-borns, the scarcity of life-critical resources (stick lodges) makes the risk of being proactive worthwhile in exchange of better chances in acquiring stick lodges.\u003c/p\u003e \u003cp\u003eIn conclusion, our study presents an example of personality differences in association with a key life history trait, the timing of birth within the season. Bush Karoo rats born later in the season showed higher proactivity and we suggest that such an association has evolved in response to the availability of survival resources, particularly the reduced availability of vacant stick lodges. Being more active, bolder, and more exploratory could be adaptive under such conditions because it could help later-borns explore more habitat to find unoccupied lodges or building materials. Our study highlights the importance of investigating not only reproduction opportunity, but also potential survival challenges to better understand the diverse mechanisms underlying the integration between life history and behavioral traits.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e \u003cp\u003e The study was conducted according to accepted international standards regarding the guidelines for the use of animals in behavioral research (Vitale et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and the legal requirements (section 20 permit) of South Africa, where the study was carried out. Ethical clearance (2022/05/02B) was provided by the Animal Research and Ethics Committee of the University of the Witwatersrand, Johannesburg, South Africa. No injuries or mortalities occurred in any study animals; all individuals were successfully released at the exact sites from where they were trapped.\u003c/p\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by a Wits-CNRS joint PhD fellowship and is part of the long-term Studies in Ecology and Evolution (SEE-Life) program of the CNRS.\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eJingyu Qiu and Carsten Schradin conceived the study. Data collection was performed by Jingyu Qiu and Lindelani Makuya, data analysis was performed by Jingyu Qiu and Heiko G. R\u0026ouml;del. The first draft of the manuscript was written by Jingyu Qiu and Heiko G. R\u0026ouml;del, all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis study is made possible by the administrative and technical support of the Succulent Karoo Research Station (registered South African NPO 122\u0026ndash;134). We are thankful to Siya Sangweni and several research assistants for managing the field site, marking, trapping and monitoring the bush Karoo rats.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this article and the supplementary information files \u0026ldquo;ESM_2.zip\u0026rdquo;.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. 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In: Norris DO, Jones RE (eds) Hormones and reproduction in fishes, amphibians, and reptiles. Springer US, Boston, MA, pp 385\u0026ndash;409\u003c/li\u003e\n\u003cli\u003eWolhuter L, Thomson J, Schradin C, Pillay N (2022) Life history traits of free-living bush Karoo rats (\u003cem\u003eOtomys unisulcatus\u003c/em\u003e) in the semi-arid Succulent Karoo. Mammal Res 67(1):73\u0026ndash;81\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"behavioral-ecology-and-sociobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"beas","sideBox":"Learn more about [Behavioral Ecology and Sociobiology](http://link.springer.com/journal/265)","snPcode":"265","submissionUrl":"https://www.editorialmanager.com/beas/default.aspx","title":"Behavioral Ecology and Sociobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"behavioral phenotype, consistent individual differences, Otomys, pace-of-life syndrome","lastPublishedDoi":"10.21203/rs.3.rs-4905612/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4905612/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn short-lived animals, individuals born earlier in the breeding season frequently reproduce within the season of birth. Consequently, it has been proposed that those born early benefit from a more proactive behavioral type to compete for reproductive resources whereas later-borns adopt a more reactive personality to conserve energy to survive through the non-breeding season and reproduce in the following year. However, being proactive could also benefit later-borns in acquiring decreasing resources in the late breeding season. We investigated personality differences depending on the date of birth in relation to resource variation in a free-living population of the bush Karoo rat (\u003cem\u003eOtomys unisulcatus\u003c/em\u003e). This species constructs stick lodges, a critical resource protecting the rats from the harsh semi-desert environments, but the availability of vacant lodges decreases with increasing population density during the breeding season. We predicted an increased occurrence of proactive phenotypes during the later breeding season, contrasting with the commonly assumed decrease in proactive phenotypes in late season due to lack of reproductive opportunity. We behaviorally phenotyped \u003cem\u003en\u003c/em\u003e= 99 individuals through repeated behavioral tests and found consistent individual differences along a proactive-reactive gradient. Most importantly, later-borns showed greater activity, boldness and exploration tendencies, indicating a more proactive personality. In addition, among early-born females, individuals which reproduced showed no differences in personality compared to those which did not reproduce. Our results indicate that seasonal differences in personality types in the bush Karoo rat may be driven by resource constraints in the late season rather than by differences in reproduction opportunities.\u003c/p\u003e","manuscriptTitle":"Higher proactivity in later-borns: effects of birth date on personality in a small mammal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-07 13:10:38","doi":"10.21203/rs.3.rs-4905612/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor Revisions Needed","date":"2024-09-30T08:45:53+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-09-08T23:04:26+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-04T13:01:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-19T02:19:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Behavioral Ecology and Sociobiology","date":"2024-08-13T04:55:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"behavioral-ecology-and-sociobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"beas","sideBox":"Learn more about [Behavioral Ecology and Sociobiology](http://link.springer.com/journal/265)","snPcode":"265","submissionUrl":"https://www.editorialmanager.com/beas/default.aspx","title":"Behavioral Ecology and Sociobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"db5c517b-6344-4559-bdce-6ddf84f95d7f","owner":[],"postedDate":"October 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-02T16:02:44+00:00","versionOfRecord":{"articleIdentity":"rs-4905612","link":"https://doi.org/10.1007/s00265-024-03531-x","journal":{"identity":"behavioral-ecology-and-sociobiology","isVorOnly":false,"title":"Behavioral Ecology and Sociobiology"},"publishedOn":"2024-11-28 15:57:43","publishedOnDateReadable":"November 28th, 2024"},"versionCreatedAt":"2024-10-07 13:10:38","video":"","vorDoi":"10.1007/s00265-024-03531-x","vorDoiUrl":"https://doi.org/10.1007/s00265-024-03531-x","workflowStages":[]},"version":"v1","identity":"rs-4905612","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4905612","identity":"rs-4905612","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}