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Behavioral Ecology and Sociobiology ISSN 0340-5443 Behav Ecol SociobiolDOI 10.1007/s00265-015-1929-6

Temporal shifts in activity of prey followinglarge predator reintroductions

Craig J. Tambling, Liaan Minnie,Jordana Meyer, Elizabeth W. Freeman,Rachel M. Santymire, John Adendorff &Graham I. H. Kerley

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ORIGINAL PAPER

Temporal shifts in activity of prey following largepredator reintroductions

Craig J. Tambling1 & Liaan Minnie1 & Jordana Meyer2,3 & Elizabeth W. Freeman2&

Rachel M. Santymire3 & John Adendorff4 & Graham I. H. Kerley1

Received: 18 February 2015 /Revised: 17 April 2015 /Accepted: 17 April 2015# Springer-Verlag Berlin Heidelberg 2015

Abstract The response of prey to predation risk variesthrough time and space. These responses relate to trade-offsbetween foraging and predator avoidance. Following the ex-tirpation of predators from many landscapes, the responsesrelated to predator avoidance may have been lost or diluted.Investigating the activity pattern of prey species on compara-ble landscapes with and without large predators provides anopportunity to understand how predators may shape prey ac-tivity and behaviour. Using camera trap data fromneighbouring fenced sections of the Addo Elephant NationalPark (Eastern Cape, South Africa), we investigated the activ-ity patterns of species exposed to large predators, where thepredators were only present in one of the sections. Our resultssuggest that prey species at risk of predation (e.g., buffalo,kudu and warthog) are more likely to be active diurnally whenco-existing with nocturnally active predators, thereby reduc-ing the activity overlap with these predators. In the absence of

predators, kudu and buffalo were more active at nightresulting in a low overlap in activity between sections.Warthog activity was predominantly diurnal in both sections,resulting in a high overlap in activity between sections. Thepresence of predators reduced the nocturnal activity of wart-hogs from 6 to 0.6 % of all warthog captures in each section.Elephants, which are above the preferred prey weight range ofthe predators and therefore have a low risk of predation,showed higher overlap in activity periodicity betweenpredator-present and predator-absent areas. Our findings sug-gest that maintaining prey with their predators has the addedbenefit of conserving the full spectrum of prey adaptivebehaviours.

Keywords African buffalo . Diel pattern . Kudu . Lion .

Predator–prey interaction . Spotted hyaena

Introduction

Predators have direct and indirect effects on prey populations(Preisser et al. 2005). Large indirect effects emerge when preyspecies alter their behaviour in response to predation risk(Creel and Christianson 2008; Hopcraft et al. 2010). Top-order predators have however been extirpated frommany eco-systems (Ripple et al. 2014) resulting in the relaxation of preybehaviour in the absence of predation risk. The behaviour ofprey species in predator-free ecosystems is unlikely to be rep-resentative of the behaviour of the same prey species prior topredator extirpation or in areas where prey species coexistwith predators. In the past 30 years, many large predators havebeen reintroduced into ecosystems fromwhich they were onceextirpated. These reintroductions of large predators provide anideal opportunity to investigate the role that predators play indriving the behavioural responses of prey species (Hayward

Communicated by M. Festa-Bianchet

Electronic supplementary material The online version of this article(doi:10.1007/s00265-015-1929-6) contains supplementary material,which is available to authorized users.

* Craig J. Tamblingcjtambling@gmail.com

1 Centre for African Conservation Ecology, Department of Zoology,NelsonMandelaMetropolitan University, Port Elizabeth 6001, SouthAfrica

2 NewCentury College, GeorgeMason University, Fairfax, VA 22030,USA

3 Davee Center for Epidemiology and Endocrinology, Lincoln ParkZoo, Chicago, IL 60614, USA

4 Addo Elephant National Park, South African National Parks,Addo 6041, South Africa

Behav Ecol SociobiolDOI 10.1007/s00265-015-1929-6

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and Somers 2009). Consequently, research has shown thatpredator reintroductions can drive the evolution of prey re-sponses (Sih et al. 2010) or the expression of learned behav-iour within generations (Berger et al. 2001).

How prey species respond to predators is a key factor inunderstanding how, why and where prey species occur on thelandscape. Prey respond to spatial variations in predation riskby avoiding risky areas within the context of the “landscape offear”, and this occurs at multiple spatial scales related to re-source acquisition and range use (Cresswell et al. 2010; Beestet al. 2013). In the same way, predation risk varies acrosstemporal scales, including climatic cycles (Mills et al. 1995),seasonal cycles (Creel andWinnie 2005), lunar cycles (Griffinet al. 2005), diel cycles (Valeix et al. 2009) and hourly varia-tions (Fenn and Macdonald 1995; Roth and Lima 2007). Atthe daily scale, the diel pattern of a prey species is a combi-nation of evolutionary physical adaptations to climate com-bined with the local selective pressures (Monterroso et al.2013). Changes in diel activity schedules may reveal howspecies cope with balancing resource acquisition and evadingpredators (Owen-Smith and Goodall 2014). Assessing wheth-er resources or predation risk influences prey space occupancycan be assessed by comparing the activity pattern of preybetween day and night, dependent on when their main preda-tor is active (Owen-Smith 2014). Predation risk may decline ifprey reduce their activity when predators are active (Lima2002) or if prey reduce their activity when predators find iteasy to locate and capture prey species (Caro 2005).

Lion (Panthera leo) and spotted hyaena (Crocuta crocuta,hereafter referred to as hyaena) are the dominant predators onthe African landscape, with up to 65 % of the energy transferbetween mammal prey and predators channelled through lionpredation (Owen-Smith and Mills 2008a). Lion and hyaenahave broad prey preference ranges (Hayward and Kerley2005; Hayward 2006; Clements et al. 2014), although theecological impact of the predator guild (dominated by thesetwo predators) varies across the African continent. Predatorsin the Serengeti National Park (Serengeti) regulate prey spe-cies below a threshold weight of 150 kg (Sinclair et al. 2003),whereas predators in the Kruger National Park (Kruger) reg-ulate prey species below a threshold weight of 1000 kg(Owen-Smith and Mills 2008a). This top-down regulation ofprey populations by direct predation and the risk of predationplays an important role in the evolution of prey activity pat-terns (Valeix et al. 2009).

Management actions (park expansion and predatorreintroductions) in the Addo Elephant National Park (Addo),South Africa, provide an opportunity to test how prey speciesalter their behaviour in response to large predatorreintroductions (Hayward and Somers 2009; Tambling et al.2012). Here, we investigate how the reintroduction and sub-sequent presence of predominantly nocturnal apex predators(lion and hyaena both reintroduced in 2003/2004) into Addo

impact the activity patterns of potential prey species.Specifically, we predict that (1) prey species that are nocturnalin the absence of predators will become increasingly diurnal,(2) diurnal prey species will remain diurnal with relatively nochange in activity pattern and (3) species that are largely in-vulnerable to predation will not alter their activity patterns inthe presence of predators.

Materials and methods

The study was conducted in the Addo Elephant National Park(Addo), which comprises seven distinct management sections.We placed camera traps in two of these sections, Nyathi(25.72°E–25.93°E, 33.33°S–33.45°S, 140 km2) and MainCamp (25.72°E–25.85°E, 33.43°S–33.57°S, 114 km2—Fig. 1). Both sections fall in the Thicket biome of SouthAfrica, represented by the Sundays Thicket and AlbanyCoastal belt, interspersed with tracts of Coega Bontveld(Mucina and Rutherford 2006). The two fully fenced sectionsare separated by fences, a main road and railway track thusprecluding movement of larger animals between them(Fig. 1). Both sections cover an area that is ecologically sim-ilar and are characterized by large tracts of dense vegetation(Thicket vegetation types) interspersed with open areas of

Fig. 1 Location of the Main Camp (predator present) and Nyathi(predator absent) sections of the Addo Elephant National Park in SouthAfrica, showing the camera trap sites and clusters (ellipses around cameralocations) and the main road and railway line separating the twoneighbouring sections

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both natural and man-made origin (Hayward and Hayward2006). Climatic data obtained at a weather station betweenthe two sections revealed considerable variation in tempera-ture over the course of our study, with minimum temperaturesranging from −1 to 24 °C and maximum temperatures rangingfrom 12 to 46 °C (South African Weather Service, unpub-lished data). Daily rainfall during our study period rangedfrom 0 to 48 mm (South African Weather Service, unpub-lished Data). Although ecotourism (primarily diurnal) takesplace in both sections, the Main Camp experiences high in-tensity ecotourism (Boshoff et al. 2007), whereas Nyathi ex-periences low intensity ecotourism.

Both sections are inhabited by a variety of large mammalsranging in size from grysbok (Raphiceros melanotis, ∼7–9 kg)to elephant (Loxodonta africana, ∼3600–5500 kg). Before2003, both sections lacked resident large predators (lion andhyaena) for over 100 years (Skead 2007). Leopard (Pantherapardus) occurs across the region at low densities, and wesuspect that their predatory effect would be low and equalacross both sections. Lions were reintroduced into the MainCamp section in 2003 and during the first 2 years after theirreintroduction consumed (calculated as % occurrence) pre-dominantly kudu (Tragelaphus strepsiceros—29 %), warthog(Phacochoerus africanus 20 %) and buffalo (Syncerus caffer17 %, Hayward et al. 2007). Hyaenas were reintroduced intothe Main Camp section in 2003 and 2004 and during the first2 years after their reintroduction consumed predominantlykudu (44%), ostrich (Struthio camelus—15%), red hartebeest(Alcelaphus buselaphus—15 %) and buffalo (15 %, Hayward2006). Between October 2007 and December 2010, lion con-sumed predominantly kudu (34 %, n=82 kills), warthog(26 %, n=62 kills), buffalo (14 %, n=33) and red hartebeest(11 %, n=26), and hyaena consumed predominantly kudu(74 %, n=32 kills) and warthog (12 %, n=5 kills, CJT, un-published data). There are no records of predation by thesetwo apex predators on elephant in Addo.

Camera traps (ScoutGuard 550V and ScoutGuard SG550,HCO Norcross, GA, USA, and Wildview STC-TGL3IR,Grand Prairie, TX, USA) were deployed in Nyathi (ten sites)and Main Camp (nine sites) between September 2009 andDecember 2010 (Fig. 1). Camera trap locations were predom-inantly along roads and cut-lines (old access roads and fencelines in the Thicket vegetation, a remnant of the historical landmanagement practices) and near waterholes in both sections,thus increasing the chances of capturing animal activity andfacilitating camera maintenance. The length of time that acamera was active at a site varied as (a) it was not alwayspossible to check cameras prior to the batteries going flat,(b) cameras were sometimes disturbed by animals, and (c)cameras were moved between sites during the course of thestudy. In some cases, camera trap locations were in closeproximity to each other, although most often, camera trapplacements were temporally separated. To reduce the chance

of trapping an individual on two nearby cameras in a shorttime period (i.e., the same movement captured by two cam-eras), we grouped nearby cameras into camera clusters (Fig. 1)and based all subsequent analysis on the camera cluster data(Nyathi = five clusters, Main camp = six clusters). We sortedcamera trap pictures of different species based on the assump-tion that multiple photographs of a species taken at a singlecamera cluster within a short period of time represented asingle capture event and considered these multiple photo-graphs as a single independent record (Akbaba and Ayaş2012). We used 30 min (Linkie and Rideout 2011; Ohashiet al. 2013) as the time to independence, as this interval is acompromise between the likelihood of capturing the sameindividual multiple times and the likelihood of missing indi-viduals (Rovero et al. 2005). For the analysis, pictures sepa-rated by the time to independence were assumed to be a rep-resentative random sample of the underlying movement pat-tern exhibited by each animal moving past each camera(Rideout and Linkie 2009).

To estimate the minimum number of pictures required toadequately represent activity in all hours of the day, we esti-mated hourly accumulation curves, similar to species accumu-lation curves (Foggo et al. 2003). We sequentially added pic-tures from the combined dataset (predator-present andpredator-absent sections) of each species and assessed wheth-er or not additional pictures included an hour when the specieswas active that had not yet been detected. Adequate samplingwas thus achieved once the relationship between activity (i.e.,hour detected as active) and the cumulative number of obser-vations reached an asymptote. Accumulation curves were cal-culated using the package “vegan” (Oksanen et al. 2012). Toassess where the accumulation curve asymptotes, we calculat-ed breakpoints using segmented models implemented in thepackage “segmented” (Muggeo 2008). Visual inspection sug-gested that using two breakpoints in the segmented relation-ship provided an adequate sampling intensity, with each seg-mented model explaining more than 95 % of the variability inthe accumulation curve. For a prey species to be included inthe analysis, the number of captures needed to exceed thesecond breakpoint, and the slope of the relationship betweendetecting additional hours and adding additional samples fol-lowing the second breakpoint was almost zero (i.e., the curvereached an asymptote).

We analysed the camera trap data to investigate (a) thebroad-scale differences in activity patterns for each prey spe-cies between the different sections and (b) what factors may bedriving shifts in activity between diurnal and nocturnal pat-terns between the sections. To broadly depict activity peaksand overlap during a 24-h period, we followed a two-stepprocedure (Linkie and Rideout 2011). Firstly, the activity pat-terns were estimated non-parametrically using kernel densityestimation with the bandwidth set at one for all estimations.Secondly, the degree of overlap in activity was estimated

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using the coefficient of overlap,Δ, which ranges from 0 to 1,where 0 indicates total separation and 1 indicates total overlapin activity patterns (Linkie and Rideout 2011). We comparedthe overlap in activity patterns of each prey species betweenthe sections where predators were present or absent. A highoverlap index suggests that activity patterns are similar regard-less of whether predators are present, and a low overlap indexsuggests that activity patterns vary in the presence and ab-sence of predators. For each of these overlap comparisons,we bootstrapped the coefficient of overlap 500 times to gen-erate 95 % confidence limits (Linkie and Rideout 2011). Allbroad-scale overlap analysis was conducted using the package“overlap” (Meredith and Rideout 2014). Although our broad-scale activity pattern assessment gives a good indication as tothe overall pattern throughout the study, the fact that sunriseand sunset vary over the course of the year (summer has4 hours more sunlight than winter) influences the onset andlength of diurnal and nocturnal periods (Nouvellet et al. 2012).This variation in day length could alter when crepuscular spe-cies are active, blurring the overlap estimates if summer andwinter activity periods are compared. To reduce the potentialimpact of activity at sunrise and sunset shifting across thestudy period, we repeated the above analysis using winter(16 March–27 September) and summer (27 September–16March) data separately.

To understand the factors that may drive the activity pat-terns of our species of interest, we investigated which covar-iates explained the proportion of diurnal and nocturnal activ-ity. Because sunrise and sunset times vary seasonally, we ad-justed the daily independent captures to local sunrise and sun-set in order to classify whether they represent diurnal or noc-turnal activity. However, seasonal variation does influence notonly the timing of day and night, but also the climatic factorsin Addo. We therefore added additional climatic information(daily rainfall, daily maximum temperature and daily mini-mum temperature) obtained from a weather station locatedbetween the two sections to each capture event. Daily rainfalland maximum temperature were extracted for the day (totalrainfall and maximum temperature between 00:00 and 24:00)of the capture, whereas minimum temperature was calculatedas the average of the current and following day of each cap-ture. We then contrasted the relative impact of the presence ofpredators and climatic variation on the proportion of diurnaland nocturnal captures for each prey species. To investigatethis, we fitted generalized linear mixed models (GLMMs witha logit link function) using the package “lme4” (Bates et al.2013) with activity period (diurnal and nocturnal) as a bino-mial response. Fixed effect predictor variables included pred-ator presence, maximum temperature, minimum temperature,and rainfall. To determine the best suite of predictor variablesexplaining the proportion of nocturnal versus diurnal activity,we used an alpha to enter approach, starting with the interceptmodel and adding variables sequentially, setting alpha = 0.05

(Quinn and Keough 2002), and excluding variables that failedto meet the criteria. We included camera cluster as a randomeffect to account for the effect of a non-matched design interms of the locations for each camera between the two sec-tions. To assess the relative importance of categorical predic-tor variables, we used the coefficients of the final nocturnalproportion model (obtained from the GLMMs) to produceodds ratios (OR) depicting the chance that a species will beactive at night if a predator is absent. All statistical analysiswas conducted in the open source statistical package R (RDevelopment Core Team 2011).

Results

During the study, camera traps were deployed in Main Camp(predator present) for 713 days (average 79±25 days per cam-era) and in Nyathi (predator absent) for 1097 days (average109±31 days per camera) with at least two to three camerasactive for ∼85 % of the time (See Fig. S1, supplementaryinformation). In total, we obtained 2293 independent capturesfrom Main Camp and 4155 independent captures fromNyathi. Sufficient captures of individuals from both sectionswere available for buffalo, warthog, kudu and elephant; datafor these species provided at least two breakpoints andaccounted for at least 98 % of the variation in the hourlyaccumulation curves (Table 1). In addition, sufficient captureswere available for these species to allow the sub-sampling ofdata into winter and summer periods for the broad-scale over-lap analysis to be conducted on the seasonal data (Table 1).

Table 1 The minimum number of camera trap image captures requiredto reach an asymptote between hours prey species were detected in theAddo Elephant National Park, South Africa, and the number of capturesfor segmented models with breakpoints indicated (N1 = first breakpoint,N2 = second breakpoint). Breakpoints reflect a change in the relationshipbetween the detection of prey species at each hour over the diel cycle andthe increasing number of captures for each species. The R2 representshow well the segmented model fits the accumulation curve. The slopeof these relationships following the second breakpoint did not differ fromzero. Sample sizes refer to the number of available independent imagesfor each species within each section and during each season (winter: 16March–27 September, summer: 27 September–16 March)

Species Breakpoint R2 N1 N2 Sample size (winter, summer)

Buffalo 1 0.978 42 66 Nyathi = 284 (143, 141)

2 0.996 21 Main Camp = 230 (122, 108)

Warthog 1 0.952 36 145 Nyathi = 444 (223, 221)

2 0.989 23 Main Camp = 754 (385, 369)

Kudu 1 0.954 54 139 Nyathi = 676 (396, 280)

2 0.991 31 Main Camp = 427 (236, 191)

Elephant 1 0.945 67 157 Nyathi = 345 (163, 182)

2 0.990 35 Main Camp = 545 (298, 247)

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During the sampling in Main Camp, we captured 14 and 53independent lion and hyaena images (data not sub-sampleddue to low sample sizes). Both species displayed predomi-nantly nocturnal activity (Fig. 2) with 79 and 94 % of allcaptures for lion and hyaena, respectively, occurring duringthe nocturnal period.

Warthog and elephant were predominantly diurnal in bothMain Camp and Nyathi, and as such displayed considerableoverlap in activity between the two sections (warthogΔ=0.79[95 % confidence limits 0.74–0.83], elephantΔ=0.82 [0.77–0.88]; Fig. 3a, d). Kudu and buffalo, on the other hand,displayed less overlap in activity between the two sections(Fig. 3b, c). Both kudu (Δ=0.60 [0.54–0.65]) and buffalo(Δ=0.55 [0.47–0.62]) had more nocturnal captures inNyathi and more diurnal captures in Main Camp (Fig. 3b,c). When compared to the range of sunrise and sunset timesover the full study, activity of all species peaked during diurnalperiods in the predator-present section (Fig. 3a–d: solid lines).In contrast, activity peaked during the crepuscular or nocturnalperiods for kudu and buffalo in the predator-absent section(Fig. 3b, c: dashed lines). These trends remained consistentwhen captures were separated into summer and winter periods(Fig. S2, S3, supplementary information).

The nocturnal activity of all species was negatively relatedto the presence of predators (Table 2), and thus, the probabilityof a species being active at night declined when large preda-tors were present. The impact of predator presence on reduc-ing nocturnal activity (based on the odds of a species beingactive at night if the predator was absent) was greatest forwarthog (OR=13.6), buffalo (OR=7.3) and kudu (OR=6.7),and lowest for elephants (OR=2.6, Fig. 4). The large warthogeffect was driven by the almost complete absence of nocturnalactivity in the predator-present section (0.6 % of all warthogcaptures) when compared to the predator-absent section (6 %of all warthog captures). In addition to the predator effect,

elephant nocturnal activity increased at higher minimum tem-peratures, whereas diurnal activity increased at higher maxi-mum temperatures (Table 2).

Discussion

Lion and hyaena in Main Camp are characterized by predom-inantly nocturnal activity patterns (Hayward and Hayward2006; Hayward and Slotow 2009; current study), and it isduring these nocturnal periods that prey species are most sus-ceptible to predation (Schaller 1972). Multisite analysis showsthat kudu, buffalo and warthog fall well within the preyweightranges for both predators (Hayward and Kerley 2005;Hayward 2006; Clements et al. 2014), and all three speciesare commonly consumed by lions or hyaenas in Addo. Thus,we predicted that nocturnal periods would be risky for thesespecies which are susceptible to predation. The difference inactivity of buffalo and kudu between the predator-present andpredator-absent sections suggests that these two species maybe responding to heightened predation pressure. Warthogs arediurnal regardless of whether predators are present or not. Thisdiurnal activity pattern most likely reflects an evolved dielpattern influenced by warthog physiology (Skinner andChimimba 2005). Despite the overall lack of nocturnal activ-ity by warthogs, the presence of predators reduced the propor-tion of nocturnal captures by an order of magnitude (6 vs.0.6 %), suggesting that predators still play an important rolein warthog activity. Elephants fall beyond the maximum pre-ferred prey weight for these two predators (Hayward andKerley 2005; Hayward 2006; Clements et al. 2014) and donot fall prey to either predator within Addo. Surprisingly, el-ephant activity patterns were similarly influenced by the pres-ence of predators, although the low odds ratio and high levelof overlap between the sections suggest that this species wasthe least affected by the presence of predators. In general, theactivity patterns of these four species in Addo were consistentwith the expectations for these species from previous studies,although for buffalo, the congruence of activity patterns toprevious studies was not immediately intuitive.

Buffalo form an important component of lion diets insouthern Africa, accounting for up to 50 % of the biomassconsumed in the Hwange National Park (Davidson et al.2013) and constituting an important seasonal prey item inthe Kruger (Owen-Smith and Mills 2008b). In both of theseparks, where buffalo coexist with lions, buffalo activity ishighest during crepuscular periods and lowest at midday(Valeix et al. 2009; Polansky et al. 2010; Owen-Smith andGoodall 2014). In Addo, buffalo activity patterns in the pres-ence of lions did not follow these observed diel trends.Surprisingly, buffalo in the predator-free section of Addohad activity patterns that closely matched Kruger andHwange, which similarly matched buffalo activity patterns

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Fig. 2 Estimates of the daily activity patterns of lion (solid line) andspotted hyaena (dashed line) in the Main Camp section of the AddoElephant National Park, South Africa. Dotted vertical lines representthe range of sunrise and sunset times over the course of the study period

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observed in other areas without predators (Winterbach andBothma 1998). The characteristically low level of activityaround midday is suggested to be a mechanism used to copewith the thermal constraints related to high ambient middaytemperatures (Owen-Smith and Goodall 2014). A closer lookat buffalo activity patterns in Hwange, however, shows thatbuffalo increase their midday activity, and this behaviour ismost likely related to predator avoidance during periods ofhigh predation risk (Valeix et al. 2009). During our study,the density of lions in Main Camp was high (13 lions/km2

[n=15 lions]) and uniformly spread across the section in fivedistinct groups (Tambling et al. 2013). Therefore, similar tothe observation in Hwange, buffalo activity patterns in MainCamp could be influenced by lion presence resulting in anincrease in diurnal activity.

In protected areas with large predators, kudus are almostexclusively diurnal, exhibiting increased nocturnal activityonly when predation pressure is low (Valeix et al. 2009).

The hyaena numbers in Addo match those of the lions, andhyaena foraging groups are predominantly small (Wentworthet al. 2011), resulting in a widely dispersed hyaena populationand associated predation risk. As both lion and hyaena areimportant predators of kudu in Addo, the complete absenceof nocturnal activity of kudu in Main Camp was not surpris-ing. In contrast, in the absence of predation pressure in Nyathi,kudu activity was highest during nocturnal and crepuscularperiods, with the least activity taking place at midday.

Regardless of predator presence or absence, warthogs arediurnal across their range (Skinner and Chimimba 2005). Thismay reflect an evolutionary trait in response to other selectivepressures (e.g., thermoregulation). Warthogs are invasive inAddo (Nyafu 2009), and diurnal culling operations are con-ducted in both Nyathi and Main Camp by Addo managementto limit their population numbers. In addition, Main Camp andNyathi differ considerably in their level of ecotourism, withthe Main Camp section open to diurnal unguided self-driven

kudu:

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Fig. 3 Estimates of the dailyactivity patterns of a warthog, bbuffalo, c kudu and d elephant inthe predator-present (solid line)and predator-absent (dashed line)sections of the Addo ElephantNational Park, South Africa. Thecoefficient of overlap (Δ),accompanied by the 95 %confidence limits in parentheses,is represented by the grey shadedareas which indicate the overlapperiods. Dotted vertical linesrepresent the range of sunrise andsunset times over the course of thestudy period

Table 2 Coefficients (effect size) for the final generalized mixed effectmodels for each species used to investigate factors driving diurnal andnocturnal activity patterns of prey species within the Addo ElephantNational Park, South Africa. All coefficients are presented in referenceto the nocturnal captures for each species (i.e., a negative coefficientindicates a negative relationship between the parameter and the numberof nocturnal captures). Significance is reported as * P<0.05, ** P<0.005,and *** P<0.0005

Intercept Predator Rainfall Minimum T Maximum T

Warthog −2.4*** −2.61*** – – –

Kudu 0.15 −1.90*** – – –

Buffalo 1.24*** −1.98*** – – –

Elephant 0.3 −0.95** – 0.06** –0.05**

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1015202530354045

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Fig. 4 Odds ratio (±95 % CL) of a species being active at night ifpredators are absent in the Addo Elephant National Park, South Africa.The odds ratio represents the odds of a species being active at night if thepredators are absent

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visitation (Boshoff et al. 2007). Ecotourism in Nyathi, on theother hand, is limited to guided drives originating from a sin-gle luxury lodge. Thus, the combination of diurnal culling andhigh density diurnal self-driven ecotourism should promote anocturnal activity pattern for warthog in response to thesediurnal disturbances in Main Camp c.f. wild boar Sus scrofa(Ohashi et al. 2013). However, warthog remained diurnal inboth sections (almost exclusively so in Main Camp with pred-ators), suggesting that these diurnal anthropogenic distur-bances in Main Camp were weak, or that predation pressureor long term evolutionary adaptation for diurnal activity wasstronger. This should also be the case for the other species thatshowed increased diurnal activity irrespective of any anthro-pogenic influences.

Across Africa, elephants are largely free from predation bylion and hyaena (but see Loveridge et al. 2006; Power andCompion 2009). Elephant activity patterns should thereforenot be influenced by lion activity or the threat of lion presence,and alternate drivers should be important for their activitypatterns. Despite a similar broad activity pattern in MainCamp and Nyathi (Δ=0.82), predator presence resulted in aslight increase in diurnal activity. In regions where lions huntelephants, predation is usually directed at the sub-adult com-ponent of the population (Loveridge et al. 2006) and takesplace at night (Power and Compion 2009). These observa-tions, and our results, suggest that although no predationwas observed in Addo, elephants may still perceive nocturnalperiods as dangerous and further investigations into the dielpatterns of elephants with and without infants may re-veal different activity pattern responses to the presenceof predators.

Temperature has a significant influence on ungulate activ-ity, with high thermal stress reducing the time available forforaging (du Toit and Yetman 2005; Owen-Smith and Goodall2014). Under high thermal stress, ungulates can either shiftactivity to cooler periods within the same diel phase (i.e.,diurnal period) or shift from a diurnal activity pattern to anocturnal activity pattern (Maloney et al. 2005). Our resultssuggest that ungulates in Addo did not respond to high ther-mal stress (maximum daily temperature) by shifting betweendiurnal and nocturnal activity patterns. However, our analysisdid not take into account fine scale (hourly) temperature var-iability, and it is this fine scale temperature variation to whichungulates may be responding. Hourly temperature variationwithin the diurnal period should allow ungulates to modulatetheir activity over the course of the day, increasing activityduring the cooler periods at dawn and dusk and reducingactivity during hot periods at midday (Owen-Smith andGoodall 2014). Thus, if predators are driving an increase indiurnal activity (as suggested by our results), understandinghow temperature influences the diurnal activity patterns ofungulates will require temperature data collected at a fine res-olution. In contrast to this idea that activity is reduced at higher

temperatures, we did detect a significant increase in diurnalactivity at higher temperatures for elephants. Elephants areoften found at waterholes during diurnal periods (Haywardand Hayward 2012), and waterhole use by elephants increasesin Addo when temperatures are high (CJT pers. obs.). Thus,movement to and from waterholes, especially on hot days, islikely to increase the capture rate of elephants, even if cameratraps are not located at waterholes.

Activity patterns are shaped by evolution with activitybursts influenced by predation risk (this study), resource ac-quisition (Monterroso et al. 2013), competitive interactions(Kronfeld-Schor et al. 2001) and temperature fluctuations(Maloney et al. 2005; Owen-Smith and Goodall 2014).Untangling the relative effects of these drivers is tricky, butinvestigating the activity patterns of prey species with respectto the activity patterns of their main predators provides anindication of the relative importance of predation pressure ingoverning prey species’ space occupancy (Owen-Smith2014). Our results suggest that the presence of predatorsplayed a significant role in species’ activity patterns, and inparticular, limiting nocturnal activity of buffalo and kudu.Along with this shift in activity pattern, buffalo also partitiontheir habitat use in time and space to minimize predation risk(Tambling et al. 2012). Putting our results into context, alongwith previous observations on activity patterns, suggests thatthe strength of the relationship between predators and prey(including the anti-predator response) may be related to eitherthe ratio of predators to prey or the density of the predators(Eriksen et al. 2011). In fenced reserves, predator den-sities are often inflated, leading to increased predator–prey ratios and predation pressure and frequent preypopulation declines (Tambling and du Toit 2005). Inthese fenced ecosystems, the impact of predators is po-tentially greater than in ecosystems where prey speciescan use large-scale spatial avoidance patterns. Our find-ings suggest that maintaining prey with predators hasthe added advantage of conserving the full spectrum ofprey adaptive behaviour.

Acknowledgments We thank the South African National Parks for thepermission to conduct the research in the Addo Elephant National Park.We thank Thando Mendala, Margeret Hook and Margeret Wisniewskafor their help in servicing the camera traps during the field work periods.The project was supported by Budget Van and Car Rental. A support wasalso received from the George Mason University and the Lincoln ParkZoological Gardens. CJTwas supported by a National Research Founda-tion Post-Doctoral Grant (71541:2010) and a Claude Leon Post-DoctoralGrant, and LM was supported by a National Research Foundation Grant(74189:2011).

Conflict of interest The authors declare that they have no conflict ofinterest.

Ethical standards The experiment conducted complies with the cur-rent laws of the country in which it was performed.

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