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

When, where, and how prey pair antipredator behaviors to natural and anthropogenic mortality risks | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 January 2025 V1 Latest version Share on When, where, and how prey pair antipredator behaviors to natural and anthropogenic mortality risks Authors : Michael Egan 0000-0002-9952-5602 [email protected] , Abigail Weber , Nicole Gorman , Michael Eichholz , Daniel Skinner , Peter Schlichting , and Guillaume Bastille-Rousseau 0000-0001-6799-639X Authors Info & Affiliations https://doi.org/10.22541/au.173640749.96014838/v1 Published Movement Ecology Version of record Peer review timeline 342 views 271 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Behavioral responses of prey to predation risk have ecological impacts that can be as great as the direct mortality. Theory often suggests that prey pair responses to risks based on the hunting mode of the predator (hunting mode hypothesis), but prey may ignore hunting mode to prioritize responding to the most lethal predators (lethality hypothesis). 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.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. 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. 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 predators, 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 risk, 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 the distribution of predators regardless of their immediate presence (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 predators during times when predation risk is highest (Lima and Bednekoff 1999, Lone et al. 2016, Creel et al. 2019). 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 only mesopredators, medium-sized predators (Gompper 2002, Prugh et al. 2009). While mesopredators 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 2009, Périquet et al. 2012). 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, predator-prey relationships must be evaluated in systems without large predators to determine the risk posed by mesopredators and improve our general understanding of how prey decide to respond to predators (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, deer experience 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) (Figure 1). Coyotes are cursorial hunters shown to induce increased vigilance in prey (Cherry et al. 2015, Schuttler et al. 2017, Gulsby et al. 2018), 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. 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 (Figure 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 to 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 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), and the risky times hypothesis in which deer vary their response to these resources corresponding to the periods of highest risk (H1T and H2T). 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). 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’. Predatio n 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. Ultimately, we produced two raster layers, one each for coyote and bobcat predation risk at a 30 x 30 m resolution and 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). 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, 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. 2005). 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. 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 random steps based on a gamma and von Mises distribution for step length and turning angle, respectively. 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. (2019). 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. 2024). 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. 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 (Figure 2; 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; Figure 2) but were more likely to shift from directed movement to encamped in areas where occurrence of either predator was higher (2.10, 1.18; Figure 2). Females were also less likely to leave the active movement state in areas where coyote (-1.51; Figure 2) or bobcat (-0.28, -0.66; Figure 2) 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; Figure 2). 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, Figure 2) and transitioned between encamped and active movement compared to low human modification (1.58, 2.13, Figure 2). 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; Figure 3), but less likely to shift from encamped to directed movement (-1.48, Figure 3). 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 (Figure 4). 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 (Figure 4). 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 (Figure 5). Males more often avoided human modification (Figure 6), though this pattern differed depending on season, state, and time of day (Figure 6). 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. 2019), 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). In line with other studies (Cherry et al. 2015, Schuttler et al. 2017, Gulsby et al. 2018), we found that deer altered their behavior in response to medium-sized carnivores, potentially leading to reduced time spent foraging. Furthermore, negative interaction effects between mesocarnivores 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 sources of risk provides indication of 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 areas, while reductions in movement are associated with antipredator defense while in risky areas. State-specific habitat selection by males supports this, in that males avoid 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. Our results reinforce other studies suggesting 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. Figures: Figure 1: Representation of the predictions of each hypothesis. 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. Figure 2: Heatmap 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). Figure 3: Heatmap 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). Figure 4: Effect 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). Figure 5: Effect 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). Figure 6: Effect 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). References Alston, J.M., Maitland, B.M., Brito, B.T., Esmaeili, S., Ford, A.T., Hays, B., Jesmer, B.R., Molina, F.J., & Goheen, J.R. (2019). Reciprocity in restoration ecology: When might large carnivore reintroduction restore ecosystems? Biological Conservation, 234, 82–89. https://doi.org/10.1016/j.biocon.2019.03.021 Bateman, A.W., Vos, M., & Anholt, B.R. (2014). When to Defend: Antipredator Defenses and the Predation Sequence. The American Naturalist, 183(6), 847–855. https://doi.org/10.1086/675903 Berger, K.M. (2006). Carnivore‐Livestock Conflicts: Effects of Subsidized Predator Control and Economic Correlates on the Sheep Industry. Conservation Biology, 20(3), 751–761. https://doi.org/10.1111/j.1523-1739.2006.00336.x Beschta, R.L., & Ripple, W.J. (2009). Large predators and trophic cascades in terrestrial ecosystems of the western United States. Biological Conservation, 142(11), 2401–2414. https://doi.org/10.1016/j.biocon.2009.06.015 Bissonette, J.A., Kassar, C.A., & Cook, L.J. (2008). Assessment of costs associated with deer–vehicle collisions: Human death and injury, vehicle damage, and deer loss. Brown, J.S. (1988). Patch use as an indicator of habitat preference, predation risk, and competition. Behavioral Ecology and Sociobiology, 22(1), 37–47. https://doi.org/10.1007/BF00395696 Chitwood, M.C., Lashley, M.A., Moorman, C.E., & DePerno, C.S. (2014). Confirmation of Coyote Predation on Adult Female White-Tailed Deer in the Southeastern United States. Southeastern Naturalist, 13(3), N30–N32. https://doi.org/10.1656/058.013.0316 Cherry, M.J., Conner, L.M., & Warren, R.J. (2015). Effects of predation risk and group dynamics on white tailed deer foraging behavior in a longleaf pine savanna. Behavioral Ecology, 26(4), 1091–1099. https://doi.org/10.1093/beheco/arv054 Clark, C.W. (1994). Antipredator behavior and the asset-protection principle. Behavioral Ecology, 5(2), 159–170. https://doi.org/10.1093/beheco/5.2.159 Cooper, W.E., & Frederick, W.G. (2010). Predator lethality, optimal escape behavior, and autotomy. Behavioral Ecology, 21(1), 91–96. https://doi.org/10.1093/beheco/arp151 Creel, S., Becker, M., Dröge, E., M’soka, J., Matandiko, W., Rosenblatt, E., Mweetwa, T., Mwape, H., Vinks, M., Goodheart, B., Merkle, J., Mukula, T., Smit, D., Sanguinetti, C., Dart, C., Christianson, D., & Schuette, P. (2019). What explains variation in the strength of behavioral responses to predation risk? A standardized test with large carnivore and ungulate guilds in three ecosystems. Biological Conservation, 232, 164–172. https://doi.org/10.1016/j.biocon.2019.02.012 Creel, S., & Christianson, D. (2008). Relationships between direct predation and risk effects. Trends in Ecology & Evolution, 23(4), 194–201. https://doi.org/10.1016/j.tree.2007.12.004 Creel, S., Dröge, E., M’soka, J., Smit, D., Becker, M., Christianson, D., & Schuette, P. (2017). The relationship between direct predation and antipredator responses: A test with multiple predators and multiple prey. Ecology, 98(8), 2081–2092. https://doi.org/10.1002/ecy.1885 Creel, S., Schuette, P., & Christianson, D. (2014). Effects of predation risk on group size, vigilance, and foraging behavior in an African ungulate community. Behavioral Ecology, 25(4), 773–784. https://doi.org/10.1093/beheco/aru050 Darimont, C.T., Fox, C.H., Bryan, H.M., & Reimchen, T.E. (2015). The unique ecology of human predators. Science, 349(6250), 858–860. https://doi.org/10.1126/science.aac4249 Dewitz, J. 2021. National Land Cover Database (NLCD) 2019 Products (ver. 3.0, February 2024) [Data set]. U.S. Geological Survey. https://doi.org/10.5066/P9KZCM54 Egan, M.E., Gorman, S., Eichholz, M.W., Skinner, D., Schlichting, P.E., & Bastille‐Rousseau, G. (2024). Accounting for spatiotemporal patterns of long-term recursion in estimating local-scale step selection. Methods in Ecology and Evolution. In Review. Fedriani, J.M., Fuller, T.K., Sauvajot, R.M., & York, E.C. (2000). Competition and intraguild predation among three sympatric carnivores. Oecologia, 125(2), 258–270. https://doi.org/10.1007/s004420000448 Fortin, D., Beyer, H.L., Boyce, M.S., Smith, D.W., Duchesne, T., & Mao, J.S. (2005). Wolves influence elk movements: behavior shapes a trophic cascade in Yellowstone National Park. Ecology, 86(5), 1320–1330. https://doi.org/10.1890/04-0953 Franke, A., Caelli, T., & Hudson, R.J. (2004). Analysis of movements and behavior of caribou (Rangifer tarandus) using hidden Markov models. Ecological Modelling, 173(2–3), 259–270. https://doi.org/10.1016/j.ecolmodel.2003.06.004 Gaynor, K.M., Brown, J.S., Middleton, A.D., Power, M.E., & Brashares, J.S. (2019). Landscapes of Fear: Spatial Patterns of Risk Perception and Response. Trends in Ecology & Evolution, 34(4), 355–368. https://doi.org/10.1016/j.tree.2019.01.004 Ganz, T, DeVivo, M., Wirsing, A., Bassing, S., Kertson, B., Walker, S., & L. Prugh. (2024). Cougars, wolves, and humans drive a dynamic landscape of fear for elk. Ecology 105: e4255. Gese, E.M., & Grothe, S. (1995). Analysis of Coyote Predation on Deer and Elk during Winter in Yellowstone National Park, Wyoming. American Midland Naturalist, 133(1), 36. https://doi.org/10.2307/2426345 Gompper, M.E. (2002). Top Carnivores in the Suburbs? Ecological and Conservation Issues Raised by Colonization of North eastern North America by Coyotes. BioScience, 52(2), 185. https://doi.org /10.1641/0006-3568(2002)052[0185:TCITSE]2.0.CO;2 Gulsby, W.D., Killmaster, C.H., Bowers, J.W., Kelly, J.D., Sacks, B.N., Statham, M.J., & Miller, K.V. (2015). White-Tailed Deer Fawn Recruitment Before and After Experimental Coyote Removals in Central Georgia. Wildlife Society Bulletin, 39(2), 248–255. Halsey, S.M., Zielinski, W.J., & Scheller, R.M. (2015). Modeling predator habitat to enhance reintroduction planning. Landscape Ecology, 30(7), 1257–1271. https://doi.org/10.1007/s10980-015-0177-5 Hewitt, D.G. (Ed.). (2011b). Biology and management of white-tailed deer. CRC Press. Kennedy, C.M., Oakleaf, J.R., Theobald, D.M., Baruch‐Mordo, S., & Kiesecker, J. (2019). Managing the middle: A shift in conservation priorities based on the global human modification gradient. Global Change Biology, 25(3), 811–826. https://doi.org/10.1111/gcb.14549 Kersch-Becker, M.F., Grisolia, B.B., Campos, M.J.O., & Romero, G.Q. (2018). The role of spider hunting mode on the strength of spider–plant mutualisms. Oecologia, 188(1), 213–222. https://doi.org/10.1007/s00442-018-4170-y Kie, J.G. (1999). Optimal Foraging and Risk of Predation: Effects on Behavior and Social Structure in Ungulates. Journal of Mammalogy, 80(4), 1114–1129. https://doi.org/10.2307/1383163 Kilgo, J.C., Ray, H.S., Vukovich, M., Goode, M.J., & Ruth, C. (2012). Predation by Coyotes on White-Tailed Deer Neonates in South Carolina. The Journal of Wildlife Management, 76(7), 1420–1430. Kohl, M.T., Ruth, T.K., Metz, M.C., Stahler, D.R., Smith, D.W., White, P.J., & MacNulty, D.R. (2019). Do prey select for vacant hunting domains to minimize a multi‐predator threat? Ecology Letters, 22(11),1724–1733. https://doi.org/10.1111/ele.13319 Labisky, R.F., & Boulay, M.C. 1998. Behaviors of bobcats preying on white-tailed deer in the Everglades. The American Midland Naturalist, 139(2), 275-281. Laundre, J.W., Hernandez, L., & Ripple, W.J. (2010). The Landscape of Fear: Ecological Implications of Being Afraid.The Open Ecology Journal, 3(3), 1–7. https://doi.org/10.2174/1874213001003030001 Leblond, M., Dussault, C., Ouellet, J., & St‐Laurent, M. (2016). Caribou avoiding wolves face increased predation by bears. Journal of Applied Ecology, 53(4), 1078–1087. https://doi.org/10.1111/1365-2664.12658 Lima, S.L., & Bednekoff, P.A. (1999). Temporal Variation in Danger Drives Antipredator Behavior: The Predation Risk Allocation Hypothesis. The American Naturalist, 153(6), 649–659. https://doi.org/10.1086/303202 Lima, S.L., & Dill, L.M. (1990). Behavioral decisions made under the risk of predation: A review and prospectus. Canadian Journal of Zoology, 68(4), 619–640. https://doi.org/10.1139/z90-092 Lingle, S. (2002). Coyote predation and habitat segregation of white-tailed deer and mule deer. Ecology, 83(7), 2037–2048. https://doi.org/10.1890/0012-9658(2002)083[2037:CPAHSO]2.0.CO;2 Lingle, S., & Pellis, S. (2002). Fight or flight? Antipredator behavior and the escalation of coyote encounters with deer. Oecologia, 131(1), 154–164. https://doi.org/10.1007/s00442-001-0858-4 Little, A.R., Webb, S. L., Demarais, S., Gee, K.L., Riffell, S.K., & Gaskamp, J.A. (2016). Hunting intensity alters movement behaviour of white-tailed deer. Basic and Applied Ecology, 17(4), 360–369.https://doi.org/10.1016/j.baae.2015.12.003 Lone, K., Mysterud, A., Gobakken, T., Odden, J., Linnell, J., & Loe, L.E. (2017). Temporal variation in habitat selection breaks the catch‐22 of spatially contrasting predation risk from multiple predators. Oikos, 126(5), 624–632. https://doi.org/10.1111/oik.03486 Luttbeg, B., Hammond, J.I., Brodin, T., & Sih, A. (2020). Predator hunting modes and predator–prey spacegames. Ethology, 126(4), 476–485. https://doi.org/10.1111/eth.12998 Marucco, F. & McIntire, E.J.B. (2010). Predicting spatio‐temporal recolonization of large carnivore populations and livestock depredation risk: wolves in the Italian Alps. Journal of Applied Ecology, 47(4), 789-798. McClintock, B.T., & Michelot, T. (2018). momentuHMM: R package for generalized hidden Markov models of animal movement. Methods in Ecology and Evolution, 9(6), 1518-1530. Messier, F., Barrette, C., & Huot, J. (1986). Coyote predation on a white-tailed deer population in southern Quebec. Canadian Journal of Zoology, 64(5), 1134–1136. https://doi.org/10.1139/z86-170 Moll, R.J., Redilla, K.M., Mudumba, T., Muneza, A.B., Gray, S.M., Abade, L., Hayward, M.W., Millspaugh, J.J., & Montgomery, R.A. (2017). The many faces of fear: A synthesis of the methodological variation in characterizing predation risk. Journal of Animal Ecology, 86(4), 749–765. https://doi.org/10.1111/1365-2656.12680 Montgomery, R.A., Moll, R.J., Say-Sallaz, E., Valeix, M., & Prugh, L.R. (2019). A tendency to simplify complex systems. Biological Conservation, 233, 1–11. https://doi.org/10.1016/j.biocon.2019.02.001 Morales, J.M., Haydon, D.T., Frair, J., Holsinger, K.E., & Fryxell, J.M. (2004). Extracting more out of relocation data: building movement models as mixtures of random walks. Ecology, 85(9), 2436–2445. https://doi.org/10.1890/03-0269 Moratz, K.L., Gullikson, B.S., Michel, E.S., Jenks, J.A., Grove, D.M., & Jensen, W.F. (2018). Assessing factors affecting adult female white-tailed deer survival in the Northern Great Plains. Wildlife Research, 45(8), 679. https://doi.org/10.1071/WR18032 Muff, S., Signer, J., & Fieberg, J. 2020. Accounting for individual‐specific variation in habitat‐selection studies: Efficient estimation of mixed‐effects models using Bayesian or frequentist computation. Journal of Animal Ecology, 89(1), 80-92. Olson, E.R., Goethlich, J., & Goudos‐Weisbecker, B. (2021). Attitudes Towards a Transient Carnivore Prior to Recolonization. Wildlife Society Bulletin, 45(2), 191–201. https://doi.org/10.1002/wsb.1166 Olson, E.R., Van Deelen, T.R., & Ventura, S.J. (2019). Variation in anti-predator behaviors of white-tailed deer (Odocoileus virginianus) in a multi-predator system. Canadian Journal of Zoology, 97(11), 1030–1041. https://doi.org/10.1139/cjz-2018-0254 Oro, D., Genovart, M., Tavecchia, G., Fowler, M.S., & Martínez‐Abraín, A. (2013). Ecological and evolutionary implications of food subsidies from humans. Ecology Letters, 16(12), 1501–1514. https://doi.org/10.1111/ele.12187 Ovenden, T.S., Palmer, S.C.F., Travis, J.M.J., & Healey, J.R. (2019). Improving reintroduction success in large carnivores through individual-based modelling: How to reintroduce Eurasian lynx (Lynx lynx) to Scotland. Biological Conservation, 234, 140–153. https://doi.org/10.1016/j.biocon.2019.03.035 Owen‐Smith, N., & Mills, M.G.L. (2008). Predator–prey size relationships in an African large‐mammal food web. Journal of Animal Ecology, 77(1), 173–183. https://doi.org/10.1111/j.1365-2656.2007.01314.x Patterson, B.R., & Messier, F. (2000). Factors Influencing Killing Rates of White-Tailed Deer by Coyotes in Eastern Canada. The Journal of Wildlife Management, 64(3), 721. https://doi.org/10.2307/3802742 Picardi, S., Basille, M., Peters, W., Ponciano, J.M., Boitani, L., & Cagnacci, F. (2019). Movement responses of roe deer to hunting risk. The Journal of Wildlife Management, 83(1), 43–51. https://doi.org/10.1002/jwmg.21576 Périquet, S., Todd-Jones, L., Valeix, M., Stapelkamp, B., Elliot, N., Wijers, M., Pays, O., Fortin, D., Madzikanda, H., Fritz, H., Macdonald, D.W., & Loveridge, A.J. (2012). Influence of immediate predation risk by lions on the vigilance of prey of different body size. Behavioral Ecology, 23(5), 970–976. https://doi.org/10.1093/beheco/ars060 Peacor, S.D., Dorn, N.J., Smith, J.A., Peckham, N.E., Cherry, M.J., Sheriff, M.J., & Kimbro, D.L. (2022). A skewed literature: Few studies evaluate the contribution of predation‐risk effects to natural field patterns. Ecology Letters , 25 (9), 2048-2061. Preisser, E.L., Orrock, J.L., & Schmitz, O.J. (2007). Predator hunting mode and habitat domain alter effects in predator-prey interactions. Ecology, 88(11), 2744–2751. https://doi.org/10.1890/07-0260.1 Prugh, L.R., Stoner, C.J., Epps, C.W., Bean, W.T., Ripple, W.J., Laliberte, A.S., & Brashares, J.S. (2009). The Rise of the Mesopredator. BioScience, 59(9), 779–791. https://doi.org/10.1525/bio.2009.59.9.9 R Core Team (2024). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. . Ripple, W.J., & Beschta, R.L. (2004). Wolves and the Ecology of Fear: Can Predation Risk Structure Ecosystems? BioScience, 54(8), 755. https://doi.org/10.1641/0006-3568(2004)054[0755:WATEOF]2.0.CO;2 Ripple, W. J., Estes, J. A., Beschta, R. L., Wilmers, C. C., Ritchie, E. G., Hebblewhite, M., … & Wirsing, A. J. 2014. Status and ecological effects of the world’s largest carnivores. Science, 343(6167), 1241484. Rohm, J.H., Nielsen, C.K., & Woolf, A. (2007). Survival of White‐Tailed Deer Fawns in Southern Illinois. The Journal of Wildlife Management, 71(3), 851–860. https://doi.org/10.2193/2006-027 Say-Sallaz, E., Chamaillé-Jammes, S., Périquet, S., Loveridge, A.J., Macdonald, D.W., Antonio, A., Fritz, H., & Valeix, M. (2023). Large carnivore dangerousness affects the reactive spatial response of prey. Animal Behaviour, 202, 149–162. https://doi.org/10.1016/j.anbehav.2023.05.014 Schmidt, K., & Kuijper, D.P.J. (2015). A “death trap” in the landscape of fear. Mammal Research, 60(4), 275–284. https://doi.org/10.1007/s13364-015-0229-x Schmitz, O.J., Beckerman, A.P., & O’Brien, K.M. (1997). Behaviorally mediated trophic cascades: effects of predation risk on food web interactions. Ecology, 78(5), 1388–1399. https://doi.org/10.1890/0012-9658(1997)078[1388:BMTCEO]2.0.CO;2 Schmitz, O.J., Hambäck, P.A., & Beckerman, A.P. (2000). Trophic Cascades in Terrestrial Systems: A Review of the Effects of Carnivore Removals on Plants. Schuttler, S.G., Parsons, A.W., Forrester, T.D., Baker, M.C., McShea, W.J., Costello, R., & Kays, R. (2017). Deer on the lookout: How hunting, hiking and coyotes affect white‐tailed deer vigilance. Journal of Zoology, 301(4), 320–327. https://doi.org/10.1111/jzo.12416 Sih, A., Englund, G., & Wooster, D. (1998). Emergent impacts of multiple predators on prey. Trends in Ecology & Evolution, 13(9), 350–355. https://doi.org/10.1016/S0169-5347(98)01437-2 Sikes, R.S., & Gannon, W.L. (2011). Guidelines of the American Society of Mammalogists for the use of wild mammals in research. Journal of Mammalogy, 92(1), 235–253. https://doi.org/10.1644/10-MAMM-F-355.1 Smith, J.A., Donadio, E., Pauli, J.N., Sheriff, M.J., Bidder, O.R., & Middleton, A.D. (2019). Habitat complexity mediates the predator–prey space race. Ecology, 100(7), e02724. https://doi.org/10.1002/ecy.2724 Stankowich, T., & Coss, R.G. (2006). Effects of predator behavior and proximity on risk assessment by Columbian black-tailed deer. Behavioral Ecology, 17(2), 246–254. https://doi.org/10.1093/beheco/arj020 Suraci, J.P., Smith, J.A., Chamaillé‐Jammes, S., Gaynor, K.M., Jones, M., Luttbeg, B., Ritchie, E.G., Sheriff, M.J., & Sih, A. (2022). Beyond spatial overlap: Harnessing new technologies to resolve the complexities of predator–prey interactions. Oikos, 2022(8), e09004. https://doi.org/10.1111/oik.09004 Thaker, M., Vanak, A.T., Owen, C.R., Ogden, M.B., Niemann, S.M., & Slotow, R. (2011). Minimizing predation risk in a landscape of multiple predators: Effects on the spatial distribution of African ungulates. Ecology, 92(2), 398–407. https://doi.org/10.1890/10-0126.1 Thieurmel, B., Elmarhraoui, A., & Thieurmel, M.B. (2019). Package ‘suncalc’. R package version 0.5. Verdolin, J.L. (2006). Meta-analysis of foraging and predation risk trade-offs in terrestrial systems. Behavioral Ecology and Sociobiology, 60(4), 457–464. https://doi.org/10.1007/s00265-006-0172-6 Wilson, R.R., Blankenship, T.L., Hooten, M.B., & Shivik, J.A. (2010). Prey-mediated avoidance of an intraguild predator by its intraguild prey. Oecologia, 164(4), 921–929. https://doi.org/10.1007/s00442-010-1797-8 Wirsing, A.J., Heithaus, M.R., Brown, J.S., Kotler, B.P., & Schmitz, O.J. (2021). The context dependence of non‐consumptive predator effects. Ecology Letters, 24(1), 113–129. https://doi.org/10.1111/ele.13614 Information & Authors Information Version history V1 Version 1 09 January 2025 Peer review timeline Published Movement Ecology Version of Record 12 Jul 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords behavior habitat selection hidden markov model predator-prey spatial ecology step selection function Authors Affiliations Michael Egan 0000-0002-9952-5602 [email protected] Southern Illinois University System View all articles by this author Abigail Weber Southern Illinois University System View all articles by this author Nicole Gorman Southern Illinois University System View all articles by this author Michael Eichholz Southern Illinois University System View all articles by this author Daniel Skinner Illinois Department of Natural Resources View all articles by this author Peter Schlichting Illinois Department of Natural Resources View all articles by this author Guillaume Bastille-Rousseau 0000-0001-6799-639X Southern Illinois University Carbondale View all articles by this author Metrics & Citations Metrics Article Usage 342 views 271 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Michael Egan, Abigail Weber, Nicole Gorman, et al. 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