Implications of location accuracy and data volume for home range estimation and fine-scale movement analysis: comparing Argos and Fastloc-GPS tracking data

Abstract

The advent of Fastloc-GPS is helping to transform marine animal tracking by allowing the collection of high-quality location data for species that surface only briefly. We show how the improved location accuracy of Fastloc-GPS compared to Argos tracking is expected to lead to far more accurate home range estimates, particularly for animals moving over the scale of a few km. We reach this conclusion using simulated data and home range estimates derived from empirical tracking data for green sea turtles (Chelonia mydas) equipped with Argos linked Fastloc-GPS tags at three different foraging areas (western Indian Ocean, Western Australia, and Caribbean). Poor-quality Argos locations (e.g., location classes A, B) produced home range estimates ranging from 10 to 100 times larger than those derived from Fastloc-GPS data, whereas high-quality Argos locations (location classes 1–3) produced home range estimates that were generally comparable to those derived from Fastloc-GPS data. However, the limited number of Argos class 1–3 locations obtained for all three turtles—an average of 14.6 times more Fastloc-GPS locations were obtained compared to Argos class 1–3 locations—resulted in blurred patterns of space use. In contrast, the high volume of Fastloc-GPS locations revealed fine-scale movements in striking detail (i.e., use of discrete patches separated by just a few 100 m). We recommend careful consideration of the effects of location accuracy and data volume when developing sampling regimes for marine tracking studies and make recommendations regarding how sampling can be standardized to facilitate meaningful spatial and temporal comparisons of space use.

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Vol.:(0123456789)
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Mar Biol (2017) 164:204
DOI 10.1007/s00227-017-3225-7
ORIGINAL PAPER
Implications oflocation accuracy anddata volume forhome
range estimation andfine-scale movement analysis: comparing
Argos andFastloc-GPS tracking data
J.A.Thomson1 · L.Börger2· M.J.A.Christianen3,4· N.Esteban2· J.-O.Laloë1·
G.C.Hays1
Received: 13 June 2017 / Accepted: 22 August 2017
© Springer-Verlag GmbH Germany 2017
Fastloc-GPS data. However, the limited number of Argos
class 1–3 locations obtained for all three turtles—an aver-
age of 14.6 times more Fastloc-GPS locations were obtained
compared to Argos class 1–3 locations—resulted in blurred
patterns of space use. In contrast, the high volume of Fast-
loc-GPS locations revealed fine-scale movements in striking
detail (i.e., use of discrete patches separated by just a few
100m). We recommend careful consideration of the effects
of location accuracy and data volume when developing sam-
pling regimes for marine tracking studies and make recom-
mendations regarding how sampling can be standardized to
facilitate meaningful spatial and temporal comparisons of
space use.
Introduction
Understanding patterns of space use by animals lies at the
heart of many ecological studies and also underpins many
efforts to make evidenced-based management decisions,
for example as part of conservation planning (Cooke 2008).
Thanks to increased accessibility of tracking technology
(Kays etal. 2015; Hays etal. 2016), both the number of taxa
tracked and the number of studies collecting movement data
across different habitats are rapidly increasing. However,
the ability to reliably detect differences in space use among
individuals, species, and locations crucially depends on the
sampling regime used including the accuracy and amount
of location data obtained (Börger etal. 2006a, b; Frair etal.
2010; Hebblewhite and Haydon 2010; Montgomery etal.
2011; McClintock etal. 2015). While the importance of the
quality and abundance of location data for studying animal
movements has been well known for some time in certain
fields, particularly terrestrial ecology (e.g., Harris etal.
1995), in other fields with a shorter tracking history, the
Abstract The advent of Fastloc-GPS is helping to trans-
form marine animal tracking by allowing the collection
of high-quality location data for species that surface only
briefly. We show how the improved location accuracy of
Fastloc-GPS compared to Argos tracking is expected to lead
to far more accurate home range estimates, particularly for
animals moving over the scale of a few km. We reach this
conclusion using simulated data and home range estimates
derived from empirical tracking data for green sea turtles
(Chelonia mydas) equipped with Argos linked Fastloc-GPS
tags at three different foraging areas (western Indian Ocean,
Western Australia, and Caribbean). Poor-quality Argos
locations (e.g., location classes A, B) produced home range
estimates ranging from 10 to 100 times larger than those
derived from Fastloc-GPS data, whereas high-quality Argos
locations (location classes 1–3) produced home range esti-
mates that were generally comparable to those derived from
Responsible Editor: P. Casale.
Reviewed by Undisclosed experts.
* J. A. Thomson
jordy.thomson@deakin.edu.au
1 School ofLife andEnvironmental Sciences, Deakin
University, Centre forIntegrative Ecology, Warrnambool,
VIC3280, Australia
2 Department ofBiosciences, Swansea University,
SwanseaSA28PP, UK
3 Institute forWetland andWater Research, Radboud
University Nijmegen, Heyendaalseweg 135,
6525AJNijmegen, TheNetherlands
4 Groningen Institute forEvolutionary Life Sciences,
University ofGroningen, P.O. Box11103,
9700CCGroningen, TheNetherlands

Mar Biol (2017) 164:204
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message is less well appreciated. As such, it is important to
revisit some of the key messages in home range estimation
to avoid methodological artefacts obscuring true differences
in space use.
In the marine context, a major advance in recent years
has been the advent of Fastloc-GPS tracking (Kuhn etal.
2009; Hazen etal. 2012; Hoenner etal. 2012). Conventional
GPS receivers need several seconds to generate a location
estimate, which has precluded their use on marine species
that only surface briefly. In contrast, Fastloc-GPS over-
comes this problem with the rapid (typically tens of mil-
liseconds) acquisition of GPS data when an animal surfaces
and subsequent post-processing to derive position estimates.
Fastloc-GPS has massively improved the accuracy of loca-
tion data compared to traditional Argos tracking and is now
widely used to track diverse marine taxa including sea turtles
(Hazel 2009; Schofield etal. 2010a, b), marine mammals
(Costa etal. 2010), and fish (Sims etal. 2009). Fastloc-GPS
tags can be deployed as data loggers, which store data for
subsequent download when the unit is retrieved, or can be
interfaced with an Argos tag (i.e., Argos linked Fastloc-GPS
tags), so that data are received by the Fastloc-GPS receiver
and then relayed via the Argos system.
Here, we consider the implications of high-resolution Fast-
loc-GPS tracking for home range estimation and fine-scale
movement analysis in sea turtles. First, we use simulations to
show the general importance of location accuracy for home
range estimation. We then support these simulations with
empirical data collected for green turtles (Chelonia mydas)
tracked using Argos linked Fastloc-GPS tags, which allowed
the utility of both the Argos and Fastloc-GPS data to be com-
pared for the same individuals. Finally, we provide recom-
mendations for how future work might proceed to identify
fine-scale patterns of space use within and among individuals,
species and study systems in the marine environment.
Materials andmethods
Simulations
To evaluate the impact of location accuracy on home range
estimation, we generated distributions of the location of
simulated animals whose available habitat size varied by
three orders of magnitude. For computational simplicity, we
drew animal locations (N=1000) from a bivariate normal
distribution within square-shaped habitats of 1, 10, 100,
and 1000km2. We considered these to be the ‘true’ animal
locations. We then used the package adehabitatHR (Calenge
2006) in R v. 3.3.2 (R Core Team 2016) to estimate the
95% home range of the animal in each habitat size via the
fixed kernel method (Worton 1989). We used the reference
bandwidth (href) as a smoothing parameter, which is suit-
able for bivariate normal data (Calenge 2006) and provides
a conservative estimate thanks to oversmoothing (Bowman
and Azzalini 1997).
We then introduced errors to the ‘true’ animal locations
to obtain home range size estimates under different levels of
location accuracy. We did so by drawing random errors from
a bivariate normal distribution with a mean of 0 and a stand-
ard deviation (SD) ranging from 0 to 2km in increments
of 0.01. This range was selected, because it would encom-
pass Fastloc-GPS errors (Hazel 2009; Dujon etal. 2014)
and most Argos location class errors excluding those with
the highest uncertainty such as classes 0 and B (Costa etal.
2010). Our aim here was not to evaluate specific location
classes, because reported errors vary considerably among
studies (Table1). Rather, we sought to assess the impact
of location accuracy along a gradient that would include
location qualities commonly encountered in sea turtle home
range studies. For simplicity, we assumed that latitudinal and
longitudinal errors were equivalent. While we are aware that
Argos error distributions tend to be elliptical, with longitu-
dinal exceeding latitudinal errors (Hays etal. 2001; Costa
etal. 2010; Boyd and Brightsmith 2013), this does not affect
our ability to illustrate the general impact of location qual-
ity on home range estimation across orders of magnitude of
animal movements.
The random errors (N=1000 for each theoretical animal)
were added to the ‘true’ simulated animal locations to cre-
ate error-added location data sets. We then used the kernel
method, as above, to estimate each animal’s 95% home range
size using the error-added locations and calculated the per-
cent error between this value and the true home range size.
This was repeated 10 times for each animal for a total of
4×10×201=8040 iterations. We calculated the mean
Table 1 Variation in Argos location class accuracies in three studies that reported the same statistics (68th percentile or 1 SD of a normal distri-
bution, in km) for latitudinal and longitudinal errors separately
Source Method Error (68th percentile, lat/long)
LC B LC A LC 0 LC 1 LC 2 LC 3
Hays etal. (2001) Stationary test on land 5.23/7.79 1.39/0.81 4.29/15.02 1.03/1.62 0.28/0.62 0.12/0.32
Vincent etal. (2002) On animals, study pool 4.596/7.214 0.762/1.244 2.271/3.308 0.494/1.021 0.259/0.485 0.157/0.295
Costa etal. (2010) On animals, at sea 4.642/8.253 2.788/4.373 1.795/2.855 0.574/0.879 0.468/0.729 0.225/0.340

Mar Biol (2017) 164:204
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percent error at each increment of SD (location error) and
smoothed the resulting curve for each simulated animal by
calculating a running mean spanning three consecutive data
points. For ease of visualization, percent error data were
log10(x+1)-transformed.
Empirical case study
We equipped green turtles with Argos linked Fastloc-GPS
tags (SPLASH10-BF tags, Wildlife Computers, Seattle,
Washington) at three sites around the world: the Chagos
Archipelago (Indian Ocean) in 2012, Shark Bay (Western
Australia) in 2016, and Bonaire (Caribbean Netherlands)
in 2016. These units provided both Argos and Fastloc-GPS
locations. To compare home range estimates from Argos
versus Fastloc-GPS data, we selected one representative data
set from each site: a green turtle tracked for 14months in the
Chagos Archipelago, one tracked for 3months in Shark Bay,
and one tracked for 5months in Bonaire. To compare the
number of Fastloc-GPS versus Argos locations obtained, we
used data from all the turtles equipped in the Chagos Archi-
pelago and Shark Bay. Since the tags deployed in Bonaire
were also programmed to relay other data (e.g., depth) at the
expense of sending Fastloc-GPS data, we did not include
these tags in the comparison of location data volume.
For Fastloc-GPS, we excluded locations with a residual
value ≥35, which is a standard procedure for Fastloc-GPS
data (Dujon etal. 2014). Then, using previously estab-
lished methods (Luschi etal. 1998; Dujon etal. 2014; Hays
etal. 2014; Christiansen etal. 2017), we removed the most
obvious Argos and Fastloc-GPS locations that were likely
erroneous. To do this, we examined each track visually and
identified locations that appeared inconsistent with adjacent
points (i.e., they were off the path of previous and subsequent
locations). Further analysis confirmed that these locations
necessitated speeds of travel that were unrealistic for sea
turtles (>200kmd−1). These steps were designed to reflect
commonly used filtering procedures for both data types, and
removed a very small proportion of locations (0.5% of Argos
locations and 0.1% of Fastloc-GPS locations).
To remove the impact of fine-scale autocorrelation,
we randomly selected a single location per day from each
location class (see below) for each turtle prior to estimat-
ing home range sizes. We used the R package adehabi-
tatHR to estimate home range size, as above. However,
we used a different smoothing approach, since the ‘real-
world’ latitude and longitude data were multi-modal (i.e.,
not bivariate normal) and using the reference bandwidth
can cause a large amount of oversmoothing in such cases,
leading to overestimation of home range size (Worton
1989; Kie 2013). Instead, using a custom script in R, for
each home range estimate, we identified the minimum h
value below which the continuous home range contour
breaks up into two or more polygons (the minimum h rule,
see Fieberg and Börger 2012 and references therein). Due
to low sample size in certain location classes, we pooled
Argos classes 1, 2, and 3 together, lumped Fastloc-GPS
locations derived from 9 satellites with those derived from
8 satellites, and excluded Argos class 0 entirely.
Subsequently, to account for the possible impact of
data volume on home range estimation, we standardized
the number of locations used to estimate home range size
across location classes. We did so for each individual by
randomly selecting 75% of the smallest sample size avail-
able in a location class for all location classes for that
turtle 10 times. We then estimated the 95% home range
size at each iteration and calculated the mean and SE for
each location class. Since our aim here was to evaluate the
trend in home range size across location classes within
each site/individual, as opposed to comparing turtle home
range sizes among sites/individuals, it was not necessary
to use the same volume of data for each turtle. Therefore,
for our present purpose, we allowed the number of loca-
tions to vary from turtle-to-turtle based on the amount of
data obtained by each tag. For the Chagos turtle, many
fewer locations were available in Argos location classes
1–3 compared to other classes, so we did not sub-sample
this location class, instead producing a single estimate of
home range size.
Results
Simulations
The degree of error in home range size estimates in our
simulations depended strongly on location accuracy (SD)
and habitat size (Fig.1). Specifically, as habitat size
increased, the accuracy of locations needed to reliably esti-
mate home range size decreased. For example, at a habitat
size of 1000km2, a location error distribution with an SD
<1.67km was necessary to produce <10% error in home
range size estimates. In contrast, at a habitat size of 1km2,
a location error distribution with an SD of <0.06km was
necessary to achieve <10% error (Fig.1). The former case
would likely include Argos location classes 1–3 and all
Fastloc-GPS locations, while the latter case would likely
only include Fastloc-GPS locations derived from ≥5
satellites.
Empirical case study
For green turtles in the Chagos Archipelago, Western Aus-
tralia, and the Caribbean, home range estimates declined by
a factor of approximately 10, 12, and 100, respectively, when

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moving from the poorest to the best location quality (Fig.2).
Argos location classes A and B dramatically overestimated
home range size, whereas Argos location classes 1–3 provided
generally comparable estimates to Fastloc-GPS data, with the
exception of the Caribbean turtle (Fig.2). However, Fastloc-
GPS tracking revealed much more restricted movements and
a much higher degree of patchiness in space use compared to
Argos tracking, which tended to blur the pattern of space use
(Fig.3). This was true even when considering only the best-
quality Argos data (i.e., location classes 1–3, Fig.4). In this
case, the sparseness of class 1–3 Argos locations meant that
details of how multiple focal patches were used by each ani-
mal went unobserved. Compared to location accuracy, stand-
ardizing data volume across location classes had a relatively
minor impact on the trend in home range size from the poorest
to best location quality for both turtles (Fig.2).
On average, there were 14.6 times (range 6.8–27.0) more
Fastloc-GPS locations obtained compared to high-quality
(location class 1–3) Argos locations, and this pattern for
more Fastloc-GPS data occurred across all individu-
als (Fig.5). This increased volume of locations underlies
the much clearer pattern of space use that emerged when
plotting the Fastloc-GPS data and the tendency of these
data to reveal how multiple small patches were used by each
individual.
Discussion
In recent years, technological advances have led to rapid
improvement in the quality of locations obtainable for
Fig. 1 Percent error between the true and error-added 95% home
range estimates for simulated animals within square-shaped habitats
of 1, 10, 100 and 1000km2 across different location qualities includ-
ing all values of SD from 0 to 2 (a) and SD ≤0.3 (b). Percent error
data are shown on a log10(x+1) scale due to large differences in
these values at high SDs, although axis labels are untransformed for
ease of interpretation. Values below the horizontal dashed line rep-
resent <10% error between the error-added and true home range size
Fig. 2 Estimated 95% home range sizes derived from different loca-
tion qualities for a green turtle tracked for 14months in the Chagos
Archipelago, western Indian Ocean (a), another tracked for 3months
in Shark Bay, Western Australia (b), and a third tracked for 5months
in Bonaire, Caribbean Netherlands. For (a) and (b), the dashed line
with triangles represents home range estimates based on all available
data (1 location per day) per location class, while the solid line with
circles represents the mean (±SE) estimate based on sub-sampled
data to standardize data volume across location classes (see “Materi-
als and Methods”). For the Chagos turtle, the estimate for Argos loca-
tion classes 1–3 is a single value based on all available locations due
to low sample size

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air-breathing marine vertebrates and some fish and,
hence, increased variability in track quality in the litera-
ture (e.g., Table2 for sea turtles). As such, consideration
of the impacts of location accuracy and data volume for
home range estimation and fine-scale movement analysis
for these species is timely. We have shown that location
accuracy can profoundly impact estimated home range
size, with exceedingly large errors likely to occur under a
combination of low location accuracy and fine-scale ani-
mal movements. Furthermore, we have shown that Fastloc-
GPS tracking can reveal movement patterns in fine detail
(i.e., patch use) insituations where Argos data cannot.
In studies looking at space use, we emphasize that it is
important to consider the level of location error inherent
in the tracking system and how this error interacts with the
scale of movement to impact the picture of space use that
emerges (see also Montgomery etal. 2011 for terrestrial
examples). Moreover, we urge caution when comparing
home range estimates obtained from different tracking sys-
tems or tag configurations that provide locations of differ-
ent levels of accuracy.
Recent movement analyses for sea turtles have been
made using light-based geolocation, radio telemetry, acous-
tic telemetry, Argos satellite tracking, and Fastloc-GPS
tracking, which have a wide range of location accuracies
Fig. 3 Argos (left panels) and Fastloc-GPS (right panels) location
distributions for a green turtle tracked for 14months in the Cha-
gos Archipelago, western Indian Ocean (a, b), another tracked for
3months in Shark Bay, Western Australia (c, d), and a third tracked
for 5months in Bonaire, Caribbean Netherlands (e, f). Argos plots
include all location data (classes A, B, 0, 1, 2 and 3), while Fastloc-
GPS plots include locations derived from ≥4 satellites. Points have
been made transparent to show location density. Note differences in
scale among plots. To emphasize the differences in scale, hashed
squares within Argos panels show the extent of the Fastloc-GPS data
for that study site
Fig. 4 Differences in movement detail provided by the most accurate
Argos data (classes 1–3, left panels) and Fastloc-GPS data (locations
derived from ≥4 satellites, right panels) for the three green turtles.
Points have been made transparent to show location density. Note
minor differences in scale among plots
Fig. 5 For nine turtles tracked using Fastloc-GPS Argos transmitters,
the proportion of Fastloc-GPS locations (derived from ≥4 satellites
and with residual values <35, filled bars) compared to high-accuracy
Argos locations (location class 1–3, open bars). Turtles 1–4 were
equipped on Diego Garcia, Chagos Archipelago, while turtles 5–9
were tagged in Shark Bay, Western Australia

Mar Biol (2017) 164:204
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(Table2). These studies all provide important space use data
that are consistent within each study. For example, Schofield
etal. (2010b) used Fastloc-GPS data from loggerhead turtles
in the Mediterranean to show that oceanic foragers had home
ranges >50 times larger than neritic foragers, while Este-
ban etal. (2017) used Fastloc-GPS to quantify the number
of clutches individual green turtles laid in a single breed-
ing season. However, while Fastloc-GPS tracking has been
available for several years, due to the lower cost of Argos
tags, many studies still rely on Argos locations (e.g., Hawkes
etal. 2011; Fujisaki etal. 2016; Shaver etal. 2016). Given
the magnitude of error in home range estimates identified in
our theoretical and empirical examples (see also Witt etal.
2010), we argue that comparison of home range estimates,
in addition to other movement metrics (e.g., Bradshaw etal.
2007), should only be made after carefully accounting for
differences in location quality between tracks. For exam-
ple, it might be of interest to examine variation in home
range size over space or time using a combination of newer
Fastloc-GPS and older Argos tracks. To do this reliably
would require decaying the GPS data by introducing random
Argos-level errors to the GPS data (similar to the approach
taken in our theoretical home range analysis) and standard-
izing sample size among tracks.
In addition to highlighting the relationship between loca-
tion accuracy, the scale of animal movements, and home
range estimation, we have demonstrated the potential for
Fastloc-GPS data to yield valuable new insights into the
patterns, drivers, and consequences of the movements of
sea turtles at very fine spatial scales (e.g., patch use dynam-
ics). This utility of Fastloc-GPS for examining fine-scale
movements will likely apply to other marine taxa that only
surface briefly including some marine mammals, birds, and
fish. As in our study, an increased number of Fastloc-GPS
locations has been noted when Argos linked Fastloc-GPS
tags have been attached to fish (Sims etal. 2009; Evans
etal. 2011). The increased number of Fastloc-GPS loca-
tions which we found is likely due to the fact that data for
a Fastloc-GPS location can be encoded in a single Argos
uplink, while many uplinks in a single satellite overpass are
required to generate an Argos location of class 1–3. As such,
the finding of a vastly greater volume of Fastloc-GPS loca-
tions compared to Argos locations when using Argos linked
Fastloc-GPS tags will likely be broadly consistent across
taxa. Furthermore, Fastloc-GPS tags can be used in data
loggers, which can increase data volume by a further order
of magnitude compared to the data volumes recoverable by
satellite (Schofield etal. 2010b).
Future comparative studies that analyze GPS-based tracks
of foraging turtles in a standardized manner hold consider-
able potential to advance our understanding of turtle space
use, trophic relationships and functional roles in coastal eco-
systems. It should be noted that, in addition to location accu-
racy and data volume (e.g., Seaman etal. 1999; Börger etal.
2006a, b), other components of home range analysis are also
known to influence estimates of home range size and should,
therefore, be accounted for when designing comparative
studies. For example, KDEs can be strongly influenced by
the smoothing parameter used (Worton 1989; Kie 2013), and
the choice of smoothing parameter will depend on the struc-
ture of the location data and the particular question being
asked (Fieberg and Börger 2012). Similarly, Service Argos
have been trying to improve the quality of their tracking
data. Specifically, Service Argos introduced a new method
of estimating platform locations which combines their tradi-
tional approach—using the Doppler shift in received uplink
frequencies and a least-squares algorithm—with interpola-
tion between locations using Kalman filtering (Lopez etal.
2014). This new method of processing tends to provide
smoother tracks, but the autocorrelation between locations
introduced by Kalman filtering will need to be considered
if these data are used in home range estimation, especially
Table 2 Summary of telemetry methods used to track sea turtle movements and their approximate location accuracy
Method Approximate location accuracy Typical movements revealed Examples
Light-based geolocation Tens to hundreds of km Long-term, coarse-scale movements
(e.g., breeding migrations) Fuller etal. (2008), Swimmer etal.
(2009)
Radio telemetry Tens of m to >1km Short-term, fine-scale movements in a
spatially restricted area Renaud etal. (1995), Whiting and Miller
(1998)
Active acoustic telemetry <10 to hundreds of m Short-term, fine-scale movements in a
spatially restricted area Ogden etal. (1983), Seminoff and Jones
(2006)
Passive acoustic telemetry <10 to hundreds of m Long-term, fine-scale movements in a
spatially restricted area Taquet etal. (2006), Thums etal. (2013)
Argos satellite tracking Hundreds of m to >10km Long-term, coarse to medium-scale
movements (e.g., breeding migra-
tions, transits between foraging sites)
Luschi etal. (1998), Papi etal. (1995),
Godley etal. (2008) (review)
Fastloc-GPS tracking Tens to hundreds of m Long-term, fine-scale movements (e.g.,
foraging patch use, breeding migra-
tions, inter-nesting movements)
Hazel (2009), Schofield etal. (2010a, b),
Dujon etal. (2014), Christiansen etal.
(2017)

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when compared with tracks without Kalman filtering. It may,
therefore, be advisable for researchers to obtain and store
the Kalman-filtered locations as well as the underlying raw
Argos locations, which may not both be provided automati-
cally by Service Argos. Doing so will create the potential to
implement more sophisticated analyses accounting for the
error of each single location. Refer also to McClintock etal.
(2015) for arguments regarding the importance of using the
error ellipse and not the error circle in movement analyses as
well as the importance of not discarding more ‘inaccurate’
locations (see Ironside etal. 2017 for a similar remark for
terrestrial GPS data).
Moreover, aspects of the movement pattern of animals
may sometimes interact with methods of data processing to
influence the picture of space use that emerges. For example,
visual observations have shown that green turtles often rest
in certain areas at night and then travel to foraging loca-
tions during the day (Bjorndal 1980). The specifics of these
movements have recently been recorded in high resolution
with Fastloc-GPS tracking (Christiansen etal. 2017), with
the finding that nighttime resting and daytime foraging areas
may be several km apart. Therefore, in this case, only using
daytime or nighttime locations, even if they are of high
resolution, would not capture the full extent of space use
(see also general discussion in Fieberg and Börger 2012).
Likewise, locations around dawn and dusk are needed to
identify migration corridors between areas occupied during
the night and day. Again, Fastloc-GPS opens up the potential
of addressing these questions, but, at the same time, com-
parative studies of space use, across individuals and across
studies, will require careful consideration of these sources
of variability.
In conclusion, our results highlight an important yet
underappreciated aspect of movement ecology study design
for air-breathing marine vertebrates and some fish. Our
understanding of the fine-scale movements of these taxa lags
well behind that of terrestrial vertebrates, which have been
tracked effectively using Argos and GPS systems for some
time. For general considerations on study design, we recom-
mend consulting the framework that has grown out of that
body of work (e.g., Seaman etal. 1999; Börger etal. 2006a,
b; Frair etal. 2010; Hebblewhite and Haydon 2010; Mont-
gomery etal. 2011; Fieberg and Börger 2012; McClintock
etal. 2015; Ironside etal. 2017). Here, we emphasize that
location accuracy relative to the expected scale of animal
movements should be a key methodological consideration
and we recommend caution when comparing home range
estimates and other movement metrics derived from tracking
systems with different location qualities and data volumes.
Acknowledgements We thank the Department of Parks and Wild-
life, Western Australia for their assistance in deploying satellite tags
in Shark Bay. Fieldwork in Bonaire was funded by the Netherlands
Organization of Scientific Research (NWO-ALW 858.14.090). We
thank Sea Turtle Conservation Bonaire for their assistance in deploy-
ing satellite tags in Bonaire. Fieldwork in the Chagos Archipelago was
supported by a Darwin Initiative Challenge Fund Grant (EIDCF008),
the Department of the Environment Food and Rural Affairs, the Foreign
and Commonwealth Office, College of Science of Swansea University,
and the British Indian Ocean Territory (BIOT) Scientific Advisory
Group of the FCO. We would like to thank Ernesto and Kirsty Berta-
relli, and the Bertarelli Foundation, for their support of this research.
We acknowledge and thank the BIOT Administration for assistance
and permission to carry out research within the Chagos Archipelago.
Compliance with ethical standards
All applicable international, national, and/or institutional guidelines
for the care and use of animals were followed. Fieldwork in Shark
Bay was conducted under Department of Parks and Wildlife (DPaW)
Regulation 17 license #SF010887 and Florida International University
IACUC approval #IACUC-15-034-CR01. Fieldwork in Bonaire was
conducted under a permit from the “Openbaar Lichaam Bonaire” nr.
558/2015-2015007762 and was performed using appropriate animal
care protocols. In the Chagos Archipelago, fieldwork was approved
by the Commissioner for the BIOT (research permit dated 2 October
2012) and Swansea University Ethics Committee, and complied with
all relevant local and national legislation. The authors have no conflicts
of interest.
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