When, where, and how prey pair antipredator behaviors to natural and anthropogenic mortality risks

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Egan, Abigail M. Weber, Nicole Gorman, Michael W. Eichholz, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5984114/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Jul, 2025 Read the published version in Movement Ecology → Version 1 posted 7 You are reading this latest preprint version Abstract Background Behavioral responses of prey to predation risk have ecological impacts that can be as great as direct mortality. Risk response involves either behavioral changes or spatial avoidance, but it is not clear how prey decide between these strategies. Theory often suggests that prey pair responses to risks based on the hunting mode of the prey (hunting mode hypothesis), but prey may ignore hunting mode to prioritize responding to the most lethal predators (lethality hypothesis). Furthermore, prey may respond to the spatial distribution of these risks (risky places hypothesis) or respond only during the periods of highest risk (risky times hypothesis). Methods To test these hypotheses, we evaluated the behavioral responses of white-tailed deer (Odocoileus virginianus) to risks from two natural mesopredators and human sources of mortality. Specifically, we determined, for each source of risk, whether deer responded with behavioral state changes or spatial avoidance and whether this behavior changed with time (diurnally and annually). We collared and tracked 40 female and 29 male deer. To determine the response of deer to risk, we collected data on the distribution of coyotes (Canis latrans), bobcats (Lynx rufus), human modification, hunters, and roads. We used hidden Markov models (HMM) to determine whether each covariate impacted the probability of transitioning between behavioral states and selection functions (SSF) to determine whether deer spatially avoided each covariate. Results Generally, deer changed behavioral state in response to both mesopredators but avoided human modification. In response to mesopredators, deer consistently shifted to slower movement behavioral states. Spatial responses to human modification varied depending on the time of day. During daylight hours, deer selected for human modification, but during the crepuscular and nighttime period, deer avoided human modification. Conclusions Space use was most strongly related to more lethal humans, providing support for the lethality hypothesis. Despite prioritizing humans, mesopredators impacted behavioral state, suggesting that mesopredators still have important impacts on prey behavior. Finally, temporal patterns of avoidance align with other studies that indicate avoidance of predators is time-dependent, but further highlight the complex push-pull relationship of human modified areas on wildlife. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction The behavioral responses of prey to predation risk can impact prey populations and the ecosystem at large (i.e. behaviorally-mediated trophic cascades) in ways that are as great or greater than the effects of direct mortality (Schmitz et al. 1997 , Creel and Christianson 2008 , Beschta and Ripple 2009 ). The indirect impacts of predation risk are the result of trade-offs between resource acquisition and safety that impact where and how an animal consumes resources (Brown 1988 , Kie 1999 , Fortin et al. 2005 , Gaynor et al. 2019 ). For example, prey may reduce their risk of mortality by avoiding habitat types that predators are most likely to occupy, which might result in the use of suboptimal habitat and reduced foraging (Fortin et al. 2005 , Gaynor et al. 2019 ). Alternatively, prey may not spatially avoid risky habitat, instead changing their behavioral state such that, if an encounter occurs, the prey may avoid a successful attack (Laundre et al. 2010 , Cherry et al. 2015 ). These behavioral state changes include encamped states (reduced movement and increased vigilance) or directed states (increased movement) which may increase safety but reduce energy intake relative to active foraging. Due to the importance of these behavioral responses, many theoretical frameworks have been developed to understand and predict how prey make decisions about which antipredator behavior to use and balance antipredator behavior with other needs. One major theoretical consideration is whether prey respond through proactive or reactive antipredator behavior. The “risky places hypothesis” suggests that prey proactively avoid risky habitats regardless of time (Creel et al. 2019 ). Alternatively, the “risky times hypothesis” predicts that prey reactively respond to the predators through strategies such as behavioral state changes or by only avoiding risky habitats when the risk is highest (Lima and Bednekoff 1999 , Lone et al. 2016). While these two hypotheses are not necessarily mutually exclusive, prey may respond differently to spatial or temporal variation in risk depending on ecological conditions. Proactive and reactive behaviors also target different parts of the predation process. Predation-related mortality involves a sequence of stages starting with an encounter between predator and prey and proceeding to a successful attack (Lima and Dill 1990 , Bateman et al. 2014 , Suraci et al. 2022 ). Proactive spatial avoidance limits encounters altogether, while reactive behavioral state changes minimize the risk of being killed given an encounter. The most common framework for understanding these decisions is based on the hunting mode of the predator (“hunting mode hypothesis”). Spatial avoidance may be most effective for predators with ambush hunting styles (many felids), because it is feasible to avoid these predators. Alternatively, behavioral state changes may be most effective for active, cursorial predators (many canids), because, in this case, the probability of encounter is driven by the predator (Preisser et al. 2007 , Smith et al. 2019 ). While this hypothesis has received support, many of the systems in which predator-prey behavior has been studied contain primarily large predators. Top predators have received the most focus because they likely have the largest ecological impacts (Ripple and Beschta 2004 , Beschta and Ripple 2009 , Ripple et al. 2014 ), but it is not clear how well inferences from large-predator systems can be transferred to other systems (Peacor et al. 2022 ). In many systems, large predators have been extirpated leaving medium-sized predators as the de facto top predator (Gompper 2002 , Prugh et al. 2009 ). While these medium-sized predators may have similar hunting-modes to comparable large-predators, they have substantially lower lethality (which we define here as the overall probability of a predator killing a prey) (Owen-Smith & Mills 2008 , Cooper and Frederick 2010 ). In contrast to the hunting mode hypothesis, prey may not respond proactively to mesopredators, instead responding proactively to the most lethal predators, which here we will refer to as the “lethality hypothesis”. Therefore, prey behavior must be evaluated in systems without large predators to improve our general understanding of how prey decide to respond to different types of risk (Creel and Christianson 2008 , Creel et al. 2019 ). Here we focus on a prominent North American ungulate, white-tailed deer ( Odocoileus virginianus ), in a system without resident populations of large carnivores. In this system, there are three sources of predation-related morality risk: coyotes ( Canis latrans ), bobcats ( Lynx rufus ), and human ‘predators’ who induce mortality through factors such as hunting and vehicle collisions (Bissonnette et al. 2008, Darimont et al. 2015 ). These three risks can be plotted on two axes related to variation in their spatial ecology and potential lethality where coyotes are the most broadly distributed risk, followed by bobcats and humans, and humans are the most lethal risk followed by coyotes and bobcats (Messier et al. 1986 , Gese et al. 1995, Chitwood et al. 2014 ) (Fig. 1 ). Coyotes are cursorial hunters shown to induce increased vigilance in prey (Cherry et al. 2015 , Schuttler et al. 2017 , Gulsby et al. 2015 ), changes in space use (Lingle 2002 ), or other antipredator behaviors (Lingle and Pellis 2002 , Olson et al. 2019 ). Bobcats are ambush predators also shown to kill adult deer, although infrequently (Labisky and Boulay 1998 , Kilgo et al. 2012 ). Humans represent a unique predation risk but are generally much more limited in terms of where and when they hunt in natural environments (Ciuti et al. 2012 ). Conversely, while coyotes and bobcats are the de facto largest predators and a common source of fawn mortality (Rohm et al. 2007 , Gese and Grothe 1995 , Patterson and Messier 2000 , Moratz et al. 2018 ), humans are a much more common source of adult mortality (Hewit 2002). We evaluated prey behavioral responses to different sources of mortality risk to better understand how prey make decisions about how to respond to predators. We tested for spatial responses using step selection functions (SSF) and tested for changes in behavioral state using hidden Markov models (HMM). We evaluated general support for two hypotheses (Fig. 1 ). The hunting mode hypothesis (H1) suggest that prey match antipredator behavior with the hunting mode for which it is most effective, in which case we predict that deer will differ in their response habitat associated with coyotes and bobcats, using spatial avoidance for bobcats and behavioral state changes for coyotes. Alternatively, the lethality hypothesis (H2) states that prey prioritize spatially avoiding the most lethal risks. Following this hypothesis, we predict that deer will differ in their response to habitats associated with humans and natural mesopredators, using spatial avoidance for humans and behavioral state changes for mesopredators. For each of these hypotheses, we evaluate the relative support for the risky places hypothesis in which deer respond consistently to the spatial distribution of predators (H1S and H2S), the risky times hypothesis in which deer vary their response to these resources corresponding to the periods of highest risk (H1T and H2T), or that deer switch between strategies based on the time when risk is the highest (H1Switch and H2Swtich). Methods Field Methods We captured and collared white-tailed deer in accordance with standards of the American Society of Mammologists (Sikes et al. 2011) with methods approved by Southern Illinois University Institutional Animal Care and Use Committee (21–028). We captured deer at two sites: primarily around Touch of Nature Outdoor Education Center (TON) near Carbondale, Illinois (37.627459 deg N, 89.153998 deg W) and land owned by the United States Army Corps of Engineers near Lake Shelbyville, Illinois (39.512753 deg N, 88.702546 deg W) (Fig. 2 ). The TON site was comprised of a mixture of forest cover with some agricultural cover and substantial exurban development. The TON property allows hunting for only a few days a year, and recreation activities such as hiking and biking are common throughout the year. The Lake Shelbyville site was comprised primarily of agricultural cover with minimal urban development and hunting is allowed throughout the area during extended periods between October and January. Both sites have similar climates with moderate winters and hot, humid summers. Deer were captured from January – March from 2020 to 2021 beginning after the end of deer hunting season and continuing until green up in each location. Additionally, we collared coyotes (n = 31) and bobcats (n = 18) at each site to estimate the relative occurrence probability of each species as an estimate of predation risk. Full details on capture methods can be found in Appendix S1. After data collection, we performed several data preparation procedures to remove potentially erroneous points and created regular trajectories for behavioral state and habitat analysis. We cleaned data based on two criteria. First, we removed any locations with a dilution of precision greater than 25. Second, we removed points for which the distance between points suggested that the deer was moving at a speed that was biologically impossible. We selected 3 km/h as the cutoff for movement speed for a time step based on visual analyses of the distribution of step lengths. Resource Covariates: We obtained landcover classification data from the national landcover database (Dewitz 2021 ). NLCD data divides the landscape into 30 x 30 m cells and classifies each into one of several landcover types. We estimated the proportion of each landcover type within a radius around each cell in the landscape. We reclassified landcover into four categories: forest, urban, agriculture, and other. For each variable, we estimated the proportion of each category within a 2 km 2 buffer surrounding each cell in the landscape. Due to correlation between landcover variables, we retained only one variable for HMM and SSF models by comparing the performance of each covariate in preliminary models. From these models, we retained the proportion of forest cover as the covariate ‘forest’. Predation Risk: We characterized predation risk based on the relative probability of occurrence of coyotes and bobcats in the study area. For each species, we calculated the relative probability of use for each cell in the landscape by fitting resource selection functions relating resources at each location to the probability of use. Potential resource covariates for each species included human modification, landcover (classified as forest, urban, or agriculture), distance to water, and distance to road. Based on this model, we predicted the relative probability of predator occurrence throughout the landscape for both coyote and bobcat respectively. We refer to these variables as ‘coyote’ and ‘bobcat’ respectively and validated these models using 10-fold cross validation (Full details on model fitting can be found in Appendix 1). These layers represent the areas where coyote and bobcat presence is most likely as a function of resources as a proxy for the risk of predation. We characterized anthropogenic risks based on several covariates describing the influence of human presence within the landscape. The impact of roads was characterized by calculating the Euclidean distance between each 30 x 30 m cell on the landscape to the nearest road based on data from the US geological survey (USGS 2019), producing a 30 x 30 m resolution raster of the distance to the nearest road, referred to as ‘distance to road’. The degree of human modification was obtained from estimates of human modification taken from Kennedy et al. 2019 . This layer represents the relative degree of human modification, on a scale from 0 to 1, based on factors related to human settlement, agriculture, transportation, and energy infrastructure. This variable is referred to throughout as ‘human modification.’ We determined the potential for hunting based on the spatial distribution of where hunting is allowed. Any landcover in urban areas, on private land, or on public land where hunting was allowed during hunting seasons were classified as the reference level 0. Lands where hunting was explicitly not allowed during any time of the year was classified as 1 producing a categorical variable describing relative safety from human hunters. Since class 1 represents the areas that are safe from hunting relative to hunting areas, we referred to this variable as ‘hunting refuge’, and a positive coefficient for this variable implies selection for refuges. Analysis Overview: We tested the effects of risk on white-tailed deer behavior using two methods. First, we tested the effect of risk variables on the probability of transitioning between behavioral states using Hidden Markov Models (HMMs). Second, we tested the effect of risk covariates on habitat selection using step-selection functions (SSFs). During this analysis, we tested the effect of risk on state-specific habitat selection based on SSFs of steps taken during each behavioral state. For each method, we ran candidate models representing each risk covariate and each combination of risk covariates. Behavioral States To test the effect of risk on deer behavior, we modeled the effect of risk variables on the probability of transition between behavioral states using an HMM (Morales et al. 2004 ). HMMs require regularly sampled data, so, for a given animal, we took steps to produce trajectories sampled at regular intervals (30 minutes for females, 1 hour for males). First, if any animal had gaps in data longer than one day, we divided this record into separate trajectories (often referred to as bursts). Second, we replaced any missing locations with NAs. We modeled the behavioral state of animals based on the turning angle and step length of consecutive steps. Based on these movement parameters, states were defined based on a gamma distribution of the step lengths and von Mises distribution of the turning angles while simultaneously accounting for the probability of transitioning between states. These models may also include variables for the transition probabilities between states, allowing us to test the effect of spatially distributed resources on the behavior of an animal. We specified a priori that movement data be classified into three behavioral states because we aimed to capture functional differences between states. Specifically, we divided steps into an “encamped” state for little movement and associated potentially with resting or cover and a high degree of safety, an “active” state for moderate movement and associated potentially with a balance between foraging and safety, and a “directed” state for longer movements and associated potentially with lower safety (Franke et al. 2004 ). To delineate states for state-specific step selection models, we ran HMMs using the full dataset for females and males, respectively. To make inferences about the impact of resources on state transitions, we divided data between seasons to estimate season-specific models. The baseline period was defined as all points before the fawning season, January to April, the fawning period included locations from May to July, and the hunting period included all locations from October to December. In each model we included the landcover covariate forest and combinations of risk covariates based on a priori models. We compared the performance of models using AICc and determined the effect of each covariate on the probability of transition between each state based on coefficients of effect of the covariate. HMMs were fit in R using the package momentuHMM (McClintock and Michelot 2018 ). Step Selection: We tested the effect of predation risk on white-tailed deer habitat selection using an SSF (Thurfjell et al. 2014 ). To ensure trajectories were regular, we divided telemetry data into bursts in the same manner as for behavioral states wherever we found missing locations. For each observed step, we generated 100 available steps based on a gamma and von Mises distribution for step length and turning angle, respectively. The mean and standard deviation for the gamma distribution and the concentration of the von Mises distribution was based on the values for observed steps. Additionally, these values were specific to the sex of the individual deer and the state during that step. Used and available steps were compared in a case-control design using a conditional logistic regression stratified by step following the methods of Muff et al. ( 2020 ). Models also included step length as a covariate to reduce bias in parameter estimation and the distance to the nearest recursion point (defined by identifying clusters of points in space and time) to account for recursive movements within the home range (Egan et al. 2025 ). For each risk covariate, we fit models of each individual risk variable, two variable models with each combination of risk variables, and interaction models that were identical to the two variable models but include an interaction between the two risk variables. Additionally, models included random slopes for the effect of each covariate on each individual. State-specific step selection was estimated by subsetting locations assigned to each behavioral state. For each behavioral state, we fit season-specific models by dividing data based on month to produce three periods. To test for differences in the response to sources of risk based on time of day, we included an interaction term between risks and time of day. We divided locations between three periods associated with behavioral patterns of deer. Day was defined as the period from the end of nautical sunrise to the beginning of nautical sunset, night was defined as the period from the end of nautical sunset to the beginning of nautical sunrise, and crepuscular was defined as the periods during sunrise or sunset. Times for sunrise and sunset were determined using the suncalc package in R (Thieurmel et al. 2019 ). All analysis were conducted using R (R version 4.4.1). Results We tracked a total of 40 females and 29 males from January 2020–December 2022, which yielded a total of 626,779 relocations after cleaning and pre-processing. Females were tracked for an average of 320 days and males an average of 270 days. During the study period, four deer were killed by hunters, one died from a vehicle collision, and no adult deer were killed by predators. Behavioral state analysis indicated that doe movement was best described by a three-state model with a low step length state (mean step length = 11 m) we refer to as encamped movement, a moderate step length state (mean step length = 37 m) we refer to as active movement, and a high step length state (mean step length = 110 m) we refer to as directed movement. Male movement was described by a similar model with states: encamped (mean step length = 23 m), active (mean step length = 52 m), and directed (mean step length = 138 m). HMMs indicated that behavioral states were distinguished by step length primarily. Turning angles differed little between states with each state showing angle distributions centered around 0 and concentrations close to 0. Based on AICc scores (Full results of model ranking can be found in Appendix 2), the probability of state transitions was related to variation in the relative probability of coyote and bobcat occurrence, but these patterns differed between seasons (Fig. 3 ; numbers represent coefficients of effect). During the baseline period, females were more likely to change to the active state from the encamped state where the probability of bobcat occurrence was high compared to areas where the probability of bobcat occurrence was low (0.58; Fig. 3 ) but were more likely to shift from directed movement to encamped in areas where occurrence of either predator was higher (2.10, 1.18; Fig. 3 ). Females were also less likely to leave the active movement state in areas where coyote (-1.51; Fig. 3 ) or bobcat (-0.28, -0.66; Fig. 3 ) occurrence was high compared to where it was low. In contrast, areas where coyote and bobcat occurrence were both high were associated with a reduced probability that females transition to or stay in the active state compared to areas where the probability of occurrence of both species was low (-0.067, 0.85, -0.92; Fig. 3 ). We observed similar patterns during the hunting season, but during fawning season, females changed behavioral state in response to areas where coyote occurrence was highest or human modification was highest. Specifically, during this period, where human modification was high, females were more likely to shift out of the directed state (2.12, 1.90, Fig. 3 ) and transitioned between encamped and active movement compared to low human modification (1.58, 2.13, Fig. 3 ). During the baseline period, the top model for males indicated that males altered their behavior in areas where coyote occurrence was high and in hunting refuges. Most notably, males were more likely to change from encamped to active in refuges compared to areas where hunting was legal (2.32; Fig. 4 ), but less likely to shift from encamped to directed movement (-1.48, Fig. 4 ). However, in all other seasons, male state transitions were most related to areas where coyote and bobcat were likely to occur. Top SSF models indicated that females selected for habitat based on the degree of human modification and, in most states and seasons, the distribution of coyotes, but not bobcats (Fig. 5 ). Females exhibited different patterns of selection depending on the state, season, and time of day. During the baseline period, females selected for human modification during the day, but avoided human modification during the night and crepuscular period. During fawning, females did not avoid human modification to the same degree, generally only avoiding human modification at night (Fig. 5 ). During hunting, females generally avoided human modification during nights and did not consistently select for human modification during the day. While coyotes were consistently present in the top model for females, females only avoided coyotes during the fawning season during directed movement (Fig. 6 ). Males more often avoided human modification (Fig. 7 ), though this pattern differed depending on season, state, and time of day (Fig. 7 ). While males did not respond to human modification during the encamped state, males strongly avoided human modification during the active state, especially during fawning and hunting season. Discussion We used behavioral state and step-selection analyses to estimate seasonal, daily, and state-specific responses by deer to multiple sources of risk, and our results have implications for how prey make decisions about how to respond to predation risk. Deer responded to all sources of risk in some way but generally paired a given risk with a specific type of antipredator behavior. This result provides support for stage specificity in antipredator behavior as a mechanism for prey to make risk related trade-offs, however the nature of this stage-specificity differed from our predictions. Specifically, we found that deer proactively responded to the most lethal predators but were reactive in that they alternated between selection and avoidance of human modification at a daily scale (H2T). This result suggests that, while prey did not respond to predators based on hunting mode, they may balance foraging and risk by responding at risky times. Conversely, we found that deer responded reactively to less lethal risks through behavioral state changes, which is demonstrated by the fact that behavioral state changes were generally related to mesopredators but exhibited only slight temporal variation. While deer did not proactively respond to mesopredators, this result suggests that mesopredators may still have important impacts on deer ecology and behavior that are mediated through differences in the way deer move. Previous empirical evaluation of the relative importance of hunting mode and lethality has yielded inconsistent results. While prey behavioral responses have been found to differ for predators of different hunting modes in systems including arthropods (Kersch-Becker et al. 2018 ), amphibians (Luttbeg et al. 2020 ), and large mammals (Moll et al. 2017 ), our study is one of few examples that compare predators in terms of both hunting mode and lethality (Say-Sallaz et al. 2023 ). In these cases, prey often respond most strongly to the most lethal or dangerous predator, with hunting mode and spatial domain having only minor impacts. For example, Say-Sallaz et al. ( 2023 ) and Thaker et al. ( 2011 ) found that larger, more lethal predators induced the strongest impacts on prey reactive spatial responses and spatial distributions, respectively. In contrast to hunting mode predictions, Leblond et al. ( 2016 ) found that caribou prioritized avoiding cursorial but highly lethal wolves over opportunistic bears. Kohl et al. ( 2019 ) also found no differences in avoidance of highly lethal predators with differing hunting modes. In support of our results, they found that elk avoided both predators by altering their behavior according to the diel patterns of the predator. Assessment of the relative role of hunting mode on prey avoidance are limited because few studies compare the impact of different predators on multiple antipredator behaviors, and results vary between these studies (Montgomery et al. 2019 , Moll et al. 2017 ). While it may seem self-evident that prey respond more to the most lethal predators, more studies should compare predators of differing lethality and multiple behavioral responses to determine the relative importance of predator attributes such as spatial domain and hunting mode. Temporal variation in behavior was particularly important for explaining the spatial relationship between deer and human modification, because human modification can have multifaceted impacts on wildlife. In general, deer avoided human modified areas during the night and crepuscular period, but often selected these areas during the day. In our study system, human modification is related to agricultural and exurban areas that see increased human presence in the evening. These areas also contain valuable food resources (Oro et al. 2013 , Kennedy et al. 2019 ), which may prompt deer to forage in these areas during the day but move away to avoid periods of elevated risk at night. This interpretation is further supported by the fact that deer, particularly females, selected for human modification most strongly from January to April when food was most scarce but avoided human modification most strongly during hunting season when humans pose the greatest risk. Alternatively, human modified areas may provide relative safety from other predators (Berger 2006 ). For example, Ganz et al. 2024 found that temporal patterns of selection and avoidance of humans may be related toward use of humans as a shield from predation, but it is unclear whether this is the case in our system. Supporting this pattern, females selected for human modified areas more during fawning season relative to males, possibly because at this time they are likely to be accompanied by more vulnerable fawns. However, in contrast to this pattern we found limited spatial avoidance of mesopredators and non-significant interaction effects between human modification and mesopredators. Whether behavioral responses are proactive or reactive has important implications for the population level consequences of predation risk, particularly in relation to two questions: are direct effects or risk effects larger and are direct effects and risk effects correlated (Creel and Christianson 2008 )? Our results suggest that prey proactively respond to more lethal predators suggesting that behavioral responses may scale with increasing direct mortality. While we did not find evidence that predator hunting mode was related to the type of behavioral response, hunting mode may still impact the effectiveness of these responses. For example, highly lethal predators with little mobility are likely to induce effective spatial avoidance, producing risk effects that trade-off with direct mortality. However, spatial avoidance may be less successful for mobile predators, resulting in risk and direct effects that are correlated. Additionally, our results suggest a potential mechanism for mesopredators to induce risk effects (Creel and Christianson 2008 , Verdolin 2006 , Creel et al. 2014 ). Our results support other studies that have found deer alter their behavioral state in response to mesopredators (Cherry et al. 2015 , Schuttler et al. 2017 , Gulsby et al. 2015 ), potentially leading to reduced time spent foraging. Furthermore, negative interaction effects suggest the possibility of interference between these predators (Fedriani et al. 2000 , Wilson et al. 2010 ). While agnostic interactions between carnivores have been noted to produce emergent spatial patterns (Sih et al. 1998 ), few studies have detected emergent impacts on behavioral state. Even if these behaviors are not ubiquitous in the population, intraspecific variation in prey behavior may have population-level impacts. For example, younger more vulnerable prey may sacrifice current year foraging and reproduction for potential future reproduction (Clark 1994 , Verdolin 2006 , Wirsing et al. 2021 ). However, to validate theoretical predictions about the trade-offs between risk effects and direct mortality in cases where direct mortality is low, it will be necessary to empirically evaluate the relationship between behavioral responses to medium-sized predators and prey fitness (Peacor et al. 2022 ). While we cannot confirm the functional role of each behavioral state without direct observations of deer behavior, the relationship between these states and risk type indicates their potential value. Many other studies have noted decreased movement rates in response to predation risk, as movement may be inherently risky (Little et al. 2016 , Picardi et al. 2019 ). Alternatively, predators may also trigger evasive movements resulting in sudden changes to fast, directed states (Stankowich and Coss 2006 ). In general, predators were associated with a decrease in the probability of transition to faster movement states, suggesting that reduced movement offered antipredator benefits. Despite this, we only noted spatial avoidance of predators by females during directed movement, suggesting that this state may help facilitate movement away from risky habitat types, while reductions in movement are associated with antipredator defense. State-specific habitat selection by males supports this, in that males avoided humans using active and directed movement, but not encamped movement. Finally, we found that deer habitat selection differed from day to night to the greatest degree in the active and encamped states. This may suggest that slower movement states are associated both with foraging and predator defense and allow deer to balance these goals by foraging at a slower rate when in potentially risky environments. Additionally, we advise caution when comparing our results to other studies, particularly those using data on the observed presence/absence of predators. Here, we use an RSF to model the probability of predator occurrence based on resources. An assumption of this approach is that these models accurately characterize the distribution of predators and that deer perceive these areas as risky. To respond to predation risk in this way, deer must be able to perceive these habitats as risky, which requires may require them to remember past encounters with predators in the area (Kashetsky et al. 2021 ). While this approach is a common proxy for spatially-distributed risk, it is necessary to understand the limitations of this data for inferring how deer perceive and respond to the distribution of risk (Gaynor et al. 2019 ). Future work could validate these results using additional data. Direct sightings of the presence or absence of predators may be obtained from camera traps or camera collars. Other studies have used olfactory cues of the risk of predation to evaluate the response of prey to indirect cues of predation. Finally, using data on the distribution of kills may produce a more direct measure of the risk of death from these sources of mortality. Our results reinforce suggestions that studies related to large-carnivore systems may not provide adequate representation of all predator-prey systems (Peacor et al. 2022 ). Specifically, we suggest that inferences gained in one system may not be transferable to systems in which predators pose vastly different levels of risk and that it may not always be possible to make predictions based on the spatial ecology and hunting behavior of the predator. This has important implications for management actions, such as potential large-carnivore reintroductions. In many areas where top predators have been extirpated, efforts have been taken to return large carnivores and, even in locations where no such efforts have taken place, occasional sightings of transient large carnivores have become more common (Alston et al. 2019 , Olson et al. 2021 ). If reintroductions are a goal for managers, it is important to understand the baseline habitat selection and risk response of local prey to inform predictions about the impacts of the addition of large carnivores. Predictive models are frequently used to guide potential reintroductions using data on the spatial ecology of large carnivores and local conditions (Marucco and McIntire 2010 , Halsey et al. 2015 , Ovenden et al. 2019 ), however, our results suggest that, if large carnivores are reintroduced, the response of prey to current predators will have little relevance. Conclusion This study has implications for our understanding of the theory surrounding how prey respond to different risks and practical considerations related to how ungulate prey, such as deer, may respond to medium-sized carnivores. Theory suggests that the relationship between predators and prey is dependent on several factors associated with the predator and the prey (Preisser et al. 2007 , Smith et al. 2019 ). Our results provide evidence of how prey respond to two sources of variation in predation risk: hunting mode and lethality, specifically that prey proactively respond to more lethal risks rather than differentiate strictly based on hunting mode. This suggests that prey behavior in systems without large carnivores will differ fundamentally from prey behavior in large-carnivore systems, and that it is not straightforward to apply inferences from large-carnivore studies to these other systems (Peacor et al. 2022 ). We found that deer managed these risks through different types of behavioral responses and temporal variation in behavioral responses. Results showing that deer alternated between selection and avoidance of human modification daily reinforce many other studies that suggest that temporal avoidance of risk may be as great or great than spatial avoidance of risky habitats (Lone et al. 2017 ). This result also highlights how human modified areas may provide both costs and benefits to deer, leading to variation in selection for these areas and the potential for deer-human interactions. Finally, we observed behavioral state responses of deer to coyote and bobcats suggesting that deer may still respond, in some way, to risks that induce little adult mortality. Taken together, these results support the notion that studies related to predator-prey behavior and risk effects that only focus on large carnivore systems may overlook important aspects of predator-prey ecology (Montgomery et al. 2017, Peacor et al. 2022 ), and it may be necessary to apply greater focus to systems that do not contain large carnivores. Declarations Competing Interests The authors have no competing interests to report Funding Funding for this study was provided by the Illinois Department of Natural Resources Author Contribution M.E.E. conceived of the study, collected data, conducted analysis, and primarily drafted the manuscript. A.M.W. and N.G. collected data and assisted drafting the manuscript. M.W.E., D.S., and P.E.S. conceived of the study, helped plan and coordinate the work, developed methodology, and assisted drafting the manuscript. 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Supplementary Files eganbsssfappendixme.docx Cite Share Download PDF Status: Published Journal Publication published 12 Jul, 2025 Read the published version in Movement Ecology → Version 1 posted Editorial decision: Accepted 26 Jun, 2025 Reviews received at journal 07 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviewers agreed at journal 21 May, 2025 Reviewers invited by journal 25 Apr, 2025 Submission checks completed at journal 25 Apr, 2025 First submitted to journal 23 Apr, 2025 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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H1s states that deer choose antipredator behavior on the basis of the spatial distribution of the predators with different hunting modes. Here we predict deer spatially avoid bobcats and change behavioral state in response to coyotes. H2s states that deer choose between antipredator behavior on the basis of the spatial distribution of predators with differing lethality. Here we predict deer spatially avoid humans and change behavioral state in response to mesopredators. H1t states that, in addition to differentiating based on hunting mode, deer only respond when the predator is most lethal. H2t states that, in addition to differentiated based on lethality, deer only respond when the predator is most lethal.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5984114/v1/4d90c8f4d9a79ebdc4826e3e.jpeg"},{"id":81515283,"identity":"a7b5d0bc-8610-4e9b-9158-e1980fd826d7","added_by":"auto","created_at":"2025-04-28 07:00:09","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":456121,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the two study areas showing home ranges for tracked deer. Home range boundaries were defined based on a 95 % minimum convex polygon.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5984114/v1/682c8740ac9339a1084d38c0.jpeg"},{"id":81515280,"identity":"1669b16f-4321-4914-a094-99e9655689c6","added_by":"auto","created_at":"2025-04-28 07:00:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30780,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap showing Hidden Markov Model results for female white-tailed deer. Numbers are coefficients for the impact of the resource on the probability of transitioning between states. Green cells represent a case in which a resource increased the likelihood of transition between states. Red cells represent a case in which a resource decreased the likelihood of transition between states. White cells were values for which the confidence interval overlapped 0. Results are shown for the top model based on AICc. Results are divided between period: baseline (January – April), fawning (May – July), and hunting (October – December).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5984114/v1/c7f1a2019ebee534197c6487.png"},{"id":81515290,"identity":"806ca0ad-55b9-4d25-92f3-ce86ddd7a165","added_by":"auto","created_at":"2025-04-28 07:00:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30512,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap showing Hidden Markov Model results for male white-tailed deer. Numbers are coefficients for the impact of the resource on the probability of transitioning between states. Green cells represent a case in which a resource increased the likelihood of transition between states. Red cells represent a case in which a resource decreased the likelihood of transition between states. White cells were values for which the confidence interval overlapped 0. Results are shown for the top model based on AICc. Results are divided between period: baseline (January – April), fawning (May – July), and hunting (October – December).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5984114/v1/38b149a91c0bd01544ef16b5.png"},{"id":81516450,"identity":"657ac71d-c228-4724-9f5b-8d20ac92e466","added_by":"auto","created_at":"2025-04-28 07:16:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":16195,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of human modification on the relative probability of selection by female white-tailed deer. Values are coefficients for the impact of human modification on the relative probability of selecting a step for each state and season. Error bars show the 95% confidence interval for each coefficient. Results are shown for each state: encamped (short step length), active (medium step length), and directed (long step length) and for each time period: baseline (Jan-April), fawning (May-July), and hunting (October-December).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5984114/v1/33cf9588ff4da8efe3e97e51.png"},{"id":81515294,"identity":"5ce97584-e928-4c6f-8591-5e7f866ab3c2","added_by":"auto","created_at":"2025-04-28 07:00:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":17902,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of coyote on the relative probability of selection by female white-tailed deer. Values are coefficients for the impact of human modification on the relative probability of selecting a step for each state and season. Error bars show the 95 % confidence interval for each coefficient. Results are shown for each state: encamped (short step length), active (medium step length), and directed (long step length) and for each time period: baseline (Jan-April), fawning (May-July), and hunting (October-December).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-5984114/v1/d1ebc28db1d3dc7e613353ce.png"},{"id":81515797,"identity":"8f5267b8-8963-45f0-9f8a-aa8be55e8b19","added_by":"auto","created_at":"2025-04-28 07:08:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":15883,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of human modification on the relative probability of selection by male white-tailed deer. Values are coefficients for the impact of human modification on the relative probability of selecting a step for each state and season. Error bars show the 95 % confidence interval for each coefficient. Results are shown for each state: encamped (short step length), active (medium step length), and directed (long step length) and for each time period: baseline (Jan-April), fawning (May-July), and hunting (October-December).\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-5984114/v1/720ae0987bc3682f41fc30f7.png"},{"id":86700102,"identity":"0998e1d1-53ad-4243-bc6f-39fcc0d19e32","added_by":"auto","created_at":"2025-07-14 16:11:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1441444,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5984114/v1/e48997f8-ba9c-421d-8ff8-730f4485bb83.pdf"},{"id":81515775,"identity":"9e30e786-b624-4088-996c-73d3706252d5","added_by":"auto","created_at":"2025-04-28 07:08:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":187452,"visible":true,"origin":"","legend":"","description":"","filename":"eganbsssfappendixme.docx","url":"https://assets-eu.researchsquare.com/files/rs-5984114/v1/ff78227c5b51307ff8e6d297.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"When, where, and how prey pair antipredator behaviors to natural and anthropogenic mortality risks","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe behavioral responses of prey to predation risk can impact prey populations and the ecosystem at large (i.e. behaviorally-mediated trophic cascades) in ways that are as great or greater than the effects of direct mortality (Schmitz et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1997\u003c/span\u003e, Creel and Christianson \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Beschta and Ripple \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The indirect impacts of predation risk are the result of trade-offs between resource acquisition and safety that impact where and how an animal consumes resources (Brown \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1988\u003c/span\u003e, Kie \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, Fortin et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Gaynor et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, prey may reduce their risk of mortality by avoiding habitat types that predators are most likely to occupy, which might result in the use of suboptimal habitat and reduced foraging (Fortin et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Gaynor et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Alternatively, prey may not spatially avoid risky habitat, instead changing their behavioral state such that, if an encounter occurs, the prey may avoid a successful attack (Laundre et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Cherry et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These behavioral state changes include encamped states (reduced movement and increased vigilance) or directed states (increased movement) which may increase safety but reduce energy intake relative to active foraging. Due to the importance of these behavioral responses, many theoretical frameworks have been developed to understand and predict how prey make decisions about which antipredator behavior to use and balance antipredator behavior with other needs.\u003c/p\u003e \u003cp\u003eOne major theoretical consideration is whether prey respond through proactive or reactive antipredator behavior. The \u0026ldquo;risky places hypothesis\u0026rdquo; suggests that prey proactively avoid risky habitats regardless of time (Creel et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Alternatively, the \u0026ldquo;risky times hypothesis\u0026rdquo; predicts that prey reactively respond to the predators through strategies such as behavioral state changes or by only avoiding risky habitats when the risk is highest (Lima and Bednekoff \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, Lone et al. 2016). While these two hypotheses are not necessarily mutually exclusive, prey may respond differently to spatial or temporal variation in risk depending on ecological conditions. Proactive and reactive behaviors also target different parts of the predation process. Predation-related mortality involves a sequence of stages starting with an encounter between predator and prey and proceeding to a successful attack (Lima and Dill \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1990\u003c/span\u003e, Bateman et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Suraci et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Proactive spatial avoidance limits encounters altogether, while reactive behavioral state changes minimize the risk of being killed given an encounter.\u003c/p\u003e \u003cp\u003eThe most common framework for understanding these decisions is based on the hunting mode of the predator (\u0026ldquo;hunting mode hypothesis\u0026rdquo;). Spatial avoidance may be most effective for predators with ambush hunting styles (many felids), because it is feasible to avoid these predators. Alternatively, behavioral state changes may be most effective for active, cursorial predators (many canids), because, in this case, the probability of encounter is driven by the predator (Preisser et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Smith et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While this hypothesis has received support, many of the systems in which predator-prey behavior has been studied contain primarily large predators. Top predators have received the most focus because they likely have the largest ecological impacts (Ripple and Beschta \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Beschta and Ripple \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Ripple et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), but it is not clear how well inferences from large-predator systems can be transferred to other systems (Peacor et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In many systems, large predators have been extirpated leaving medium-sized predators as the de facto top predator (Gompper \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Prugh et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). While these medium-sized predators may have similar hunting-modes to comparable large-predators, they have substantially lower lethality (which we define here as the overall probability of a predator killing a prey) (Owen-Smith \u0026amp; Mills \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Cooper and Frederick \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In contrast to the hunting mode hypothesis, prey may not respond proactively to mesopredators, instead responding proactively to the most lethal predators, which here we will refer to as the \u0026ldquo;lethality hypothesis\u0026rdquo;. Therefore, prey behavior must be evaluated in systems without large predators to improve our general understanding of how prey decide to respond to different types of risk (Creel and Christianson \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Creel et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHere we focus on a prominent North American ungulate, white-tailed deer (\u003cem\u003eOdocoileus virginianus\u003c/em\u003e), in a system without resident populations of large carnivores. In this system, there are three sources of predation-related morality risk: coyotes (\u003cem\u003eCanis latrans\u003c/em\u003e), bobcats (\u003cem\u003eLynx rufus\u003c/em\u003e), and human \u0026lsquo;predators\u0026rsquo; who induce mortality through factors such as hunting and vehicle collisions (Bissonnette et al. 2008, Darimont et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These three risks can be plotted on two axes related to variation in their spatial ecology and potential lethality where coyotes are the most broadly distributed risk, followed by bobcats and humans, and humans are the most lethal risk followed by coyotes and bobcats (Messier et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1986\u003c/span\u003e, Gese et al. 1995, Chitwood et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Coyotes are cursorial hunters shown to induce increased vigilance in prey (Cherry et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Schuttler et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Gulsby et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), changes in space use (Lingle \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), or other antipredator behaviors (Lingle and Pellis \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Olson et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Bobcats are ambush predators also shown to kill adult deer, although infrequently (Labisky and Boulay \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, Kilgo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Humans represent a unique predation risk but are generally much more limited in terms of where and when they hunt in natural environments (Ciuti et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Conversely, while coyotes and bobcats are the \u003cem\u003ede facto\u003c/em\u003e largest predators and a common source of fawn mortality (Rohm et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Gese and Grothe \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1995\u003c/span\u003e, Patterson and Messier \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Moratz et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), humans are a much more common source of adult mortality (Hewit 2002).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe evaluated prey behavioral responses to different sources of mortality risk to better understand how prey make decisions about how to respond to predators. We tested for spatial responses using step selection functions (SSF) and tested for changes in behavioral state using hidden Markov models (HMM). We evaluated general support for two hypotheses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The hunting mode hypothesis (H1) suggest that prey match antipredator behavior with the hunting mode for which it is most effective, in which case we predict that deer will differ in their response habitat associated with coyotes and bobcats, using spatial avoidance for bobcats and behavioral state changes for coyotes. Alternatively, the lethality hypothesis (H2) states that prey prioritize spatially avoiding the most lethal risks. Following this hypothesis, we predict that deer will differ in their response to habitats associated with humans and natural mesopredators, using spatial avoidance for humans and behavioral state changes for mesopredators. For each of these hypotheses, we evaluate the relative support for the risky places hypothesis in which deer respond consistently to the spatial distribution of predators (H1S and H2S), the risky times hypothesis in which deer vary their response to these resources corresponding to the periods of highest risk (H1T and H2T), or that deer switch between strategies based on the time when risk is the highest (H1Switch and H2Swtich).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eField Methods\u003c/h2\u003e \u003cp\u003e We captured and collared white-tailed deer in accordance with standards of the American Society of Mammologists (Sikes et al. 2011) with methods approved by Southern Illinois University Institutional Animal Care and Use Committee (21\u0026ndash;028). We captured deer at two sites: primarily around Touch of Nature Outdoor Education Center (TON) near Carbondale, Illinois (37.627459 deg N, 89.153998 deg W) and land owned by the United States Army Corps of Engineers near Lake Shelbyville, Illinois (39.512753 deg N, 88.702546 deg W) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The TON site was comprised of a mixture of forest cover with some agricultural cover and substantial exurban development. The TON property allows hunting for only a few days a year, and recreation activities such as hiking and biking are common throughout the year. The Lake Shelbyville site was comprised primarily of agricultural cover with minimal urban development and hunting is allowed throughout the area during extended periods between October and January. Both sites have similar climates with moderate winters and hot, humid summers. Deer were captured from January \u0026ndash; March from 2020 to 2021 beginning after the end of deer hunting season and continuing until green up in each location. Additionally, we collared coyotes (n\u0026thinsp;=\u0026thinsp;31) and bobcats (n\u0026thinsp;=\u0026thinsp;18) at each site to estimate the relative occurrence probability of each species as an estimate of predation risk. Full details on capture methods can be found in Appendix S1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter data collection, we performed several data preparation procedures to remove potentially erroneous points and created regular trajectories for behavioral state and habitat analysis. We cleaned data based on two criteria. First, we removed any locations with a dilution of precision greater than 25. Second, we removed points for which the distance between points suggested that the deer was moving at a speed that was biologically impossible. We selected 3 km/h as the cutoff for movement speed for a time step based on visual analyses of the distribution of step lengths.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResource Covariates:\u003c/h3\u003e\n\u003cp\u003eWe obtained landcover classification data from the national landcover database (Dewitz \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). NLCD data divides the landscape into 30 x 30 m cells and classifies each into one of several landcover types. We estimated the proportion of each landcover type within a radius around each cell in the landscape. We reclassified landcover into four categories: forest, urban, agriculture, and other. For each variable, we estimated the proportion of each category within a 2 km\u003csup\u003e2\u003c/sup\u003e buffer surrounding each cell in the landscape. Due to correlation between landcover variables, we retained only one variable for HMM and SSF models by comparing the performance of each covariate in preliminary models. From these models, we retained the proportion of forest cover as the covariate \u0026lsquo;forest\u0026rsquo;.\u003c/p\u003e\n\u003ch3\u003ePredation Risk:\u003c/h3\u003e\n\u003cp\u003eWe characterized predation risk based on the relative probability of occurrence of coyotes and bobcats in the study area. For each species, we calculated the relative probability of use for each cell in the landscape by fitting resource selection functions relating resources at each location to the probability of use. Potential resource covariates for each species included human modification, landcover (classified as forest, urban, or agriculture), distance to water, and distance to road. Based on this model, we predicted the relative probability of predator occurrence throughout the landscape for both coyote and bobcat respectively. We refer to these variables as \u0026lsquo;coyote\u0026rsquo; and \u0026lsquo;bobcat\u0026rsquo; respectively and validated these models using 10-fold cross validation (Full details on model fitting can be found in Appendix 1). These layers represent the areas where coyote and bobcat presence is most likely as a function of resources as a proxy for the risk of predation.\u003c/p\u003e \u003cp\u003eWe characterized anthropogenic risks based on several covariates describing the influence of human presence within the landscape. The impact of roads was characterized by calculating the Euclidean distance between each 30 x 30 m cell on the landscape to the nearest road based on data from the US geological survey (USGS 2019), producing a 30 x 30 m resolution raster of the distance to the nearest road, referred to as \u0026lsquo;distance to road\u0026rsquo;. The degree of human modification was obtained from estimates of human modification taken from Kennedy et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e. This layer represents the relative degree of human modification, on a scale from 0 to 1, based on factors related to human settlement, agriculture, transportation, and energy infrastructure. This variable is referred to throughout as \u0026lsquo;human modification.\u0026rsquo; We determined the potential for hunting based on the spatial distribution of where hunting is allowed. Any landcover in urban areas, on private land, or on public land where hunting was allowed during hunting seasons were classified as the reference level 0. Lands where hunting was explicitly not allowed during any time of the year was classified as 1 producing a categorical variable describing relative safety from human hunters. Since class 1 represents the areas that are safe from hunting relative to hunting areas, we referred to this variable as \u0026lsquo;hunting refuge\u0026rsquo;, and a positive coefficient for this variable implies selection for refuges.\u003c/p\u003e\n\u003ch3\u003eAnalysis Overview:\u003c/h3\u003e\n\u003cp\u003eWe tested the effects of risk on white-tailed deer behavior using two methods. First, we tested the effect of risk variables on the probability of transitioning between behavioral states using Hidden Markov Models (HMMs). Second, we tested the effect of risk covariates on habitat selection using step-selection functions (SSFs). During this analysis, we tested the effect of risk on state-specific habitat selection based on SSFs of steps taken during each behavioral state. For each method, we ran candidate models representing each risk covariate and each combination of risk covariates.\u003c/p\u003e\n\u003ch3\u003eBehavioral States\u003c/h3\u003e\n\u003cp\u003eTo test the effect of risk on deer behavior, we modeled the effect of risk variables on the probability of transition between behavioral states using an HMM (Morales et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). HMMs require regularly sampled data, so, for a given animal, we took steps to produce trajectories sampled at regular intervals (30 minutes for females, 1 hour for males). First, if any animal had gaps in data longer than one day, we divided this record into separate trajectories (often referred to as bursts). Second, we replaced any missing locations with NAs. We modeled the behavioral state of animals based on the turning angle and step length of consecutive steps. Based on these movement parameters, states were defined based on a gamma distribution of the step lengths and von Mises distribution of the turning angles while simultaneously accounting for the probability of transitioning between states. These models may also include variables for the transition probabilities between states, allowing us to test the effect of spatially distributed resources on the behavior of an animal. We specified a priori that movement data be classified into three behavioral states because we aimed to capture functional differences between states. Specifically, we divided steps into an \u0026ldquo;encamped\u0026rdquo; state for little movement and associated potentially with resting or cover and a high degree of safety, an \u0026ldquo;active\u0026rdquo; state for moderate movement and associated potentially with a balance between foraging and safety, and a \u0026ldquo;directed\u0026rdquo; state for longer movements and associated potentially with lower safety (Franke et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). To delineate states for state-specific step selection models, we ran HMMs using the full dataset for females and males, respectively. To make inferences about the impact of resources on state transitions, we divided data between seasons to estimate season-specific models. The baseline period was defined as all points before the fawning season, January to April, the fawning period included locations from May to July, and the hunting period included all locations from October to December. In each model we included the landcover covariate forest and combinations of risk covariates based on a priori models. We compared the performance of models using AICc and determined the effect of each covariate on the probability of transition between each state based on coefficients of effect of the covariate. HMMs were fit in R using the package momentuHMM (McClintock and Michelot \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStep Selection:\u003c/h2\u003e \u003cp\u003eWe tested the effect of predation risk on white-tailed deer habitat selection using an SSF (Thurfjell et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). To ensure trajectories were regular, we divided telemetry data into bursts in the same manner as for behavioral states wherever we found missing locations. For each observed step, we generated 100 available steps based on a gamma and von Mises distribution for step length and turning angle, respectively. The mean and standard deviation for the gamma distribution and the concentration of the von Mises distribution was based on the values for observed steps. Additionally, these values were specific to the sex of the individual deer and the state during that step. Used and available steps were compared in a case-control design using a conditional logistic regression stratified by step following the methods of Muff et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Models also included step length as a covariate to reduce bias in parameter estimation and the distance to the nearest recursion point (defined by identifying clusters of points in space and time) to account for recursive movements within the home range (Egan et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For each risk covariate, we fit models of each individual risk variable, two variable models with each combination of risk variables, and interaction models that were identical to the two variable models but include an interaction between the two risk variables. Additionally, models included random slopes for the effect of each covariate on each individual. State-specific step selection was estimated by subsetting locations assigned to each behavioral state. For each behavioral state, we fit season-specific models by dividing data based on month to produce three periods. To test for differences in the response to sources of risk based on time of day, we included an interaction term between risks and time of day. We divided locations between three periods associated with behavioral patterns of deer. Day was defined as the period from the end of nautical sunrise to the beginning of nautical sunset, night was defined as the period from the end of nautical sunset to the beginning of nautical sunrise, and crepuscular was defined as the periods during sunrise or sunset. Times for sunrise and sunset were determined using the suncalc package in R (Thieurmel et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). All analysis were conducted using R (R version 4.4.1).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eWe tracked a total of 40 females and 29 males from January 2020\u0026ndash;December 2022, which yielded a total of 626,779 relocations after cleaning and pre-processing. Females were tracked for an average of 320 days and males an average of 270 days. During the study period, four deer were killed by hunters, one died from a vehicle collision, and no adult deer were killed by predators. Behavioral state analysis indicated that doe movement was best described by a three-state model with a low step length state (mean step length\u0026thinsp;=\u0026thinsp;11 m) we refer to as encamped movement, a moderate step length state (mean step length\u0026thinsp;=\u0026thinsp;37 m) we refer to as active movement, and a high step length state (mean step length\u0026thinsp;=\u0026thinsp;110 m) we refer to as directed movement. Male movement was described by a similar model with states: encamped (mean step length\u0026thinsp;=\u0026thinsp;23 m), active (mean step length\u0026thinsp;=\u0026thinsp;52 m), and directed (mean step length\u0026thinsp;=\u0026thinsp;138 m). HMMs indicated that behavioral states were distinguished by step length primarily. Turning angles differed little between states with each state showing angle distributions centered around 0 and concentrations close to 0.\u003c/p\u003e \u003cp\u003eBased on AICc scores (Full results of model ranking can be found in Appendix 2), the probability of state transitions was related to variation in the relative probability of coyote and bobcat occurrence, but these patterns differed between seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; numbers represent coefficients of effect). During the baseline period, females were more likely to change to the active state from the encamped state where the probability of bobcat occurrence was high compared to areas where the probability of bobcat occurrence was low (0.58; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) but were more likely to shift from directed movement to encamped in areas where occurrence of either predator was higher (2.10, 1.18; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Females were also less likely to leave the active movement state in areas where coyote (-1.51; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) or bobcat (-0.28, -0.66; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) occurrence was high compared to where it was low. In contrast, areas where coyote and bobcat occurrence were both high were associated with a reduced probability that females transition to or stay in the active state compared to areas where the probability of occurrence of both species was low (-0.067, 0.85, -0.92; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We observed similar patterns during the hunting season, but during fawning season, females changed behavioral state in response to areas where coyote occurrence was highest or human modification was highest. Specifically, during this period, where human modification was high, females were more likely to shift out of the directed state (2.12, 1.90, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and transitioned between encamped and active movement compared to low human modification (1.58, 2.13, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring the baseline period, the top model for males indicated that males altered their behavior in areas where coyote occurrence was high and in hunting refuges. Most notably, males were more likely to change from encamped to active in refuges compared to areas where hunting was legal (2.32; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), but less likely to shift from encamped to directed movement (-1.48, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, in all other seasons, male state transitions were most related to areas where coyote and bobcat were likely to occur.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTop SSF models indicated that females selected for habitat based on the degree of human modification and, in most states and seasons, the distribution of coyotes, but not bobcats (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Females exhibited different patterns of selection depending on the state, season, and time of day. During the baseline period, females selected for human modification during the day, but avoided human modification during the night and crepuscular period. During fawning, females did not avoid human modification to the same degree, generally only avoiding human modification at night (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). During hunting, females generally avoided human modification during nights and did not consistently select for human modification during the day. While coyotes were consistently present in the top model for females, females only avoided coyotes during the fawning season during directed movement (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Males more often avoided human modification (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), though this pattern differed depending on season, state, and time of day (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). While males did not respond to human modification during the encamped state, males strongly avoided human modification during the active state, especially during fawning and hunting season.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe used behavioral state and step-selection analyses to estimate seasonal, daily, and state-specific responses by deer to multiple sources of risk, and our results have implications for how prey make decisions about how to respond to predation risk. Deer responded to all sources of risk in some way but generally paired a given risk with a specific type of antipredator behavior. This result provides support for stage specificity in antipredator behavior as a mechanism for prey to make risk related trade-offs, however the nature of this stage-specificity differed from our predictions. Specifically, we found that deer proactively responded to the most lethal predators but were reactive in that they alternated between selection and avoidance of human modification at a daily scale (H2T). This result suggests that, while prey did not respond to predators based on hunting mode, they may balance foraging and risk by responding at risky times. Conversely, we found that deer responded reactively to less lethal risks through behavioral state changes, which is demonstrated by the fact that behavioral state changes were generally related to mesopredators but exhibited only slight temporal variation. While deer did not proactively respond to mesopredators, this result suggests that mesopredators may still have important impacts on deer ecology and behavior that are mediated through differences in the way deer move.\u003c/p\u003e \u003cp\u003ePrevious empirical evaluation of the relative importance of hunting mode and lethality has yielded inconsistent results. While prey behavioral responses have been found to differ for predators of different hunting modes in systems including arthropods (Kersch-Becker et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), amphibians (Luttbeg et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and large mammals (Moll et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), our study is one of few examples that compare predators in terms of both hunting mode and lethality (Say-Sallaz et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In these cases, prey often respond most strongly to the most lethal or dangerous predator, with hunting mode and spatial domain having only minor impacts. For example, Say-Sallaz et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Thaker et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) found that larger, more lethal predators induced the strongest impacts on prey reactive spatial responses and spatial distributions, respectively. In contrast to hunting mode predictions, Leblond et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that caribou prioritized avoiding cursorial but highly lethal wolves over opportunistic bears. Kohl et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) also found no differences in avoidance of highly lethal predators with differing hunting modes. In support of our results, they found that elk avoided both predators by altering their behavior according to the diel patterns of the predator. Assessment of the relative role of hunting mode on prey avoidance are limited because few studies compare the impact of different predators on multiple antipredator behaviors, and results vary between these studies (Montgomery et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Moll et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While it may seem self-evident that prey respond more to the most lethal predators, more studies should compare predators of differing lethality and multiple behavioral responses to determine the relative importance of predator attributes such as spatial domain and hunting mode.\u003c/p\u003e \u003cp\u003eTemporal variation in behavior was particularly important for explaining the spatial relationship between deer and human modification, because human modification can have multifaceted impacts on wildlife. In general, deer avoided human modified areas during the night and crepuscular period, but often selected these areas during the day. In our study system, human modification is related to agricultural and exurban areas that see increased human presence in the evening. These areas also contain valuable food resources (Oro et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Kennedy et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which may prompt deer to forage in these areas during the day but move away to avoid periods of elevated risk at night. This interpretation is further supported by the fact that deer, particularly females, selected for human modification most strongly from January to April when food was most scarce but avoided human modification most strongly during hunting season when humans pose the greatest risk. Alternatively, human modified areas may provide relative safety from other predators (Berger \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). For example, Ganz et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e found that temporal patterns of selection and avoidance of humans may be related toward use of humans as a shield from predation, but it is unclear whether this is the case in our system. Supporting this pattern, females selected for human modified areas more during fawning season relative to males, possibly because at this time they are likely to be accompanied by more vulnerable fawns. However, in contrast to this pattern we found limited spatial avoidance of mesopredators and non-significant interaction effects between human modification and mesopredators.\u003c/p\u003e \u003cp\u003eWhether behavioral responses are proactive or reactive has important implications for the population level consequences of predation risk, particularly in relation to two questions: are direct effects or risk effects larger and are direct effects and risk effects correlated (Creel and Christianson \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)? Our results suggest that prey proactively respond to more lethal predators suggesting that behavioral responses may scale with increasing direct mortality. While we did not find evidence that predator hunting mode was related to the type of behavioral response, hunting mode may still impact the effectiveness of these responses. For example, highly lethal predators with little mobility are likely to induce effective spatial avoidance, producing risk effects that trade-off with direct mortality. However, spatial avoidance may be less successful for mobile predators, resulting in risk and direct effects that are correlated. Additionally, our results suggest a potential mechanism for mesopredators to induce risk effects (Creel and Christianson \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Verdolin \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Creel et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Our results support other studies that have found deer alter their behavioral state in response to mesopredators (Cherry et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Schuttler et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Gulsby et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), potentially leading to reduced time spent foraging. Furthermore, negative interaction effects suggest the possibility of interference between these predators (Fedriani et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Wilson et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). While agnostic interactions between carnivores have been noted to produce emergent spatial patterns (Sih et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), few studies have detected emergent impacts on behavioral state. Even if these behaviors are not ubiquitous in the population, intraspecific variation in prey behavior may have population-level impacts. For example, younger more vulnerable prey may sacrifice current year foraging and reproduction for potential future reproduction (Clark \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1994\u003c/span\u003e, Verdolin \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Wirsing et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, to validate theoretical predictions about the trade-offs between risk effects and direct mortality in cases where direct mortality is low, it will be necessary to empirically evaluate the relationship between behavioral responses to medium-sized predators and prey fitness (Peacor et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile we cannot confirm the functional role of each behavioral state without direct observations of deer behavior, the relationship between these states and risk type indicates their potential value. Many other studies have noted decreased movement rates in response to predation risk, as movement may be inherently risky (Little et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Picardi et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Alternatively, predators may also trigger evasive movements resulting in sudden changes to fast, directed states (Stankowich and Coss \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In general, predators were associated with a decrease in the probability of transition to faster movement states, suggesting that reduced movement offered antipredator benefits. Despite this, we only noted spatial avoidance of predators by females during directed movement, suggesting that this state may help facilitate movement away from risky habitat types, while reductions in movement are associated with antipredator defense. State-specific habitat selection by males supports this, in that males avoided humans using active and directed movement, but not encamped movement. Finally, we found that deer habitat selection differed from day to night to the greatest degree in the active and encamped states. This may suggest that slower movement states are associated both with foraging and predator defense and allow deer to balance these goals by foraging at a slower rate when in potentially risky environments.\u003c/p\u003e \u003cp\u003eAdditionally, we advise caution when comparing our results to other studies, particularly those using data on the observed presence/absence of predators. Here, we use an RSF to model the probability of predator occurrence based on resources. An assumption of this approach is that these models accurately characterize the distribution of predators and that deer perceive these areas as risky. To respond to predation risk in this way, deer must be able to perceive these habitats as risky, which requires may require them to remember past encounters with predators in the area (Kashetsky et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While this approach is a common proxy for spatially-distributed risk, it is necessary to understand the limitations of this data for inferring how deer perceive and respond to the distribution of risk (Gaynor et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Future work could validate these results using additional data. Direct sightings of the presence or absence of predators may be obtained from camera traps or camera collars. Other studies have used olfactory cues of the risk of predation to evaluate the response of prey to indirect cues of predation. Finally, using data on the distribution of kills may produce a more direct measure of the risk of death from these sources of mortality.\u003c/p\u003e \u003cp\u003eOur results reinforce suggestions that studies related to large-carnivore systems may not provide adequate representation of all predator-prey systems (Peacor et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Specifically, we suggest that inferences gained in one system may not be transferable to systems in which predators pose vastly different levels of risk and that it may not always be possible to make predictions based on the spatial ecology and hunting behavior of the predator. This has important implications for management actions, such as potential large-carnivore reintroductions. In many areas where top predators have been extirpated, efforts have been taken to return large carnivores and, even in locations where no such efforts have taken place, occasional sightings of transient large carnivores have become more common (Alston et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Olson et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). If reintroductions are a goal for managers, it is important to understand the baseline habitat selection and risk response of local prey to inform predictions about the impacts of the addition of large carnivores. Predictive models are frequently used to guide potential reintroductions using data on the spatial ecology of large carnivores and local conditions (Marucco and McIntire \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Halsey et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Ovenden et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), however, our results suggest that, if large carnivores are reintroduced, the response of prey to current predators will have little relevance.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study has implications for our understanding of the theory surrounding how prey respond to different risks and practical considerations related to how ungulate prey, such as deer, may respond to medium-sized carnivores. Theory suggests that the relationship between predators and prey is dependent on several factors associated with the predator and the prey (Preisser et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Smith et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our results provide evidence of how prey respond to two sources of variation in predation risk: hunting mode and lethality, specifically that prey proactively respond to more lethal risks rather than differentiate strictly based on hunting mode. This suggests that prey behavior in systems without large carnivores will differ fundamentally from prey behavior in large-carnivore systems, and that it is not straightforward to apply inferences from large-carnivore studies to these other systems (Peacor et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe found that deer managed these risks through different types of behavioral responses and temporal variation in behavioral responses. Results showing that deer alternated between selection and avoidance of human modification daily reinforce many other studies that suggest that temporal avoidance of risk may be as great or great than spatial avoidance of risky habitats (Lone et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This result also highlights how human modified areas may provide both costs and benefits to deer, leading to variation in selection for these areas and the potential for deer-human interactions. Finally, we observed behavioral state responses of deer to coyote and bobcats suggesting that deer may still respond, in some way, to risks that induce little adult mortality. Taken together, these results support the notion that studies related to predator-prey behavior and risk effects that only focus on large carnivore systems may overlook important aspects of predator-prey ecology (Montgomery et al. 2017, Peacor et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and it may be necessary to apply greater focus to systems that do not contain large carnivores.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have no competing interests to report\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eFunding for this study was provided by the Illinois Department of Natural Resources\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.E.E. conceived of the study, collected data, conducted analysis, and primarily drafted the manuscript. A.M.W. and N.G. collected data and assisted drafting the manuscript. M.W.E., D.S., and P.E.S. conceived of the study, helped plan and coordinate the work, developed methodology, and assisted drafting the manuscript. G.B.R. conceived of the study, planned and coordinated work, developed methodology, assisted with data analysis, and assisted drafting the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available in a figshare repository upon acceptance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlston JM, Maitland BM, Brito BT, Esmaeili S, Ford AT, Hays B, Jesmer BR, Molina FJ, Goheen JR. (2019). Reciprocity in restoration ecology: When might large carnivore reintroduction restore ecosystems? 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Ecol Lett. 2021;24(1):113\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ele.13614\u003c/span\u003e\u003cspan address=\"10.1111/ele.13614\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"movement-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"move","sideBox":"Learn more about [Movement Ecology](http://movementecologyjournal.biomedcentral.com/)","snPcode":"40462","submissionUrl":"https://submission.nature.com/new-submission/40462/3","title":"Movement Ecology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5984114/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5984114/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBehavioral responses of prey to predation risk have ecological impacts that can be as great as direct mortality. Risk response involves either behavioral changes or spatial avoidance, but it is not clear how prey decide between these strategies. Theory often suggests that prey pair responses to risks based on the hunting mode of the prey (hunting mode hypothesis), but prey may ignore hunting mode to prioritize responding to the most lethal predators (lethality hypothesis). Furthermore, prey may respond to the spatial distribution of these risks (risky places hypothesis) or respond only during the periods of highest risk (risky times hypothesis).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test these hypotheses, we evaluated the behavioral responses of white-tailed deer (Odocoileus virginianus) to risks from two natural mesopredators and human sources of mortality. Specifically, we determined, for each source of risk, whether deer responded with behavioral state changes or spatial avoidance and whether this behavior changed with time (diurnally and annually). We collared and tracked 40 female and 29 male deer. To determine the response of deer to risk, we collected data on the distribution of coyotes (Canis latrans), bobcats (Lynx rufus), human modification, hunters, and roads. We used hidden Markov models (HMM) to determine whether each covariate impacted the probability of transitioning between behavioral states and selection functions (SSF) to determine whether deer spatially avoided each covariate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenerally, deer changed behavioral state in response to both mesopredators but avoided human modification. In response to mesopredators, deer consistently shifted to slower movement behavioral states. Spatial responses to human modification varied depending on the time of day. During daylight hours, deer selected for human modification, but during the crepuscular and nighttime period, deer avoided human modification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpace use was most strongly related to more lethal humans, providing support for the lethality hypothesis. Despite prioritizing humans, mesopredators impacted behavioral state, suggesting that mesopredators still have important impacts on prey behavior. Finally, temporal patterns of avoidance align with other studies that indicate avoidance of predators is time-dependent, but further highlight the complex push-pull relationship of human modified areas on wildlife.\u003c/p\u003e","manuscriptTitle":"When, where, and how prey pair antipredator behaviors to natural and anthropogenic mortality risks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 07:00:04","doi":"10.21203/rs.3.rs-5984114/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-06-26T06:29:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-07T05:21:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224558282210578119768046686455932050158","date":"2025-06-02T17:55:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122509072429733665706753510157200410217","date":"2025-05-21T05:51:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-25T13:20:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-25T11:51:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Movement Ecology","date":"2025-04-23T14:20:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"movement-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"move","sideBox":"Learn more about [Movement Ecology](http://movementecologyjournal.biomedcentral.com/)","snPcode":"40462","submissionUrl":"https://submission.nature.com/new-submission/40462/3","title":"Movement Ecology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7d0ff5ac-17ed-415b-a389-4325449ce79c","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-14T16:09:50+00:00","versionOfRecord":{"articleIdentity":"rs-5984114","link":"https://doi.org/10.1186/s40462-025-00576-z","journal":{"identity":"movement-ecology","isVorOnly":false,"title":"Movement Ecology"},"publishedOn":"2025-07-12 15:58:01","publishedOnDateReadable":"July 12th, 2025"},"versionCreatedAt":"2025-04-28 07:00:04","video":"","vorDoi":"10.1186/s40462-025-00576-z","vorDoiUrl":"https://doi.org/10.1186/s40462-025-00576-z","workflowStages":[]},"version":"v1","identity":"rs-5984114","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5984114","identity":"rs-5984114","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}