Performance of GPS units for deployment on semiaquatic animals
Performance of GPS units for deployment on semiaquatic animals
Lia Schlippe Justicia 0 1 2
Frank Rosell 0 1 2
Martin MayerID 0 1 2
0 1 Faculty of Technology, Natural Sciences, and Maritime Sciences, Department of Natural Sciences and Environmental Health, University of South-Eastern Norway , Bø i Telemark , Norway , 2 Faculty of Biology, University of Barcelona , Barcelona , Spain , 3 Department of Bioscience, Aarhus University , Aarhus , Denmark
1 Funding: Funding was provided by the University of South-Eastern Norway. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
2 Editor: Suzannah Rutherford, Fred Hutchinson Cancer Research Center , UNITED STATES
Global Positioning System (GPS) technology is widely used in wildlife research to study animal movement and habitat use. In order to evaluate the quality and reliability of GPS data, the factors influencing the performance of these devices must be known, especially for semiaquatic species, because terrestrial and aquatic habitat might affect GPS performance differently. We evaluated the location error and fix success rate of three GPS receiver models in stationary tests and on a semi-aquatic mammal, the Eurasian beaver (Castor fiber). The location error during stationary tests was on average 15.7 m, and increased with increasing canopy closure, slope, and horizontal dilution of precision, potentially leading to the erroneous classification of GPS positions when studying habitat use in animals. In addition, the position of the GPS antenna (flat versus 90˚ tilted) affected the location error, suggesting that animal behavior affects GPS performance. The fix success rate was significantly higher during stationary tests compared to when GPS units were deployed on beavers (94% versus 86%). Further, GPS receivers did not obtain any positions underwater and underground, the latter potentially allowing the estimation of activity periods in animals that use lodges or burrows as shelter. We discuss the possibilities for data screening, the use of buffer zones along the shoreline, and combination with other data loggers to avoid the erroneous classification of GPS positions when studying habitat use.
Competing interests: The authors have declared
that no competing interests exist.
Global positioning system (GPS) technology allows the remote data collection of animal
positions and movements [1, 2]. Therefore, GPS units are a valuable tool for estimating animal
home range sizes [3, 4], studying habitat use and resource selection [5, 6], as well as movement
patterns and migratory routes [7–9]. The use of GPS telemetry has several technical advantages
compared to conventional very high frequency (VHF) triangulation techniques  or Argos
satellite positioning , as it is more accurate, and available for 24h a day with position
updates available in rapid succession . Since its initial development, GPS tracking has
significantly improved due to the reduction in size and weight, increased battery life, lowered
costs, and large capacity of data storage . These improvements now allow us to track small
species such as hedgehogs (Erinaceus europaeus)  and ovenbirds (Seiurus aurocapilla) .
However, raw data obtained from these methods can contain error and bias that require
rigorous and objective testing . To estimate a location, a GPS device must receive
information from 3 satellites, and this reliance on satellites can cause two types of errors in the GPS
performance. The first one is the location error (LE), defined as the Euclidian distance between
each GPS-generated location and the true location, which occurs when the GPS unit records a
location that is inaccurate . The second error type is unsuccessful fix acquisition that
occurs when the GPS cannot acquire signals from enough satellites to generate a location
estimate, which results in missing location data [17, 18]. This is measured as fix success rate
(FSR), defined as the number of successful fix attempts divided by the total attempted fixes.
The magnitude of both errors depend on technological [19–21], environmental [18, 22–24]
and behavioral factors [16, 25]. The main technological factors affecting LE and FSR are the
number of satellites and satellite geometry, expressed as dilution of precision (DOP) including
horizontal DOP (HDOP). Smaller DOP values indicate wider spacing between satellites,
which potentially minimizes triangulation error and, therefore, increases position accuracy
(i.e., lower LE) . Lewis, Rachlow  suggested removing GPS positions with DOP
values > 5 and 2-D positions (3 satellites) to increase accuracy.
Several studies have assessed the effects of different habitat and climate variables on GPS fix
acquisition, such as habitat type, topography, water submersion, precipitation, cloud cover,
and vegetation characteristics (tree height, tree diameter and canopy closure) [22–24, 27]. For
example, studies on white-tailed deer (Odocoileus virginianus) and wolves (Canis lupus) found
that vegetation closure had a negative effect on GPS accuracy , because it can block or
reflect satellite signals, leading to higher LE values [29, 30]. Similar effects can occur in rugged
terrain and built-up areas, leading to a reduced FSR . Further, water submersion was
shown to negatively affect GPS accuracy due to the inability of GPS signals to properly
propagate in water . For example, Costa, Robinson  found that the LE was smaller in sea
lions (Zalophus californianus; Z. wollebaeki) and fur seals (Arctocephalus pusillus pusillus; A. p.
doriferus), which spend more time on the surface and make shorter dives compared to species
that make long dives followed by shorter surface intervals like elephant seals (Mirounga
angustirostris). Moreover, the behavior of tracked animals can bias the FSR and LE by causing
variations in signal reception due to body obstruction and changes in antenna position . In
grizzly bears (Ursus arctos), GPS fix success decreased when individuals were bedding, thereby
moving the GPS antenna toward the side or ground, leading to a reduced reception of satellite
signals . The error introduced by animal behavior is still largely unexplored because of the
difficulties of setting up suitable field tests . Conversely, animal behavior itself can help to
identify erroneous locations. For example, Bjørneraas, Van Moorter  developed a method
for screening GPS data based on unrealistic species-specific speed and distance travelled
between consecutive locations.
In this study, we evaluated factors affecting FSR and LE of three models of GPS receivers
along riparian habitat. Riparian habitat is important for many semiaquatic species, including
insects , amphibians , reptiles , birds  and mammals [41, 42], and acts as
migration route and corridor connecting habitat patches . A specific challenge when
studying habitat use in semiaquatic animals is the assignment of an individual being in water
or on land, respectively, requiring a high GPS accuracy. However, to our knowledge, the
performance of GPS units in riparian habitat was only investigated in one study using Eurasian
otters (Lutra lutra) .
We hypothesized that 1) habitat variables (canopy closure, slope, water submersion and
being underground), 2) technical variables (HDOP and the number of satellites), and 3)
antenna angle (as crude measure of animal body posture) would affect LE and FSR. We
predicted that GPS performance would decrease (i.e., increased LE and decreased FSR) with 1)
increasing canopy closure, 2) slope, and 3) when GPS units were underwater and underground
due to GPS signal obstruction. Further, we predicted GPS performance to increase with 4)
increasing number of satellites and lower HDOP values, and 5) when the antenna faced toward
the sky compared to a tilted antenna position. In addition, we tested 6) if FSR depended on
weather conditions, i.e., temperature and precipitation. Finally, we compared the FSR obtained
from stationary tests to the FSR calculated from GPS data obtained from a semiaquatic
mammal, the Eurasian beaver (Castor fiber) to investigate the influence of animal behavior.
Materials and methods
Study area and species
The study area was located in Telemark county, southeast Norway (59˚23’ N, 09˚09’ E) and
consisted of two connected rivers, the Gvarv and Saua, which both empty into Lake Norsjø.
The climate in the area is cool continental with a mean annual temperature of 4.6 ˚C and an
average annual rainfall of 790 mm . The area is dominated by semi-agricultural land, and
riverbanks are lined with riparian woodland structures .
We previously deployed GPS units on Eurasian beavers (hereafter, beaver) to study habitat
selection and spatial movement patterns [46–48]. Beavers are large, nocturnal, herbivorous
rodents  that inhabit freshwater systems . They move relatively close to the shoreline,
both when being on land and in water, respectively [47, 51]. Further, they spend approximately
half of their activity time on land and the other half in water, whereas the proportion of time
spent on land and in water, respectively, varies with individual age . Beavers build lodges
or dens that are used as shelter during the day, although they may also return to them during
We conducted stationary tests from March–May 2017 and March–April 2018 with six GPS
receivers of three different models (two units per model): 1) GIG 134A micro GPS, 2) PinPoint
75 micro GPS (both Sirtrack, Havelock North, New Zealand), and 3) TGB-317/315GX
(Telenax, Playa del Carmen, Mexico). Models GIG 134A micro (not produced any longer) and
TGB-317/315GX attempted to take a fix for 180 sec and obtained conventional GPS positions
(cold start). Model PinPoint 75 micro attempted to take a fix for 70 sec, because it only stored
satellite information (GPS positions were calculated post-processing). All receiver models
stored data onboard and had to be recovered for data download. We selected 36 sites (Fig 1) of
varying canopy closure (range: 0–95%; mean ± SD: 47 ± 34%), and slope (range: 0–56˚;
17 ± 17˚) to cover the variation of the habitat within our study area. Site selection considered
different slope levels within three categories of vegetation height (low: 0–1.5 m, medium: 1.5–
10 m, and high: >10 m), allowing us to disentangle between slope and vegetation
height/canopy closure (31 different sites). Additionally, we selected five extra sites to test the GPS
receivers on the water surface (two sites), underwater (two sites: 14 and 37 cm underwater,
respectively) and one site underground (60 cm) in an inactive beaver den. We measured the
slope, vegetation height, and canopy closure at the true GPS location for each test site. Canopy
closure was measured as the proportion of sky obscured by vegetation , and was estimated
by averaging spherical densitometer readings recorded in the four cardinal directions .
Further, we measured the average tree diameter measured at breast height and the basal area,
defined as the amount of area occupied by tree stems, within a radius of 10 m from the true
GPS location at each test site. The true location of each site was determined using a
highFig 1. Study area in South-Eastern Norway, showing the 36 test sites (red crosses) where we tested the performance of GPS
receivers (large map). The small map shows GPS locations (grey dots) for one exemplary test site, and the picture shows a Eurasian
beaver (Castor fiber), equipped with a GPS unit on its lower back.
precision GPS unit with <1 m precision (Topcon FC-250, Topcon Positioning Systems, CA,
United States, https://www.topconpositioing.com). All six GPS receivers were placed
simultaneously on a 30 cm high burlap bag filled with straw, simulating a beaver, with the GPS
antenna directly facing skyward (0˚). We programmed the units to record one GPS position
every 15 minutes between 1900 and 0700 h (49 possible fixes per night), i.e., during the activity
time of beavers . GPS units were programmed to not record positions during the day,
because beavers are not active then . To evaluate the effect of GPS antenna position
(simulating animal behavior) on FSR and LE, we conducted an additional test at 11 of the 36 test
sites (March–April 2018; T26-T36, S1 Table). This test was only conducted for the four
Sirtrack models, because the two Telenax units were lost during fieldwork the previous year. We
placed the GPS receivers at each site for two consecutive days, one day positioned at 0˚ (GPS
antenna facing directly skyward), representing swimming/being in water, and one day at 90˚,
representing sitting, grooming and feeding behaviors . The bearing of the GPS antenna
when facing 90˚ was chosen randomly. At all remaining sites (T1-T25, S1 Table) units were
deployed for one day only (0˚). Information stored for each successful GPS fix included fix
number, fix date, fix time, latitude, longitude, number of satellites, and HDOP. We obtained
the precipitation (mm) and mean temperature (˚C) from Gvarv meteorological station
(Meteorological Institute of Norway; URL: https://www.met.no/), located in the middle of our study
area, for each test site on the day we conducted the test. Additionally, we used GPS data
collected from 58 beavers (48 beavers were tagged with the G1G 134A, six with the TGB-317/
315GX, and four with the PinPoint 75 micro) from 2009–2016 . Beavers were trapped at
night from a boat using landing nets, and GPS units were glued to the lower back using a two
component resin , with the GPS antenna facing skyward when the beaver was swimming
. For details on capture, handling and GPS tagging see Graf, Mayer  and Steyaert,
All trapping and handling procedures were approved by the Norwegian Experimental Animal
Board (FOTS id 742, id 2170, 2579, 4384, 6282, 8687) and the Norwegian Directorate for
Nature Management (2008/14367 ART-VI-ID, archive code 444.5, 446.15/3, 14415), which
also granted permission to conduct fieldwork in the study area. Our study met the ASAB /ABS
Guidelines for the treatment of animals in research and teaching ASAB/ABS.
We calculated LE as the Euclidean distance in meters between the GPS-measured location and
the true location. FSR was calculated for each GPS day by dividing the number of successfully
obtained fixes by the maximum number of possible fixes.
Canopy closure was positively correlated with vegetation height, tree diameter and basal
area (Spearman correlation coefficient r > 0.6 in all cases). Thus, we only included canopy
closure in the analysis. Further, because HDOP and the number of satellites used for a fix were
highly correlated (r = -0.69), we only included the HDOP in the analysis.
To analyze the LE (dependent variable, log-transformed to meet the assumption of
normality), we used a mixed-effects linear regression with a Gaussian distribution. For this analysis,
we included all test sites, but only the days when units were placed facing skyward (0˚). We
included the HDOP, GPS model, slope, canopy closure, and the interaction of canopy closure
x slope as fixed effects and the GPS unit as random intercept to control for non-independence
of the data. To investigate FSR (dependent variable; using all test sites, but only days with units
facing skyward), we used a mixed-effects logistic regression with a binomial error distribution.
We included the GPS model, mean daily temperature, daily precipitation (mm), canopy
closure, slope, and the interaction of canopy closure x slope as fixed effects and the GPS unit as
random intercept (we did not include more interactions to avoid overfitting the models). To
investigate the effect of the GPS angle (simulating body posture) on LE and FSR, we analyzed
the subset of data where we changed the angle of the GPS antenna (11 sites: T26-T36, S1
Table) using the same model structure, but including the GPS angle (0˚ versus 90˚) and the
interaction of GPS model x GPS angle as fixed effects. Finally, to study the influence of animal
behavior, we assessed differences in FSR between stationary tests (201 GPS days) and GPS
units deployed on 54 beavers (771 GPS days) using an unpaired t-test.
Model selection was based on Akaike’s Information Criterion for small sample sizes (AICc
values, Table 1), selecting the model with the lowest AICc value . We used the dredge
function in R package MuMIn  to create a set of candidate models including all possible
combinations of fixed effects and the above mentioned interactions. If ΔAICc was < 4 in two or
more models, we performed model averaging . Parameters that included zero within their
95% confidence intervals (CI) were considered as uninformative . Data are shown as
mean ± standard deviation (SD) unless otherwise stated. All statistical analyses were carried
using the free software R 3.2.5 .
1) Location error
2) Fix success rate
In total, we obtained 9,624 location fixes from 33 stationary test sites (we did not obtain any
fixes underwater and underground). Within these fixes, 96.8% were 3-D locations ( 4
satellites) and 3.2% 2-D locations (3 satellites). The LE associated with 3-D fixes was significantly
smaller compared to 2-D fixes (15.1 ± 19.8 m versus 33.7 ± 53.2 m, t-test: t = 4.47; p < 0.001).
The mean number of obtained satellites per fix was 5.4 ± 1.5 (range: 3–12 satellites) and the
mean HDOP was 2.6 ± 2.1 (range: 0.6–45.6).
The mean LE of all GPS positions was 15.7 ± 21.9 m, and ranged between 0.0 and 364.5 m
(S1 Fig). When comparing between models (excluding days when the GPS antenna was tilted
by 90˚), the LE of the GIG 134A micro (14.8 ± 16.2 m) was significantly larger compared to
the TGB-317/315GX (13.5 ± 20.1 m) and the PinPoint 75 micro (13.8 ± 20.1 m; ANOVA:
F = 3.72, p = 0.024). The LE increased with increasing HDOP (Table 2, Fig 2). Further, the
1) Location error GPS model (TGB-317/315GX) GPS model (TGB PinPoint 75 micro) HDOP
2) Fix success rate
Fig 2. The effect of horizontal dilution of precision (HDOP, upper graph) and the interaction of canopy closure x
slope (lower graph) on the location error of GPS receivers during stationary tests. Slope was categorized into ‘flat’
(0–20˚) and ‘steep’ (> 20˚) for reasons of better visualization.
interaction of canopy closure x slope revealed that the LE increased with increasing canopy
closure and more so in steeper slopes (Table 2, Fig 2). Analyzing the subset of sites where we
shifted the GPS angle showed that the LE was markedly lower when the GPS receiver was
facing skyward, i.e., at 0˚ compared to when tilted by 90˚ (14.2 ± 18.6 versus 22.5 ± 31.6 m;
Estimate ± SD = 0.49 ± 0.04; 95% CI: 0.42; 0.57). The LE of GPS model PinPoint 75 micro
increased more when tilted by 90˚ compared to the GIG 134A micro (Fig 3). Removing
HDOP values > 5 and < 4 available satellites led to a data loss of 9% and improved the mean
LE by 1.7 m to 14.0 ± 17.0 m. This procedure removed many outliers (mean ± SD of removed
GPS positions: 69.0 ± 21.6 m), but not all. When removing HDOP values > 4 and < 5 available
satellites, the LE further improved to 11.9 ± 13.1 m and resulted in a data loss of 27.9%.
The overall FSR of all units during stationary tests was 94.2 ± 14.8%. FSR differed between
models with the GIG 134A micro (98.1 ± 1.8) performing better compared to the PinPoint 75
micro (90.5 ± 20.4) and TGB-317/315GX (93.7 ± 15.1), respectively (Table 2). FSR increased
with increasing temperature, precipitation, and canopy closure, and decreased with increasing
slope (Table 2, Fig 4). The interaction of canopy closure x slope was uninformative. When
analyzing the data for the model GIG 134A micro only, only slope had a negative effect on FSR
and all other variables were uninformative (results not shown). A separate analysis of the 11
sites where we shifted the GPS angle revealed that the FSR of the model GIG 134A micro was
not affected by the GPS angle, but the PinPoint 75 micro had an increased FSR when the units
Fig 4. The effect of mean temperature (top left), precipitation (top right), canopy closure (bottom left) and slope (bottom right) on the fix success rate
of GPS receivers during stationary tests.
were tilted by 90˚ (Fig 5). Moreover, none of the GPSs recorded positions underwater or
The FSR of GPS units that were deployed on beavers was significantly lower compared to
the stationary tests (86.2 ± 10.3% versus 94.2 ± 14.8%; t-test: t = -17.11; p < 0.001). The FSR of
GPS units deployed on beavers was significantly different between two GPS receiver models,
with the GIG 134A micro performing better compared to the TGB-317/315GX (86.6% versus
Fig 5. The effect of the GPS angle (as measure of body posture) on the fix success rate of two GPS receiver models
during stationary tests.
83.5%, t-test: t = 2.13; p = 0.035). We did not obtain any field data from the PinPoint 75 micro
as we only recovered one unit, which did not record any data (we did not find the other three
When studying animal movement and habitat use, it is crucial to know the quality of obtained
GPS data and to understand the error-inducing mechanisms . As predicted, LE depended
on both habitat and technical variables as well as body posture, which might lead to an
erroneous categorization of GPS positions when studying habitat use. Data screening can help to
remove GPS positions with large LE , and alternative methods, like the combination with
accelerometers, can be used to avoid wrong habitat assignment. The mean FSR for all GPS
models during stationary tests was > 90%, which was higher compared to the FSR of GPS
units deployed on beavers. This difference was likely caused by animal behavior. We discuss
the possibility of using FSR to calculate activity times of burrow-living animals.
The mean LE was 15.7 m, in accordance with the typical range (10 to 30 m) reported by other
studies [26, 27, 61]. The results from our stationary tests indicate that LE is affected by both
technical (HDOP and GPS model) and habitat (canopy closure and slope) variables, as well as
animal body posture (GPS angle). LE increased with increasing HDOP, and removing 2-D
positions and HDOP values > 5 reduced the LE, removing many, but not all outliers.
Removing lower HDOP values and positions < 5 satellites further increased the LE, but led to
a large data loss. Hence, we suggest to follow the guideline by Lewis, Rachlow , i.e.
removing 2-D positions and DOP values > 5. Further, LE differed among the three GPS models.
Nevertheless, this difference was within one meter, making the LE of the three models
comparable. When simulating varying body posture by changing the GPS angle, we found that the
LE was markedly increased when the GPS antenna was tilted by 90˚, consistent with previous
studies testing collar position and orientation [16, 62]. LE also increased in locations with
dense canopy cover and more so in steeper slopes. These results suggest that there will be a
larger LE associated with land positions compared to GPS positions on water, because the GPS
is directly facing skyward when an animal is swimming (depending on the attachment
method) and because there is no slope and less obstruction by vegetation. Canopy closure
varies with the vegetative season, potentially also leading to variation of the LE associated with
land positions over the course of the year. Beavers are often sitting when on land , and
they spend much time foraging in forest , i.e., in areas with dense vegetation cover,
suggesting that the effects of GPS angle, slope and canopy closure amplify each other. Further, we
previously found that older beavers spend comparatively more time on land , which
indicates that individual differences in animal behavior might also affect LE (in this case via an
altered body posture and increased canopy closure on land). Generally, beavers in our study
area (equipped with the GPS model GIG 134A micro) were shown to spend most of the time
close to shore (on average 15 m), both when being on land and in water . This distance is
similar to the LE obtained in our study, and could result in inaccurate estimates when studying
habitat use, resource selection [1, 27], and when calculating behavioral time budgets due to
misclassification of land and water positions, respectively. This is important, because beavers
(and other semi-aquatic animals) are in water or on land for very different purposes. For
example, beavers use water to travel [48, 63] and spend much of their time on land foraging
. Another source of uncertainty are map errors that in the case of riparian habitats can
vary depending on the water level.
The FSR in our study was generally high (> 90%) and varied among models and with canopy
closure, slope and weather conditions. The FSR of the GPS model GIG 134A micro was close
to 100% and was more robust compared to the other two models; e.g., it was not affected by a
varying GPS angle or weather conditions. Surprisingly, canopy closure and precipitation had a
positive effect on the FSR of the models TGB-317/315GX and PinPoint 75 micro, a finding
previously reported for canopy closure . Although canopy closure, slope and precipitation
had an effect on the FSR, their effect was comparatively small as FSR remained > 93% in all
cases. In contrast, temperatures < 0 ˚C led to a marked decrease in FSR for the models
TGB317/315GX and PinPoint 75 micro, suggesting that FSR decreases during the colder months of
The FSR of GPS units deployed on beavers was 86%, lower than in stationary tests, a finding
similar to other studies [14, 20, 64]. Nevertheless, the FSR was higher or comparable to results
reported for GPS devices deployed on other mammals. For example, a FSR of 85% was
recorded for white-tailed deer (Odocoileus virginiaunus) , 81% for pygmy rabbits
(Brachylagus idahoensis) , and 68% for otters . The reduction of performance between
stationary tests and field trials might be explained by animal behavior [22, 66]. GPS units did not
record any positions underground as reported previously [65, 67]. Thus, the most likely
explanation for the reduced FSR was, because beavers return to their lodge/den during their activity
period. Sharpe and Rosell  reported that beavers spend ca. 32% of their time budget inside
the lodge, and based on acceleration data we estimated that beavers spend ca. 10% of their
active time inside the lodge (MM, unpublished results). The large proportion of time spent
inside the lodge found by Sharpe and Rosell  might be owed to an observer bias, because
these beavers were observed throughout the night from a motorboat using spotlights, which
might have impacted the beavers’ natural behavior. This is a good example how GPS
technology can be used to reduce observer bias, and to estimate principal activity periods of animals
that use burrows or dens as shelter. Further, GPSs did not record any positions underwater
(independent of submersion depth). However, beavers typically dive for short periods of time
(on average < 30 sec) and spend < 3% of their nightly activity on diving . GPS receivers
attempted to acquire locations for 3 min (the PinPoint 75 micro for 70 sec). Therefore, diving
activity probably had little influence on the FSR. For other semi-aquatic species that spend
more time underwater, e.g. crocodiles  and turtles , the FSR could potentially be used
to quantify the time spend in/under water after initial calibration and depending on the fix
The LE in our study was on average 15.7 m, which is similar to the average distance that
beavers stay from the shoreline , making it hard to reliably categorize GPS positions into
being in water versus on land. Data screening can improve the LE via removing 2-D positions
and positions with HDOP values > 5 . Additionally, the use of buffer areas along the
shoreline could be used to remove uncertain GPS positions. For example, a buffer zone the
size of the LE (i.e., 15 m in our case) could be created to remove all GPS positions therein. In
the case of the beaver, this is not feasible, because it would result in the removal of 60% of all
data (results not shown). However, it might be possible for species that forage further from the
water, e.g. hippopotami (Hippopotamus amphibius) .
We suggest that the best solution to identify land versus water positions is using the GPS
unit in combination with an accelerometer. Graf, Wilson  used accelerometers attached on
the lower back of beavers (same location as GPS units in this study) to record changes in body
posture and body movement, which were then used to identify different behaviors. This
enables to link GPS positions to specific behavior, e.g. if an animal was sitting, walking or
swimming. Consequently, this could be used to assign GPS positions to land or water,
respectively, allowing for more precise estimates of habitat use and behavioral time budgets, and
could potentially also resolve the problem of inaccurate maps and changing water levels.
Further, as GPS devices in our study were placed at the same body position as the accelerometers,
we could relate specific behaviors to an increased LE, which would allow controlling for
inaccurate positions. For example, when beavers are standing the GPS is turned 90˚ , leading
to an increased LE. To increase battery life, GPS units could be programmed to only take
positions when an animal is active, determined by acceleration data and accelerometers could be
programmed to only record data at the same time as the GPS obtains a position. We conclude
that the use of GPS telemetry is an effective tool to collect detailed location data suitable to
study animal home range size, spatial movement patterns, space use, and habitat selection in
semi-aquatic animals, although some limitations still exist. Future research should aim to
quantify how to increase the certainty of location data by combining GPS units with other data
S1 Table. Overview of the 36 test sites in Southeast Norway showing the fix success rate
and location error separately for the three GPS models. Model TGB-317/315GX was only
We thank Alex Briggs for his large contribution to the fieldwork, Shane Frank for statistical
advice, and Patricia Graf for input regarding accelerometer data. The University of
South-Eastern Norway funded this study.
Conceptualization: Martin Mayer.
Data curation: Martin Mayer.
Formal analysis: Lia Schlippe Justicia, Martin Mayer.
Funding acquisition: Frank Rosell.
Methodology: Lia Schlippe Justicia, Martin Mayer.
Writing – original draft: Lia Schlippe Justicia.
Writing – review & editing: Frank Rosell, Martin Mayer.
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