Insights into the Pace-of-Life Syndrome hypothesis: Exploring the Influence of personality on Movement Ecology in Crotalus atrox

Insights into the Pace-of-Life Syndrome hypothesis: Exploring the Influence of personality on Movement Ecology in Crotalus atrox | 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 Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Insights into the Pace-of-Life Syndrome hypothesis: Exploring the Influence of personality on Movement Ecology in Crotalus atrox Oceane Da Cunha, Joshua J. Mead, Braulio A. Sanchez, Kajaya J. Pollard, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4681611/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The pace-of-life syndrome hypothesis predicts individuals, populations, and species that experience different ecological conditions will differ in spatial ecology resulting in contrasting life history strategies. We investigated the effect of personality on spatial ecology of the western diamond-backed rattlesnake ( Crotalus atrox ), and tested the predictions that bolder, more active, and exploratory individuals will move more and have larger territories compared to shyer, less active individuals. We tracked 14 rattlesnakes for one year and assessed their personality in captivity across four different axes: activity, boldness, exploration, and reactivity. Bolder and more active individuals travelled more than shy, less active individuals, but only during the non-mating season. Shy individuals increased movement significantly more than bold individuals during the mating season, thus leading to no significant difference in movements between shy and bold individuals during the mating season. Moreover, less bold and less active individuals were more responsive to changes in reproductive status. These results suggest the existence of two different strategies for resources acquisition within the same population, indicating that the pace-of-life syndrome hypothesis may be context-dependent. Ultimately, we show that the existence of different life-history strategies within the same population are dependent upon the reproductive status of these individuals. Behavioral Ecology Pace-of-life syndrome hypothesis life-history spatial ecology personality Figures Figure 1 Figure 2 INTRODUCTION To thrive within a given environment, individuals must efficiently gather food and find mates to reproduce [ 1 ]. These resources are limited, and their acquisition depends on how individuals choose to distribute their energy and time among various activities, along with prevailing environmental conditions [ 2 ]. Due to these constraints, animals must continuously adapt their behavior to meet conflicting demands [ 3 ]. Different species, or even different individuals within the same population, respond to these trade-offs by employing contrasting strategies, leading to differential life-history outcomes (e.g. Bonter et al. 2013; Patrick et al. 2017). Uncovering the mechanisms behind these strategies is essential for grasping fundamental concepts ranging from response to environmental stress [ 6 ] to community assemblage [ 7 ]. Life history can be seen as a set of rules governing three types of allocations: 1) energy to competing functions, 2) time to competing activities, and 3) reproductive energy to competing offspring [ 2 ]. In accordance with the pace-of-life syndrome hypothesis, closely related species are anticipated to exhibit distinct sets of physiological traits that have evolved in concordance with their specific life-history characteristics [ 8 , 9 ]. These trait suites are likely to be collectively influenced by ecological conditions thus favoring divergent life-history strategies [ 10 ]. While the pace-of-life syndrome hypothesis has been verified in different species [ 9 , 11 , 12 ], the integration of behavioral traits, particularly personality traits, within this hypothesis is a more recent development [ 10 ]. Personality traits are defined as inter-individual behavioral differences that are consistent through time and these traits can be correlated, thus forming behavioral syndromes [ 13 ]. Personality traits have the potential to influence each rule governing life-history resulting in individuals with different life histories in a population. To be able to allocate energy to competing functions, animals must first acquire resources. To acquire resources, most animals must move. The movement ecology paradigm hypothesizes that individual movement is controlled by three internal factors: navigation capacity (e.g. spatial information gathering and processing), motion capacity (e.g. means of movement), and internal state of the focal individual. These factors are themselves influenced by external factors such as biotic and abiotic components of the environment[ 14 ]. The internal state of an individual refers to the physiological and behavioral drivers of movements, determining the ultimate and proximate causes for movement [ 14 , 15 ]. According to this definition, personality fits within the internal state of an individual and has the potential to lead to individual differences in movement strategies [ 16 ]. Individual differences in behavior have been shown to influence home range size [ 17 ], habitat use [ 18 ], and local movement rate within species [ 19 , 20 ]. This intraspecific variation in space use can lead to different life-history strategies, affecting individual fitness [ 21 , 22 ]. Additionally, the costs and benefits of these different space use strategies may fluctuate over time, depending on the abiotic and biotic conditions [ 23 ]. The connection between movement and personality has been primarily investigated in the context of dispersal, i.e. long distance movements [ 16 , 18 ]. Dispersal only constitutes a minimal fraction of an animal's lifetime movement, while local movements (i.e. small-scale movements) contribute to most movements an animal makes. Despite the importance of local movements, only a few studies focused on the relationship between personality and local movements [ 17 , 18 , 24 – 26 ]. Local movements are important determinants of ecological interactions [ 27 – 30 ], the formation of individual niches [ 18 , 31 ], and thus, community dynamics and species coexistence [ 24 – 26 , 32 , 33 ]. Despite both personality and movement ecology research focusing on individuals as the primary unit of study and how they react to environmental stimuli, these two fields have remained surprisingly separated (Nilsson et al. 2014). Connecting movement ecology and personality will help illuminate the evolutionary implications of animal movement driving fitness outcomes associated with personalities, potentially leading to differences in life-history strategies [ 16 , 34 , 35 ]. The main goal of this study was to investigate the effect of individual behavioral differences on the spatial ecology of the western diamond-backed rattlesnake ( Crotalus atrox ) by combining telemetry and behavioral assays. Due to previous work showing that Crotalus atrox individuals display different personality types [ 36 ] and that C. atrox individuals vary immensely in movement and territory sizes [ 37 , 38 ], C. atrox can be used as a model for testing the correlation between personality and spatial ecology. Moreover, as a mesopredator, rattlesnakes’ foraging decisions are influenced by both apex predators [ 39 ] and prey availability [ 40 ]. Rattlesnakes are likely under considerable pressure to decide when and where to forage, resulting in trade-offs between resource acquisition and predator avoidance. Crotalus atrox was hypothesized to exhibit individual differences in space use and movement in the field that could be explained by individual differences in personality. Bolder, more active, more explorative individuals were predicted to have larger home ranges and higher movement rates than shy, less active and explorative individuals, thus following the pace-of-life syndrome hypothesis. To test these predictions, fourteen C. atrox were radiotracked for a year to collect spatial data and then, their personality was assessed in captivity. MATERIAL AND METHODS Study site Animal collection and field work was conducted at the Indio Mountains Research Station (IMRS), a 161 km 2 property managed by The University of Texas at El Paso. IMRS is located within the Chihuahuan Desert approximately 42 km southwest of Van Horn in Hudspeth County (Texas, USA). The property includes most of the Indio Mountains and the southern spur of the Eagle Mountains, with an elevation ranging from 900 m to 1,600m. Within these elevations, the flora is classified as Chihuahuan Desert Scrub and is mostly represented by creosote bushes ( Larrea tridentata ), lechugillas ( Agave lechugilla ), black grama ( Boutela eriopoda ), and yuccas ( Yucca sp. ). Telemetry and field observations Fourteen western diamond-backed rattlesnakes ( Crotalus atrox ) were captured on IMRS property and equipped with temperature-sensitive radiotransmitters (Holohil Systems Ltd., Carp, ON, Canada, SI-2T, 9.0 g). Radiotransmitters were surgically implanted into the coelomic cavity following a modified protocol based on Hardy and Greene (2000). Transmitters never exceeded more than 5% of the snake's body mass. Prior to surgery, the rattlesnakes were anesthetized with isoflurane using the open-drop method. Surgical instruments underwent sterilization in a benzalkonium chloride solution for a minimum of 30 minutes, while benches were sanitized with 70% isopropyl alcohol. All procedures were conducted while wearing single-use sterile gloves. A 1.25 cm longitudinal incision was made into the coelomic cavity at two-thirds of the snout-vent length anterior to the cloaca, through which the transmitter was inserted. The transmitter antenna was then placed subcutaneously towards the head along the body, using a cannula that was subsequently removed [ 41 , 42 ]. Rattlesnakes were observed for 48 hours to monitor recovery before being released at the original capture site. Rattlesnakes were radiotracked using an R-1000 telemetry receiver coupled with a RA-150 Yagi antenna (Communication Specialist, INC). Each rattlesnake was radiotracked for a full year with seven individuals radiotracked between 2020–2021 and seven others between 2021–2022 with the first tracking event being in June of 2020 and the last tracking event in August of 2022. The tracking regime was adapted based on rattlesnake biological activity. Rattlesnakes were radiotracked twice a week during the active season (April-October) with a minimum of two days between tracking events. Rattlesnakes were only tracked once every two weeks during the inactive season (November-March) as previous studies have shown snakes to be inactive during this time [ 37 , 38 ]. As spatial strategies are known to vary between behavioral states for this species at this site [ 37 ], the active season was divided into two for analyses based on behavioral observations: non-mating season (April-July ) and mating season (August-October). For each tracking event, the following data were recorded: date, time, GPS coordinates, behavior, microhabitat data and weather data (see supplementary materials for a complete list). After a year of being radiotracked and their personality assessed (see below), transmitters were removed, and snakes were released at their exact most recent capture site. Animal collection was authorized by the Texas Parks and Wildlife under permit number SPR-0290-019. All animal procedures adhered to the ethical guidelines of The University of Texas at El Paso and were pre-approved by The University of Texas at El Paso Institutional Animal Care and Use Committee (protocol number: A-201905-2_1447328-2). Individual difference tests After a full year of being radiotracked, all 14 rattlesnakes were brought to the laboratory at UTEP and were housed individually in ventilated plastic boxes (839 × 457 × 304 mm) lined with paper towels in a room maintaining a temperature of 25°C for approximately eight weeks. To minimize stress, each snake had access to a plastic hide and water was provided ad libitum throughout captivity. Food was not offered throughout captivity as desert rattlesnakes eat infrequently and have minimal energy requirements [ 43 ]. The personality of each snake was assessed following four axes: activity, exploration, boldness, and reactivity following the protocol described in Da Cunha et al. 2023 [ 36 ]. Activity levels were evaluated by measuring the duration (in seconds) that an individual spent moving within an enclosure over a 24-hour period. Exploration, the propensity to venture into new areas, was assessed using two types of open-field tests. For the first test (Explo1), individuals were gently placed in the center of a 112 cm diameter circle marked on the floor of an arena. As a proxy for exploration (Explo1), the latency to move and to leave the circle was extracted from video recordings (in seconds). For the second proxy of exploration (Explo2), each individual was placed in an arena where the floor was sectioned off into equal sized rectangles for 60 minutes. The number of tongue flicks and the number of squares crossed by the head were extracted from the video recordings (Explo2). Boldness, the propensity of an individual to take risk [ 44 ], was measured by using an emergence test. Each of these tests produced two variables that were used for statistical analysis. Snakes were placed in a plastic box in an arena for 120 min and the time for the snake’s head to emerge and the time for the complete body to emerge from the box (in seconds) were extracted from recordings to assess individual boldness level. Finally, reactivity, defined as the response to a simulated predator attack [ 45 ], was assessed using a restrain test. Individuals were immobilized (i.e. restrained) in a tight plastic tube for a maximum duration of five minutes and the rattling duration (in seconds) during the restraining process was recorded and used as a proxy for reactivity. Before starting any behavioral trials, snakes were acclimated to captivity for a minimum of one week. On average, only one trial was conducted per day. Each trial was repeated once to assess repeatability. To eliminate bias, a single observer scored most of the videos without knowing the snake’s identity. The activity trials were the only trials for which multiple observers scored the videos. To control bias between observers, different observers were tasked to score the same 24 h activity recording and it was ensured that the difference between observers was less than 5%. Spatial metrics All spatial metrics were calculated using the package adehabitatHR [ 46 ] and move [ 47 ] in R [ 48 ] for both non-mating and mating seasons using GPS points collected during radiotracking events. For each tracking event, we collected one GPS point within a 5-meter radius of the snake using a handheld GPS (Garmin Oregon 700), which has an accuracy of 3 meters. Home range size was estimated based upon the 95% Minimum Convex Polygon (MCP) and upon the 95% kernel density (KD95). Core use area was based on the 50% kernel density (KD50). For all kernel estimators, the reference bandwidth selector was chosen as it is more robust to variations in sampling intensity [ 49 ]. Movement rate (meters/day) and total distance travelled (meters) for each individual were also calculated for each season. Statistical analysis All statistical analyses were performed in R [ 48 ]. To estimate the repeatability of behavioral traits between repetitions, generalized linear mixed-effects models (GLMM) fitted via restricted maximum likelihood were used via the rptR package in R [ 50 ]. In these models, repetition number was treated as a fixed effect, while individual identification was considered a random effect. To investigate the effect of behavioral traits on the spatial ecology of Crotalus atrox , linear models were used with spatial metrics included as response variables. Data distributions were verified using Shapiro-wilk test on the residuals of the models. Data were mostly untransformed as they already followed a Gaussian distribution. When the residuals did not follow a Gaussian distribution, the response variable was log-transformed (see Table II for a list of variables that were log-transformed). Because of the small sample size (n = 14), only one behavioral trait was included in each model as a fixed effect. Sex was also included as a fixed effect. Some variables (latency to leave the circle, latency to move, and time for head to emerge) were not tested as they are highly correlated to the other exploration and boldness variables [ 36 ]. Because multiple linear models were performed on the same response variable, the level of significance was set to α < 0.01 according to Bonferroni correction. RESULTS Repeatability of behavioral differences The results of the generalized linear mixed-effects models (GLMM) fitted via restricted maximum likelihood used to test the repeatability of individual differences are presented in Table I. All the variables measured were significantly repeatable through time with repeatability coefficients varying from 0.586 to 0.848 (moderately to highly repeatable). Table I Summary of repeatability estimates calculated via GLMMs fitted by restricted maximum. Trial Variable measured Repeatability Confidence intervals Statistical significance Activity Time spent moving (s) 0.817 0.55–0.944 p < 0.001 Reactivity Time spent rattling (s) 0.826 0.57–0.942 p < 0.001 Bold Latency for head to emerge (s) 0.733 0.376–0.915 p < 0.001 Latency for body to emerge (s) 0.593 0.139–0.855 p < 0.001 Explo1 Latency to move (s) 0.586 0.107–0.869 p < 0.001 Latency to leave arena (s) 0.715 0.351–0.906 p < 0.001 Explo2 Number of squares crossed 0.847 0.589–0.955 p < 0.001 Number of tongue flicks 0.848 0.6–0.953 p < 0.001 Influence of behavioral differences on spatial ecology The results of the linear models used to test the relationship between behavioral differences and spatial metrics are presented in table II. For brevity, the influence of sex was only included in this table when personality scores had a significant effect on spatial metrics. Boldness had a significant effect on movement rate (m/day) during the non-mating season with bolder individuals travelling more per day than shy individuals (Fig. 1a). Females travelled significantly less per day than males during the non-mating season (Table II, Fig. 1a). Activity had a near significant effect on total distance travelled (m) with more active individuals travelling longer distances than less active individuals (Fig. 1b). Females travelled significantly less than males during the non-mating season (Table II, Fig. 1b). A linear model also showed that boldness also had a significant effect on the difference in movement rate between season (Est. = 0.008, SE = 0.003, t = 3.039, p = 0.0113; Fig. 2) with shy individuals increasing their movement rate more than bold individuals during the mating season. Table II Summary of linear models results used to investigate the effects of personality and sex on different spatial metrics. Sex is only included for significant relationships between spatial metrics and personality scores. MCP stands for Minimum Convex Polygon (95%), K95 for kernel density (95%), K50 for core use area (50%), MR for Movement Rate, and TDT for Total Distance Travelled. Significant results are bolded (p < 0.01). * denotes when the response variable was log-transformed to follow a Gaussian distribution. Response Season Personality score Estimate SE t-value df p value MCP Mating Exploration (tongue flicks) 0.000 0.004 0.069 11 0.947 Exploration (squares) -0.004 0.034 -0.119 11 0.908 Boldness (body out) 0.001 0.001 0.797 11 0.442 Activity 0.000 0.001 -0.443 11 0.666 Reactivity -0.080 1.590 -0.050 11 0.961 Non-mating Exploration (tongue flicks)* 0.000 0.000 -0.143 11 0.889 Exploration (squares)* -0.002 0.003 -0.508 11 0.622 Boldness (body out)* 0.000 0.000 -0.718 11 0.487 Activity* 0.000 0.000 1.460 11 0.172 Reactivity -0.019 0.019 -1.022 11 0.329 K95 Mating Exploration (tongue flicks) -0.026 0.029 -0.920 11 0.377 Exploration (squares) -0.265 0.225 -1.175 11 0.265 Boldness (body out)* 0.000 0.000 -0.178 11 0.862 Activity* 0.000 0.000 0.175 11 0.864 Reactivity 0.266 0.160 1.658 11 0.126 Non-mating Exploration (tongue flicks) 0.010 0.220 0.469 11 0.648 Exploration (squares) 0.137 0.174 0.799 11 0.448 Boldness (body out)* 0.000 0.000 -1.921 11 0.081 Activity* 0.000 0.000 1.218 11 0.249 Reactivity -0.162 0.125 -1.295 11 0.222 K50 Mating Exploration (tongue flicks) -0.004 0.005 -0.884 11 0.396 Exploration (squares) -0.037 0.037 -1.000 11 0.339 Boldness (body out) 0.000 0.001 0.364 11 0.723 Activity 0.000 0.001 -0.615 11 0.551 Reactivity 0.040 0.027 1.499 11 0.162 Non-mating Exploration (tongue flicks) 0.001 0.003 0.242 11 0.813 Exploration (squares) 0.008 0.228 0.364 11 0.723 Boldness (body out) -0.001 0.001 -1.968 11 0.075 Activity 0.000 0.000 -0.058 11 0.954 Reactivity -0.027 0.015 -1.812 11 0.097 MR Mating Exploration (tongue flicks)* 0.007 0.008 0.886 11 0.394 Exploration (squares) 0.044 0.069 0.645 11 0.532 Boldness (body out) 0.002 0.002 1.039 11 0.321 Activity* 0.000 0.001 -0.002 11 0.999 Reactivity 0.014 0.052 0.264 11 0.797 Non-mating Exploration (tongue flicks) 0.000 0.000 0.521 11 0.613 Exploration (squares) 0.111 0.888 1.250 11 0.237 Boldness (body out) -0.007 0.002 -3.463 11 0.005 Sex (with boldness) 18.102 8.060 2.246 11 0.002 Activity 0.000 0.000 1.870 11 0.088 Reactivity -0.058 0.069 -0.839 11 0.419 TDT Mating Exploration (tongue flicks) 0.310 0.711 0.436 11 0.671 Exploration (Squares) 3.504 5.683 0.617 11 0.550 Boldness (body out) 0.264 0.146 1.806 11 0.098 Activity 0.042 0.093 0.445 11 0.665 Reactivity 2.085 4.291 0.486 11 0.637 Non-mating Exploration (tongue flicks) -0.335 0.604 -0.554 11 0.590 Exploration (squares) -1.934 4.899 -0.395 11 0.701 Boldness (body out) -0.213 0.126 -1.687 11 0.120 Activity 0.149 0.067 2.239 11 0.047 Sex (with Activity) 2435 485.6 4.914 11 0.0005 Reactivity 0.139 3.702 -0.038 11 0.971 DISCUSSION The main goal of this study was to investigate the effect of individual behavioral differences on the spatial ecology of western diamond-backed rattlesnakes. The main hypothesis was partially supported as bolder and more active individuals travelled more than shy, less active individuals during the non-mating season. Thus, shyer individuals increased their movement rate significantly more than bolder individuals during the mating season resulting in no significant difference in movement between personality types during the breeding season. Generally, these results support the pace-of-life syndrome hypothesis only during the non-breeding season. Boldness had a significant effect on movement rate (m/day) during the non-mating season. Indeed, bolder individuals moved more on average than shyer individuals. The same relationship has been previously described in other species [ 19 , 20 ]. For most of their time, rattlesnakes stay hidden in burrows or within vegetation to stay concealed from predators [ 51 , 52 ]. When hunting, rattlesnakes stay immobile for long periods of time [ 53 ] rendering them difficult to spot by predators, even if the rattlesnake is not under the cover of vegetation [ 42 ]. Because of this, moving is risky for rattlesnakes as they are more likely to get killed while moving on the surface [ 54 ]. Although boldness has been correlated with habitat use [ 18 ], dispersal rate [ 44 ], and home range size [ 17 ] in other species, no significant correlation between boldness and home range size was found in this study. In the case of home range size, bolder individuals usually exhibit larger home range and core use area [ 18 , 26 ] indicating a potential relationship between boldness and territoriality. Territoriality has been described in one species of snake [ 55 ], but generally snakes do not appear to be territorial. The lack of a relationship between boldness and home range size in the western diamond-backed rattlesnakes might be explained by their non-territorial nature. Although activity did not have a significant effect on the total distance travelled according to the Bonferroni correction, the level of significance was still under 0.05. The Bonferroni correction has been subject to numerous critics, especially in the field of ecology (e.g. Moran 2003; García 2004). For this reason, the influence of activity is still discussed but these results must be interpreted with caution. Activity has been related to several spatial metrics in other species including core area size [ 58 ] and dispersal [ 59 , 60 ]. In C. atrox , activity, boldness, and sociability form a behavioral syndrome [ 36 ] meaning that in this study, bolder individuals are also more active. In this study, active and bold individuals tend to travel longer distances at a faster rate than less active and shy individuals. These characteristics are commonly found in superficial (or fast) explorer individuals [ 61 ] showing that these results support the pace-of-life syndrome hypothesis [ 10 ]. Superficial explorers tend to move rapidly while being less sensitive to environmental stimuli and changes [ 16 ]. Superficial explorers reduce their stay at a specific spot, exploiting less resources [ 62 ]. Because of this, these individuals might need to travel longer distances to reach their resources requirements, even if it means taking more risks. On the other hand, less active and shy individuals are usually considered slow explorers, moving less and slower than fast explorers [ 61 ]. Slow explorers tend to carefully explore their environment, making them more sensitive to environmental variations [ 16 ]. These different behavioral types are maintained in the population as they each perform better in different conditions [ 62 ]. Indeed, slow explorers might be more successful than fast explorers when resources are unequally distributed on the landscape but might be outperformed by fast explorers when resources are unpredictable [ 16 ]. In general, boldness and activity are known to influence growth, survival, and reproduction success (reviewed in Réale et al. 2007). For example, bolder bighorn sheep ( Ovis canadensis ) males had higher lifetime reproductive success [ 63 ] and higher survival rates than shyer individuals [ 64 ]. Boldness also influenced positively body mass gain in several species [ 65 , 66 ].While individuals were found to move differently across the landscape based on their personality during the non-mating season in this study, the biological implications of this difference remain to be investigated. In this study, personality influenced the movement ecology of C. atrox only during the non-mating season (April-July), showing that this relationship was dependent on the reproductive state of this species. The non-mating season represents four out of the seven active months of this species, and thus personality influenced the majority of the active season. During the mating season, male rattlesnakes face a trade-off between searching for mates and hunting due to their ambush foraging strategy that requires they remain sedentary [ 67 ]. As a result, males usually choose to decrease their foraging activity to invest more in their reproductive output [ 67 , 68 ]. Rattlesnake males actively locate females and as a result, often travel more during the mating season [ 37 , 69 ]. The movement of males is closely linked to their reproductive success, and males who travel along straighter paths or longer distances tend to experience a higher frequency of mating events [ 69 , 70 ]. Therefore, increased movement of all males regardless of personality type during the mating season might be a result of sexual selection to increase reproductive success [ 69 ]. While on average, males increased their movement during the mating season, the difference in movement rate varied between individuals and was dependent on boldness. Shyer individuals increased their movement rate significantly more than bolder individuals during the mating season. According to the pace-of-life syndrome hypothesis, slow explorers should be more responsive than superficial explorers to environmental and social changes, such as reproductive status of other individuals [ 16 , 71 , 72 ]. Thus, the results of this study support the pace-of-life syndrome hypothesis as slow explorers responded more to a change in reproductive status. Slow-explorers tend to act based upon prior knowledge from personal and social origins whereas fast-explorers tend to make decisions independently from information available [ 73 , 74 ]. These differences might be explained by variation in the incorporation of information based on personality type [ 74 ] with slow-explorers being more behaviorally flexible than fast-explorers when incorporating information [ 75 , 76 ]. These contrasting strategies can be beneficial or maladaptive depending on the environmental context [ 74 ]. Although using prior knowledge might generally be safer [ 77 ], especially in a more constant or resource-scarce environment, ignoring prior knowledge might be more rewarding, particularly in more variable and higher quality habitats [ 78 , 79 ]. Male rattlesnakes are hypothesized to use three different strategies to find receptive females: 1) following scent-trails left by females; 2) using efficient search patterns to locate females; or 3) using prior experience to return to locations where females were previously found [ 80 ]. Scent-trailing does not appear to be an effective method to locate mates over extensive distances, as it largely relies on the chances of a male to encounter the trail of a female. In line with evidence showing that slow-explorers tend to base their decisions on prior knowledge and social cues [ 74 ], slow-explorer rattlesnakes might use a combination of all three strategies to locate mates leading to an increase in movement rate during the mating season. On the other hand, fast-explorers are less accurate when incorporating information in their decision-making processes [ 74 ]. 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Anim Behav 54:329–334. 10.1006/anbe.1996.0418) Ruiz-Gomez M, de Huntingford L, Øverli FA, Thörnqvist Ø, Höglund P-O E (2011) Response to environmental change in rainbow trout selected for divergent stress coping styles. Physiology Behavior 102:317–322. 10.1016/j.physbeh.2010.11.023) Herborn KA, Heidinger BJ, Alexander L, Arnold KE (2014) Personality predicts behavioral flexibility in a fluctuating, natural environment. Behav Ecol 25:1374–1379. 10.1093/beheco/aru131) Kurvers RHJM, Van Oers K, Nolet BA, Jonker RM, Van Wieren SE, Prins HHT, Ydenberg RC 2010 Personality predicts the use of social information. Ecol Lett 13, 829–837. ( 10.1111/j.1461-0248.2010.01473.x) Smit JAH, van Oers K (2019) Personality types vary in their personal and social information use. Anim Behav 151:185–193. 10.1016/j.anbehav.2019.02.002) Mathot KJ, Godde S, Careau V, Thomas DW, Giraldeau L-A (2009) Testing dynamic variance-sensitive foraging using individual differences in basal metabolic rates of zebra finches. Oikos 118:545–552. 10.1111/j.1600-0706.2009.17357.x) Coppens CM, de Boer SF, Koolhaas JM 2010 Coping styles and behavioural flexibility: towards underlying mechanisms. Philosophical Trans Royal Soc B: Biol Sci 365, 4021–4028. ( 10.1098/rstb.2010.0217 ) Wolf M, van Doorn GS, Weissing FJ (2008) Evolutionary emergence of responsive and unresponsive personalities. Proceedings of the National Academy of Sciences 105, 15825–15830. ( 10.1073/pnas.0805473105 ) Herborn KA, Macleod R, Miles WTS, Schofield ANB, Alexander L, Arnold KE 2010 Personality in captivity reflects personality in the wild. Anim Behav 79, 835–843. ( 10.1016/j.anbehav.2009.12.026 ) Heinen VK, Stephens DW 2016 Blue jays, Cyanocitta cristata , devalue social information in uncertain environments. Anim Behav 112, 53–62. ( 10.1016/j.anbehav.2015.11.015 ) Coupe B (2002) Pheromones, search patterns, and old haunts: how do male timber rattlesnakes (Crotalus horridus) locate mates. Biology of the Vipers. Eagle Mountain Publishing, USA, pp 139–148. 7705 North Wyatt Earp Avenue, Eagle Mounlain, Utah 84043 Supplementary Materials Supplementary Materials is not available with this version. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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 Advisory Board 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-4681611","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322330377,"identity":"e26482bf-633f-4f74-bb13-7ce6d65e77c3","order_by":0,"name":"Oceane Da Cunha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYFCCBBBxgIGfgbEBWZiZsBbJBpgWNmK1GByACRDSws+e/PAz74478sbXDrc9+LinjsHgfvOxBwwV1okNOLRI9jwzluY988xw2+3EdsMZzw4zGBxjSzdgOJOOU4vBjQQzZt62w4xALW3SPAeALjzGYybB2HYYj5b0byAt9ptnA7X8OQB02DH+bxKM//BpyQHbkrhBGqiF4QAzyBY2CcYG3Foke94US85tO5w8A+gwyZ4Dh3kkj6WZGyQcSzfGpYWfPX3jh7dth237Z6c/k/hxoE6O7/DhZw8+1FjL4tKCAXgYQDGTQKxyGGAjVcMoGAWjYBQMbwAAt/ZeE09k4HQAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0009-3842-3707","institution":"The University of Texas at El Paso","correspondingAuthor":true,"prefix":"","firstName":"Oceane","middleName":"Da","lastName":"Cunha","suffix":""},{"id":322330378,"identity":"631d60e0-54fc-4d38-9420-2e79d1d712b0","order_by":1,"name":"Joshua J. Mead","email":"","orcid":"","institution":"The University of Texas at El Paso","correspondingAuthor":false,"prefix":"","firstName":"Joshua","middleName":"J.","lastName":"Mead","suffix":""},{"id":322330379,"identity":"7ce35da8-b640-4dc7-a851-f9b7961d0a73","order_by":2,"name":"Braulio A. Sanchez","email":"","orcid":"","institution":"The University of Texas at El Paso","correspondingAuthor":false,"prefix":"","firstName":"Braulio","middleName":"A.","lastName":"Sanchez","suffix":""},{"id":322330380,"identity":"e8043de8-915d-4c78-8bce-993256fea4d4","order_by":3,"name":"Kajaya J. Pollard","email":"","orcid":"","institution":"The University of Texas at El Paso","correspondingAuthor":false,"prefix":"","firstName":"Kajaya","middleName":"J.","lastName":"Pollard","suffix":""},{"id":322330381,"identity":"ee9967f6-9e11-4d55-b39d-a90ee5077b76","order_by":4,"name":"Jerry D. Johnson","email":"","orcid":"","institution":"The University of Texas at El Paso","correspondingAuthor":false,"prefix":"","firstName":"Jerry","middleName":"D.","lastName":"Johnson","suffix":""},{"id":322330382,"identity":"8a2d2230-ce75-4c0a-8234-f81470387d5f","order_by":5,"name":"Brett M. 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Boldness is measured here as the total length of the boldness trial (7200s) minus emergence time, meaning that bolder individuals have larger score in seconds. Results from linear model show that boldness had a significant effect on movement rate (see table II).\u003c/p\u003e\n\u003cp\u003e(b) Effect of activity (measured as time spent moving within 24 hours in seconds) on total distance traveled (m) during the non-mating season on 14 radio-tracked rattlesnakes (M=10, F=4). Results from linear model show that activity had a near significant effect on movement rate (see table II).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4681611/v1/3c0ca55350dd3d61951839bf.png"},{"id":60172714,"identity":"c7613dc8-74b7-4c4c-9cad-c7e1a47bddd6","added_by":"auto","created_at":"2024-07-12 15:17:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132991,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of boldness on Δ\u003cem\u003emovement rate \u003c/em\u003e(m/day in the field) between non-mating and mating season 14 \u003cem\u003eCrotalus atrox\u003c/em\u003e (M=10, F=4). Boldness is measured here as the total length of the boldness trial (7200s) minus emergence time, meaning that bolder individuals have larger score in seconds. Individuals are considered bolder when they have a shorter emergence time. Results from the linear model shows that boldness had a significant effect Δ\u003cem\u003emovement rate \u003c/em\u003e(p=0.013).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4681611/v1/f41cdd105a4f2585846dcea7.png"},{"id":60173813,"identity":"53a0eafd-df2d-4b65-94fd-d6f7ef6a3363","added_by":"auto","created_at":"2024-07-12 15:25:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1134835,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4681611/v1/9d8d44ff-00e6-402f-aac1-16598a98324c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eInsights into the Pace-of-Life Syndrome hypothesis: Exploring the Influence of personality on Movement Ecology in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCrotalus atrox\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eTo thrive within a given environment, individuals must efficiently gather food and find mates to reproduce [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These resources are limited, and their acquisition depends on how individuals choose to distribute their energy and time among various activities, along with prevailing environmental conditions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Due to these constraints, animals must continuously adapt their behavior to meet conflicting demands [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Different species, or even different individuals within the same population, respond to these trade-offs by employing contrasting strategies, leading to differential life-history outcomes (e.g. Bonter et al. 2013; Patrick et al. 2017). Uncovering the mechanisms behind these strategies is essential for grasping fundamental concepts ranging from response to environmental stress [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] to community assemblage [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLife history can be seen as a set of rules governing three types of allocations: 1) energy to competing functions, 2) time to competing activities, and 3) reproductive energy to competing offspring [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In accordance with the pace-of-life syndrome hypothesis, closely related species are anticipated to exhibit distinct sets of physiological traits that have evolved in concordance with their specific life-history characteristics [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These trait suites are likely to be collectively influenced by ecological conditions thus favoring divergent life-history strategies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While the pace-of-life syndrome hypothesis has been verified in different species [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], the integration of behavioral traits, particularly personality traits, within this hypothesis is a more recent development [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Personality traits are defined as inter-individual behavioral differences that are consistent through time and these traits can be correlated, thus forming behavioral syndromes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Personality traits have the potential to influence each rule governing life-history resulting in individuals with different life histories in a population.\u003c/p\u003e \u003cp\u003eTo be able to allocate energy to competing functions, animals must first acquire resources. To acquire resources, most animals must move. The movement ecology paradigm hypothesizes that individual movement is controlled by three internal factors: navigation capacity (e.g. spatial information gathering and processing), motion capacity (e.g. means of movement), and internal state of the focal individual. These factors are themselves influenced by external factors such as biotic and abiotic components of the environment[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The internal state of an individual refers to the physiological and behavioral drivers of movements, determining the ultimate and proximate causes for movement [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. According to this definition, personality fits within the internal state of an individual and has the potential to lead to individual differences in movement strategies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Individual differences in behavior have been shown to influence home range size [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], habitat use [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and local movement rate within species [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This intraspecific variation in space use can lead to different life-history strategies, affecting individual fitness [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additionally, the costs and benefits of these different space use strategies may fluctuate over time, depending on the abiotic and biotic conditions [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe connection between movement and personality has been primarily investigated in the context of dispersal, i.e. long distance movements [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Dispersal only constitutes a minimal fraction of an animal's lifetime movement, while local movements (i.e. small-scale movements) contribute to most movements an animal makes. Despite the importance of local movements, only a few studies focused on the relationship between personality and local movements [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Local movements are important determinants of ecological interactions [\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], the formation of individual niches [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and thus, community dynamics and species coexistence [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Despite both personality and movement ecology research focusing on individuals as the primary unit of study and how they react to environmental stimuli, these two fields have remained surprisingly separated (Nilsson et al. 2014). Connecting movement ecology and personality will help illuminate the evolutionary implications of animal movement driving fitness outcomes associated with personalities, potentially leading to differences in life-history strategies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe main goal of this study was to investigate the effect of individual behavioral differences on the spatial ecology of the western diamond-backed rattlesnake (\u003cem\u003eCrotalus atrox\u003c/em\u003e) by combining telemetry and behavioral assays. Due to previous work showing that \u003cem\u003eCrotalus atrox\u003c/em\u003e individuals display different personality types [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and that \u003cem\u003eC. atrox\u003c/em\u003e individuals vary immensely in movement and territory sizes [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], \u003cem\u003eC. atrox\u003c/em\u003e can be used as a model for testing the correlation between personality and spatial ecology. Moreover, as a mesopredator, rattlesnakes\u0026rsquo; foraging decisions are influenced by both apex predators [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and prey availability [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Rattlesnakes are likely under considerable pressure to decide when and where to forage, resulting in trade-offs between resource acquisition and predator avoidance. \u003cem\u003eCrotalus atrox\u003c/em\u003e was hypothesized to exhibit individual differences in space use and movement in the field that could be explained by individual differences in personality. Bolder, more active, more explorative individuals were predicted to have larger home ranges and higher movement rates than shy, less active and explorative individuals, thus following the pace-of-life syndrome hypothesis. To test these predictions, fourteen \u003cem\u003eC. atrox\u003c/em\u003e were radiotracked for a year to collect spatial data and then, their personality was assessed in captivity.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy site\u003c/h2\u003e \u003cp\u003eAnimal collection and field work was conducted at the Indio Mountains Research Station (IMRS), a 161 km\u003csup\u003e2\u003c/sup\u003e property managed by The University of Texas at El Paso. IMRS is located within the Chihuahuan Desert approximately 42 km southwest of Van Horn in Hudspeth County (Texas, USA). The property includes most of the Indio Mountains and the southern spur of the Eagle Mountains, with an elevation ranging from 900 m to 1,600m. Within these elevations, the flora is classified as Chihuahuan Desert Scrub and is mostly represented by creosote bushes (\u003cem\u003eLarrea tridentata\u003c/em\u003e), lechugillas (\u003cem\u003eAgave lechugilla\u003c/em\u003e), black grama (\u003cem\u003eBoutela eriopoda\u003c/em\u003e), and yuccas (\u003cem\u003eYucca sp.\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTelemetry and field observations\u003c/h2\u003e \u003cp\u003eFourteen western diamond-backed rattlesnakes (\u003cem\u003eCrotalus atrox\u003c/em\u003e) were captured on IMRS property and equipped with temperature-sensitive radiotransmitters (Holohil Systems Ltd., Carp, ON, Canada, SI-2T, 9.0 g). Radiotransmitters were surgically implanted into the coelomic cavity following a modified protocol based on Hardy and Greene (2000). Transmitters never exceeded more than 5% of the snake's body mass. Prior to surgery, the rattlesnakes were anesthetized with isoflurane using the open-drop method. Surgical instruments underwent sterilization in a benzalkonium chloride solution for a minimum of 30 minutes, while benches were sanitized with 70% isopropyl alcohol. All procedures were conducted while wearing single-use sterile gloves. A 1.25 cm longitudinal incision was made into the coelomic cavity at two-thirds of the snout-vent length anterior to the cloaca, through which the transmitter was inserted. The transmitter antenna was then placed subcutaneously towards the head along the body, using a cannula that was subsequently removed [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Rattlesnakes were observed for 48 hours to monitor recovery before being released at the original capture site.\u003c/p\u003e \u003cp\u003eRattlesnakes were radiotracked using an R-1000 telemetry receiver coupled with a RA-150 Yagi antenna (Communication Specialist, INC). Each rattlesnake was radiotracked for a full year with seven individuals radiotracked between 2020\u0026ndash;2021 and seven others between 2021\u0026ndash;2022 with the first tracking event being in June of 2020 and the last tracking event in August of 2022. The tracking regime was adapted based on rattlesnake biological activity. Rattlesnakes were radiotracked twice a week during the active season (April-October) with a minimum of two days between tracking events. Rattlesnakes were only tracked once every two weeks during the inactive season (November-March) as previous studies have shown snakes to be inactive during this time [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. As spatial strategies are known to vary between behavioral states for this species at this site [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], the active season was divided into two for analyses based on behavioral observations: non-mating season (April-July ) and mating season (August-October). For each tracking event, the following data were recorded: date, time, GPS coordinates, behavior, microhabitat data and weather data (see supplementary materials for a complete list). After a year of being radiotracked and their personality assessed (see below), transmitters were removed, and snakes were released at their exact most recent capture site.\u003c/p\u003e \u003cp\u003eAnimal collection was authorized by the Texas Parks and Wildlife under permit number SPR-0290-019. All animal procedures adhered to the ethical guidelines of The University of Texas at El Paso and were pre-approved by The University of Texas at El Paso Institutional Animal Care and Use Committee (protocol number: A-201905-2_1447328-2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIndividual difference tests\u003c/h2\u003e \u003cp\u003eAfter a full year of being radiotracked, all 14 rattlesnakes were brought to the laboratory at UTEP and were housed individually in ventilated plastic boxes (839 \u0026times; 457 \u0026times; 304 mm) lined with paper towels in a room maintaining a temperature of 25\u0026deg;C for approximately eight weeks. To minimize stress, each snake had access to a plastic hide and water was provided \u003cem\u003ead libitum\u003c/em\u003e throughout captivity. Food was not offered throughout captivity as desert rattlesnakes eat infrequently and have minimal energy requirements [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe personality of each snake was assessed following four axes: activity, exploration, boldness, and reactivity following the protocol described in Da Cunha et al. 2023 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Activity levels were evaluated by measuring the duration (in seconds) that an individual spent moving within an enclosure over a 24-hour period. Exploration, the propensity to venture into new areas, was assessed using two types of open-field tests. For the first test (Explo1), individuals were gently placed in the center of a 112 cm diameter circle marked on the floor of an arena. As a proxy for exploration (Explo1), the latency to move and to leave the circle was extracted from video recordings (in seconds). For the second proxy of exploration (Explo2), each individual was placed in an arena where the floor was sectioned off into equal sized rectangles for 60 minutes. The number of tongue flicks and the number of squares crossed by the head were extracted from the video recordings (Explo2). Boldness, the propensity of an individual to take risk [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], was measured by using an emergence test. Each of these tests produced two variables that were used for statistical analysis. Snakes were placed in a plastic box in an arena for 120 min and the time for the snake\u0026rsquo;s head to emerge and the time for the complete body to emerge from the box (in seconds) were extracted from recordings to assess individual boldness level. Finally, reactivity, defined as the response to a simulated predator attack [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], was assessed using a restrain test. Individuals were immobilized (i.e. restrained) in a tight plastic tube for a maximum duration of five minutes and the rattling duration (in seconds) during the restraining process was recorded and used as a proxy for reactivity. Before starting any behavioral trials, snakes were acclimated to captivity for a minimum of one week. On average, only one trial was conducted per day. Each trial was repeated once to assess repeatability. To eliminate bias, a single observer scored most of the videos without knowing the snake\u0026rsquo;s identity. The activity trials were the only trials for which multiple observers scored the videos. To control bias between observers, different observers were tasked to score the same 24 h activity recording and it was ensured that the difference between observers was less than 5%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSpatial metrics\u003c/h2\u003e \u003cp\u003eAll spatial metrics were calculated using the package \u003cem\u003eadehabitatHR\u003c/em\u003e [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and \u003cem\u003emove\u003c/em\u003e [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] in R [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] for both non-mating and mating seasons using GPS points collected during radiotracking events. For each tracking event, we collected one GPS point within a 5-meter radius of the snake using a handheld GPS (Garmin Oregon 700), which has an accuracy of 3 meters. Home range size was estimated based upon the 95% Minimum Convex Polygon (MCP) and upon the 95% kernel density (KD95). Core use area was based on the 50% kernel density (KD50). For all kernel estimators, the reference bandwidth selector was chosen as it is more robust to variations in sampling intensity [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Movement rate (meters/day) and total distance travelled (meters) for each individual were also calculated for each season.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed in R [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. To estimate the repeatability of behavioral traits between repetitions, generalized linear mixed-effects models (GLMM) fitted via restricted maximum likelihood were used via the \u003cem\u003erptR\u003c/em\u003e package in R [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In these models, repetition number was treated as a fixed effect, while individual identification was considered a random effect.\u003c/p\u003e \u003cp\u003eTo investigate the effect of behavioral traits on the spatial ecology of \u003cem\u003eCrotalus atrox\u003c/em\u003e, linear models were used with spatial metrics included as response variables. Data distributions were verified using Shapiro-wilk test on the residuals of the models. Data were mostly untransformed as they already followed a Gaussian distribution. When the residuals did not follow a Gaussian distribution, the response variable was log-transformed (see Table II for a list of variables that were log-transformed). Because of the small sample size (n\u0026thinsp;=\u0026thinsp;14), only one behavioral trait was included in each model as a fixed effect. Sex was also included as a fixed effect. Some variables (latency to leave the circle, latency to move, and time for head to emerge) were not tested as they are highly correlated to the other exploration and boldness variables [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Because multiple linear models were performed on the same response variable, the level of significance was set to α\u0026thinsp;\u0026lt;\u0026thinsp;0.01 according to Bonferroni correction.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eRepeatability of behavioral differences\u003c/h2\u003e \u003cp\u003eThe results of the generalized linear mixed-effects models (GLMM) fitted via restricted maximum likelihood used to test the repeatability of individual differences are presented in Table I. All the variables measured were significantly repeatable through time with repeatability coefficients varying from 0.586 to 0.848 (moderately to highly repeatable).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTable I\u003c/strong\u003e \u003cp\u003eSummary of repeatability estimates calculated via GLMMs fitted by restricted maximum.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable measured\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRepeatability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConfidence intervals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistical significance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime spent moving (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55\u0026ndash;0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReactivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime spent rattling (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.57\u0026ndash;0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatency for head to emerge (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.376\u0026ndash;0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\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\u003eLatency for body to emerge (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.139\u0026ndash;0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExplo1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatency to move (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.107\u0026ndash;0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\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\u003eLatency to leave arena (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.351\u0026ndash;0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExplo2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of squares crossed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.589\u0026ndash;0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\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\u003eNumber of tongue flicks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u0026ndash;0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eInfluence of behavioral differences on spatial ecology\u003c/h2\u003e \u003cp\u003eThe results of the linear models used to test the relationship between behavioral differences and spatial metrics are presented in table II. For brevity, the influence of sex was only included in this table when personality scores had a significant effect on spatial metrics. Boldness had a significant effect on movement rate (m/day) during the non-mating season with bolder individuals travelling more per day than shy individuals (Fig.\u0026nbsp;1a). Females travelled significantly less per day than males during the non-mating season (Table II, Fig.\u0026nbsp;1a). Activity had a near significant effect on total distance travelled (m) with more active individuals travelling longer distances than less active individuals (Fig.\u0026nbsp;1b). Females travelled significantly less than males during the non-mating season (Table II, Fig.\u0026nbsp;1b). A linear model also showed that boldness also had a significant effect on the difference in movement rate between season (Est. = 0.008, SE\u0026thinsp;=\u0026thinsp;0.003, t\u0026thinsp;=\u0026thinsp;3.039, p\u0026thinsp;=\u0026thinsp;0.0113; Fig.\u0026nbsp;2) with shy individuals increasing their movement rate more than bold individuals during the mating season.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTable II\u003c/strong\u003e \u003cp\u003eSummary of linear models results used to investigate the effects of personality and sex on different spatial metrics. Sex is only included for significant relationships between spatial metrics and personality scores. MCP stands for Minimum Convex Polygon (95%), K95 for kernel density (95%), K50 for core use area (50%), MR for Movement Rate, and TDT for Total Distance Travelled. Significant results are bolded (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). * denotes when the response variable was log-transformed to follow a Gaussian distribution.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"8\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePersonality score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (tongue flicks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.947\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (squares)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.908\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoldness (body out)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.442\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.666\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.961\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\u003eNon-mating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (tongue flicks)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.889\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (squares)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.622\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoldness (body out)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.487\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActivity*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.172\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (tongue flicks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.377\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (squares)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.265\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoldness (body out)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.862\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActivity*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.864\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.126\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\u003eNon-mating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (tongue flicks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.648\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (squares)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.448\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoldness (body out)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.081\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActivity*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.249\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (tongue flicks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.396\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (squares)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.339\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoldness (body out)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.723\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.551\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.162\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\u003eNon-mating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (tongue flicks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.813\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (squares)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.723\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoldness (body out)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.075\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.954\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (tongue flicks)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.394\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (squares)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.532\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoldness (body out)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.321\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActivity*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.999\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.797\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\u003eNon-mating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (tongue flicks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.613\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (squares)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.237\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoldness (body out)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.005\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSex (with boldness)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.002\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.088\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (tongue flicks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.671\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (Squares)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.550\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoldness (body out)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.098\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.665\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.637\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\u003eNon-mating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (tongue flicks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.590\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploration (squares)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.701\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoldness (body out)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.120\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.047\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSex (with Activity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e485.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0005\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReactivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe main goal of this study was to investigate the effect of individual behavioral differences on the spatial ecology of western diamond-backed rattlesnakes. The main hypothesis was partially supported as bolder and more active individuals travelled more than shy, less active individuals during the non-mating season. Thus, shyer individuals increased their movement rate significantly more than bolder individuals during the mating season resulting in no significant difference in movement between personality types during the breeding season. Generally, these results support the pace-of-life syndrome hypothesis only during the non-breeding season.\u003c/p\u003e\n\u003cp\u003eBoldness had a significant effect on movement rate (m/day) during the non-mating season. Indeed, bolder individuals moved more on average than shyer individuals. The same relationship has been previously described in other species [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. For most of their time, rattlesnakes stay hidden in burrows or within vegetation to stay concealed from predators [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]. When hunting, rattlesnakes stay immobile for long periods of time [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e] rendering them difficult to spot by predators, even if the rattlesnake is not under the cover of vegetation [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. Because of this, moving is risky for rattlesnakes as they are more likely to get killed while moving on the surface [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]. Although boldness has been correlated with habitat use [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e], dispersal rate [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e], and home range size [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] in other species, no significant correlation between boldness and home range size was found in this study. In the case of home range size, bolder individuals usually exhibit larger home range and core use area [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] indicating a potential relationship between boldness and territoriality. Territoriality has been described in one species of snake [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e], but generally snakes do not appear to be territorial. The lack of a relationship between boldness and home range size in the western diamond-backed rattlesnakes might be explained by their non-territorial nature.\u003c/p\u003e\n\u003cp\u003eAlthough activity did not have a significant effect on the total distance travelled according to the Bonferroni correction, the level of significance was still under 0.05. The Bonferroni correction has been subject to numerous critics, especially in the field of ecology (e.g. Moran 2003; Garc\u0026iacute;a 2004). For this reason, the influence of activity is still discussed but these results must be interpreted with caution. Activity has been related to several spatial metrics in other species including core area size [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e] and dispersal [\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e]. In \u003cem\u003eC. atrox\u003c/em\u003e, activity, boldness, and sociability form a behavioral syndrome [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e] meaning that in this study, bolder individuals are also more active. In this study, active and bold individuals tend to travel longer distances at a faster rate than less active and shy individuals. These characteristics are commonly found in superficial (or fast) explorer individuals [\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e] showing that these results support the pace-of-life syndrome hypothesis [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. Superficial explorers tend to move rapidly while being less sensitive to environmental stimuli and changes [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. Superficial explorers reduce their stay at a specific spot, exploiting less resources [\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e]. Because of this, these individuals might need to travel longer distances to reach their resources requirements, even if it means taking more risks. On the other hand, less active and shy individuals are usually considered slow explorers, moving less and slower than fast explorers [\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e]. Slow explorers tend to carefully explore their environment, making them more sensitive to environmental variations [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. These different behavioral types are maintained in the population as they each perform better in different conditions [\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e]. Indeed, slow explorers might be more successful than fast explorers when resources are unequally distributed on the landscape but might be outperformed by fast explorers when resources are unpredictable [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. In general, boldness and activity are known to influence growth, survival, and reproduction success (reviewed in R\u0026eacute;ale et al. 2007). For example, bolder bighorn sheep (\u003cem\u003eOvis canadensis\u003c/em\u003e) males had higher lifetime reproductive success [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e] and higher survival rates than shyer individuals [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e]. Boldness also influenced positively body mass gain in several species [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e].While individuals were found to move differently across the landscape based on their personality during the non-mating season in this study, the biological implications of this difference remain to be investigated.\u003c/p\u003e\n\u003cp\u003eIn this study, personality influenced the movement ecology of \u003cem\u003eC. atrox\u003c/em\u003e only during the non-mating season (April-July), showing that this relationship was dependent on the reproductive state of this species. The non-mating season represents four out of the seven active months of this species, and thus personality influenced the majority of the active season. During the mating season, male rattlesnakes face a trade-off between searching for mates and hunting due to their ambush foraging strategy that requires they remain sedentary [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e]. As a result, males usually choose to decrease their foraging activity to invest more in their reproductive output [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e]. Rattlesnake males actively locate females and as a result, often travel more during the mating season [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]. The movement of males is closely linked to their reproductive success, and males who travel along straighter paths or longer distances tend to experience a higher frequency of mating events [\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e]. Therefore, increased movement of all males regardless of personality type during the mating season might be a result of sexual selection to increase reproductive success [\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eWhile on average, males increased their movement during the mating season, the difference in movement rate varied between individuals and was dependent on boldness. Shyer individuals increased their movement rate significantly more than bolder individuals during the mating season. According to the pace-of-life syndrome hypothesis, slow explorers should be more responsive than superficial explorers to environmental and social changes, such as reproductive status of other individuals [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e]. Thus, the results of this study support the pace-of-life syndrome hypothesis as slow explorers responded more to a change in reproductive status. Slow-explorers tend to act based upon prior knowledge from personal and social origins whereas fast-explorers tend to make decisions independently from information available [\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e]. These differences might be explained by variation in the incorporation of information based on personality type [\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e] with slow-explorers being more behaviorally flexible than fast-explorers when incorporating information [\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e]. These contrasting strategies can be beneficial or maladaptive depending on the environmental context [\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e]. Although using prior knowledge might generally be safer [\u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e], especially in a more constant or resource-scarce environment, ignoring prior knowledge might be more rewarding, particularly in more variable and higher quality habitats [\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e]. Male rattlesnakes are hypothesized to use three different strategies to find receptive females: 1) following scent-trails left by females; 2) using efficient search patterns to locate females; or 3) using prior experience to return to locations where females were previously found [\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e]. Scent-trailing does not appear to be an effective method to locate mates over extensive distances, as it largely relies on the chances of a male to encounter the trail of a female. In line with evidence showing that slow-explorers tend to base their decisions on prior knowledge and social cues [\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e], slow-explorer rattlesnakes might use a combination of all three strategies to locate mates leading to an increase in movement rate during the mating season. On the other hand, fast-explorers are less accurate when incorporating information in their decision-making processes [\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study demonstrated that western diamond-backed rattlesnakes exhibit differences in their movement ecology according to their personality. The effect of behavioral differences on movement was dependent on the reproductive status of individuals. During the mating season, males adjusted their movements, with shy individuals showing a larger increase in movement rate than bold individuals. The results of this study suggest the existence of two different strategies for resources acquisition, thus supporting the pace-of-life syndrome hypothesis under specific contexts (i.e. non-breeding season) [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. While this study provides new insights into the relationship between personality and ecology, further research is required to validate and uncover the mechanisms and consequences of these strategies for this species.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDavies NB, Krebs JR, West SA (2012) An introduction to behavioral ecology. John Wiley \u0026amp; Sons, Ltd., West Sussex, Nicholas B. Davies, John R. Krebs, and Stuart A. West\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunham AE, Grant BW, Overall KL (1989) Interfaces between Biophysical and Physiological Ecology and the Population Ecology of Terrestrial Vertebrate Ectotherms. Physiological Zool 62:335\u0026ndash;355\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWerner EE, Anholt BR (1993) Ecological Consequences of the Trade-Off between Growth and Mortality Rates Mediated by Foraging Activity. 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Eagle Mountain Publishing, USA, pp 139\u0026ndash;148. 7705 North Wyatt Earp Avenue, Eagle Mounlain, Utah 84043\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Supplementary Materials","content":"\u003cp\u003eSupplementary Materials is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"The University of Texas at El Paso","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pace-of-life syndrome hypothesis, life-history, spatial ecology, personality","lastPublishedDoi":"10.21203/rs.3.rs-4681611/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4681611/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe pace-of-life syndrome hypothesis predicts individuals, populations, and species that experience different ecological conditions will differ in spatial ecology resulting in contrasting life history strategies. We investigated the effect of personality on spatial ecology of the western diamond-backed rattlesnake (\u003cem\u003eCrotalus atrox\u003c/em\u003e), and tested the predictions that bolder, more active, and exploratory individuals will move more and have larger territories compared to shyer, less active individuals. We tracked 14 rattlesnakes for one year and assessed their personality in captivity across four different axes: activity, boldness, exploration, and reactivity. Bolder and more active individuals travelled more than shy, less active individuals, but only during the non-mating season. Shy individuals increased movement significantly more than bold individuals during the mating season, thus leading to no significant difference in movements between shy and bold individuals during the mating season. Moreover, less bold and less active individuals were more responsive to changes in reproductive status. These results suggest the existence of two different strategies for resources acquisition within the same population, indicating that the pace-of-life syndrome hypothesis may be context-dependent. Ultimately, we show that the existence of different life-history strategies within the same population are dependent upon the reproductive status of these individuals.\u003c/p\u003e","manuscriptTitle":"Insights into the Pace-of-Life Syndrome hypothesis: Exploring the Influence of personality on Movement Ecology in Crotalus atrox","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-12 15:17:54","doi":"10.21203/rs.3.rs-4681611/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6ee6568a-b06d-4a61-b27f-bba30faa9032","owner":[],"postedDate":"July 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34084445,"name":"Behavioral Ecology"}],"tags":[],"updatedAt":"2024-07-12T15:17:54+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-12 15:17:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4681611","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4681611","identity":"rs-4681611","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}