Red deer exhibit spatial and temporal responses to hiking activity

Red deer exhibit spatial and temporal responses to

hiking activity

Authors: Marion, Solène, Demšar, Urška, Davies, Althea L., Stephens,

Philip A., Irvine, R. Justin, et al.

Source: Wildlife Biology, 2021(3)

Published By: Nordic Board for Wildlife Research

URL: https://doi.org/10.2981/wlb.00853

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Red deer exhibit spatial and temporal responses to hiking activity

Solène Marion, Urška Demšar, Althea L. Davies, Philip A. Stephens, R. Justin Irvine and Jed A. Long

S. Marion (https://orcid.org/0000-0001-9774-7386) ✉ (srhm@st-andrews.ac.uk), U. Demšar, A. L. Davies and J. A. Long, School of Geography

and Sustainable Development, Irvine Building, Univ. of St Andrews, St Andrews, Scotland, UK. – SM, and R. J. Irvine, e James Hutton Inst.,

Craigiebuckler, Aberdeen, Scotland, UK. – P. A. Stephens, Conservation Ecology Group, Dept of Biosciences, Durham Univ., Durham, England,

UK. RJI also at: Frankfurt Zoological Society, Addis Ababa, South Africa Street, Ethiopia. JAL also at: Dept of Geography and Environment,

Western Univ., London, Ontario, Canada.

Outdoor recreation has the potential to impact the spatial and temporal distribution of animals. We explore interactions

between red deer Cervus elaphus and hikers along a popular hiking path in the Scottish Highlands. We placed camera traps

in transects at diﬀerent distances (25, 75 and 150 m) from the path to study whether distance from hiker activity inﬂu-

ences the number of deer detected. We compared this with the detection of red deer in an additional, spatially isolated area

(one km away from any other transects and the hiking path). We collected count data on hikers at the start of the path and

explored hourly (red deer detection during daytime), daily, diurnal (day versus night) and monthly spatial distributions

of red deer. Using generalized linear mixed models with forward model selection, we found that the distribution of deer

changed with the hiking activity. We found that fewer red deer were detected during busy hourly hiking periods. We found

that during daytime, more red deer were detected at 150 m than at 25 m. Moreover, during the day, red deer were detected

at a greater rate in the isolated area than around the transects close to the path and more likely to be found close to the path

at night. is suggests that avoidance of hikers by red deer, in this study area, takes place over distances greater than 75 m

and that red deer are displaced into less disturbed areas when the hiking path is busy. Our results suggest that the impact

of hikers is short-term, as deer return to the disturbed areas during the night.

Keywords: camera traps, outdoor activity, recreation interaction, spatio-temporal distribution, ungulate

Non-consumptive outdoor recreation activities such as hik-

ing, biking and skiing are increasing in popularity glob-

ally (Cordell 2008) and can have unintended impacts on

animals. e consequences of outdoor recreation activity

depend on the taxon, the type of recreation and its inten-

sity (Monz et al. 2013, Larson et al. 2016). An avoidance

response is one of the consequences of recreation activity and

can be deﬁned as a change in animal residency patterns, such

as change in home range or change in movement behaviour,

to reduce interaction with human activity (spatial avoid-

ance) (Wakeﬁeld and Attum 2006, Bateman and Fleming

2017, Coppes et al. 2017). Avoidance responses can also

be deﬁned as a change in the animal’s activity patterns in

disturbed areas, such as spending less time in these areas or

avoiding periods during which the disturbance takes place

(temporal avoidance) (Neuhaus and Mainini 1998, Bateman

and Fleming 2017, Fuglei et al. 2017).

Spatial avoidance of human recreation can induce various

animal’s responses such as increased stress levels (Barja et al.

2007), reduced population size (Wolfe et al. 2000) and

increased energy consumption (Cassirer et al. 1992). ese

responses depend on whether an animal is displaced into

equivalent or less suitable habitats (Gill et al. 2001) and

whether displaced animals expend more energy moving to

avoid human outdoor recreation (Cassirer et al. 1992, Nel-

lemann et al. 2010). is spatial avoidance behaviour repre-

sents a trade-oﬀ between the risk from the disturbance versus

the cost of movement (Lima and Dill 1990, Gill et al. 2001).

Temporal avoidance of outdoor recreation, especially in

ungulates, can also co-occur with spatial avoidance and can

be observed at a range of timescales: hourly, diurnal (day

versus night), daily or seasonal. Hourly avoidance implies

the return of an animal to its pre-disturbance location in

the short term (Reilly et al. 2017). Outdoor recreation

activity can also cause diurnal changes in animal activities

(Fuglei et al. 2017, Reilly et al. 2017), aﬀecting the diﬀer-

ent activities that animals undertake during day and night,

which are essential for maintaining species’ feeding and

sleeping patterns (Fuglei et al. 2017), and mating practices

(Frey et al. 2017). Finally, seasonal avoidance reﬂects the

Wildlife Biology 2021: wlb.00853

doi: 10.2981/wlb.00853

Subject Editor: Pia Anderwald. Editor-in-Chief: Ilse Storch. Accepted 1 July 2021

is work is licensed under the terms of a Creative Commons

Attribution 4.0 International License (CC-BY) <http://

creativecommons.org/licenses/by/4.0/>. e license permits

use, distribution and reproduction in any medium, provided the

original work is properly cited.

Downloaded From: https://bioone.org/journals/Wildlife-Biology on 16 Sep 2024

avoidance of an area during a speciﬁc period of the year, for

instance, as a result of variations in recreation intensity (e.g.

winter sports; Olson et al. 2017).

To study the diﬀerent spatial and temporal scales of

avoidance, we focused on the interaction between red deer

Cervus elaphus and hikers in Scotland. In this country, red

deer hunting (done through stalking and in Scotland often

referred to as such) is culturally important to some sectors

of society and is an economic asset for some land managers

(Macmillan and Phillip 2008). e culling of red deer is also

necessary to manage grazing and browsing impacts on veg-

etation (Albon et al. 2007) due to absence of natural preda-

tors. Furthermore, mountains, moors and woodlands that

are red deer habitat, are also attractive for hiking in Scot-

land. Indeed, the Land Access Reform (Scotland) Act 2003

provides rights to access Scotland’s countryside to anyone

engaged in outdoor recreation. With a popular hiking cul-

ture in the country, especially in the Highlands where most

of the red deer are located, there is potential for increased

human–wildlife interaction with consequences for the tem-

poral and spatial distribution of red deer (Sibbald et al.

2011). Our study complements previous studies in Scotland

(Sibbald et al. 2011, O’Neill 2016) which used GPS track-

ing and direct observation to monitor red deer interactions

with hikers at a very ﬁne spatio-temporal scale. Here, camera

traps are used to capture the impact of hikers on red deer

spatio-temporal distribution over a much longer period of

time and across a larger sample of hiking activities.

We ﬁrst explore how hiker activity impacts red deer

presence at four temporal scales: hourly (red deer temporal

detection during a quiet or a busy hiking hour), daily (red

deer temporal detection during a quiet or a busy hiking day)

and diurnal (detection during day versus during night), and

across ﬁve months. At the same time, we explore how hikers

may inﬂuence red deer presence at two spatial scales: ﬁrst, we

compare red deer numbers at three distances from a hiking

path, and second, red deer presence in areas near the path

(less than 150 m) versus in an isolated area (more than one

km away from the path). We test three hypotheses related to

the spatio-temporal behaviour of red deer. e ﬁrst hypoth-

esis is whether red deer avoid higher-intensity recreation;

here we expect that red deer will be observed less frequently

close to the path during a busy hour and a busy day (avoid-

ance hypothesis). e second hypothesis is whether red deer

appear more frequently near the path during the night than

during the day; we expect to detect more red deer during the

night near the path (diurnal hypothesis). Our third hypoth-

esis is that hiking activity inﬂuences red deer detection at a

larger spatio-temporal scale (displacement hypothesis). e

expectation is that red deer detection will be greater in the

isolated area (distant from the hiking path) compared to

areas in closer proximity to the hiking path.

Material and methods

Study area

e study area was a 2746 ha land holding (estate) in Glen

Lyon, Perthshire, Scotland (56°37′04.5″N, 4°10′50.7″W)

(Fig. 1). is area is managed for red deer hunting, which

occurs every year from the end of August to mid-October

for the male (stag) season and from mid-October to mid-

February for the female (hind) season. During our period

of data collection (below), the number of hunting days was

10 in 2017, 24 in 2018 and 18 in 2019. e number of

people involved generally varied between 2 and 5 people and

the location of the hunt is typically targeted at speciﬁc parts

of the estate which vary depending on the weather condi-

tions. In this area, hunters are the main predators of red

deer. In 2019, the population of red deer on this estate was

approximatively 382 (13.91 deer km

−2

; Deer Management

Plan, Breadalbane DMG). e estate is not fenced, so deer

can roam freely across the landscape and across neighbour-

ing properties. Red deer are not fed in this area, but mineral

(salt) licks are present in various locations. e estate is also

used for summer sheep Ovis aries grazing. Other terrestrial

resident animals include, for example, small populations of

badgers Meles meles, red foxes Vulpes vulpes and pine martens

Martes martes.

Vegetation in the area consists of a mixture of open heather

Calluna vulgaris, grassland (e.g. Agrostis capillaris, Antho-

xanthum odoratum or Muhlenbergia rigens) and peat (e.g.

Sphagnum compactum or Eriophorum vaginatum), typical of

the Scottish Highlands, with some plantation (commercial

conifer plantation) and semi-natural broadleaf woodland

cover conﬁned to low-lying areas. e estate includes a

17 km circular hiking route that takes in four Munros

(mountains with summits over 914 m). Climbing as many of

the 282 Munros as possible is popular with Scottish hikers.

e recreation trail in our study area and the four Munros to

which it gives access, are therefore very attractive to hikers.

Data collection

Data collection occurred over three periods: from the begin-

ning of August to mid-November 2017, from mid-June to

the end of October 2018 and from the end of May to end of

October 2019, for a total of 7077 camera trap survey eﬀort

days. We chose these three periods of data collection to rep-

resent intensive times for hiking activity which overlap with

the calving season and hunting period. us, this busy time

represents the period where the hiking activity can poten-

tially interfere with red deer movement and consequently

aﬀect red deer management on the estate.

e spatio–temporal distribution of red deer along the

hiking path was quantiﬁed using transects of three camera

traps at distances of 25, 75 and 150 m on one side of the

hiking path (the transects were perpendicular to the path).

e choice of these distances was informed by two previous

studies in Scotland which suggested that red deer maintain

a distance of 100 m (O’Neill 2016) to 250 m from hiking

paths (Sibbald et al. 2011). However, these studies were

carried out in low elevation areas with limited topographic

variation, higher recreational visitor numbers (as high as 300

tourists per day) and in landscapes with more forest cover

(Sibbald et al. 2011). In contrast, our study area usually sees

only dozens of hikers on a busy day in a landscape character-

ised by relatively low-growing vegetation and more marked

changes in elevation. Furthermore, in the case of our study,

we expected that the hiking activity was largely associated

with the hiking path and this study design aimed to capture

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the ﬁner scale response of red deer along the hiking path.

We therefore designed the study to detect the response of

red deer at a higher resolution up of to 150 m (i.e. being

able to detect red deer at multiple points from 0 to 150 m)

as we expected the hikers to impact red deer distribution at

a ﬁner scale.

e transects were set up in three diﬀerent points along

the hiking path: East (TE), West (TW) and North (TN) in

all three years (2017–2018–2019) (Fig. 1). e locations of

these transects were chosen to be close to the hiking path but

separated from one another (by more than 3 km along the

hiking path). A separate transect (in 2019 only) was set up

in an isolated area of the landscape with no path nearby (the

closest distance to the hiking path was 1 km). e location

of this isolated transect was chosen for its general landscape

similarity to the other areas; that is, the presence of a high

elevation ridge (such as in TE, TW and TN) with vegeta-

tion similar to that of the East and West areas. e north

transects and the isolated transect are similar due to the pres-

ence of a ridge, but the vegetation is not identical nor is the

amount of hiking activity.

e number of cameras and their set up in each year var-

ied due to availability of functioning equipment (Supporting

information) and insuﬃcient cameras were available to deploy

at every position in every year. However, we ensured that the

camera distribution among the transects provided us with an

overview of the spatial distribution of the red deer on both sides

of the hiking path (south and north part of the hiking path)

and aimed to limit eﬀects that diﬀerent camera brands might

have at diﬀerent locations of the path and thus we rotated our

camera trap locations each year. erefore, in 2017, the east

and the west areas each consisted of four transects for a total

of 12 camera traps per area and the north transect consisted of

three transects for a total of nine camera traps. ese transects

of cameras were alternated between the north and south side

of the hiking path approximatively every two weeks. In 2018

and 2019, the east and the west areas comprised two tran-

sects each for total of six camera traps per area (south side of

the trails used) and the north area consisted of one transect of

three camera traps (south side in 2018 and north side in 2019)

(see the Supporting information for details).

We used wooden poles to position each camera trap at

a height of approximatively 1.10 m and, as dictated by ter-

rain (i.e. ﬁeld of view not running up slope), facing either

away from or parallel to the hiking path. We calibrated every

camera trap to trigger as many photos as possible per detec-

tion to increase the chance of clear photos to more accurately

estimate (i.e. count) red deer numbers from camera images.

is depended on the individual camera model; three for the

Bushnell, eight for the Browning and 10 for the Reconyx

cameras. Cameras were calibrated to re-trigger with minimal

delay. We visited each camera trap at least once a month to

collect SD memory cards, change batteries and perform gen-

eral maintenance.

We recorded the dominant vegetation type at each cam-

era location, focusing on the direction that the camera was

facing. For this we used an existing vegetation classiﬁcation

protocol (JNCC 2010) and identiﬁed the following ﬁve veg-

etation types: wet dwarf shrub heath, dry dwarf shrub heath,

montane vegetation, bare peat and blanket bog.

Figure 1. Study area in Scotland, with locations of camera trap transects grouped in three areas close to the hiking path (red dashed line)

and a fourth transect in an isolated area. e blue triangle shows the people counter location near the start of the hiking trail.

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To count how many people entered the path we used

a Chambers RadioBeam People Counter RBX_EB, which

allowed us to record the number of hikers passing by the

counter every hour and every day (Fig. 1). is counter was

present for all camera trap periods of activity. is single

counter was placed at the start of the circular hiking path.

To account for the fact that our counter was positioned to

double-count hikers (i.e. when they entered and when they

exited the path), we ﬁrst took the total number of hikers

the counter captured per day and divided this by two. e

number obtained was the maximum of hikers present in the

area during a day. For each hour of this day, we added the

hourly number of hikers detected in that hour to the num-

ber of hikers detected in the previous hour until the daily

maximum number of hikers was reached. After this, we sub-

tracted the hourly counts until we reached 0 (typically later

in the evening). e counter is an eﬃcient way to record the

overall number of hikers present on the study area but the

direct link with camera traps detections is limited due to the

counter’s distance from the camera traps and the individual

behaviour of the hikers (speed and navigation choice). Due

to an interference problem with the automated counter (false

detections triggered by vegetation growing in the detection

zone), three brief periods were removed from our analysis

(18 Jun 2018 to 24 Jun 2018, 7 Jul 2018 to 9 Jul 2018 and 3

Aug 2019 to 15 Aug 2019). Camera trap photos taken dur-

ing these periods were also removed from all analyses involv-

ing the number of hikers (i.e. the avoidance hypothesis).

To assess whether most of the hikers stayed on the track

and whether the hiking intensity was similar amongst the

diﬀerent camera traps, we collected GPS tracking data from

hikers. During the three-year period of our study, we sam-

pled 60 days of hiking activity using GPS tracking. ese

days were evenly distributed between weekday and week-

end days. We approached hikers starting their walk between

07:00 and 13:00 and asked them to carry a GPS tracker

(i-Blue 747proS GPS Trip Recorder) after explaining the aim

of the project. If they agreed, one GPS tracker was given to

each group of hikers. Hikers were asked to leave their GPS

tracker in a drop box located at the end of the path. We used

GPS data to estimate the percentages of hikers performing

the full hiking loop, passing the counter on their way in, and

out of the upland portion of the trail, and next to each of our

camera traps transects. We performed a kernel density esti-

mation of all the GPS tracks using a 10 m kernel bandwidth.

Data processing and analysis

Red deer can stay in the same area, including in front of a

camera, for long periods of time, resulting in a large number

of images for the same detection event (e.g. when foraging in

front of a camera). us, to avoid counting the same animal

multiple times, we speciﬁed red deer observations as being

independent when more than 10 min elapsed between two

consecutive detections. Previous studies have used diﬀerent

times to determine independent camera trap observations

(e.g. 30 min; Sollmann 2018) but we decided to use 10 min

due to the small size of the study area and the relatively large

number of animals.

We separated the statistical analysis by hourly and daily

levels of hiking activity and night versus day. is separation

made it possible to focus on speciﬁc temporal scales: during

the day only for the impact of the level of hiking activity, at

hourly and daily temporal scales (avoidance hypothesis), and

at a broader temporal scale with the comparison between

night and day (i.e. the period ‘day’ as a proxy of hiking activ-

ity; diurnal hypothesis).

We used the people counter data to assess the overall

number of hikers present on the path for each hour, each

day and each month (Fig. 2, Supporting information). We

classiﬁed the level of hiking activity for each hour and each

day as ‘quiet’ or ‘busy’. e level of activity ‘quiet’ was when

the number of hikers was below the hourly mean number

of hikers (8) and ‘busy’ when above this mean (8) (Fig. 2a).

We also used this number to classify quiet versus busy days.

We used this categorization as a proxy for hiking activity

instead of the raw number of hikers as we did not expect

a linear relationship between the number of hikers and red

deer spatio-temporal avoidance, and to limit uncertainty due

to the counter itself. However, to assess the impact of diﬀer-

ent levels of hiking activity, we also calculated quartiles of

the total number of hikers per day: 25%: 0; 50%: 4; 75%:

11 (Supporting information). We used the sunset and sun-

rise times of each day as delimiters of night and daytime.

As the location of the study area is at 56.6° latitude, sunrise

and sunset times vary substantially across the observational

period, from 04:23 to 07:15 and 16:35 to 22:11 respectively.

We obtained the exact sunset and sunrise times for each day

using the function sunriset from the package maptools (1.0-

2; Bivand and Lewin-Koh 2015) in R ver. 3.5.2 (<www.r-

project.org>).

Detection rate (DR)

For the purpose of our analysis, we deﬁned the level of sur-

vey eﬀort associated with each camera trap location as the

number of days when that camera trap was working. e

survey eﬀort took into consideration that some cameras were

working only during busy days or only during quiet days. We

calculate the average detections per camera day. is average

was called the detection rate (hereafter DR) of each camera.

We resampled many of the same locations more than

once over the course of our study (Supporting information).

us, we summed the number of detections and the number

of working days for each camera trap location. We ﬁrst visu-

ally compared the DR of each camera for each distance from

the hiking path during diﬀerent periods of hiking activity:

hourly (quiet versus busy), daily (quiet versus busy) and dur-

ing day and night using comparative boxplots. In the next

section, we used the statistical modelling to further explore

these results.

Drivers of red deer detection

To incorporate additional environmental variables (e.g.

elevation and vegetation) into our hourly and daily hiking

activity (busy versus quiet) and time of day analyses (day

versus night), we used three generalized linear mixed mod-

els (GLMMs) with a negative binomial distribution for the

dependent variable (detection counts; Table 1). Our study

site is characterized by low-lying vegetation and an open

landscape; thus topography is the main factor inﬂuencing

red deer detectability. Cameras were placed so that they faced

either parallel to or away from the direction of the hiking

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trail. We therefore expect relatively uniform detectability

amongst the diﬀerent camera trap locations, which warrants

use of a GLMM rather than a model that explicitly models

the detection process (Gorosito et al. 2016). For the hiking

activity (only during daytime), the dependent variables were

the sum of the detections per camera for each hour or each

day. For the time of day analysis, the dependent variable was

the sum of the detections per camera during night or day

of each day the camera trap was working. Four ﬁxed eﬀects

were included in our models: three class variables (factors)

which included distance from the path (close (25 m), moder-

ate (75 m) or far (150 m)), vegetation type (ﬁve classes listed

above), and the level of hiking activity (busy versus quiet)

or the time of the day (day versus night) and one continu-

ous variable: elevation (in metres). We used an interaction

term between the distance and level of hiking activity for the

Figure 2. Number of hikers over the periods of camera trap deployment. (a) Distribution of the cumulative number of hours classiﬁed as

quiet (≤ 8) or busy (> 8) from the automatic counter. (b) Mean number of hikers per hour and (c) per day. In both panels, error bars

indicate the standard deviation.

Table 1. Generalized linear mixed model (GLMMs) dependent variables and model covariates. Two separate models with a negative bino-

mial distribution were used: one for the hiking activity (daytime data only), one for the time of the day (all data). (×) indicates an interaction

term (**) indicates the category used as a reference.

GLMM analysis

Dependent variables Model covariates Type of variables and number of levels

a) Hiking activity Sum of the detections per camera o

during quiet or busy periods

Distances from hiking path Categorical: 3 levels (25**, 75 and 150 m)

Elevation (in meters) Continuous

Level of hiking activity Categorical: 2 levels (quiet versus busy**)

Distances × level of hiking

activity

Interaction term

b) Period of day Sum of the detections per camera

during the night or the day

Distances from hiking path Categorical: 3 levels (25**, 75 and 150 m)

Elevation Continuous

Time of the day Categorical: 2 levels (day versus night**)

Distances × time of the day Interaction term

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ﬁrst model and between the distance and the time of the day

for the second model. After testing for collinearity amongst

the potential variables, we removed vegetation from the satu-

rated model to avoid correlation (i.e. vegetation varies with

elevation; Zuur et al. 2017). e transect ID and the camera

trap location ID were used as nested random eﬀects. We used

the packages glmmTMB (1.0.2.1, Brooks et al. 2017) to ﬁt

the most complex model. en, we ﬁt diﬀerent models using

the ‘dredge’ function in the R package MuMIn (1.43.17,

Bartoń 2020). is function ﬁt models of all the possible

combinations of the above covariates, presented in Table 1.

We then selected models using the Akaike information cri-

terion corrected for small sample sizes (AICc) (Akaike et al.

1973). Following Richards et al. (2011), we retained for

inference all models with ΔAICc < 6, except those that were

more complicated versions of any model with a lower AIC.

Hourly and monthly variations: area comparison

To test if daily red deer detection diﬀered between the isolated

area and areas within 150 m of the hiking path, we compared

daily detection patterns using kernel density curves of the tem-

poral records from each area (east, west, north and isolated).

For this, we used the non-parametric approach suggested by

Ridout and Linkie (2009) where camera trap detections are

considered as random samples from a continuous distribution

over 24 h and used to estimate a probability density function

(Lashley et al. 2018). We also calculated the hikers’ kernel

density curves using the automatic counter information.

From the two daily activity curves for red deer and hikers,

we calculated the coeﬃcient of overlap Δ which estimates the

temporal overlap in activity between two species: red deer and

hikers (Ridout and Linkie 2009, Niedballa et al. 2019). is

coeﬃcient ranges from 0 (no temporal overlap) to 1 (full tem-

poral overlap) and is the joint area under the probability den-

sity functions of the estimated daily activity density curves for

both species. ree diﬀerent methods can be used to estimate

Δ and we used the estimator Δ

as it is the most appropriate

for sample sizes larger than 50 (Ridout and Linkie 2009). It

uses vectors of densities estimated at the time of observation

of the two species. We calculated the 95% conﬁdence interval

of these estimates from 1000 bootstrap samples (of red deer

and hikers). Finally, to incorporate seasonal variations in red

deer detection, we also examined the monthly coeﬃcient of

overlaps for each area. For this, we used the R package overlap

(0.3.3, Meredith and Ridout 2014).

Results

Hiking activity

e peak in hiking activity occurred during the middle of

the day with a mean of 12 hikers at 12:00 (Fig. 2b), with a

mean of 13 hikers during the weekend and ﬁve hikers dur-

ing weekdays (Fig. 2c). e hourly mean number of hik-

ers was constant between May and November with a slight

decrease over time (highest hiking activity in June and lowest

in October) (Supporting information).

We collected 252 hikers’ GPS tracks from which 83% of

the hikers completed the full delimited hiking loop and 90%

of the hikers tracked did not went go track (Fig. 3); some hik-

ers only completed some part of the hiking loop but did not

went oﬀ track (i.e. they walked the same way in and out).

Hikers were entering and exiting the area by going through

the laser counter 98% of the times. e percentages of hikers

walking on the hiking path close to our camera traps were

87% in the east area, 94% in the west area and 86% in the

north area. We did not record any hikers walking close to our

isolated transect. us, the percentage of hikers visiting each

area is comparable to each other and the counter information

can be use as indicator of the hiking intensity in the study area.

Detection rates

We obtained 3054 independent detections of red deer from

7077 camera trap survey eﬀort days. For each distance, the

DR (sum of detections per survey eﬀort (sum of days)) was

higher during quieter hiking hours (avoidance hypothesis,

Fig. 4a) and lower closer to the path than further away (Fig.

4a–b). At the day scale, we did not detect more red deer dur-

ing quiet hiking days than during busy days (Fig. 4b). ere

were more detections of red deer during night than day at

close and moderate distances (Fig. 4c) (diurnal hypothesis).

To ensure that these results are not dependent on the

choice of the point at which to separate quiet and busy hours

and days we used diﬀerent quantiles of the total hourly num-

ber of hikers as separators (Supporting information). e

results were similar to the mean when the median and the

quantile were used as separators, with more detection dur-

ing quiet hours than during busy hours and no diﬀerence

between quiet and busy days.

Drivers of red deer detection

To identify determinants of red deer detection, we focused

on the diﬀerence between busy versus quiet hours and days

(hiking activity; avoidance hypothesis) and night versus day

(diurnal hypothesis), also taking account of the elevation as

an environmental covariate (Table 2). We found that the

hourly detection of red deer was well explained by the hik-

ing activity, the distance from the path and the elevation (as

they were covariates of the best model in Table 2a). Hiking

activity and elevation activity were the only variables retained

in each model of the conﬁdence set (ΔAICc < 6). We found

that more deer were detected during quiet hiking hours and

more frequently far 150 m from the hiking path than near, at

25 m (Avoidance hypothesis) (Table 3a). At the day scale, we

found that the detection of red deer was well explained by the

distance from the hiking path and the elevation (Table 2b).

We also found that more red deer were detected further away

from the hiking path than at 25 m (Table 3b). Moreover, we

also found that more red deer were detected during night

than during days (diurnal hypothesis, Table 3c) with more

red deer detected further away from the path than at 25 m

during the day (Table 3c). Finally, we found that fewer red

deer were detected in lower elevation (Table 2, 3).

Hourly and monthly variations: comparison of

areas

We explored the detection patterns of deer and hikers in

each of the four transect groups separately (displacement

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hypothesis; Fig. 5). For both the East and West groups, we

observed highest deer presence from around 18:00 until

06:00 (respective DR between 0.06 and 0.04), with low red

deer presence (near 0) outside this period (Fig. 5a). In the

North group, red deer were mainly detected between 18:00

and 22:00 (DR around 0.01), with a few detections between

midnight and 9:00 (DR between 0 and 0.05). As in the east

and west groups, fewer animals were detected in the middle

(DR close to 0) of the day. Red deer detections at the isolated

site show a peak in the morning around sunrise (DR around

0.02) with a second detection peak around 21:00 or around

sunset (DR between 0.02 and 0.04). During daytime, red

deer were more detected in the isolated area than in other

locations at the same time (DR around 0.01).

e percentage of temporal overlap between red deer and

hikers for the east and west groups was 30.8% and 33.6%,

respectively (Fig. 5b). In the north area, this percentage was

46.8%. In the isolated area, the percentage of temporal overlap

was 67.8%, representing a large diﬀerence over the three tran-

sect areas. e bootstrap CI between the isolated area and path

areas did not overlap which showed that red deer detection in

the isolated area and in areas close to the hiking path diﬀered

signiﬁcantly. We also compared the detection patterns and the

coeﬃcient of overlap for each month separately and found

similar overlap patterns in each area (Supporting information).

Discussion

Our study focused on how hiking activity inﬂuences the spa-

tial and temporal distribution of red deer in an area of the

Scottish Highlands that includes a popular hiking path. We

showed that red deer avoided areas within 150 m of the hik-

ing path during busy hiking times (avoidance hypothesis)

and during daylight more generally (diurnal hypothesis).

We also showed that during daytime, more red deer were

detected at 150 m than at 25 m. In contrast, there was no

diﬀerence in detection at close distances to the path during

night versus during day. We also showed that during busy

hiking periods (daylight), red deer were more active in an

isolated area (> 1 km away from the trail) than in areas adja-

cent to the path (displacement hypothesis).

Sibbald et al. (2011) also found diﬀerences in red deer

use patterns between quiet and busy days. However, they

detected this diﬀerence within 100 m of the path, whereas

we found more deer at 150 m during the day. Red deer

avoided the hiking path during busy hiking periods and

seem to keep a distance greater than 75 m during quiet peri-

ods. e diﬀerence in results between Sibbald et al. (2011)

and our study might be due to the more varied topography

and generally open, low-growing vegetation, which increases

visibility over longer distances. Speciﬁcally, the open land-

scape of our study area might increase the direct sighting of

hikers by red deer. In this upland terrain, spatial avoidance of

hikers by red deer appears to result in displacement of deer to

distances greater than 75 m from the hiking path.

In our study, red deer were more frequently detected dur-

ing night than during day in areas close to the path (< 150

m). is aligns with Coppes et al. (2017) who found that

red deer occupy less disturbed areas during the day and move

towards human recreational areas at night. To explore the

eﬀects of human activity on a broader spatial scale, we com-

pared our transects along the hiking path with an isolated

transect in a similar habitat situated remote from the hiking

path (more than one km away). During day, and thus during

period when hiking typically occurs, red deer were more fre-

Figure 3. Density of hiking activity derived from GPS tracks (n = 252) and camera trap transects in our study area in Glen Lyon, Scotland.

e density values were calculated using kernel density estimation with a bandwidth of 10 m. is shows that the majority of GPS hikers

kept to the hiking trail (which corresponds to the areas with high density – in red) with only occasional individuals exiting the trail and

crossing the area through the gullies.

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quently detected in the isolated area. erefore, spatial pat-

terns of avoidance have an important temporal component.

e daily pattern of red deer detection in our study shows

the expected crepuscular diurnal pattern of activity with a

peak activity in the morning and in the evening (Georgii

1981). is is evident at both the isolated and path tran-

sects which might explain the diﬀerence in ﬁndings between

the hourly and daily scales. At the daily scale, few deer were

detected in the middle of the day when most of the hik-

ing activity occurred, while the overall number of red deer

can be high due to early and late detection. However, higher

daylight detection in the isolated area may indicate that the

deer moved from areas near the path to more distant terrain

to avoid hikers. In Scotland, the home range of adult female

Figure 4. Detection rate (DRs; the ratio of the sum of the number of detections to the total number of working camera days) for red deer,

where each point is a camera trap location. (a) DRs during busy versus quiet hours (the mean number of hikers every hour (mean = 8) was

used as the separator) compared across the three distances. (b) DRs during busy versus quiet days. (c) DRs during night versus daytime

(delimitated using the sunrise and sunset times of each day) compared across the three distances.

Table 2. Selected GLMMs (ΔAICc < 6) and variables retained for red deer detection depending on the hour hiking activity (a), the day hiking

activity (b) or the period of the day (night versus day, c). The dependent variables were the number of detections per camera trap during each

hour (a), each day (b) or during the day and night of each day (c). Each model was ﬁtted using a negative binomial family and the transect ID

and the camera trap location were used as a nested random effect.

Model retained

df LogLik AICc ΔAIC Weight

a) Hiking activity hour

Hiking + distance + elevation 8

−4774.09

9564.2 0 0.76

Hiking + elevation 6

−4778.12

9568.2 4.06 0.1

b) Hiking activity day

Distance + elevation 7

−2457.2

4928.4 0 0.629

Elevation 5

−2461.53

4933.1 4.64 0.062

c) Period of the day

Period × distance + elevation 10

−6579.14

13 178.3 0 0.986

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red deer varies from one to ﬁve km

, while adult males range

over greater distances and areas (Clutton-Brock and Albon

1989). e distance between our isolated site and the tran-

sects near the hiking path ranges from 1 km to 2.5 km and

falls within these estimates of home range size, making daily

travel between locations possible. us, disturbances result-

ing from hiking activity may have generated a consistent

behavioural pattern where the deer favour less disturbed

areas. e deer are not fenced in and are free to roam into

neighbouring properties so comparing these ﬁndings with an

area of lower human activity, such as a site outside our study

area with no hiking activity, could improve understanding of

this process of displacement.

We did not observe a diﬀerence in the hourly/daily tem-

poral distribution of red deer detections during busy hiking

months (e.g. June) compared with other months. Previous

studies have identiﬁed a seasonal response to disturbance,

relating to peak tourism months. For example, a study in

Norway found a diﬀerence in reindeer escape distance behav-

iour in relation to annual tourism patterns (Reimers et al.

2006). is distinction may be due to large diﬀerences in the

level of recreational activity between the summer and winter

in some areas, which is perhaps less marked in our study

area (i.e. the area is still accessed by hikers into October and

November).

Some aspects of our approach could be considered for

improvement in future work. First, due to technical limita-

tions, we collected data in only one isolated area away from

the path, and thus the presence of red deer during the day

in this area – which diﬀered from the areas next to the path

– may be due to location speciﬁcity. Further studies using

multiple isolated areas could be used to assess the diﬀerences

in hourly detection of red deer. Second, our study lacks a

before–after–control–impact design, so the long-term eﬀect

Table 3. Results of the best generalized linear mixed-effects models (GLMMs) of red deer detection depending on the hour hiking activity (a),

the day hiking activity (b) or the period of the day (night versus day, c). In (b) we also presented the hiking pressure GLMM which correspond to

our hypothesis as the hiking variable was not retained in the best model. Bolded rows show statistically signiﬁcant variables (p-value < 0.05).

Covariates

Estimate SE

a) Hiking activity hour

Best model: hiking + distance + elevation

Lognormal R

: R

m = 0.09, R

C = 0.17

Intercept −3.260 0.694

Distance 25 m Ref Ref

Distance 75 m 0.230 0.254

Distance 150 m 0.704 0.25

Elevation −0.004 0.001

Hiking (busy) Ref Ref

Hiking (quiet) 0.793 0.093

b) Hiking activity day

Best model: distance + elevation

Lognormal R

: R

m = 0.17, R

C = 0.27

Intercept −0.114 0.699

Distance 25 m Ref Ref

Distance 75 m 0.244 0.266

Distance 150 m 0.766 0.261

Elevation −0.004 0.001

Hiking pressure: distance × hiking

Lognormal R

: R

m = 0.09, R

C = 0.35

Intercept −3.031 0.330

Distance 25 m Ref Ref

Distance 75 m 0.310 0.279

Distance 150 m 0.871 0.274

Hiking (busy) Ref Ref

Hiking (quiet) −0.074 0.179

Distance 25 m × hiking (quiet)

Ref Ref

Distance 75 m × hiking (quiet)

−0.192 0.254

Distance 150 m × hiking (quiet)

−0.047 0.228

c) Period of the day

Best model: period × distance + elevation

Lognormal R

: R

m = 0.19, R

C = 0.42

Intercept 1.376 0.865

Distance 25 m Ref Ref

Distance 75 m 0.129 0.296

Distance 150 m 0.337 0.296

Elevation −0.005 0.001

TOD (night) Ref Ref

TOD (day) −0.864 0.102

Distance 25 m × period (day)

Ref Ref

Distance 75 m × period (day)

0.279 0.142

Distance 150 m × period (day)

0.625 0.132

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Figure 5. (a) Hourly total detection rates of red deer for each transect group (east, west, north and isolated). Cameras in the east, west and

north groups are close to the path (< 150 m), while the isolated group is distant from the path (> 1 km). e grey area shows the period

between the earliest and latest sunrise and sunset times. (b) Activity patterns of red deer (red lines) and hikers (blue dashed lines) for each

transect group. e grey shading shows the overlap in activity between red deer and hikers in the study area at time of the day. Activity

overlap was estimated from 1000 bootstrap samples of red deer camera trap detection and hikers counter information.

Downloaded From: https://bioone.org/journals/Wildlife-Biology on 16 Sep 2024

of hiking may have already resulted in a change to the spa-

tial distribution of red deer, such that animals preferentially

occupy surroundings areas (i.e. outside our focal area). A

wider comparison, at a broader geographical scale, could

help provide more insight into deer behavioural responses

and how these change with varied levels of hiking. Finally, in

this work, we assessed only the detection of red deer. Addi-

tional factors, such as the number of deer, their sex, the pres-

ence of forage competitors (e.g. sheep) and the presence of

juveniles (Neuhaus and Mainini 1998) could also impact the

spatio–temporal distribution of red deer and their interac-

tion with hikers (i.e. increase or reduce interaction). Poten-

tial interactions between these factors would also beneﬁt

from further study.

Conclusion

Deﬁning the spatial and temporal scales of interactions

between outdoor recreation activities and animals is crucial

to understand and manage these processes. We showed that

the impacts of hiking activity on red deer detections are

most likely occurring at an hourly scale, with more deer

detected during quiet hiking hours. At the daily scale and

at small distances (between 25 and 150 m), we observed

more red deer at 150 m from the path during the day. We

observed lower detection rates during the day than at night.

However, these results did not appear to depend on time

of year (within the times of year we studied). Our results

align with previous studies but, by extending the evidence

to more heterogeneous upland terrain, they provide new

insights into the spatial and temporal scale of disturbance

of red deer by outdoor recreation activity. e impact of

this on the ability of deer managers to locate animals and

carry out stalking or management culls will depend on how

this displacement aﬀects the ease of stalking access to the

areas that red deer now occupy. A better understanding of

how hiking inﬂuences deer detection within upland land-

scapes has the potential to reduce conﬂict between diﬀerent

users (e.g. land managers, sporting, recreation) and sup-

port social, economic and ecological elements of sustain-

able management.

Acknowledgments – is project is funded through a joint

James Hutton Inst. and Univ. of St Andrews collaborative

PhD Studentship. We would like to thank the Carnegie

Trust and the British Deer Society for their ﬁnancial support.

We would like to thank Elie Ancrenaz, Connor Milton and

Lucy Miller for their help with the data collection. We also

acknowledge North Chesthill Estate for their ﬁnancial and

logistical support.

Funding – is project is funded through a joint James Hutton

Inst. and Univ. of St Andrews collaborative PhD Studentship,

the Carnegie Trust and the British Deer Society.

Conﬂict of interest – e authors declare that they have no

conﬂict of interest.

Permit – e hikers’ GPS tracking experiment was approved

by the University Teaching and Research Ethics Committee

(UTREC) of the University of St Andrews (approval no.

GG13615). Participants were asked to give written informed

consent and the consent form was approved by UTREC.

Author contributions

JAL, RJI and PAS conceived the original idea and obtained

the funding. SM collected and analysed the data. SM per-

formed the statistical analysis with contributions from JAL,

UD, ALD and PAS. JAL, UD and ALD provided supervi-

sion at the diﬀerent stages of the project. SM wrote the ﬁrst

version of the manuscript. All authors contributed critically

to the drafts and gave ﬁnal approval for publication.

Data availability statement

Data are available from the Dryad Digital Repository: <https://

doi.org/10.5061/dryad.v9s4mw6tz> (Marion et al. 2021).

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