Introduction

For predators to successfully feed on prey, they need to complete different sequential stages in the predation process, namely: search, detect (or encounter), chase, capture, handle and consume prey (Bateman et al. 2014). At each stage of the predation process, prey in their turn can use antipredator responses to reduce predation risk (Uetz and Hieber 1994; Creel and Creel 2002; Creel 2018). In particular, prey invests most antipredator effort in avoiding detection or becoming chased (Fuiman and Magurran 1994; but see Bateman et al. 2014), as the chance to avoid being killed presumably decreases further in the predation sequence (Endler 1991).

To reduce the probability of becoming detected by a predator, prey may avoid habitats with predators (Lima and Dill 1990), hide in dens (Olsson et al. 2008), spend more time in habitats that provide cover (e.g., tall vegetation) (Caro 2005; Hopewell et al. 2005), reduce movement (Eccard and Liesenjohann 2014; Weterings et al. 2016), or use camouflage (Stevens and Ruxton 2019). For example, elk moved to more dense vegetation to decrease their predation risk, after wolves arrived in Yellowstone National Park (Gude and Garrott 2003; Creel and Winnie 2005; Gude et al. 2006). Similarly, European hare spent more time in tall vegetation when red foxes were more active (Weterings et al. 2018).

To reduce the probability of becoming chased, prey may spend more time vigilant to improve detection of approaching predators (Creel et al. 2017). Besides, prey can escape before the chase (Cooper and Frederick 2007) or select habitats that allow escaping at a later time (Heithaus et al. 2009), or show a predator that it has been seen (Holley 1993) to avoid becoming chased. For example, zebras increased their time spent vigilant when lions were present (Périquet et al. 2012). Furthermore, when predation risk increased, guinea pigs increased their time spent vigilant when the distance to cover increased (Cassini 1991). Similarly, during high predation risk, gerbils forage close to bush microhabitat, to allow escape into cover (Kotler et al. 1991).

Presumably, predation risk imposes constraints on prey by increasing the need to make alternative choices (Hawlena and Perez-Mellado 2009). The use of antipredator behavior depends on the context (Périquet et al. 2017) and comes with a cost, such as physiological costs (Zanette et al. 2014), movement costs (Weterings et al. 2016), foraging costs (Creel et al. 2017), nutritional costs (Christianson and Creel 2010) or energetic costs (MacLeod et al. 2014). Insight into the costs of antipredator behavior on nutrient intake is important, because prolonged nutritional deficits could eventually lead to a reduced birth- and survival rate (Creel and Christianson 2008; Cresswell 2010).

Increased vigilance can come at a cost to nutrient intake (i.e., costly vigilance) when an animal stops processing food (i.e., chewing, lubricating and swallowing), while scanning the environment (Hik 1994; Abramsky et al. 2002; Fortin et al. 2004; Creel 2018). For example, dark-eyed juncos foraged more on whole seeds when predation risk increased, to allow visual scanning of the surrounding, even though whole seeds were less profitable than seeds without a shell (Lima 1988). In contrast to prey in short vegetation, animals in tall vegetation stop processing food to improve auditory detection of approaching predators (Benhaiem et al. 2008), as visual detection of approaching predators in tall vegetation is difficult (Hopewell et al. 2005; Riginos and Grace 2008). As a consequence, spending time in tall vegetation can come at a cost to nutrient intake when the food in tall vegetation is nutrient-poor (i.e., in nitrogen and phosphorus) (Shipley 2007; but see Hodges and Sinclair 2003) or contains a high percentage of fibers that increases the handling time of food (Barboza et al. 2009). Nevertheless, prey can often compensate for the costs of antipredator behavior (Luttbeg et al. 2003). To compensate for the low-quality food or the increase in handling time, herbivores in tall vegetation could spend more time foraging (i.e., searching and cropping) to ingest sufficient nutrients (Heuermann et al. 2011). Additionally, as a result of stress, prey can adjust the composition of their diet, thereby changing the types of nutrients or energy ingested (Hawlena and Schmitz 2010b). However, animals may only show compensatory behavior in a specific context. For example, European hares in tall vegetation only spend more time foraging when the vegetation contains low edible biomass (Weterings et al. 2018).

Prey that better match their defense towards predators more often focus on escape tactics (i.e., reactive response), while species that poorly match their defense towards the predator more often focus on avoidance tactics (i.e., proactive response; Dellinger et al. 2019). Prey that is effective in avoiding predator detection is often less effective in escaping predators (Creel et al. 2014). Creel (2018) suggested that predation risk that is unpredictable and of short duration (i.e., an encounter) does not affect the nutrient intake of prey. However, this has not been tested yet for cryptic herbivores (Creel 2018), because nutrient intake for nocturnal or crepuscular herbivores is very difficult to observe or measure in the field, especially when in cover (Ashby 1972).

Therefore, we experimentally tested the short-term effect of a predator–prey encounter on the nutrient intake of a small herbivore. To understand this effect, we additionally studied the time spent costly vigilant, the vegetation height in which the animals were present, and the time spent foraging.

As a case study, we focused on the European hare (Lepus europaeus) as a cryptic herbivore, which is a widespread species in North-western Europe (NDFF 2020). The European hare is a solitary non-central place forager that is common in open grassland areas (Barnes and Tapper 1986) but can use tall vegetation as cover or resting places (Neumann et al. 2011). Predation risk strongly affects hare behavior and space use (Weterings et al. 2016, 2019). European hares spend approximately half of their active time being vigilant (Lush et al. 2015) and use crypsis or a flight response to escape predators (Focardi and Rizzotto 1999).

We hypothesize that after a predator–prey encounter in short vegetation, hares (1) spend more time in tall vegetation that provides more cover (Neumann et al. 2011; Weterings et al. 2018) (Table 1). As a consequence, in contrast to short vegetation, hares (2) spend more time costly vigilant in tall vegetation (Monclús and Rödel 2008; Trefry and Hik 2009) to improve auditory detection of approaching predators (Benhaiem et al. 2008). Following, in contrast to short vegetation, hares (3) will spend more time foraging in tall vegetation (Shipley 2007) to compensate for any losses in nutrient intake (Heuermann et al. 2011). Therefore, overall, we expect that a predator–prey encounter does not affect the nutrient intake of the European hare.

Table 1 Overview of hypotheses tested
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Materials and methods

Study site

All data used in this study were collected by Weterings et al. (2018) in 2014–2015 in a coastal dune landscape (52°33′N, 4°38′E) in the Netherlands. In this dune landscape, we focused on two study sites (275 and 50 ha) with a population of European hares (±15 hares/km2). The areas consisted of patches of grass, thicket, brushwood, and forest.

Research design and data collection

In October 2014 nine hares were caught with Speedset static hare nets (height 45 cm, with 13 cm full mesh; JB’s Nets, Alexandria, UK), blindfolded (Paci et al. 2012), and kept in darkened boxes temporarily to reduce stress. Five hares were caught from the Koningsbos area and four hares from the Vennewater area. Hares were equipped with a lightweight GPS-ACC collar (69 g, 1.8% ± 0.2 SD of a hare’s body weight) that included a radio link for wireless communication (Type A, E-obs GmBH, Gruenwald, Germany) to minimize disturbance of the hares. Sex and life stage were determined by Stroh's method (juvenile < 1 year/adult > 1 year). Healthy hares (weight 2981–4400 g) were tagged without sedation (Gerritsmann et al. 2012). All handling of the hares was approved by the Wageningen University Animal Experiment Committee (no. 2014034.b) and followed the EU Directive 2010/63 on the protection of animals used for scientific purposes.

To investigate the costs of a predator–prey encounter on European hare nutrient intake, we conducted a playback experiment between 16th of December 2014 and 21st of January 2015. Even though different cues can be used to trigger a response to increased predation risk (Prugh et al. 2019), playbacks are often used in field studies to investigate immediate responses to predator–prey encounters (Clinchy et al. 2012). Moreover, for species that rely more on sound than sight and smell, such as hares (Łopucki et al. 2017), playbacks are generally more meaningful in assessing prey response to encounters (Jarvis 2004). Furthermore, playbacks may often be more alarming than visual cues (Cohen et al. 2009). Hares that participated in the playback experiment were selected based on their spatial distribution to maximize the distance between individual hares treated. Based on the GPS locations of individual hares, hares within 300 m of each other were assigned the same treatment. We used playbacks of conspecific alarm calls of hares instead of playbacks of predators, because prey often responds more strongly to conspecific alarm calls than to playbacks of predators (Schmidt et al. 2008; Magrath et al. 2014). Conspecific alarm calls may warn conspecifics of predators (Smith 1965; Sherman 1977; Zuberbühler et al. 1999; Blumstein 2007), but may also communicate directly to predators that have been detected (Digweed and Rendall 2009a, b; Hasson 1991; Sherman 1985; Woodland et al. 1980).

The playback experiment consisted of three different treatments (1) playbacks of conspecific alarm calls of hares, (2) playbacks of sheep (control playback), and (3) no sound. The playback experiment consisted of three blocks of four days. In every treatment block, hares were either exposed to the treatments from audio boxes (Foxpro Fury2, FOXPRO inc. Lewistown) or to no sound at all (Supplementary materials 1, Table S1). Different treatments occurred within the same block; however, hare and sheep playbacks were never used both within the same block. The three treatment blocks were chosen to control for changes in daylight and weather conditions. Weather data were collected from the weather station in IJmuiden (approximately 10 km from Castricum) (Koninklijk Nederlands Meteorologisch Instituut 2020). After each treatment block there were at least five days without playbacks to avoid carry-over effects (e.g., Petrovan et al. 2012). Ten different combinations of three playback fragments of each 40 s were placed in random order. To avoid habituation, fragments were never used more than two times (McGregor et al. 1992). Playbacks were played for 40 s at 20:00 h (CET), because hare activity and foraging behavior peaked during that time (Hansen 1996), thereby increasing the chance of triggering anti-predator responses during foraging. Audio boxes were placed 50 m south from the core location of GPS activity of a given hare at 20:00 h on previous days (mean distance between boxes = 1117 ± 1882 m), with the largest speaker directed towards the north to standardize distribution of playbacks in different directions.

Costly vigilance and foraging time

To investigate time spent costly vigilant and time spent foraging, we recorded accelerometer (ACC) data of collared hares in three axes, every two minutes for 24 h a day with a frequency of 10.54 Hz per axis. To interpret the accelerometer data, a hand-held video camera was used to record behavior of collared hares in the field. We video-recorded the behavior of eight hares to account for individual variation between hares (see Brivio et al. 2021); six hares from this study site and two hares from another coastal dune habitat on Schiermonnikoog island. Behavioral observations of the latter two hares in a comparable habitat were only added to improve the classification of the accelerometer data of this study, not to explain our hypothesis, because the GPS collars used and the sampling design was exactly the same. The six hares in our two study sites were observed when hares were expected to be the most visible and active (7:00–10:00 and 13:00–16:00). We recorded a total of 9225 s of behavior (mean 1153 s ± 1509 SD per observation).

Vegetation height

To investigate the vegetation height, we measured the vegetation height at five orthogonal locations in six random 2 × 2 m quadrants in each of the 20 vegetation types (n = 120) (Agriculture; flower-rich grasslands; bulb fields; dune grasslands; Burnet rose, creeping willow-, blackberry thicket; bare sand; calcareous dune grassland; calcareous dune valleys; deciduous forest; coniferous forest; former agriculture; other; other forests; reed swamp; reed swamp communities; herbaceous, fault, and mantle communities; thickets; nutrient-rich grasslands; nutrient-rich pioneer communities, flood meadows, and pace vegetation; near-shore communities). Next, we recorded GPS locations of individual hares every 12 min for 24 h a day, and used ArcGIS (version 10.7) to link the GPS location of the hares with the average height of the vegetation type at that location.

Nutrient intake

To test the effect of predation risk on nutrient intake, the available food quantity (i.e., edible biomass) and the nutrient concentration of each vegetation type was measured. We collected samples of edible biomass for seven of the most important plant species in the diet of hares (i.e., Festuca rubra, Agrostis capillaris, Poa pratensis, Holcus lanatus, Poa trivialis, Taraxacum officinale, Rubus caesisus; Kuijper et al. 2008; Weterings et al. 2018) and a commercial flower bulb species using the hand-pluck method (de Vries and Schippers 1994). Edible biomass (i.e., the green plant parts that have a high nutritional value and are selected by hares; Homolka 1987) were collected by the hand-pluck method in six randomly placed circular plots (10 m radius) up to 50 cm in height in each vegetation type (n = 120). Plant parts were air-dried, stored, and chemically analyzed for the concentration of phosphorus (P). We chose phosphorus to investigate nutrient intake, because phosphorus plays an important role in the body of animals, involving the skeletal formation, energy storage, metabolism, nerve impulse transmission and muscle contraction (Barboza et al. 2009) that could facilitate flight from predators. Furthermore, phosphorus is considered one of the most important nutrients for hares (Miller 1968).

Data preparation

Costly vigilance and foraging time

The video recordings of hare behavior were used to label one-second segments of ACC data that only consisted of the same behavior. Hare behavior was classified into six postures (i.e., sitting, sitting alert, standing, standing on hind legs, movement, and jumping), and six activities (i.e., chewing, cropping, grooming, scratching, shaking, and stretching) using the software Avidemux (2.6.6). Labeled segments of ACC data (training data) were used to classify unlabeled segments ACC data into behaviors using Decision Tree (accuracy 80.96% ± 0.75 SD) in the AcceleRater software (Resheff et al. 2014). We used the time sitting alert as a proxy for the time spent costly vigilance, and cropping time as a proxy for foraging time. We chose cropping time instead of chewing time to determine the foraging time, because cropping time was classified with higher precision and recall than chewing time.

Even though there is a trade-off between foraging time and chewing time (Spalinger and Hobbs 1992), nutrient intake increases when chewing time as well as foraging time increases (Gross et al. 1993).

Vegetation height

To calculate the fraction of time hares spent in a certain vegetation height in an hour, the GPS location of the hares was linked to a high-resolution GIS map (1:5000) of the different vegetation types (Everts et al. 2008, 2009). However, whenever hares were present in multiple vegetation types within an hour, we calculated the weighted vegetation height.

Nutrient intake

We calculated the relative nutrient intake of hares by multiplying the time spent foraging by the phosphorus concentration in the edible biomass (Fig. 1).

Fig. 1
figure 1

The four-step process of food intake with the formula that shows the proxies used for foraging time and nutrient intake

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The average edible biomass (g/m2) was calculated for each vegetation type by summing the amount of edible biomass (g) of all plant species in one square meter of a vegetation type up to 50 cm in height. The average content of phosphorus in every vegetation type was calculated by averaging the percentage of phosphorus in the edible biomass present in the vegetation type, weighted by their volume per square meter up to 50 cm in height (see Weterings et al. 2018).

Data analysis

We explored the data using the protocol of Zuur et al. (2010) to identify potential statistical problems. Because all males were juveniles, we could not investigate the effect of life stage in our analysis. We used Generalized Additive Mixed Models (GAMMs) in R (R Core Team 2021; R package ‘mgcv’ version 1.9-0 (Wood 2017)) to test the effect of the treatment on the fraction of time spent costly vigilant by hares (n = 1390) (i.e., beta distribution), the average vegetation height (n = 1227) in which the hares were present (i.e., Gaussian distribution), the fraction of time spent foraging (n = 1342) (i.e., beta distribution) and on the phosphorous intake by hares (n = 1342) (i.e., Gaussian distribution). GAMMs describe highly nonlinear relationships between response and explanatory variables using smoothing functions (Guisan et al. 2002). In total we investigated 168 h (7 days times 24 h) of response by the hares. All four global models included the treatment, the control variables sex, body weight, temperature, wind speed, rainfall, prior treatment, time of day and the interaction treatment*time of day. Because hares shift between short and tall vegetation during a day at dusk and dawn (Schai-Braun et al. 2012), we included time of day and the interaction treatment*time of day in the analysis. The variable ‘prior treatment’ was added to control for any carryover effects by the treatment the day before. The prior treatment on the first day of a treatment block was categorized as no treatment. Additionally, we transformed (1) the amount of rainfall into presence-absence data, because the data mainly showed zeros, and (2) phosphorous intake (log(x + 1)) because the data were right-skewed. We included vegetation height as an explanatory variable in the models that investigated time spent costly vigilant (Hopewell et al. 2005; Riginos and Grace 2008), foraging time (Heuermann et al. 2011) and phosphorous intake (Shipley 2007). However, vegetation height was excluded from the foraging time model and the phosphorous intake model due to multicollinearity. We found no multicollinearity between the other control variables. All continuous covariates were standardized to compare the effect size within and between models. Hare ID and treatment day block within hare ID were considered random factors. We excluded one hare from the analysis, because we did not identify its sex. A GAMM with cyclic smoother was used to model the effects during the time of day, to avoid discontinuity between subsequent days. Temporal autocorrelation among subsequent hours within a time block was addressed by including an autocorrelation structure, modeling a decreasing degree of autocorrelation with increasing temporal distance between data points. We chose an autoregressive (AR(1)) covariance type for individual time blocks at each site as this resulted in the best fit. The Akaike Information Criterion (AIC) was used to select the final model using the ‘base’ R-package (version 3.6.1). We validated the final model using the ‘MuMIn’ R-package (version 1.43.17) (Bartoń and Bartoń 2020) to plot the residuals against the predicted value and all the covariates.

Results

Compared to a playback of sheep and no playback, hares only spent more time foraging after an alarm call of a conspecific hare (Table 2).

Table 2 Results of the Generalized Additive Mixed Models (final models) on the effect of type of treatment on the costly vigilance, vegetation height in which the hares were present, foraging time, and phosphorous intake (log(x + 1)) of European hare (Lepus europaeus)
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Costly vigilance

We found that the playback of conspecific alarm calls did not affect the amount of time the hares spent costly vigilant (Wald X2 [2] = 0.343, p = 0.710). However, between 6 AM and 4 PM hares spent more time costly vigilant (Wald X2 [8] = 8.303, p < 0.001; Fig. 2), more specifically when present in tall vegetation (Wald X2 [1] = 22.605, p < 0.001).

Fig. 2
figure 2

Estimated marginal mean time spent costly vigilant (fraction per day) (grey band: 95% CI) plotted against time of day, based on 1390 h of observations of 9 hares in 2 sites. Start treatment was at 20:00 h (black arrows). Results based on Generalized Additive Mixed Model (GAMM)

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Vegetation height

We found that the playback of conspecific alarm calls did not affect the average vegetation height in which hares were present (Wald X2 [2] = 0.201, p = 0.818). However, between 6 AM and 4 PM (Wald X2 [8] = 37.01, p < 0.001), hares spent significantly more time in taller vegetation (Fig. 3), which corresponds to the time when the hares spent more time costly vigilant.

Fig. 3
figure 3

Estimated marginal mean vegetation height per hour in which hares were found (grey band: 95% CI) plotted against time of the day, based on 1227 h of observations of 9 hares in 2 sites. Reference category: Treatment = control, Sex = females. Start treatment was at 20:00 h (black arrows). Results based on Generalized Additive Mixed Model (GAMM)

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Foraging time

Hares spent more time foraging after a playback of a conspecific alarm call compared to a playback of sheep, and no sound (Wald X2 [2] = 10.42, p = 0.005); Fig. 4). Hares with a lower bodyweight spent more time foraging (Wald X2 [1] = 7.16, p = 0.007). Furthermore, hares spent more time foraging in absence of rain compared to the presence of rain (Wald X2 [1] = 6.49, p = 0.011). Finally, time of day was not significantly related to the time spent foraging (Wald X2 [1] = 0.204, p = 0.651). Hares thus spent a similar amount of time per hour foraging throughout the day and the night.

Fig. 4
figure 4

Estimated marginal mean time spent foraging (fraction per day) (error bars: 95% CI) plotted against treatment, based on 1342 h of observations of 9 hares in 2 sites. Reference category: Bodyweight = 4253.15 g, Wind speed = 10.54 m/h, Rain = absence. Means with similar letters do not differ significantly. Results based on Generalized Additive Mixed Model (GAMM)

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Phosphorous intake

We found that the playback of conspecific alarm calls did not affect the phosphorus intake of hares (Wald X2 [2] = 0.088, p = 0.915). However, the phosphorus intake of hares was significantly affected by time of day (Wald X2 [7] = 4.81, p < 0.001; Fig. 5), with a gradual increase in intake between 4AM and 4PM (i.e., during daytime in tall vegetation), after which the phosphorus intake decreased again (i.e., during night time in short vegetation). Moreover, hares had a significantly higher phosphorus intake in the absence of rain compared to the presence of rain (Wald X2 [1] = 13.64, p < 0.001).

Fig. 5
figure 5

Estimated marginal means of phosphorous intake (95% CI) plotted against time of the day, based on 1342 h of observations of 9 hares in 2 sites. Reference category: Treatment = control, Rain = no rain, Prior treatment = no treatment, Bodyweight  (z-score) = 0. Start treatment was at 20:00 h (black arrows). Results based on Generalized Additive Mixed Model (GAMM)

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Discussion

We investigated the effect of a predator–prey encounter on the phosphorous intake of a small cryptic herbivore, the European hare. As a response to conspecific alarm calls, (1) hares did not increase their time spent costly vigilant in tall vegetation, (2) hares did not spend more time in tall vegetation, but (3) increased their time spent foraging in short and tall vegetation. Still, the phosphorus intake of hares was not affected. Therefore, the antipredator response of the European hare to our playback treatment did not come with a nutritional cost in phosphorus. Hares could possibly adjust their diet to compensate for losses in nutrient intake (Engelhart and Muller-Schwarze 1995; Epple et al. 1993; Pfister et al. 1990; Sullivan and Crump 1984). Alternatively, food intake could interact with safety to affect dietary responses to predation. For example, L. catesbeianus tadpoles adjust their body nutrient stoichiometry in response to predation risk (Guariento et al. 2015). Besides, Eurasian siskins that did not reduce their food intake rate with increased predation risk, showed more effective behavior to detect or escape from a predator, compared to individuals that did reduce their food intake (Pascual and Senar 2014). Additionally, hares that forage on an energy-rich diet that reduces gut-content weight could be more successful in escaping predators (Schai-Braun et al. 2015). Furthermore, in response to an encounter, prey possibly compensate for the loss in carbohydrates, which are spent during a flight response (Hawlena and Schmitz 2010a). In contrast, prey probably do not need to compensate for a loss in phosphorus or calcium, as these nutrients are not spent during an escape, but are required for stronger bones (Rinehart and Hawlena 2020). The latter could explain the lack of a nutritional cost in phosphorus as a result of the hare treatment observed in our study. Nevertheless, because the reactive response acts late in the predation sequence and occurs less frequent (see also Barnier et al. 2014; Christianson and Creel 2010), the effect on prey ecology at a population level would be limited (Creel 2018).

The time spent costly vigilant did not increase as a result of the ‘risky’ playback of conspecific alarm calls of hares. It is unlikely that this behavioral response was shown because of the type of playback used, as hares did respond to our cue by increasing their time spent foraging. Besides, playbacks of conspecific alarm calls are used successfully to trigger antipredator behavior in many other studies (e.g., Blumstein et al. 2001; Cameron and du Toit 2005; Lung and Childress 2007; McDonough and Loughry 1995). Our results did show that in contrast to short vegetation, hares in tall vegetation spent more time being costly vigilant, probably to improve auditory detection of approaching predators due to low visibility (Marboutin and Aebischer 1996; Benhaiem et al. 2008; Riginos and Grace 2008). Differences in (costly) vigilance seemed, therefore, more related to differences in visibility due to vegetation cover than a change in predation risk initiated by our playback.

As a response to the treatment simulating a predator–prey encounter, we hypothesized that prey would shift habitat and move to taller vegetation containing more cover (Neumann et al. 2011). In contrast, hares did not move to tall vegetation, but moved to tall vegetation during the transition from night to day. Hares shift from tall to short vegetation during dusk and vice versa during dawn (Schai-Braun et al. 2012). Possibly, hares did not shift habitat to avoid predators in open vegetation, because their defense matched predator attack ecology in open and short vegetation during the night (Dellinger et al. 2019). Short vegetation allows hares to detect approaching predators (Hewson 1977) and to escape predators by flight (Weterings et al. 2016). However, hares only use flight as an escape tactic in open habitat during the night, and make use of crypsis in closed habitat during the day. We, therefore, suggest that hares have an advantage escaping predation in short vegetation during the night, while they have an advantage avoiding predation in tall vegetation during the day (Dellinger et al. 2019).

In contrast to the control treatment, playback of conspecific alarm calls increased the time spent foraging by hares throughout the day, no matter the length of the vegetation. Prey species increase their time spent foraging during high levels of predation risk, when this risk is uniformly spread over the landscape (i.e., they have no place to hide) and missed opportunity costs for foraging are therefore low (Eccard et al. 2008; Eccard and Liesenjohann 2014). This could apply to the European hare, which as a non-central place forager, does not have a refuge or burrow. However, snowshoe hares (Lepus americanus) (also a non-central place forager) did show a decrease in time spent foraging at high predation risk (Liu et al. 2014). Small mammals that can hide, decrease their time spend foraging during high predation risk (Verdolin 2006). Nevertheless, Mazza et al. (2019) showed that prey that could outrun their predator seem to accept predation risk rather than avoid predation risk and adjust their foraging behavior, compared to prey that cannot outrun their predator (see also Dellinger et al. 2019). Besides, our prey could behaved more boldly during winter time, because the available food choices are limited during this season (Kervola 2019). Moreover, if the risk of starvation is high, prey will forage in unsafe habitats (Sih 1980, 1982).

In contrast to studies that report hares resting in cover during day time (see e.g., Tapper and Barnes 1986), our results show that hares foraged in cover during the day. Possibly hares require a specific nutritional diet that can only be satisfied by foraging in two different types of habitats (i.e., short and tall vegetation) (see e.g., sparrows; Tinbergen 1980), as mobile species often use multiple habitats to fulfill their biological needs (Firle et al. 1998; Doniol-Valcroze et al. 2012). Interestingly, during day time (between 8:00 a.m. and 18:00 p.m.) when hares were found in tall vegetation, hares had a higher phosphorus intake in contrast to night time when hares were found in short vegetation. Furthermore, our results do show that hares with a lower bodyweight spent more time foraging. This could be an effect of the life stage of hares, or could corroborate with the hypothesis of mass-dependent predation risk (MDPR) (see Gosler et al. 1995; Kullberg et al. 1996; MacLeod et al. 2005). Nevertheless, we think that earlier scientists did not observe hares foraging during the day time, because hares spent most of their time in cover during day time and are thus very difficult to observe (e.g., Marboutin and Aebischer 1996). Most recent studies did not use accelerometers that can continuously record behavior of small mammals during their circadian rhythm, even in concealed habitats (see e.g., Botts et al. 2020). However, variation between individuals (e.g., in sex, age, body size, collar tightness) may affect the recorded values of accelerometers and need to be considered during data analysis (Brivio et al. 2021).

Overall, we found that playback of conspecific alarm calls of hares did not affect the antipredator responses of hares including their phosphorus intake. Animals consume food resources as a complex mixture of nutrients in varying levels of availability to maximize their response to predation (Zaguri et al. 2022), while minimizing the effects of toxics (Kirmani et al. 2010). In contrast to the consumption of phosphorus, hares could select an energy-rich diet, enabling them to run faster and escape from predators (Schai-Braun et al. 2015) (see hypothesis of mass-dependent predation risk: MacLeod et al. 2005), while meeting their daily energy demands. Additionally, the nutritional costs of an encounter can also affect the consumption of carbohydrates by prey, as carbohydrates are spent during the flight response. Furthermore, prey anti-predator responses (i.e., vigilance or a habitat shift) seem strongly related to the escape tactics of prey species that can differ between different habitats, while it can also differ during the time of the day. After all, more detailed field studies on the effects of predation risk on the nutrient intake of prey species are necessary to get a better insight into species’ anti-predator-food tactics.