DOI: 10.5061/dryad.05qfttf95
title: Reactive response to predation risk affects foraging time of hares, yet not their phosphorus intake

description: Antipredator responses could affect nutrient intake, which could lead to
 nutritional deficits. However, little is known about the antipredator
 response of small herbivores because most are nocturnal or crepuscular and
 therefore very difficult to study in the field. Therefore, we
 experimentally assessed the effect of a reactive response to predation
 risk on the nutrient (i.e., phosphorous) intake of the European hare
 (Lepus europaeus) using three different playback sounds. Additionally, we
 studied the time spent being costly vigilant, the time spent foraging, and
 the vegetation height in which the hares were present using accelerometers
 and GPS. Our results showed that elevated predation risk from our playback
 experiment did not affect the (1) phosphorus intake, (2) time spent being
 costly vigilant, and (3) time spent in tall vegetation. However, elevated
 predation risk did increase the time spent foraging. Possibly hares spent
 more time foraging with an increased predation risk because hares cannot
 seek refuge from predators. Additionally, the effect on phosphorus intake
 could be weak because phosphorous intake does not benefit a flight escape,
 while the reactive response acts late in the predation sequence limiting
 the effect on hare ecology. Prey anti-predator responses seem strongly
 related to the escape tactics of prey species that can differ between
 different habitats and the time of the day. More detailed field studies
 are necessary to get a better insight into species’ anti-predator-food
 tactics.
description: 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/km<sup>2</sup>). 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 &lt; 1
 year/adult &gt; 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 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 were 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 were measured. We collected samples of edible biomass for
 seven of the most important plant species in the diet of hares (i.e.,
 <em>Festuca rubra, Agrostis capillaris, Poa pratensis, Holcus
 lanatus, Poa trivialis, Taraxacum officinale, Rubus caesisus</em>;
 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 of 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 on 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).
 The average edible biomass (g/m<sup>2</sup>) 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 on costly vigilance (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.
description: This README file was generated on 2023-12-11 by Samara Brandsen &
 Louise Vermorken.   1\. Title of Dataset: Reactive response to predation
 risk affects foraging time of hares, yet not their phosphorus intake 2\.
 Author Information   Name: Samara Brandsen Institution: Van Hall
 Larenstein University of Applied Sciences, The Netherlands Email:
       Name: Louise Vermorken
 Institution: Van Hall Larenstein University of Applied Sciences, The
 Netherlands Email:     Name: Henry
 Kuipers    Institution: Van Hall Larenstein University of Applied
 Sciences, The Netherlands Email:     Name:
 Inger K. De Jonge        Institution: Wageningen University & Vrije
 Universiteit Amsterdam, The Netherlands Email: 
   Name: Sip van Wieren Institution: Wageningen University, The Netherlands
   Name: Martijn Weterings        Institution: Van Hall Larenstein
 University of Applied Sciences & Wageningen University, The
 Netherlands Email:     3\. Date of data
 collection (single date, range, approximate date): 2014-2015 4\.
 Geographic location of data collection: in a coastal dune landscape
 (52°33′N, 4°38′E) in the Netherlands 5\. Information about funding sources
 that supported the collection of the data: Funding Netherlands
 Organization for Scientifc Research (NWO), 023.001.222, Martijn Weterings.
   SHARING/ACCESS INFORMATION   1\. Licenses/restrictions placed on the
 data: None 2\. Links to publications that cite or use the data: Brandsen,
 S., Vermorken, L.S., Kuipers, H.J., van Wieren, S.E., de Jonge, I.K. &
 Weterings, M.J.A. (2023). Reactive response to predation risk affects
 foraging time of hares, yet not their phosphorus intake. Mammalian Biology
 3\. Links to other
 publicly accessible locations of the data: None 4\. Links/relationships to
 ancillary data sets: None 5\. Was data derived from another source?
 Partially (see A) A. If yes, list source(s): Weather data (temperature and
 rainfall) was obtained from the weather station IJmuiden(approximately 10
 km from Castricum) (Koninklijk Nederlands Meteorologisch Instituut 2020).
 Koninklijk Nederlands Metereologisch Instituut. (2020). Klimatologie:
 daggegevens van het weer in Nederland. Retrieved on June 15 2020, from
 6\.
 Recommended citation for this dataset: Brandsen, S., Vermorken, L.S.,
 Kuipers, H.J., van Wieren, S.E., de Jonge, I.K. & Weterings, M.J.A.
 (2023). Data from: Reactive response to predation risk affects foraging
 time of hares, yet not their phosphorus intake. Dryad Digital Repository.
   DATA & FILE OVERVIEW
   1\. File List:   A) DATASET HAAS GOED.csv B) MODEL2_Hare_model2Final.sav
 C) MODEL3_Feeding_model_zonderHEIGHT.sav D) MODEL4_SPSS_IntakePNIEUW.sav  
 2\. Relationship between files, if important: File B, C and D are derived
 from file A. 3\. Additional related data collected that was not included
 in the current data package: None 4\. Are there multiple versions of the
 dataset? No       A. If yes, name of file(s) that was updated: NA
             i. Why was the file updated? NA             ii. When was the
 file updated? NA  
 \#########################################################################
   DATA-SPECIFIC INFORMATION FOR: DATASET HAAS GOED.csv 1\. Number of
 variables: 12 2\. Number of cases/rows: 1391 3\. Variable List: * Hare.ID
 = Hare identification * Sex = Sex of hare * Bodyweight = Bodyweight of
 hare in g (numeric) * Treatment_dayblockID = Number of treatment day  *
 Temp = Average temperature in degrees Celsius (numeric) *
 Average_Windspeed.0.1m = Average windspeed in m/h (numeric) * Treatment =
 Sound (Hare, Sheep, and Control) * Prior_treatment = Treatment what was
 given prior to the new treatment * Hour_start_sound = Hour since the start
 of the sound * Average_height = Average vegetation height in centimeters
 (numeric) * Fac_Costly_Vigilance = Amount of time sitting alert plus time
 sitting (factor) * Total_Rainfall.0.1mm = Rainfall   4\. Missing data
 codes: None 5\. Specialized formats or other abbreviations used: None  
 \#########################################################################
   DATA-SPECIFIC INFORMATION FOR: MODEL2_Hare_model2Final.sav   1\. Number
 of variables: 11 2\. Number of cases/rows: 1227 3\. Variable List: *
 HareID = Hare identification * Sex = Sex of hare * Bodyweight = Bodyweight
 of hare in g (numeric) * Treatment_dayblockID = Number of treatment day  *
 Temp = Average temperature in degrees Celsius (numeric) *
 Average_Windspeed01m = Average windspeed in m/h (numeric) * Treatment =
 Sound (Hare, Sheep, and Control) * Prior_treatment = Treatment what was
 given prior to the new treatment * Hour_start_sound = Hour since the start
 of the sound * Rainfall_class = Rainfall (presence/absence) *
 Average_height = Average vegetation height in centimeters 4\. Missing data
 codes: None 5\. Specialized formats or other abbreviations used: None  
 \#########################################################################
   DATA-SPECIFIC INFORMATION FOR: MODEL3_Feeding_model_zonderHEIGHT.sav 1\.
 Number of variables: 12 2\. Number of cases/rows: 1342 3\. Variable List:
 * HareID = Hare identification * Sex = Sex of hare * Bodyweight =
 Bodyweight of hare in g (numeric) * Treatment_dayblockID = Number of
 treatment day  * Date = Date of treatment  * Temp = Average temperature in
 degrees Celsius (numeric) * Average_Windspeed01m = Average windspeed in
 m/h (numeric) * Treatment = Sound (Hare, Sheep, and Control) *
 Hour_start_sound = Hour since the start of the sound  * Fac_Feeding =
 Amount of time foraging in minutes * Timeofday = Time of day (factor) *
 RainClass = Rainfall (presence/absence)   4\. Missing data codes: None 5\.
 Specialized formats or other abbreviations used: None  
 \#########################################################################
   DATA-SPECIFIC INFORMATION FOR: MODEL4_SPSS_IntakePNIEUW.sav 1\. Number
 of variables: 11 2\. Number of cases/rows: 1342 3\. Variable List: *
 HareID = Hare identification * Sex = Sex of hare * Bodyweight = Bodyweight
 of hare in g (numeric) * Treatment_dayblockID = Number of treatment day  *
 Temp = Average temperature in degrees Celsius (numeric) *
 Average_Windspeed01m = Average windspeed in m/h (numeric) * Treatment =
 Sound (Hare, Sheep, and Control) * Prior_treatment = Treatment what was
 given prior to the new treatment * Hour_start_sound = Hour since the start
 of the sound * log_Foodintake10000 = Phosphorous intake * RainClass =
 Rainfall (presence/absence) 4\. Missing data codes: None 5\. Specialized
 formats or other abbreviations used: Non