Spatial and temporal plasticity in free-ranging dogs in sub-Antarctic Chile

Abstract

Free-ranging owned dogs are a conservation concern worldwide, but knowledge on their movement ecology is only recently increasing. To examine unsupervised dog movements into wilderness, we attached Global Positioning System devices to 33 village and four rural dogs on a sub-Antarctic island in Chile during the four seasons of a year (n = 86115 locations). This corresponded to a quarter of the local free-ranging dog population based on a photographic mark-recapture survey. The largest maximum distance to the owner´s home was 20.4 km. The median home range size ranged between 15.8 (spring) and 24.4 ha (summer), but with great individual variation (1.6 ha - 148.8 km²). Nine individuals had home ranges > 100 ha in at least one seasonal monitoring; seven individuals performed excursions spending 1 - 6 nights in pristine nature, and two individuals accompanied tourists on trekking trips lasting 3 - 6 days. Remarkably; village dogs were quite active at night (40.7% of the locations). Top-ranked habitats in the compositional analysis of habitat use of village dogs were forest and infrastructure. However, coasts were also important at second order and peatbog at third order habitat selection. Our study revealed a high temporal and spatial plasticity of dog movement in sub-Antarctic ecosystems, likely interacting with wildlife. We conclude that future research should address predictors of problematic animals, which have been treated as “outliers” in many studies. In Chile, the control of legislation and education beyond the mere owner should be improved wherever dogs occur near sensitive wilderness areas.

Full text

Applied Animal Behaviour Science 250 (2022) 105610
Available online 21 March 2022
0168-1591/© 2022 Elsevier B.V. All rights reserved.
Spatial and temporal plasticity in free-ranging dogs in sub-Antarctic Chile
Elke Schüttler
a
,
b
,
c
,
*
, Lorena Saavedra-Aracena
a
, Jaime E. Jim´
enez
d
a
Sub-Antarctic Biocultural Conservation Program, Universidad de Magallanes, Teniente Mu˜
noz 166, Puerto Williams, Chile
b
Cape Horn International Center (CHIC), Puerto Williams, Chile
c
Institute of Ecology and Biodiversity, Department of Ecological Sciences, Casilla 653, Santiago, Chile
d
Department of Biological Sciences and Advanced Environmental Research Institute (AERI), University of North Texas, 1155 Union Circle #305220, Texas 76203-
5017, USA
ARTICLE INFO
Keywords:
Activity
Canis familiaris
Habitat use
Home range
Movement ecology
Subsidized predator
ABSTRACT
Free-ranging owned dogs are a conservation concern worldwide, but knowledge on their movement ecology is
only recently increasing. To examine unsupervised dog movements into wilderness, we attached Global Posi-
tioning System devices to 33 village and four rural dogs on a sub-Antarctic island in Chile during the four seasons
of a year (n =86115 locations). This corresponded to a quarter of the local free-ranging dog population based on
a photographic mark-recapture survey. The longest maximum distance to the owner´s home was 20.4 km. The
median home range size ranged between 15.8 (spring) and 24.4 ha (summer), but with great individual variation
(1.6 ha - 148.8 km
2
). Nine individuals had home ranges >100 ha in at least one seasonal monitoring; seven
individuals performed excursions spending 1–6 nights in pristine nature, and two individuals accompanied
tourists on trekking trips lasting 3–6 days. Remarkably, village dogs were quite active at night (40.7% of the
locations). Top-ranked habitats in the compositional analysis of habitat use of village dogs were forest and
infrastructure. However, coasts were also important at second order and peatbog at third order habitat selection.
Our study revealed a high temporal and spatial plasticity of dog movement in sub-Antarctic ecosystems, likely
interacting with wildlife. We conclude that future research should address predictors of problematic animals,
which have been treated as “outliers” in many studies. In Chile, the control of legislation and education beyond
the mere owner should be improved wherever dogs occur near sensitive wilderness areas.
1. Introduction
Estimated in almost a billion individuals, domestic dogs (Canis
familiaris) are the most abundant carnivore species worldwide (Gomp-
per, 2014). In other words, there is one dog for eight people on earth -
under different outdoor conditions. Leashed dogs have limited access to
nature, free-ranging owned or abandoned dogs have partial or full ac-
cess, and feral dogs occur in nature without human help (Boitani et al.,
2017). All dogs, but particularly those without restriction, can cause
conicts with wildlife through predation, competition, disease trans-
mission, and hybridization (reviewed in Doherty et al., 2016; Hughes
and Macdonald, 2013; Twardek et al., 2017; Young et al., 2011, who
also reviewed the positive effects of dogs in conservation). More subtle
impacts are disturbance of wildlife by dogs resulting in higher vigilance
levels (Vanak et al., 2009), shift in activity patterns (Zapata-Ríos and
Branch, 2016), or reduced foraging times (Suraci et al., 2016). Most dogs
are subsidized predators (Gompper, 2014), and as such they reach
remarkably high population densities (e.g., 2.5–10.3 rural dogs/km
2
in
Tanzania, Lembo et al., 2008), outnumbering native carnivores (e.g.,
3–85 times more abundant in Brazil, Paschoal et al., 2016). The high
densities of dogs, together with their often-unrestricted mobility,
explain why domestic dogs are among the group of invasive mammalian
predators with the most pervasive impacts on vertebrates (188 spp.,
Doherty et al., 2017).
In the context of conservation, movement ecology is an emerging
discipline holding a promise for enhancing wildlife management plan-
ning (Allen and Singh, 2016). A better understanding of an animal´s
space use can inform about the causes and patterns of movement and
how these are linked to environmental change (conceptual framework in
Nathan et al., 2008). In fact, if movement data for endangered mobile
species is available, it is generally reected in species status assessments
(Fraser et al., 2018). The rapid recent technological progress in accuracy
and resolution of tracking data allows a quasi-continuous record of how,
where, and why animals move (Kays et al., 2015). Along with the
* Corresponding author at: Sub-Antarctic Biocultural Conservation Program, Universidad de Magallanes, Teniente Mu˜
noz 166, Puerto Williams, Chile.
E-mail address: elke.schuttler@umag.cl (E. Schüttler).
Contents lists available at ScienceDirect
Applied Animal Behaviour Science
journal homepage: www.elsevier.com/locate/applanim
https://doi.org/10.1016/j.applanim.2022.105610
Received 25 November 2021; Received in revised form 7 March 2022; Accepted 16 March 2022

Applied Animal Behaviour Science 250 (2022) 105610
2
development of rigorous analytical methods to maximize the extraction
of information contained in autocorrelated data (e.g., Fleming et al.,
2015), the range of scientic questions answered by tracking data is
steadily growing. In the eld of biological invasions, tackling the causes,
patterns, and consequences of movement of invasive species is central to
adequately design control actions (Nathan et al., 2008) and reveal the
links to the dynamics of human movement and afliated pets (Jeltsch
et al., 2013).
A better understanding of the movements of free-ranging dogs for
conservation is particularly relevant in parts of the world where these
roam freely, which is true in many countries in Africa, Asia, and South
America (Reece, 2005; Warembourg et al., 2021). Fortunately, during
the last decade dog movements have been increasingly tracked, for
example in Chad, to determine whether hunting activities of owners
inuenced the space use of dogs (Wilson-Aggarwal et al., 2021); in
China, to evaluate exposure to canine distemper virus in wild pandas
(Jin et al., 2017); and in Peru, to assess movement patterns across urban
landscapes for rabies control (Raynor et al., 2020). One general pattern
is that most dogs stay close to their owner´s home: Half of the xes were
recorded <100 m from home in Mexico (Ruiz-Izaguirre et al., 2015) and
80% within 200 m from home in Chile (Sepúlveda et al., 2015). How-
ever, maximum distances of individual dogs reached as far as 10.4 km in
Chile (P´
erez et al., 2018) and even 30 km in Australia (Meek, 1999).
Similarly, home range estimations (95% kernel isopleths) varied among
the tracked dogs of each study, ranging for example between 1–97 ha in
Kenya (Muinde et al., 2021) and 31–1161 ha in Australia (van Bommel
and Johnson, 2014). Also, the dogs´habitat use differed from study to
study: foraging habitat along rivers (Meek, 1999), at beaches (Ruiz-I-
zaguirre et al., 2015), in agroforests (Dos Santos et al., 2018), in pasture
habitats (Sepúlveda et al., 2015), and in crop elds (Parsons et al.,
2016). Night forays in free-ranging dogs (van Bommel and Johnson,
2014; Ruiz-Izaguirre et al., 2015), but also diurnal activity patterns
(Sepúlveda et al., 2015) have been described. Finally, seasonal space use
has been studied to a lesser degree (e.g., van Bommel and Johnson,
2014; Wilson-Aggarwal et al., 2021).
In Chile, there is one dog for every four people (Gompper, 2014),
while 31–50% of town dogs (Acosta-Jamett et al., 2010; Schüttler et al.,
2018) and 67–92% of rural dogs are allowed to roam free (Acosta-Ja-
mett et al., 2010; Sepúlveda et al., 2014; Silva-Rodríguez and Sieving,
2012). During the past years, studies on the Chilean dog-wildlife-society
problem have increased. Researchers have addressed the impacts of
free-ranging dogs on livestock (Montecino-Latorre and San Martín,
2018), endangered wildlife (Beltrami et al., 2021; Silva-Rodríguez and
Sieving, 2012), and human-associated factors of dog movement (Saa-
vedra-Aracena et al., 2021; Villatoro et al., 2016). In parallel, the Chil-
ean government has increased its awareness of the dog problem. In
2017, a new Chilean law (Nr. 21.020) aimed at regulating responsible
pet ownership, instructing the dog´s connement to the owner´s property;
meanwhile, the situation of feral dogs remains unsolved. An attempt to
include feral dogs into the Hunting Law in 2015 failed two months later
(Decrees Nr. 65 and 6), among other aspects, due to the denition of
feral dogs. They were dened as dogs in packs at a distance >400 m of a
settlement or isolated rural home, irrespective of the possible presence
of roaming owned dogs.
Our aim was to contribute knowledge on the movement ecology of
free-ranging dogs, from a little-known part of the world perspective. We
performed this study in the context of conservation, examining dog
foraging movements in a sensitive wilderness setting to inform man-
agement policies. Specically, we focused on assessing dog (1) ranging
distances to the owner´s home, (2) home range estimation during the four
seasons of the year, (3) diel activity patterns, and (4) habitat use. This
study provides a rst insight into how dogs move through sub-Antarctic
wilderness. The discussion of the results shows the global relevance of
dog connement wherever dogs roam free next to wildlife and high-
lights the need to strengthen research on the causes of individual
variability.
2. Methods
2.1. Ethical approval
We obtained signed informed consent from each participant by
reading together the aims and procedure of the study, anonymous data
storage, access to the results, lack of risks, and benets from partici-
pating (i.e., a 3-kg bag of dog food was provided to each dog owner). The
Scientic Ethical Committee of the University of Magallanes, Chile,
certied ethical approval of the instrument (Certicate 03/30/2016).
2.2. Study area
Our study was carried out in southernmost Chile (55◦S), on Navarino
Island (~2500 km
2
, Fig. 1). This region belongs to the Magellanic sub-
Antarctic ecoregion (Rozzi et al., 2012), classied as an archipelago
with low human impact (Jacobson et al., 2019), which is covered by
southern beech forests (Nothofagus spp.), peatbogs (Sphagnum spp.),
Andean habitats above the tree line, and shrublands. The climate is cool
and under oceanic inuence (-2 ◦C to +10 ◦C variation over the year);
water bodies are largely ice-bound in the winter (Tuhkanen et al., 1990).
The 2200 inhabitants live concentrated in the town of Puerto Williams.
Their income is based on artisanal shery, tourism, and small-scale
livestock farming. Terrestrial infrastructure is limited to the northern
coast of Navarino, some logging trails, and trekking routes, whereas
most of the island is pristine sub-Antarctic wilderness. However, new
maritime infrastructure, new roads, and land parceling and settling will
likely increase the access of people and free-ranging dogs to wilderness
in the near future.
Following a survey based on questionnaires, around one third of
owned dogs roam free on Navarino Island (31%, Schüttler et al., 2018)
and camera-trap data points to the existence of feral dogs (Contardo
et al., 2020). Free-ranging dogs represent a major conservation concern,
because (1) no native terrestrial predators exist and hence, dogs take the
role as a new guild of apex predators (along with domestic cats Felis catus
and American mink Neovison vison as medium-sized introduced preda-
tors, Schüttler et al., 2019), and (2) they might severely impact the
southernmost population of guanacos (Lama guanicoe), considered at
local extinction risk by Gonz´
alez (2010). Silva Rochefort and
Root-Bernstein (2021) propose that native camelids in Chile are
particularly vulnerable to predation by dogs as they may have evolved in
the absence of pack-hunting cursorial predators.
2.3. Dog censuses
For one year, we conducted seasonal censuses of free-ranging dogs in
the streets of Puerto Williams (May 2015, September 2015, November
2015, and March 2016). During each census, we walked through all the
streets of the village taking photos of all dogs without restriction of
movement, including dogs resting in courtyards with open access to a
street. This was repeated at different times during the four days of each
census. As some dogs had similar physical aspects, to assure the correct
identication of individuals, three independent reviewers revised the
photographic catalog and differences were discussed until reaching
consensus.
2.4. Dog-owner recruitment
We recruited 35 dog owners in Puerto Williams by revising photos of
the photographic catalog of the dog census together with the Munici-
pality’s veterinarian and selected those free-ranging dogs from which
roaming behavior was believed; limited to one adult (≥1 year) dog per
owner. We then approached the dog owners and asked them whether
they would participate in our study. In the rural area, we asked all six
farmers located on the northern coast of Navarino Island who owned
dogs to participate in the study. Only two dog owners in Puerto Williams
E. Schüttler et al.

Applied Animal Behaviour Science 250 (2022) 105610
3
refused to participate in the study, whereas all rural households
participated. During the one-year-long study in 2016/2017, four owners
(2 urban/2 rural) refused to participate in the second/third seasonal
monitoring (reasons given were risks of injury due to the collar). Upon
recruitment and in a face-to-face approach, we asked owners to answer a
questionnaire containing 22 questions on the dog’s age, sex, husbandry,
and hunting behavior, as well as the dog-owner relationship and basic
personal data of the owner. To be able to distinguish between forays of
unaccompanied dogs’ excursions and those together with the owner, the
owners were given a 3-weeks calendar in which they were asked to make
notes on the dates and places outside the village they visited together
with their monitored dog. Finally, the town veterinarian performed an
in-situ health check (general inspection and 9-point body condition
score following Laamme, 1997). The data of the summer surveys of 12
of these dogs have been used in Saavedra-Aracena et al. (2021) to
address the inuence of the dog-owner bond on dog movement.
2.5. GPS-tracking
We equipped a total of 41 dogs (35 village dogs and 6 rural dogs)
with commercial Global Positioning System (GPS) devices (Igot-U GT-
600, Mobile Action, Taiwan, 37 g) in sealed leather bags strapped to
adjustable animal collars around the neck. The weight of the GPS device
was always below 0.5% of the animal´s weight and all devices were
retained after completing the seasonal surveys to recharge the batteries.
Movement data was collected at 10-min intervals, only when an animal
was moving, during a 3-week period in the four seasons of the year
(between February 2016 and March 2017). With four dogs we could not
continue to work (two lost their collars during the rst survey, one dog
got lost and one dog changed owner). In nine occasions, dogs lost their
GPS collars (but in ve occasions these were found and returned by local
people), in only one occasion the data could not be retrieved from the
GPS unit. We repeated the monitoring in seven occasions, either when
the GPS got lost or when only some days were recorded. In summary, we
were able to collect the complete seasonal data for 26 village dogs and
for two rural dogs. Once the 3-week period was completed, the GPS
device from each dog was retrieved and the locations were checked
together with the owner on a digital map on a computer screen, unless
the owner declined to do so. We asked each owner whether during the
monitoring period the dog had accompanied trips (by car or walks
together with the owner) outside the village. Any car trip (depicted as a
route) was cross-checked with the owner and excluded from the anal-
ysis. That way, we ltered the data to use only presumably unaccom-
panied locations of the dogs.
2.6. Data accuracy
To estimate the accuracy of the GPS locations, we performed static
and mobile tests (Cargnelutti et al., 2006; Camp et al., 2016). We
compared the xes of the Igot-U GPS devices with the position described
by a portable highly sensitive GPS receiver (Garmin GPSmap® 60CSx).
For the static tests, we placed a GPS tag ~50 cm above ground to
simulate the height of a dog and recorded the xes every 10 min for
24 h. A total of 9 static tests were performed inside houses, in the forest,
and in peatbog habitats. For the mobile tests, we performed three 1-hour
walks with a leashed dog carrying a GPS device maintaining a distance
<5 m from the GPS receiver carried on the side. We then calculated the
errors by computing the differences between the readings on the GPS
device and receiver. The accuracy of the static tests was greatest for
peatbog (average error 13.2 m, range 1–117.5 m, n =447), followed by
forest (15.7, 0.7–85.3, n =414), and least inside houses (29.5,
0.5–1055.2, n =317), whereas the mobile tests had an average error of
Fig. 1. Study area showing the principal ecosystems and tracking data (n =86115) of 37 free-ranging dogs with unsupervised movements (a) on Navarino Island (b)
in southern Chile (c).
E. Schüttler et al.

Applied Animal Behaviour Science 250 (2022) 105610
4
6.2 m (0.2–27.3, n =15). We found that xes with a difference >50 m
between the GPS device and receiver (3.9%, n =1208) presented
abnormal elevation parameters recorded by the GPS device. We nally
excluded those xes with abnormal elevation parameters and with
inconsistent chronological history. Of 93092 xes we discarded 2907
(3.1% of the data) due to erroneous recording and 4070 locations which
were either replicates or from accompanied movements (nal
n=86115).
2.7. Data analysis
The population size of free-ranging dogs in Puerto Williams was
estimated with closed capture models in program MARK (White and
Burnham, 1999). We used three model types, constant capture and
recapture probability, time varying, and behavioral response to capture.
Best models were those with lowest values of the Akaike Information
Criterion adjusted for small sample size (AICc, Burnham and Anderson,
2002).
We used the software @trip PC (Mobile Action, Taiwan) to download
the locations and then analyzed the data in the R Environment (R Core
Team, 2021). We calculated the linear distances from each dog´s location
to its home and used descriptive statistics to describe them. Home ranges
were calculated through continuous time movement modeling (Cal-
abrese et al., 2016) using the ctmm package in program R. When we
visually evidenced range residence in the variograms of each dog´s sea-
sonal monitoring, we tted isotropic and anisotropic versions of
Ornstein-Uhlenbeck (OU) and OU Foraging (OUF) processes, which as-
sume autocorrelation in the data. We also tted models under the in-
dependent identically distributed (IID) data assumption. After AICc
model selection, the best model was used to estimate home ranges via
autocorrelated kernel density estimation (95% AKDE, Fleming et al.,
2015), whereas the IID model was used to provide the traditional 95%
KDE home range estimation. To adjust for over-estimation, for three
dogs with multiple overnight excursions we used the low value of the
95% condence intervals of the 95% AKDE estimate. The aquatic habitat
of the Beagle Channel was treated as a physical barrier. Therefore, we
created a buffer of 243 m from the coastline, beyond which we deleted
the home range area (calculated by taking the farthest location of a
swimming dog, 236.8 m, and adding the GPS error of 6.2 m). We applied
a Friedman test to compare home range sizes between seasons and
subsequently paired Wilcoxon signed rank tests with Bonferroni cor-
rections. To assign activity during day versus nighttime we calculated
daily sunlight times using R-package suncalc. We used two-sample tests
for equality of proportions with Bonferroni corrections to compare
whether the proportions of locations at day or at night were signicantly
different in seven types of habitats (Andean, coast, forest, infrastructure,
lakes, peatbog, and shrubland).
Finally, we used compositional analysis to determine habitat use
(Aebischer et al., 1993). This analysis compares proportional habitat use
versus availability through log-ratio transformation and evaluates the
signicance of nonrandom habitat use by using randomization tests (at
p<0.05). Based on the matrix of the mean differences between the used
and available log-ratios, habitats are then ranked from most to least
preferred (the times a habitat is used more than another in the ranking
matrix), but ranks can be interchangeable when habitats are not signi-
cantly more used. Habitat selection was considered at two levels: (i) home
range use within the study area or second order selection and (ii) habitat
use within the home range or third order selection (Johnson, 1980). For
this analysis, we dened the study area as the area covered by the 95%
AKDE estimates of all 33 village dogs adjusted for over-estimation as
described above. The home ranges were the individual AKDE estimates
and the single locations within the home ranges were used for third order
selection. We determined second order habitat selection for all village
dogs for each season, while we used a subset of nine village dogs with
>5% of their locations outside the urban area for third order selection,
again for each season. Rural dogs were only four individuals, while for
compositional analysis a minimum of six individuals are needed
(Aebischer et al., 1993); therefore, as a reference, we only provide per-
centages of locations per habitat pooled across all seasons. Following the
recommendations of Aebischer et al. (1993) in R-package adehabitatHS
zero values in the available habitat matrix were replaced by 0.01 and
statistical signicance in these matrices were determined by weighted
mean lambdas instead of usual lambdas. We performed this analysis for
ve habitat types excluding all locations within the urban area
(n =75422): coastal, forest, infrastructure, peatbogs, and pastures and
shrubland, hereafter shrubland (n =3990). Coastal included coastal
habitat and the buffer of 243 m from the coastline, and infrastructure
referred to roads, trails, and rural infrastructure (i.e., landll, industrial
areas, rural houses). Poorly used Andean habitats (n =656 locations of
4 dogs) and lakes (n =101 locations of 5 dogs) were excluded from the
analysis to reduce the number of null cells in the matrices (see Aebischer
et al., 1993). Due to this fact, the nal sample sizes of the eight analyses
(4 seasons for 2nd and 3rd order analyses each) vary. We used ArcMap
10.4 (ESRI, Redlands, USA) and the projection WGS 84/UTM zone 19S for
geoprocessing and mapping. The land cover classication (30 m resolu-
tion, year 2016) was updated by visual inspection in Google Earth TM
(Version 7.1.2.2600). For the background of our gures, we downloaded
satellite images from Bing Maps (3 m resolution) available through SAS
Planet (Version 171130).
3. Results
Dog owners had a mean age of 43.5 years (range 22–70), 56.8% were
female. The highest education levels were 35.2% technical or university
formation, 32.4% high school, and 32.4% less than high school. The
mean number of dogs per household was 2.5 (range 1–7).
3.1. Dog background
Most of the 37 participating dogs (summary of characteristics in
Table 1) received basic standard care; 70.3% were sterilized and 89.2%
received parasite treatment. Only 21.6% of the dogs were fed mainly
with leftovers. However, when asking the owners what the dog had
eaten the day before, the fraction of leftovers was higher (37.8%). Over
half of the dogs (54.1%) had ideal weight (body condition scores BCI 4
and 5, Laamme, 1997), and only 16.2% was underweight (BCI 1–3).
The questionnaires also gave insights into dog-animal interactions:
24.3% of the dogs brought prey home when returning from roaming
(mainly birds and body parts of livestock, both categories 36.4% each)
and 86.5% were observed harassing other animals, mostly livestock
(48.1%), followed by birds (22.2%), and other dogs (20.4%).
Table 1
Characteristics of 37 free-ranging GPS-tracked dogs in southern Chile. The data
is based on dog owner questionnaires and a health check by a veterinarian.
Parameter Value
Number of village/rural dogs 33/4
Born on Navarino Island (%) 89.2
Dogs owned for company (% village dogs) 87.9
Males/females (%) 54.1/45.9
Mean age of dogs in years (SD, range) 4.5 ±2.4 (1.3–10)
Uncastrated/unsterilized dogs (%) 29.7
Dogs vaccinated against rabies (%) 45.9
Dogs with parasite treatment (%) 89.2
Dogs receiving pet food (pellet) and/or meat (%) 48.7
Dogs receiving leftovers (%) 21.6
Dogs receiving a mix of the above (%) 29.7
Dogs fed in more than one household (%) 56.8
Mean body condition score of dogs (SD, range) 5 ±1.7 (1–9)
Dogs free-ranging during day and night (%) 75.7
Dogs free-ranging during day only (%) 24.3
Dogs having brought home prey (%) 24.3
Dogs observed harassing animals (%) 86.5
E. Schüttler et al.

Applied Animal Behaviour Science 250 (2022) 105610
5
3.2. Dog census
The mean number of free-ranging dogs in the village estimated
through capture-recapture models was 131.8 ±2 individuals (95%
Condence Interval 129.8–139) for summer, 140.7 ±6.1 individuals
(133.5–159.6) for autumn, 131 ±3 individuals (127.4–140.1) for
winter, and 126.2 ±1.4 individuals (125.2–132.4) for spring. For spring
the best model accounted for temporal variation in the capture and
recapture probabilities, for autumn for differences in the encounter
probability, and for summer and winter the best models had no behav-
ioral or temporal variations.
3.3. Movement ecology
In total, we collected tracking data of 37 free-ranging dogs (33
village dogs, 4 rural dogs) with unsupervised movements (n =86115)
with a mean of 647.5 ±290.6 locations (median =611, range =
102–1495) during 133 seasonal 3-week monitoring sessions with a mean
duration of 17.9 ±5.6 days (19, 4−34). The devices failed to collect
data during 3.4 ±3.8 days (2, 0−17) over the sessions. Considering the
mean number of individuals counted over the year, we monitored 24.9%
of the free-ranging village dog population.
The mean distance to the dogs´homes was 601.8 ±2074.4 m (median =
41.7 m, range =0–19.1 km, n =23025) during summer, 268 ±971.7 m
(40 m, 0–20.4 km, n =21289) during autumn, 192.5 ±688.5 m (36.2 m,
0–11.2 km, n =20154) during winter, and 259.4 ±891.7 m (32.7 m,
0–11.2 km, n =21647) during spring. Maximum distances to the
owners´homes were >500 m for all dogs, but one, during at least one of the
four seasons, 32 dogs (86.5%) had maximum distances >1 km, eight dogs
(21.6%) >5 km, and ve dogs (13.5%) >10 km. The longest distance
Fig. 2. Tracking data (points), 95% AKDE home ranges (shapes) and daily movements (lines) of free-ranging dogs (unsupervised movements) on Navarino Island in
southern Chile. (a) Tracking data of 25 village dogs with home ranges ≤100 ha during summer; (b) Relocation data of ve village dogs (P1, P2, P3, P4, P5) with
home ranges >100 ha during summer; (c) Seasonal relocation data and home ranges for one village dog (P1); (d) Daily excursions of four village dogs (P1, P2, P6,
P7, in different colors) >500 m from the urban area during one season each; (e) Overnight excursions (colors representing different dates) of a rural dog (P8) during
autumn including day and nighttime locations (spatial data anonymized to avoid that rural owner can be identied); (f) Village dog (P4) following a trekking trail
during summer. Gray lines indicate low and high values of the 95% condence intervals of the 95% AKDE estimate (one individual is represented with its low value
to adjust for over-estimation).
E. Schüttler et al.

Applied Animal Behaviour Science 250 (2022) 105610
6
(20.4 km) was traveled by a village dog during the day to an area without
infrastructure nor trails and predominated by peatbogs.
Mean home range size of dogs during summer was 710.4
±2706.4 ha (median =21, range =1.6–14879.1, n =32), during
autumn 292.1 ±1065.5 ha (24.4, 5.1–5922.4, n =36), during winter
174.5 ±617.4 (16.2, 2–3378.7, n =34), and during spring 220.8
±882.8 ha (15.8, 1.9–4845.8, n =30). Nine different dogs (8 village, 1
rural) had home ranges >100 ha in at least one seasonal monitoring
(2299.1 ±3521.5, 690, 116.5–14879.1, n =19) and four individuals
repeatedly (during 3 or 4 seasonal monitoring sessions). When checking
whether home ranges were different among seasons, the overall com-
parison was signicant (Х
2
(3) =13, p =0.005, n =28), but with a
small effect size (Kendall’s W =0.16, on a scale from 0 to 1 =large
effect). Signicantly larger home ranges only occurred in autumn versus
winter (paired Wilcoxon signed rank test, V =350, p =0.003, n =28),
not among other seasons. Finally, AKDE home range estimates were
0.8–2 times bigger than the conventional KDE estimates (AKDE 53.6
±250.8 ha, median =16.2, range =1.6–2466.3, n =107, and KDE
39.3 ±175.5 ha, 14.8, 1.7–1779, n =107). Fig. 2(a-c) illustrates these
ndings for selected dogs.
Having a closer look on excursions, we found that six village dogs
and one rural dog spent at least one night outside the urban area during
all seasons. Considering only village dogs performing daily excursions
>500 m away from the urban limit, almost half of them (16 or 48.5%)
were engaged in at least one excursion during the sampling period, nine
dogs (27.3%) veered away more than twice (mean 3.4 ±2.8, range 1–9
times). The locations of two village dogs followed complete trekking
trails with a duration of 3–6 days during summer of which owners
conrmed they followed tourists. Only three individuals were recorded
on the local landll, but one of them went on repeated occasions. Fig. 2
(d-f) highlights selected data on excursions.
Regarding the dogs’ activity patterns, we distinguished between lo-
cations recorded during daytime, dened as the time between sunrise
and sunset, and at night. Rural dogs were equally active during day
(49.8%) and night (50.2%, n =4747 locations), but village dogs tended
to be slightly more active during the day (59.4%, n =80169). Village
dogs had 40.7% of their locations during the night in the urban area
(n =75422) and 38.3% in nature (n =4747, Fig. 3). When comparing
the number of locations in seven habitats (Andean, coastal, forest,
infrastructure, lakes, peatbog, shrubland) during the day (n =3645)
dogs used signicantly more infrastructure and coastal habitat than at
night (n =2528; two-sample tests for equality of proportions of loca-
tions, Х
2
=12.1, p =0.004 and Х
2
=40.2, p <0.001, respectively),
whereas in forest and peatbogs dogs were signicantly more active
during the night (Х
2
=29.5, p <0.001 and Х
2
=11.7, p =0.004,
respectively). The number of locations during the day compared to those
at night had no statistical differences in Andean habitat, lakes, and
shrubland.
Among the 80169 locations of the 33 village dogs only 5.9% were
recorded outside the urban radius. Of those, almost half were recorded
in forest (49.1%), followed by infrastructure (15.7%), Andean habitat
(13.8%), coastal (7.1%), shrubland (6.2%), peatbog (6%), and lakes
(2.1%). Second order habitat selection (i.e., home ranges within the
study area) was nonrandom for all four seasons (Wilks’ lambdas
λ≤0.38, p <0.002, Fig. 4). For summer, habitat of village dogs was
ranked (most to least preferred) infrastructure >forest >coastal
>peatbogs >shrubland and for the other three seasons forest >coastal
>infrastructure >peatbogs >shrubland. However, between the three
top-ranked habitat types the intensity of habitat selection was similar,
but these were signicantly more used than the remaining habitat types,
being shrubland the least preferred overall (Fig. 4). Third order habitat
selection (i.e., locations within home ranges) of village dogs with >5%
locations outside the urban area was signicant for summer and autumn
only at p =0.1 level (λ≤0.14) and clearly random for winter and spring
seasons (λ≤0.13, p >0.1, Fig. 5). Infrastructure and peatbogs were
most preferred habitats during summer and autumn; for summer, also
forest habitat was among the top-ranked habitat, whereas coastal
habitat was the least ranked habitat type for all four seasons. The four
rural dogs had 47.6% of their locations in shrubland, 30.1% in forest,
8.3% in peatbog, 6.7% in Andean habitat, 3.9% in coastal, and 3.5% in
roads/trails (n =1471); rural infrastructure (n =4473) was excluded as
this area represented their homes.
4. Discussion
This study describes the movement ecology of 37 owned dogs
Fig. 3. Daily activity of 33 free-ranging village dogs during 24 h in southern Chile in natural versus the urban area, without day/night difference (a, n =80169
locations). Activity of village (33) and rural (4) dogs in habitats outside the urban area/rural homes, in infrastructure (b, n =748 of 28 dogs), coastal habitat (c,
n=394 of 32 dogs), forest (d, n =2772 of 33 dogs), and peatbog (e, n =407 of 16 dogs).
E. Schüttler et al.

Applied Animal Behaviour Science 250 (2022) 105610
7
allowed to roam free unsupervised on a sub-Antarctic island in southern
Chile. As expected, dogs generally stayed close to the owners´homes,
with median and mean distances to their homes of 33–42 m and
193–602 m, respectively, depending on the season. However, 86.5% of
the dogs had maximum distances >1 km. This is more distant compared
to other studies, which detail that, for example, 36% of rural dogs were
engaged in 77% of 51 forays >200 m away from home (Sepúlveda et al.,
2015) or nding a village dog at ≥1 km from their home had less than a
10% chance (Ruiz-Izaguirre et al., 2015). Our study also revealed that
almost half of the tracked dogs (48.5%) roamed beyond a radius of
500 m from the town limit, which shows how problematic the distinc-
tion between free-ranging owned and feral dogs for legislation is.
Indeed, this was one of the reasons for the failed law intent in Chile
(Degree Nr. 6, 2015). The maximum distance traveled in this study
(southernmost Chile, 20.4 km) was more than usually reported: 1.5 km
in Tibet (Vaniscotte et al., 2011), 2 km in Kyrgyzstan (van Kesteren
et al., 2013), ~4 km in Mexico (Ruiz-Izaguirre et al., 2015), 4.3 km in
south-central Chile (Sepúlveda et al., 2015), 4.5 km in Tanzania (Par-
sons et al., 2016), 10.4 km in southern Chile (P´
erez et al., 2018), and
17 km in Australia (Sparkes et al., 2014). Only in an earlier study in our
study area (Saavedra-Aracena et al., 2021) a dog veered as far as
28.4 km and in Meek et al. (1999), Australia, distances of up to 30 km
were common in two of 10 free-ranging dogs. The wider roaming of dogs
in our study may be explained by the fact that we did not select dogs
randomly, but focused on dogs from which was believed (but not evi-
denced) they roamed. This shows how important it is that movement
research discloses the selection procedure of dogs. At the same time, a
random selection procedure cannot always be achieved due to the
refusal of owners to participate (Sepúlveda et al., 2015) or because of
handling difculties of certain dogs (e.g., van Kesteren et al., 2013).
Although the general pattern of staying close or relatively close to
home has been acknowledged by several authors (e.g., van Kesteren
et al., 2013, Sepúlveda et al., 2015), in most studies there were some
dogs behaving as exceptions from the rule, as we can see from the
different maximum distances. Data from those dogs are often called
“outliers” (P´
erez et al., 2018) or “not representative” (Ruiz-Izaguirre
et al., 2015), and not seldom excluded from analysis (e.g., Sparkes et al.,
2014). Outliers as inuential values can dominate the results. Particu-
larly in regression-type analyses (e.g., dening predictors of movement),
they are not easy to deal with and although transforming the data or
choosing probability distributions which allow a greater variation for
means might be an option, outliers are often removed (Zuur et al., 2009).
Generalization is a problem when studying free-ranging dogs, as,
apparently, those dogs with an extreme movement behavior are of
special conservation concern. Fortunately, some studies quantitatively
describe the behavior of “extreme” dogs. Those were, for example, 9.3%
(8 dogs out of 86) with more than 25% of their locations in the rural area
(P´
erez et al., 2018), 4.3% (1 out of 23) using water channels to travel up
to 14 km away from home (Raynor et al., 2020), 6.9% (4 out of 58) with
home ranges >10 ha and 1.7% (1 out of 58) with home ranges >100 ha
(Molloy et al., 2017), and 13.5% (5 out of 37) with excursions >10 km
away from town (this study).
In contrary to maximum distances traveled that might only reect
“exploratory forays” (Sparkes et al., 2014), home ranges are more
representative for movement patterns. However, studies are difcult to
compare as the methodologies differ in relation to the number of dogs
monitored, the sampling effort, the home range estimator, etc. (Table 2).
Studies conducted during the last decade reported home ranges of up to
21 km
2
(P´
erez et al., 2018), others up to 10 ha (Vaniscotte et al., 2011),
whereas median values of the 95% extent were generally below 10 ha
(Table 2). Compared to those studies, we report the largest median home
range (24.4 ha, summer estimate) and largest upper range (149 km
2
).
The latter corresponded to an individual dog on trekking routes lasting
for several days, which could have been excluded from home range
analysis as a type of extraordinary behavior. However, as already
mentioned, we believe that extreme movement in free-ranging dogs is of
special interest and should be carefully reported. In our study, two dogs
accompanied tourists on 3–6-day trekking trips into sub-Antarctic wil-
derness, highlighting the role of tourism as a possible driver of dog
movement where the access of dogs is not controlled.
Beyond this special case, some authors (e.g., Meek, 1999; Sparkes
et al., 2014) suggest that the variability in home range sizes could be
explained by the Resource Dispersion Hypothesis (Macdonald, 1983), i.
e., dogs needing to extend their forays where resources are poor, similar
to wild carnivores (e.g., Ethiopian wolves Canis simensis, Tallents et al.,
2012; foxes Vulpes vulpes, Bino et al., 2010). Other reasons for roaming
far are biological, husbandry-related, and environmental factors. How-
ever, studies are not concordant in their ndings. Some found that male
Fig. 4. Habitat selection of village dogs outside the urban area in southern Chile. Second order habitat selection was analyzed for all village dogs and third order
habitat selection for dogs with >5% of their locations outside the urban area. Different sizes of icons mean signicantly different ranks. Shrubland was deleted from
the third order winter habitat selection as availability was only given for one animal. λ=Wilks’ lambda, p =p-value, n =number of individuals.
E. Schüttler et al.

Applied Animal Behaviour Science 250 (2022) 105610
8
(Dürr et al., 2017; Sparkes et al., 2014; Vaniscotte et al., 2011), intact
dogs (Dürr et al., 2017; Molloy et al., 2017; but see Garde et al., 2015),
younger (P´
erez et al., 2018, but see Muinde et al., 2021), and
malnourished dogs (Molloy et al., 2017; Ruiz-Izaguirre et al., 2015, but
see P´
erez et al., 2018) roamed further. Others report that during the dry
season dogs had more extended movement patterns than during the wet
season (Maher et al., 2019; Wilson-Aggarwal et al., 2021). Recently,
Saavedra-Aracena et al. (2021) in addition to the more classical pre-
dictors, addressed the inuence of the owner’s bond on roaming
behavior. The focus of our study was on movement ecology, not pre-
dictors, but we believe that further studies are needed to better under-
stand predictors of movement, including novel aspects until now not
addressed, such as personality.
Even if only a small percentage of dogs roam far from home, this is
still relevant for conservation policies, particularly next to protected
areas. A single dog can severely affect prey populations (e.g., surplus
killing of kiwis Apteryx australis, Taborsky, 1988). Moreover, due to the
often-high population density of dogs (Gompper, 2014) a small per-
centage of far-roaming dogs is still a considerable number of carnivores
for the ecosystem and thus very impactful. For example, in our study,
nine dogs (27%) of the 33 village dogs had >5% of their locations in
natural areas. Considering the results of the street dog census (126–141
free-roaming village dogs in total, depending on the season), an
extrapolated 34–38 individuals could enter natural areas frequently.
Taking only into account the two dogs following tourists over several
days into wilderness, an extrapolated 8–9 individuals out of the total
population could behave similarly. Finally, invasive predators pose a
severe threat to native fauna in insular ecosystems (Doherty et al., 2016)
as on islands, where prey often lack behavioral or evolutive defense
strategies (Banks and Dickman, 2007). This is the case in our study
system, where native species have been long isolated from mammalian
predators (Schüttler et al., 2009).
Activity patterns indicated an almost even pattern of diurnal (59% of
locations) and nocturnal (41%) movements. In southern Chile, 80% of
forays >200 m away from home took place during the day (Sepúlveda
et al., 2015); in Brazil, Madagascar, and Australia free-ranging dogs
were also photographed predominantly during the day (Silva et al.,
2018; Sparkes et al., 2016). Yet, crepuscular activity was observed in
feral dogs in Ecuador (Zapata-Ríos and Branch, 2016) and nocturnal
habits in wild-living dogs in Australia (Sparkes et al., 2016). A reason for
the apparent difference in activity patterns between free-ranging versus
feral dogs might be that in free-ranging dogs human and dog activity are
often related (Dos Santos et al., 2018; Wilson-Aggarwal et al., 2021).
Social activity (e.g., following a person, playing with a person, jumping)
was one of the main behaviors shown by free-ranging village dogs in
southern Chile (Garde et al., 2015). In fact, dogs preferred to be petted
by unfamiliar persons over food in India (Bhattacharjee et al., 2017),
and the presence of dogs at sandy beaches was associated with
beach-goers, not necessarily owners (Cort´
es et al., 2021). This
dog-human link could explain why in our study dogs used infrastructure
and coastal habitat more during the day - where likely more human
activity takes place -, in contrast to forest or peatbog habitat more fre-
quented during the night.
Finally, our study found preference for forest, infrastructure, and
coastal habitat across the seasons, and avoidance of shrubland. Hence,
dogs were not habitat specialists, but used a mix of man-made and
natural habitats available. A preference for forest in free-ranging dogs
has been acknowledged previously (Dos Santos et al., 2018; Ribeiro
et al., 2018), although this would depend on the density of the under-
story of the forest (Sepúlveda et al., 2015). In our study area, old-growth
forests have poorly developed understory and therefore, may not
represent a barrier for movement or prey concealment (Contardo et al.,
2020). Coasts might be interesting to dogs in our study area as those
habitats harbor plenty of coastal birds. The presence of dogs at beaches
has been associated to the harassment of shorebirds (Cort´
es et al., 2021),
scavenging of carcasses (Schlacher et al., 2015), and nest predation of
geese (Schüttler et al., 2009) and sea turtles (Ruiz-Izaguirre et al., 2015).
But dogs made also use of road and trails, as earlier described (Sepúl-
veda et al., 2015; Warembourg et al., 2020). On a ner scale, individuals
(n =9) preferred peatbogs, as earlier revealed by camera-trap data
(Contardo et al., 2020), highlighting those ecosystems as areas deserving
special conservation efforts.
5. Conclusion
Our ndings reveal a relatively high spatial (movement patterns,
habitat use) and temporal (seasonal, daily) plasticity in free-ranging
owned dogs moving through pristine sub-Antarctic environments.
Although we could not directly address the impacts of dogs, this plas-
ticity gives dogs access to a wide range of recognized interactions with
wildlife. Although most dogs stayed relatively close to their homes, our
study showed that excursions into natural areas were not only limited to
“extreme dogs.” These dogs are the one that should receive a higher
attention in future research, particularly with respect to predictors of
movement and their impact on wildlife. The ndings of our study also
show the need of legislation to restrict the movement of dogs. Pet
ownership strategies must acknowledge that dogs are active during the
night, mainly in the absence of human presence. On the other hand, also
people who are not owners of dogs must be educated when visiting
sensible natural settings as they can be drivers of dog movement. Free-
ranging dogs are social animals often owned for company and if owners
take advantage of this fact and acknowledge their responsibility, wildlife
will benet.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
Table 2
Selection of 10 dog movement studies during the last decade, highlighting the variability in methodologies and results. R =rural dogs, U =urban dogs, V =village
dogs. AKDE =Autocorrelated Kernel Density Estimation, BRB =Biased Random Bridge method, CHP =Hull Polygon method, MCP =Maximum Convex Polygon, T-
LoCoH =Time Localized Convex Hulls. Dashes indicate that size was not reported. In this study, median home range size was based on summer data, in Raynor et al.
(2020) on water channel usage, only.
GPS-tracked dogs (n, type) Sampling effort in days,
median or mean ±SD (range)
95% home range in ha,
median (range)
Site Reference
37 V, R 19 (4–34) 24.4 (1.6–14879, AKDE) Chile This study
73 U, R 5.1 (1–6.3) 9.3 (4.1–14.3, kernel) Kenya Muinde et al. (2021)
100 R 2.5 (1.2–4.7) 7.7 (1.1–103, kernel) Chad Warembourg et al. (2021)
23 U - (4–28) 1.8 (0–370, T-LoCoH) Peru Raynor et al. (2020)
86 V 3–11 0.04 (0.1–2097, MCP) Chile P´
erez et al. (2018)
135 V 2–16 4.5 (0.9–40.5, kernel) Australia Dürr et al. (2017)
58 R 1 (0.9–4.3) 3.1 (0.9–131, BRB) Australia Molloy et al. (2017)
12 R 115 ±11 (9–174) - (31–1161, kernel) Australia van Bommel and Johnson (2014)
37 V 0.8 ±0.4 (0.06–2) 2.3 (-, CHP) Kyrgyzstan van Kesteren et al. (2013)
87 V - (1–4) - (0–10, MCP) Tibet Vaniscotte et al. (2011)
E. Schüttler et al.

Applied Animal Behaviour Science 250 (2022) 105610
9
the work reported in this paper.
Acknowledgments
This study would not have been possible without the kind partici-
pation of dog owners and their dogs. We would also like to thank those
people who found and returned lost GPS devices. Veterinary Silvia
Llanos supported the recruitment and health check of the dogs, and
Mauricio Bahamonde gave us time in his radio program for the
dissemination of the project to the local community. The Chilean Na-
tional Commission for Scientic and Technological Research (PAI-
CONICYT, Call 2014, No. 79140024, ANID/BASAL FB210018) funded
our research.
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