TY - JOUR
AU - Cherry, Michael J
AB - Abstract Prey species often mitigate predation risk through alteration of spatiotemporal diel activity patterns whereby prey access high-quality resources in risky areas during predator downtimes. However, dominance hierarchies exist in some prey species, and temporal partitioning is a mechanism thought to reduce aggressive intraspecific interactions. How demographic-specific responses to predation risk influence intraspecific temporal partitioning in prey are largely unknown and could be key to understanding the effects of predators on intraspecific interactions in prey. To assess the effects of predation risk on intraspecific interactions in white-tailed deer (Odocoileus virginianus), we monitored deer diel activity during the fawning season in four pairs of predator exclusion and control plots (~40 ha) from 2015 to 2018 using 16 camera traps. We examined the effect of predation risk on diel activity of males, females, and nursery groups by comparing the within-group coefficient of activity overlap (d̂) across predator exclusion and control plots. We then examined within-treatment activity overlap between groups in the predator exclosure and control plots. All groups maintained different diel activity patterns in safe and risky areas. Unconstrained by predation risk, all groups behaved more similarly, and interspecific group overlap was greater in the predator exclusion plots than control plots. Male-nursery group overlap exhibited the strongest treatment effect, increasing 24% in predator exclusion plots (d̂ = 0.91, confidence interval [CI]: 0.87–0.95) relative to control plots (d̂ = 0.67, CI: 0.57–0.76). Our results suggest predators increase heterogeneity in prey behavior and may be important drivers of behavioral processes, such as temporal partitioning, that minimize antagonistic intraspecific interactions of prey. Introduction Predation risk can influence prey traits resulting in nonconsumptive effects on the population demography of prey and lower trophic levels (Gaynor et al. 2019). However, predation risk is often experienced differently across demographic groups within a prey population (Cherry et al. 2015; Gulsby et al. 2018; Crawford et al. 2019). Prey can mitigate predation risk via behavioral modifications that increase predator detectability or decrease probability of predator encounter. Spatially, prey species may alter habitat use and avoid high-risk patches to minimize exposure to predation risk (Winnie and Creel 2007). However, such avoidance can be costly, particularly when risky patches are of relatively high quality (Lima and Dill 1990). Thus, prey often modify temporal diel activity patterns to access risky places during predator downtimes, which can decouple variation in predation risk and herbivory patterns by allowing herbivores to access risky areas (Kohl et al. 2018; Smith et al. 2019). Further, use of temporal refugia may limit nutritional effects of predation risk if prey can maintain access to resources in risky areas during safe times. Expression of this antipredator strategy is likely to vary across species-specific demographic groups with risk of predation and necessity of predator encounter avoidance rather than early detection or evasion of detected predators. While predator-mediated shifts in diel activity may be important for mitigating nonconsumptive effects on population- and community-level processes, they may also have important implications for intraspecific interactions. Sexual segregation, or the spatial separation of sexes outside of the breeding season, has been described in many dimorphic species and has been suggested as a mechanism that limits intraspecific competition (Weckerly 1998; Ruckstuhl and Neuhaus 2000). Risk of predation, risk tolerances, and antipredator strategies often vary across demographic groups, and this variation has been proposed as a driver of sexual segregation in ungulates (Main et al. 1996). While numerous drivers of sexual segregation have been postulated, Main and Coblentz (1990) articulate the foundation of the predation risk hypothesis (PRH; also called the reproductive-strategy hypothesis by Main et al. [1996]), which predicts that females should make decisions based on predation risk and offspring safety while males should prioritize energetic intake to maintain competitive body mass for breeding competition. Sexual segregation is typically considered in the context of spatial separation; however, the sexes may also exhibit temporal resource partitioning (Crawford et al. 2019), a strategy that may reduce demographic-specific predation risk and aggressive intraspecific interactions. Thus, exploitation of temporal refugia to access risky places should vary demographically in a landscape with temporally dynamic risk. Although behavioral responses of prey to predation risk have garnered much attention, the relationship between predator-mediated diel activity patterns and intraspecific interactions of prey are poorly understood and could be key to understanding the effects of predators on intraspecific interactions in prey. White-tailed deer (Odocoileus virginianus; henceforth, deer) exhibit demographically variable diel activity patterns to navigate spatial variation in predation risk (Crawford et al. 2019; Higdon et al. 2019; Stone et al. 2019). In the tropical savannas of southwestern Florida, deer were strongly diurnal despite hot humid conditions, a strategy that minimized the probability of encountering their primary predator, the nocturnal Florida panther (Puma concolor coryi; Crawford et al. 2019). Moreover, the strength of predator avoidance was closely linked to the reproductive chronology of deer such that female and male risk tolerance increased during the fawning and breeding seasons, respectively. However, Crawford et al. (2019) did not evaluate the effect of fawn presence on female behavior. Higdon et al. (2019) showed that groups of deer containing fawns, or nursery groups, exhibited reduced temporal overlap with their primary predator, the coyote (Canis latrans), relative to males or females. Demographic-specific predation risk varies spatially and temporally based on predator community composition, and predator-specific prey selection can variably affect population growth rates. In landscapes, devoid of an efficient predator of adult deer, the relative importance of demographic-specific survival on population growth may shift as the predator community becomes dominated by mesocarnivores (Prugh et al. 2009). In southeastern North America, coyote populations have increased dramatically in recent decades contributing to relatively high, localized fawn predation rates (Kilgo et al. 2010; Nelson et al. 2015; Chitwood et al. 2015; Shuman et al. 2017). In the absence of larger carnivores, coyotes are dominant fawn predators, and coyote predation risk can affect deer foraging behavior (Cherry et al. 2015; Gulsby et al. 2018) and space use (Conner et al. 2016). Deer likely experience spatial variation in coyote predation risk as coyotes select habitat patches nonrandomly (Stevenson et al. 2018), and land cover drives spatial variation in abundance of coyotes (Cherry, Howell, et al. 2017) and fawn survival (Gulsby et al. 2017). However, it is unknown if deer use of temporal refugia, which exist in downtimes of coyote diel activity patterns (Higdon et al. 2019), to exploit risky patches varies across demographic groups influencing intraspecific interactions. In the context of deer, the PRH for sexual segregation hinges on the notion that females caring for offspring, or nursery groups, will prioritize offspring safety and be more risk averse than males (Main et al. 1996). Thus, shifts in diel activity patterns of nursery groups should result in decreased exposure to risk and give rise to differences in temporal diel activity patterns of males, females, and nursery groups. We developed a field experiment to assess the effects of predation risk on demographic variation in diel activity patterns of deer. We experimentally manipulated predation risk and tested the hypotheses that 1) predation risk would influence diel activity patterns in males, females, and nursery groups and 2) interdemographic diel activity overlap of males, females, and nursery groups would differ in risky and safe areas. We predicted nursery groups would exhibit the strongest responses to the predation risk treatment because they are the demographic group experiencing the greatest level of direct predation. Furthermore, early-life antipredator strategies which attempt to minimize encounters with predators, including bedding for long periods between lactation (i.e., hider strategy), cryptic coloration, and alarm bradycardia (Jacobsen 1979; Carl and Robbins 1988; Shuman et al. 2018), demonstrate fawns rely heavily on avoiding encounters with predators likely because, relative to adult deer, they are poorly equipped to flee from predators once detected. We predicted that nursery groups exposed to predation risk would exploit diurnal temporal refugia to avoid primarily nocturnal coyotes. We predicted females traveling without fawns would be less risk averse and males would exhibit the weakest response to predator exclusion as a function of their larger body size. We predicted predation risk would increase differences in behavior among males, females, and nursery groups and that diel activity overlaps between these groups would be greater in the predator free areas. STUDY AREA Our research took place at the Jones Center at Ichauway (henceforth, Ichauway), a 12,000-ha ecological research site located in southwestern Georgia, USA (Figure 1a,b). Ichauway was dominated by ~7250 ha of longleaf pine stands. Other forest types included slash (Pinus elliottii) and loblolly pine (Pinus taeda) forests, mixed pine and hardwood forests, lowland hardwood hammocks, oak barrens, and cypress–gum (Taxodium ascendens–Nyssa biflora) limesink ponds (Boring 2001). Prescribed fire is the primary forest management tool used for restoration and maintenance of longleaf woodlands. Deer densities on Ichauway were approximately 12 per km2 (B. T. Rutledge, personal communication). Coyotes and bobcats are the only extant mammalian predators of deer on our site (Nelson et al. 2015), and predators influence deer foraging behavior (Cherry et al. 2015), space use (Conner et al. 2016), and may suppress recruitment (Howze et al. 2009; Cherry, Morgan, et al. 2016; Conner et al. 2016). However, coyotes appear to be the primary predator of deer, killing approximately five times more fawns than bobcats (Nelson et al. 2015). On Ichauway, deer are an important component of coyote diets throughout the year, but consumption peaks during the fawning season (Cherry, Turner, et al. 2016). Figure 1 Open in new tabDownload slide Data were collected (a) in southwestern Georgia, USA at (b) the Jones Center at Ichauway, a ~12,000 ha ecological research station. (c) Two infra-red motion triggered cameras were deployed from May 2015 through September 2018 in each predator exclusion plot and control plot (d) for a total of 16 continuously monitored traps across 27 trap sites. Figure 1 Open in new tabDownload slide Data were collected (a) in southwestern Georgia, USA at (b) the Jones Center at Ichauway, a ~12,000 ha ecological research station. (c) Two infra-red motion triggered cameras were deployed from May 2015 through September 2018 in each predator exclusion plot and control plot (d) for a total of 16 continuously monitored traps across 27 trap sites. METHODS Experimental design We selected eight approximately 40-ha sites completely surrounded by sandy roads, of similar habitat composition, and located in the northern third of the study site. We randomly selected four of these plots to receive terrestrial predator exclusion treatment; the remaining four unfenced plots served as controls (Figure 1c). During 2004, at sites chosen for predator exclusion, we constructed a woven-wire (1.22 m tall, 10 × 20 cm mesh) fence with electric wire attached to E2000 (Twin Mountain Fence Company, San Angelo, TX) or Cyclops Super (Taylor Fence, Inc., Ashville, AL) electrical fence chargers along the top, middle, and bottom to deter predators from climbing over or digging under fences (Figure 1d). We removed all trees outside of exclosures that had branches overlapping the fence to ensure predators did not use overhanging branches to trespass. At the onset of predator exclusion, we trapped mammalian predators from within exclosures using a combination of soft-catch (Woodstream Corp., Lititz, PA, USA) and cage (Tomahawk Live Trap Company, Tomahawk, WI, USA) traps and relocated captured animals just outside of the exclosure in which the animal was captured. Predators targeted for capture included raccoons (Procyon lotor), Virginia opossums (Didelphis virginiana), striped skunks (Mephitis mephitis), gray foxes (Urocyon cinereoargenteus), red foxes (Vulpes vulpes), coyotes, and bobcats (Lynx rufus). We trapped each exclosure periodically to remove any predators that may have breached the fence. We monitored fences twice weekly for dig-ins and to ensure the electric wires were functioning. Adult deer are frequently observed jumping over the fences. While we do not know how soon after birth fawns can jump over the fences, we have observed fawns that appeared 3–4-month-old jump the fences. This experimental infrastructure was continuously maintained since 2004, has successfully manipulated the distribution of predators, and has resulted in effects on prey survival (Smith et al. 2013), cause-specific mortality (Conner et al. 2011), behavior (Cherry et al. 2015), body size (Morris and Conner 2019), scavenging community dynamics (Turner et al. 2020), and trophic interactions (Cherry, Warren, et al. 2016). Animal capture and handling followed recommendations of the American Society of Mammalogists (Sikes et al. 2011) and took place under Georgia Scientific Collecting Permit number 29-WJH-13-203. Camera traps Camera traps are an effective tool in experiments studying predator–prey interactions (Smith et al. 2020); therefore, we developed a camera survey in predator exclosure and control plots. Following prescribed burns during February and March of 2015 and 2017, we searched all plots and identified five well-used game trails within each predator exclosure and control plot. In 2017, the five game trails included two that were monitored during the previous fire cycle (i.e., 2015–2017) and three additional sites. We deployed motion-detecting camera traps (HCO VH400, HCO Outdoors Inc., Norcross, GA; henceforth, traps) to monitor diel deer activity on all identified trails. We set traps approximately 1.0 m above the ground and generally oriented either north or south to reduce glare associated with low sun angles. We programmed camera traps with default sensitivity and a 30-s delay between detections. After two weeks, we retrieved data, and the two camera trap sites in each plot with the greatest number of deer detections were maintained for long-term data collection. Thus, two traps were operational in each predator exclusion and control plot (total of 16 traps) from May 2015 through September 2018, although trap locations within plots may have changed to maximize detection of deer following fire in 2017 (Figure 1c). All traps were visited weekly for data retrieval and maintenance (e.g., vegetation control, battery replacement, etc.) and the date-time of visitation recorded for correcting erroneous image date-time stamps associated with camera malfunction or improper programming. Data All adult individuals in each deer detection image received a demographic classification as male, female, or unknown, while any image containing a fawn was classified as a nursery group. We defined a female detection as an image containing an adult female (>1 year old) where no fawn was detected. Therefore, our category of female included reproductively inactive females and reproductively active females during solitary feeding forays. We acknowledge this class likely included nursery groups where the fawn was present but not detected, which would obscure differences between females and nursery groups. We defined detection of a nursery groups as any image containing at least one fawn, regardless of the presence of other deer. We considered detections of lone fawns as representative of nursery groups because fawns rarely move without their mother during this life stage (Hirth 1985). We included images containing adult male and female deer as a detection of each. Additionally, each record was associated with its date-time stamp of detection, and a predator exclusion treatment classification based on trap site location. All detections classified with an unknown demographic group were excluded from analyses. To maximize independence of detections, we only used detections separated by > 5 min at each site. We included detections occurring during July–September as, across years, these months accounted for 97.4% of fawn detections. For context and graphical representation of diel coyote activity, we analyzed GPS relocation data collected on 17 adult resident coyotes during 2011–2013. For 6 days per week, relocations were recorded at 13-h intervals, and one day per week, locations were recorded every 15 min. Because estimation of diel activity curves from GPS-telemetry data requires relatively high location acquisition rates, we included only records acquired at a 15-min fix rate during July–September (n = 11,877). Analysis We calculated time-stamp kernel density activity curves for males, females, and nursery groups in predator exclosure and control plots. We estimated the coefficient of overlap (d̂) in predator exclosures and controls for males, females, and nursery groups. We then estimated the diel activity coefficient of overlap for pairings of males, females, and nursery groups in both predator exclosures and control plots. We estimated coefficient of overlap for all comparisons using Program R v3.4.6 (R Core Team 2019) and package overlap (Ridout and Linkie 2009). We obtained 95% confidence intervals (CIs) for our estimates via 10,000 parametric bootstrapping iterations and deemed differences in diel activity overlap between demographic groups or treatments significant when neither mean fell within the CIs of the other. To characterize coyote diel activity patterns, we estimated a diel activity curve from coyote GPS-telemetry data (Lashley et al. 2018). To make coyote movement data comparable to deer camera data, we calculated the mean step length within 128, 11.25-min intervals per day which is the same temporal resolution used for estimating deer activity from image timestamps. We sampled each binned interval the number of times equal to the mean step length for that bin. This provided count data per interval, similar to camera detection data, and created a kernel density activity curve. RESULTS We recorded 4116 independent deer detections during July, August, and September of 2015–2018 (female: 2590, male: 890, nursery: 636) at 27 trap sites. We maintained two camera traps per plot and observed 3244 deer in predator exclosures and 872 deer in control plots. Greater detection in the predator exclosure plots was particularly apparent for nursery groups. Only 95 of 636 nursery groups were detected in controls plots. However, we had sufficient data to estimate activity curves for males, females, and nursery groups in predator exclosures and control plots (Supplementary Table 1; Lashley et al. 2018). Our estimated coyote diel activity curve depicted relatively high nocturnal activity that decreased dramatically following sunrise and increased following sunset (Figure 2). Figure 2 Open in new tabDownload slide Intrademographic diel activity density curves and the coefficient of overlap (d; shaded area under both deer activity curves), for (a) deer nursery groups, (b) female deer, and (c) male deer in control plots (solid black) and predator exclusion plots (dashed blue) relative to diel activity curves of coyotes (solid red) estimated from GPS-telemetry data. Diel deer activity curves were estimated using timestamps from camera trap data collected July–September 2015–2018 while the coyote activity curve was estimated using GPS-telemetry data collected during the same months in 2011–2013. All data were collected at the Jones Center at Ichuaway, Newton, GA, USA. Vertical bars indicate mean sunrise and sunset during data. Figure 2 Open in new tabDownload slide Intrademographic diel activity density curves and the coefficient of overlap (d; shaded area under both deer activity curves), for (a) deer nursery groups, (b) female deer, and (c) male deer in control plots (solid black) and predator exclusion plots (dashed blue) relative to diel activity curves of coyotes (solid red) estimated from GPS-telemetry data. Diel deer activity curves were estimated using timestamps from camera trap data collected July–September 2015–2018 while the coyote activity curve was estimated using GPS-telemetry data collected during the same months in 2011–2013. All data were collected at the Jones Center at Ichuaway, Newton, GA, USA. Vertical bars indicate mean sunrise and sunset during data. We observed effects of predation risk on diel activity patterns for males, females, and nursery groups; however, the magnitude of the effect was variable. Surprisingly, male diel activity patterns differed the most between predator exclosure and controls plots (d̂ = 0.77, CI: 0.71–0.83), followed by nursery groups (d̂ = 0.79, CI: 0.71–0.87) and females (d̂ = 0.89, CI: 0.86–0.93; Figure 2). In the absence of predation risk, nursery groups increased activity at night and during dawn when coyotes active was high. However, males and females increased diurnal activity in the absence of predation risk. Nursery group diel activity overlap with males was 24% greater in exclusion plots (d̂ = 0.91, CI: 0.87–0.95) than control plots (d̂ = 0.67, CI: 0.76–0.57; Figure 3). Similarly, overlap between nursery groups and females increased 18% in predator exclusion plots (d̂ = 0.95, CI: 0.92–0.98) relative to control plots (d̂ = 0.77, CI: 0.68–0.84). Lastly, male and female diel activity overlap was 4% greater in predator exclosure plots (d̂ = 0.93, CI: 0.90–0.96) than control plots (d̂ = 0.89, CI: 0.83–0.94). Figure 3 Open in new tabDownload slide Interdemographic estimates of diel activity overlap (d; shaded area under both deer activity curves) and activity density curves obtained via kernel density estimation of image timestamps from camera trap data for male deer (dotted black), female deer (black dashed), and nursery groups (solid black) in control and predator exclusion plots relative to diel coyote activity curves (solid red) estimated using GPS-telemetry data. Camera trap data were collected during July, August, and September of 2015–2018, whereas coyote GPS-telemetry data were collected in 2011–2013 during the same months. All data were collected at the Jones Center at Ichauway, Newton, GA, USA. Measures of error are 95% confidence intervals. Figure 3 Open in new tabDownload slide Interdemographic estimates of diel activity overlap (d; shaded area under both deer activity curves) and activity density curves obtained via kernel density estimation of image timestamps from camera trap data for male deer (dotted black), female deer (black dashed), and nursery groups (solid black) in control and predator exclusion plots relative to diel coyote activity curves (solid red) estimated using GPS-telemetry data. Camera trap data were collected during July, August, and September of 2015–2018, whereas coyote GPS-telemetry data were collected in 2011–2013 during the same months. All data were collected at the Jones Center at Ichauway, Newton, GA, USA. Measures of error are 95% confidence intervals. The relatively low overlap between nursery groups and both males and females in control plots was a function of strongly crepuscular diel activity patterns for males and females while the majority of nursery group activity occurred midday to early evening. However, the relatively high overlap of all groups in the predator exclusion plots was a function of both increased evening activity of males and females as well as increased morning crepuscularity of nursery groups relative to the control plots. Discussion Our results demonstrate predation risk influences diel activity patterns of males, females, and nursery groups. In the absence of predation risk, these demographic groups of deer behaved more similarly, and diel activity overlap was greater for all pairings of groups in the predator exclosure plots than the control plots. Under reduced predation risk, deer accepted greater intraspecific diel activity overlap. We found that, when exposed to predators, nursery groups concentrated diel activity during diurnal periods characterized by relatively low coyote diel activity, and in the absence of predators, nursery groups increased morning activity. We found support for our prediction that males, females, and nursery groups would maintain different activity patterns in safe and risky areas. However, we assumed each group would increase activity during risky times in safe areas and use risky areas during safe times (Lone et al. 2017; Kohl et al. 2018; Smith et al. 2019). As predicted, nursery groups were primarily diurnal and maintained diel activity patterns that minimized potential for predator encounters in risky areas. Our findings support previous research suggesting fawns or nursery groups are largely diurnal (Jackson et al. 1972; Higdon et al. 2019), and our results demonstrate that diurnal diel activity is an antipredator behavior that increases in risky areas. Interestingly, males and females increased diurnal activity during a predator downtime in the safety of the predator exclosure plots. One potential explanation for this counterintuitive result is that we are relying on faulty assumptions pertaining to predation risk. We assume diel activity of coyotes is a good representation of predation risk. However, coyotes rely heavily on visual cues (Wells 1978) and adult deer may be more susceptible to coyote predation during the day when the coyote’s visual advantage is greatest, despite lower coyote activity. On our site, adult female deer were more vigilant during day than night (Cherry et al. 2017). Therefore, males and females may be more active during the day in the predator exclosures because, in the presence of predators, there are elevated costs of foraging during day. Conversely, fawns have small body sizes relative to adults and use cryptic coloration, hiding, and alarm bradycardia to minimize visual cues (Jacobsen 1979; Carl and Robbins 1988); thus, predation risk for nursery groups may be more directly linked to predator activity and adult risk may be due to predator visual advantage. Demographic groups are likely vulnerable during different phases of the predatory sequence (Andelt et al. 1999; Lingle 2001) such that the efficacy and expression of antipredator behaviors vary across demographic groups of deer (Lashley et al. 2014; Cherry et al. 2015; Biggerstaff et al. 2017; Stone et al. 2017). The PRH predicts that larger-bodied males should exploit riskier resources under the assumption that their larger body size decreases susceptibility to predation (Ruckstuhl and Neuhaus 2000); however, males in our study exhibited the greatest shift in diel activity patterns in response to predator exclusion. Within the same experimental infrastructure, Cherry et al. (2015) documented males were generally more vigilant, and their foraging behavior was more sensitive to predator exclusion than either females or juveniles. Furthermore, fawns are generally less vigilant than adults (Lashley et al. 2014; Stone et al. 2017), and on our site, foraging behavior of juveniles was insensitive to spatial variation in predation risk (Cherry et al. 2015). Stronger antipredator responses by males than nursery groups or females in our system have a couple of important implications. First, strong antipredator behaviors in males relative to females and nursery groups may allow males to access riskier areas. Second, assuming males are less susceptible to direct predation than fawns, our system is an example of a nonlinear relationship between mortality risk and strength of antipredator behavioral responses across demographic groups within a species (Creel and Christianson 2008). Females exhibited the weakest response to predator exclusion. This group included reproductively inactive females that either were not bred or lost their fawns, and reproductively active females on solitary foraging bouts. These individuals likely exhibit risk-prone behaviors to acquire sufficient energy to become reproductively active, recuperate from gestation, or meet high energetic demand of lactation (Oftedal 1985). Similarly, in southern Florida, female deer increased spatiotemporal overlap with their primary predator, Florida panthers, during the fawning and fawn-rearing seasons, likely to meet increased energetic demands (Crawford et al. 2019). The ultimate outcome of the group-specific antipredator responses is that males, females, and nursery groups maintain greater diel activity overlap in the absence of predators. Presumably, this pattern emerges because the activity decision-making process for each demographic group is simplified by the removal of the predation risk variable. Thus, predation risk induces group-specific antipredator responses that make males, females, and nursery groups behave more differently. This result refutes findings of Biggerstaff et al. (2017) who found that diel activity overlap between male and female deer did not increase with predation risk. However, they did not manipulate predation risk and may not have surveyed over sufficient variation in predation risk to observe effects of predation risk on diel activity overlap. Our experiment demonstrates that predation risk increases heterogeneity in prey behavior. Deer have structured dominance hierarchies within and among demographic groups (Donohue et al. 2013; Michel et al. 2016; Stone et al. 2019), and in our system, the presence of a male reduces the probability of feeding for juveniles and females during fawning (Cherry et al. 2015). While the dominate individuals often achieve access to the desired resource, the interactions can be costly for all in involved and can result in decreased feeding of both males and females (Biggerstaff et al. 2017; Gulsby et al. 2018). Therefore, assuming diel activity overlap is representative of the probability of an intraspecific interaction, we observed covariation in prey tolerance for predation risk and intraspecific interactions (Sih et al. 1985) as deer accepted greater levels of intraspecific diel activity overlap in low-risk areas. Studies of predation risk effects often focus on the effects of risk on individual prey responses and how those responses scale up to affect population demography or ecosystem-level processes (Werner and Peacor 2003; Zanette et al. 2011; Gaynor et al. 2019), yet relatively little attention has been given to the effects of predation risk on intraspecific interactions in prey. Our results suggest predators increase heterogeneity in prey behavior and may be important drivers of behavioral processes, such as temporal partitioning, that minimize antagonistic intraspecific interactions of prey. We provide evidence that 1) males, females, and nursery groups maintain different diel activity patterns in safe and risky areas; 2) nursery groups increase diurnal activity in the temporal refugia of predator downtimes in risky areas; and 3) predation risk increases behavioral heterogeneity in prey by inducing variable antipredator responses across demographic groups. FUNDING This research was funded internally by The Jones Center at Ichauway (grant no.: JCI2020-06). Acknowledgments We would like to thank H. Tisell, A. Warren, K. Underhill, K. Paolini, L. Pipino, S. Cahill, G. Rigsby, S. Pesi, N. Klopmeier, N. Moore, N. Yetke, J. Sands, and M. Taig-Johnston for their contributions to data collection and equipment maintenance. Additionally, we would like to thank the Jones Center at Ichauway for logistical support and funding. Data availability: Analyses reported in this article can be reproduced using the data provided by Crawford et al. (2020). References Andelt WF , Phillips RL, Gruver KS, Guthrie JW. 1999 . Coyote predation on domestic sheep deterred with electronic dog-training collar . Wildl Soc Bull . 1 : 12 – 8 . Google Scholar OpenURL Placeholder Text WorldCat Biggerstaff MT , Lashley MA, Chitwood MC, Moorman CE, DePerno CS. 2017 . Sexual segregation of forage patch use: support for the social-factors and predation hypotheses . 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Google Scholar OpenURL Placeholder Text WorldCat © The Author(s) 2020. Published by Oxford University Press on behalf of the International Society for Behavioral Ecology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
TI - Predation risk increases intraspecific heterogeneity in white-tailed deer diel activity patterns
JO - Behavioral Ecology
DO - 10.1093/beheco/araa089
DA - 2021-03-02
UR - https://www.deepdyve.com/lp/oxford-university-press/predation-risk-increases-intraspecific-heterogeneity-in-white-tailed-Rb00b9A0eJ
SP - 41
EP - 48
VL - 32
IS - 1
DP - DeepDyve
ER -