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


Abstract and Figures

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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Mar Biol (2017) 164:204
DOI 10.1007/s00227-017-3225-7
Implications oflocation accuracy anddata volume forhome
range estimation andfine-scale movement analysis: comparing
Argos andFastloc-GPS tracking data
J.A.Thomson1 · L.Börger2· M.J.A.Christianen3,4· N.Esteban2· J.-O.Laloë1·
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
100m). We recommend careful consideration of the eects
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.
Understanding patterns of space use by animals lies at the
heart of many ecological studies and also underpins many
eorts to make evidenced-based management decisions,
for example as part of conservation planning (Cooke 2008).
Thanks to increased accessibility of tracking technology
(Kays etal. 2015; Hays etal. 2016), both the number of taxa
tracked and the number of studies collecting movement data
across dierent habitats are rapidly increasing. However,
the ability to reliably detect dierences 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 etal. 2006a, b; Frair etal.
2010; Hebblewhite and Haydon 2010; Montgomery etal.
2011; McClintock etal. 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 etal.
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 dierent 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
1 School ofLife andEnvironmental Sciences, Deakin
University, Centre forIntegrative Ecology, Warrnambool,
VIC3280, Australia
2 Department ofBiosciences, Swansea University,
SwanseaSA28PP, UK
3 Institute forWetland andWater Research, Radboud
University Nijmegen, Heyendaalseweg 135,
6525AJNijmegen, TheNetherlands
4 Groningen Institute forEvolutionary Life Sciences,
University ofGroningen, P.O. Box11103,
9700CCGroningen, TheNetherlands
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 dierences
in space use.
In the marine context, a major advance in recent years
has been the advent of Fastloc-GPS tracking (Kuhn etal.
2009; Hazen etal. 2012; Hoenner etal. 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 etal. 2010a, b), marine mammals
(Costa etal. 2010), and fish (Sims etal. 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 andmethods
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 1000km2. 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 dierent 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 2km in increments
of 0.01. This range was selected, because it would encom-
pass Fastloc-GPS errors (Hazel 2009; Dujon etal. 2014)
and most Argos location class errors excluding those with
the highest uncertainty such as classes 0 and B (Costa etal.
2010). Our aim here was not to evaluate specific location
classes, because reported errors vary considerably among
studies (Table1). 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 etal. 2001; Costa
etal. 2010; Boyd and Brightsmith 2013), this does not aect
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 etal. (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 etal. (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 etal. (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
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 14months in the
Chagos Archipelago, one tracked for 3months in Shark Bay,
and one tracked for 5months 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 etal. 2014). Then, using previously estab-
lished methods (Luschi etal. 1998; Dujon etal. 2014; Hays
etal. 2014; Christiansen etal. 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 o the path of previous and subsequent
locations). Further analysis confirmed that these locations
necessitated speeds of travel that were unrealistic for sea
turtles (>200kmd1). 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 dierent 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.
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 1000km2, a location error distribution with an SD
<1.67km was necessary to produce <10% error in home
range size estimates. In contrast, at a habitat size of 1km2,
a location error distribution with an SD of <0.06km 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
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
Mar Biol (2017) 164:204
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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
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 1000km2 across dierent 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 dierences 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 dierent loca-
tion qualities for a green turtle tracked for 14months in the Chagos
Archipelago, western Indian Ocean (a), another tracked for 3months
in Shark Bay, Western Australia (b), and a third tracked for 5months
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
Mar Biol (2017) 164:204
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air-breathing marine vertebrates and some fish and,
hence, increased variability in track quality in the litera-
ture (e.g., Table2 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) insituations 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 etal. 2011 for terrestrial
examples). Moreover, we urge caution when comparing
home range estimates obtained from dierent tracking sys-
tems or tag configurations that provide locations of dier-
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 14months in the Cha-
gos Archipelago, western Indian Ocean (a, b), another tracked for
3months in Shark Bay, Western Australia (c, d), and a third tracked
for 5months 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 dierences in
scale among plots. To emphasize the dierences in scale, hashed
squares within Argos panels show the extent of the Fastloc-GPS data
for that study site
Fig. 4 Dierences 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 dierences 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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(Table2). These studies all provide important space use data
that are consistent within each study. For example, Schofield
etal. (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 etal. (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
etal. 2011; Fujisaki etal. 2016; Shaver etal. 2016). Given
the magnitude of error in home range estimates identified in
our theoretical and empirical examples (see also Witt etal.
2010), we argue that comparison of home range estimates,
in addition to other movement metrics (e.g., Bradshaw etal.
2007), should only be made after carefully accounting for
dierences 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 etal. 2009; Evans
etal. 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 etal. 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 etal. 1999; Börger etal.
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 etal.
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 etal. (2008), Swimmer etal.
Radio telemetry Tens of m to >1km Short-term, fine-scale movements in a
spatially restricted area Renaud etal. (1995), Whiting and Miller
Active acoustic telemetry <10 to hundreds of m Short-term, fine-scale movements in a
spatially restricted area Ogden etal. (1983), Semino and Jones
Passive acoustic telemetry <10 to hundreds of m Long-term, fine-scale movements in a
spatially restricted area Taquet etal. (2006), Thums etal. (2013)
Argos satellite tracking Hundreds of m to >10km Long-term, coarse to medium-scale
movements (e.g., breeding migra-
tions, transits between foraging sites)
Luschi etal. (1998), Papi etal. (1995),
Godley etal. (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 etal. (2010a, b),
Dujon etal. (2014), Christiansen etal.
Mar Biol (2017) 164:204
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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 etal.
(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 etal. 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 etal. 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 eectively 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 etal. 1999; Börger etal. 2006a,
b; Frair etal. 2010; Hebblewhite and Haydon 2010; Mont-
gomery etal. 2011; Fieberg and Börger 2012; McClintock
etal. 2015; Ironside etal. 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 dierent 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 Aairs, the Foreign
and Commonwealth Oce, 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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... Restricted movement patterns have been commonly reported from foraging hawksbill turtles tracked in other areas [27][28][29][30]. The animals tracked in the present study exhibited similar restricted movements (Additional file 1: Table S1). ...
... The animals tracked in the present study exhibited similar restricted movements (Additional file 1: Table S1). The Red Sea hawksbill turtles exhibited similarly sized home range and core use areas as from other regions, with some smaller and some larger (Additional file 1: Table S1) [27][28][29][30]. Home ranges may simply reflect the physical constraints where each animal resides. ...
... Our results showed more than a tenfold difference in the home range and core area estimations when using GPS-derived locations compared to the Argos-derived locations (see also [27]). While home range estimations from both data types indicated highly constrained foraging grounds when compared to the available habitat, the difference in area estimation had a pronounced effect on finer-scale analyses, such as the benthic habitat classification (see Additional file 1: Fig. S1). ...
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Background Hawksbill turtles ( Eretmochelys imbricata ) are Critically Endangered throughout their global range, and concerningly little is known about this species in the Red Sea. With large-scale coastal development projects underway in the northern Red Sea, it is critical to understand the movement and habitat use patterns of hawksbill turtles in this environmentally unique region, so that effective conservation strategies can be implemented. We satellite tagged three hawksbill turtles, one 63 cm curved carapace length adult male captured near Wahlei Island, one 55 cm turtle captured in the Gulf of Aqaba, and one 56 cm turtle suffering from a floating syndrome which was captured at Waqqadi Island, rehabilitated, and released at Waqqadi Island. Turtles were tracked for 156, 199, and 372 days between October 2020 and November 2021. Results We calculated the home ranges and core use areas of hawksbill turtles using kernel-density estimations and found that each turtle showed high fidelity to their foraging sites. Home ranges calculated with GPS-derived locations ranged between 13.6 and 2.86 km 2, whereas home ranges calculated with Argos-derived locations ranged from 38.98 to 286.45 km ² . GPS-derived locations also revealed a higher proportion of time spent in coral and rock habitats compared to Argos, based on location overlap with the Allen Coral Reef Atlas. We also found that turtles were making shallow dives, usually remaining between 0 and 5 m. Conclusions While the number of tracked turtles in this study was small, it represents an important contribution to the current understanding of spatial ecology among foraging hawksbill turtles globally, and provides the first-ever reported hawksbill turtle tracking data from the Red Sea. Our results suggest that protecting coral reef habitats and implementing boating speed limits near reefs could be effective conservation measures for foraging hawksbill turtles in the face of rapid coastal development.
... Satellite and archival tags were deployed on 59 female green turtles during the 2018, 2019 and 2020 breeding seasons, between August 13 and November 8. In 2018, 20 turtles were equipped with SPOT-375B tags (99 × 55 × 21 mm, 152 g, ® Wildlife Computers), which rely on the Argos satellite system only and have an accuracy of hundreds of meters to >1 km (Thomson et al. 2017). In 2019 and 2020, FastGPS tags (F6G 376B, 115 × 64 × 43.5 mm, 220 g, ® Lotek), which provide both Argos and GPS locations (mean ± SD accuracy of fast-acquisition GPS ranging from 172.0 ± 317.5 m for a minimum of 4 satellites to 26.0 ± 19.2 m for a maximum of 8 satellites; Hazel 2009), were de ployed on 9 and 15 females, respectively. ...
... The Argos tracking data were first filtered by removing the class Z locations, corresponding to the lowest location class provided by the Argos service (considered as error locations; Witt et al. 2010, Thomson et al. 2017). All GPS locations were obtained from at least 4 satellites. ...
... Yet, the fact that we did not detect a year effect when comparing only 2019 (14 406 nests) with 2020 (59 676 nests) suggests that competition for resting sites is not noticeable in the waters surrounding Poilão Island, or perhaps the difference in abundance was not sufficient to lead turtles to explore larger areas to find suitable resting sites. Alternatively, the observed year effect may potentially be influenced by the use of the Argos system in 2018, which is less accurate and can lead to larger home ranges (Thomson et al. 2017). Further deployments using the same tag type across more years may elucidate this matter. ...
Understanding the spatial distribution of wildlife is fundamental to establish effective conservation measures. Tracking has been key to assess movement patterns and connectivity of sea turtles, yet some regions of great significance are largely understudied. We tracked 44 green turtles from the largest rookery in the Eastern Atlantic, on Poilão Island, Guinea-Bissau, during the 2018 through 2020, to assess their inter-nesting movements, connectivity with nearby islands and fidelity to inter-nesting sites. Additionally, we investigated individual and environmental factors that may guide inter-nesting distribution and assessed the adequacy of a marine protected area to support this population during the breeding period. Green turtles had an overall home range of 124.45 km2, mostly occupying a restricted area around Poilão Island, with 52% of this home range falling within the no-take zone of the João Vieira-Poilão Marine National Park. Turtles exhibited strong fidelity to inter-nesting sites, likely as a strategy to save energy. Only two turtles performed significant excursions out of the Park, and connectivity between Poilão and nearby islands within the Park was limited. Larger turtles and turtles tagged later in the nesting season tended to have smaller core areas and home ranges, thus, experience may potentially benefit energy saving. This study highlights the importance of a marine protected area for the conservation of one of the largest green turtle breeding populations globally, and hints on ways to further increase its effectiveness.
... However, the potential effects of survey frequency and sample size have yet to be explored for UASs. This directly contrasts with the wealth of studies exploring the effects of the number of animals and sampling frequency for remote tracking datasets, despite the potential for similar issues with frequency and sample size effects (Thomson et al., 2017;Sequeira et al., 2019;Shimada et al., 2020). For instance, home range size estimates are particularly impacted by sample size and bias towards individuals (Borger et al., 2006;Plotz et al., 2016;Thomson et al., 2017), therefore, it is essential to identify key parameters impacting the interpretation of UAS surveys. ...
... This directly contrasts with the wealth of studies exploring the effects of the number of animals and sampling frequency for remote tracking datasets, despite the potential for similar issues with frequency and sample size effects (Thomson et al., 2017;Sequeira et al., 2019;Shimada et al., 2020). For instance, home range size estimates are particularly impacted by sample size and bias towards individuals (Borger et al., 2006;Plotz et al., 2016;Thomson et al., 2017), therefore, it is essential to identify key parameters impacting the interpretation of UAS surveys. ...
... As a case in point, 1000s of sea turtles have been individually GPS-tracked at breeding and foraging grounds globally (Hays and Hawkes, 2018). However, at given sites, only small numbers of animals (10s) are typically tracked, with a very strong bias towards adult females (Thomson et al., 2017;Lamont and Iverson, 2018). Yet, there is clear evidence that males and females use breeding sites differently and, often, dynamically (James et al., 2005;Arendt et al., 2011b;Schofield et al., 2013). ...
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Quantifying how animals use key habitats and resources for their survival allows managers to optimise conservation planning; however, obtaining representative sample sizes of wildlife distributions in both time and space is challenging, particularly in the marine environment. Here, we used unoccupied aircraft systems (UASs) to evaluate temporal and spatial variation in the distribution of loggerhead sea turtles (Caretta caretta) at two high-density breeding aggregations in the Mediterranean, and the effect of varying sample size and survey frequency. In May–June of 2017 to 2019, we conducted 69 surveys, assimilating 10,075 inwater turtle records at the two sites. Optimal time interval between surveys to capture the dynamics of aggregations over the breeding period was <2-week intervals and >500 turtles (from the combined surveys). This minimum threshold was attributed to the core-area use of female turtles shifting across surveys in relation to wind direction to access warmer nearshore waters and male presence. Males were more widely distributed within aggregations than females, particularly in May when mating encounters were high. Most males were recorded swimming and oriented parallel to shore, likely to enhance encounter rates with females. In contrast, most females were generally stationary (resting on the seabed or basking), likely to conserve energy for reproduction, with orientation appearing to shift in relation to male numbers at the breeding area. Thus, by identifying the main factors regulating the movement and distribution of animals, appropriate survey intervals can be selected for appropriate home range analyses. Our study demonstrates the versatility of UASs to capture the fine-scale dynamics of wildlife aggregations and associated factors, which is important for implementing effective conservation.
... However, a significant advantage of Fastloc GPS devices is that they acquire GPS ephemeris data in 10 s of milliseconds, compared to 5-12 s for SWIFT GPS devices, and will therefore be more suitable for briefly surfacing marine animals. This is a domain where Fastloc GPS devices have been widely applied with great success [48,49], and where SWIFT GPS devices will have little utility. Fastloc devices also process and compress signals on-board the tag, which can then be transmitted over the Argos network [50]-which is not currently a feature for SWIFT GPS devices. ...
Full-text available
The remote collection of animal location data has proliferated in recent decades, and higher-frequency data are increasingly available with battery-saving optimisations such as ‘snapshot’ algorithms that acquire GPS satellite data and post-process locations off-board. This is the first study to assess the effects of vegetation and topography on the fix success rate and location error of global positioning system (GPS) devices that use the SWIFT fix algorithm, developed by Lotek. To assess fix success rate (FSR—the proportion of successful fixes compared to the total number of attempts) and location error (LE), we conducted a stationary test at a predominately forested site on the South Island of New Zealand. The overall FSR was 83% (± 15.3% SD), which was affected strongly by canopy closure above 90%. Half of the locations were within 8.65 m of the true location, 79.7% were within 30 m, and 95% of locations were within 271 m. When 6 or more satellites were used, this reduced to 4.92 m and 18.6 m for 50% and 95%, respectively. Horizontal dilution of precision (HDOP), the number of satellites, and canopy closure all influenced location error. To field test the fix success rate of SWIFT GPS devices, we deployed them on forest-dwelling parrots with 2 and 3-h fix intervals, which showed similar FSR results to the stationary test when cavity-nesting individuals were removed (FSR mean ± SD = 81.6 ± 5.0%). The devices lasted an average of 147 days before depleting the battery, resulting in an average of 1087 successful fixes per individual at an average time of 9.38 s to acquire the GPS ephemeris, resulting in an average of 3.73 attempted locations per mAh of battery for PinPoint 350 devices. Our study provides a baseline for fix success rates and location errors under forested conditions that can be used for future SWIFT GPS tracking studies.
... Upon surfacing, the SPLASH10-F-321A satellite tags transmitted location data, including both ARGOS and Fastloc GPS locations. For subsequent analyses, we only report on GPS positions based on their higher accuracy for estimating home range and fine-scale habitat use patterns (Dujon et al., 2014;Thomson et al., 2017). Additionally, the satellite transmitters were programmed to record and archive dive-depth, light levels, and ambient sea temperature. ...
Full-text available
The behaviour and spatial use patterns of juvenile manta rays within their critical nursery habitats remain largely undocumented. Here, we report on the horizontal movements and residency of juvenile reef manta rays (Mobula alfredi) at a recently discovered nursery site in the Wayag lagoon, Raja Ampat, Indonesia. Using a multi-disciplinary approach, we provide further corroborative evidence that the lagoon serves as an important M. alfredi nursery. A total of 34 juvenile rays were photo-identified from 47 sightings in the sheltered nursery between 2013–2021. Five (14.7%) of these individuals were resighted for at least 486 days (~1.3 years), including two juveniles resighted after 641 and 649 days (~1.7 years), still using the nursery. Visually estimated (n=34) disc widths (DW) of juveniles using the nursery site ranged from 150–240 cm (mean ± SD: 199 ± 19), and the DW of two juveniles measured using drones were 218 and 219 cm. Five juveniles were tracked using GPS-enabled satellite transmitters for 12–69 days (mean ± SD: 37 ± 22) in 2015 and 2017, and nine juveniles were tracked using passive acoustic transmitters for 69–439 days (mean ± SD: 182 ± 109) from May 2019–September 2021. Satellite-tracked individuals exhibited restricted movements within Wayag lagoon. The minimum core activity space (50% Utilisation Distribution-UD) estimated for these five individuals ranged from 1.1–181.8 km2 and the extent of activity space (95% UD) between 5.3–1,195.4 km2 in area. All acoustically tagged individuals displayed high residency within the nursery area, with no acoustic detections recorded outside the lagoon in the broader Raja Ampat region. These juveniles were detected by receivers in the lagoon throughout the 24 h diel cycle, with more detections recorded at night and different patterns of spatial use of the lagoon between day and night. The observed long-term residency of juvenile M. alfredi provides further compelling evidence that the Wayag lagoon is an important nursery area for this globally vulnerable species. These important findings have been used to underpin the formulation of management strategies to specifically protect the Wayag lagoon, which will be instrumental for the survival and recovery of M. alfredi populations in Raja Ampat region.
... Titanium and Inconel flipper tags have enabled the tracking of movements and habitat use with individual marine turtles across decades (Limpus and Limpus 2003) while current satellite telemetry technology is limited to tracking turtles over short durations, often less than a year, as in the current study. Over time, advances in satellite tag technology, such as the advent of Fastloc-Global Positioning System (GPS) that has allowed for more accurate locations and can receive signals in just milliseconds, have facilitated the tracking of air-breathing marine organisms that may only surface briefly (Dujon et al. 2014; Thomson et al. 2017). Collectively, the information derived from either CMR or telemetry studies has been used for analysis of fine-scale movements and behaviours of marine turtles during migration, and identification of foraging sites (Limpus and Limpus 2001;Troëng et al. 2005;Shimada et al. 2020). ...
Full-text available
Marine turtles encounter different threats during various life-history stages. Therefore, understanding their movements and spatial distribution is crucial for effectively managing these long-lived migratory organisms. This study combines satellite telemetry data with long-term capture-mark-recapture data derived from flipper tag studies to determine distribution patterns of endangered loggerhead turtles ( Caretta caretta ) during post-nesting migrations from different eastern Australian nesting sites. Individuals from the K’gari-Fraser Island and Great Barrier Reef island rookeries typically migrated northward, whereas individuals from mainland rookeries migrated equally northward and southward. Despite this difference in foraging distribution, loggerheads from the different rookeries did not differ substantially in their migration duration or distance travelled. The foraging distribution identified from successful satellite tag deployments represented 50% of the foraging distribution identified from a large long-term flipper tag recovery database. However, these satellite telemetry results have identified new migration and foraging habitats not previously recognised for loggerhead turtles nesting in eastern Australia. Additionally, they support the conclusion from a past study using flipper tag recovery data that the mainland nesting turtles migrate to different foraging grounds than the turtles nesting on Great Barrier Reef islands. Collectively, the two data sources provide valuable data on the migration route, habitat distribution and ecological range for a threatened genetic stock of loggerhead turtles.
... L'acquisition de données spatio-temporelles de localisation des individus à fine échelle est nécessaire. Ces données peuvent ensuite être utilisées pour des analyses de l'utilisation de l'espace à l'échelle individuelle (Thomson et al., 2017 ...
Prévenir les risques d’épidémies est devenu un enjeu sanitaire et économique mondial, comme en témoigne l’émergence récente du SARS-COV-2. Cette Thèse vise à améliorer les connaissances sur l’utilisation de l’espace des chauves-souris frugivores (Pteropodidae) dans des environnements modifiés par l’homme. Ce travail mobilise des données de télémétrie satellitaire chez (i) la roussette de Lyle (Pteropus lylei), espèce réservoir du virus Nipah en Asie, et (ii) la chauve-souris à tête de marteau (Hypsignathus monstrosus), impliquée dans la circulation du virus Ebola en Afrique. La population étudiée de roussette de Lyle était déjà connue pour se nourrir préférentiellement dans les zones résidentielles d’un environnement fragmenté au Cambodge. La chauve-souris à tête de marteau, dont l’utilisation des habitats était méconnue, a été étudiée dans une région forestière en République du Congo – épicentre d’épidémies humaine d’Ebola en 2001–2005. De plus, des données de captures directes de chauves-souris ont été collectées dans cette dernière région. Il ressort de ces travaux que la chauve-souris à tête de marteau se nourrit préférentiellement dans les terres agricoles qui entourent les petits villages forestiers. Les individus de roussette de Lyle visitent davantage d’aires d’alimentation dans l’habitat préférentiel durant la nuit, tandis que les chauves-souris à tête de marteau y passent plus de temps sans multiplier le nombre d’aires visitées. Ces deux espèces bénéficient ainsi des ressources anthropiques à l’échelle de la population selon deux stratégies de déplacements individuels, qui sont possiblement ajustées selon le degré de fragmentation de l’environnement. Chez la chauve-souris à tête de marteau, les aires d’alimentation dans la forêt sont délaissées par les individus qui restent longtemps dans le site d’accouplement durant la nuit, ce qui suggère un rôle des terres agricoles dans l’établissement et le maintien des sites d’accouplement. Au cours de nuits successives, les deux espèces revisitent davantage une aire d’alimentation lorsqu’elles y ont passé beaucoup de temps lors de leur dernière visite. Par ailleurs, une communauté de sept espèces de chauves-souris frugivores a été identifiée dans la région étudiée en Afrique. La probabilité d’occurrence de quatre espèces était plus importante dans les villages, tandis que les autres espèces n’étaient pas influencées par l’habitat. L’ensemble de ces travaux fournit de nouvelles informations sur l’utilisation de l’espace des chauves-souris frugivores dans le cadre de leurs activités nocturnes d’alimentation et de reproduction. Ces données pourraient être intégrées dans des modélisations épidémiologiques visant à mieux comprendre les interactions entre les chauves-souris frugivores, les humains ou les animaux domestiques, ainsi que les voies de transmission de pathogènes.
... We found that comparisons of quantitative measures of home-range area, core-use areas, and even movement rates across sirenian species or studies need to be interpreted with caution due to the differing data collection or analytical methods employed. Home-range estimates can vary greatly with the location accuracy of the tracking method (e.g., Thomson et al. 2017), with the smoothing parameter or bandwidth used in kernel density analyses (Kie 2013), and with the tracking duration of the animal. An individual's home range is often estimated as the 95% utilization distribution of its sightings or PTT/GPS locations; this area can encompass large areas that extend several kilometers beyond any actual locations (e.g., de Iongh et al. 1998). ...
The coastal marine and inland freshwater environments inhabited by manatees and dugongs around the world are spatially heterogeneous and highly dynamic over a range of time scales, often aligned with predictable geophysical cycles (tidal, diel, seasonal). Central to sirenian adaptations for meeting these varied ecological challenges is plasticity in their movement behavior , which allows them to find and utilize resources that are key to their survival and reproduction, to escape risks posed by predators and humans, and to leave habitats that become inhospitable. The development and deployment of animal-borne GPS tags have tremendously advanced our knowledge in the domain of small spatio-temporal scales by providing highly accurate locations many times per day. The recent addition of multi-sensor biologgers is further deepening our understanding of the connections between fine-scale behavioral changes and environmental features experienced by the animal. Individual sirenians generally show strong site fidelity within a season to one or a small number of high-use core areas within their home range. Manatees and dugongs usually move at a leisurely pace within and between habitats that provide forage, shelter, thermal refuge, and (for coastal manatees) fresh water. As marathon swimmers, sirenians can sustain a cruising speed of ~2 to 4 km/h for lengthy periods, but when threatened, they can briefly sprint at speeds up to 30 km/h. A common theme across species, ecosystems, and spatio-temporal scales is that access to forage is often constrained due to environmental fluctuations, including tidal cycles in coastal systems, seasonal water level cycles in flood-pulse river systems, and seasonal temperature changes in higher-latitude regions. Sirenians negotiate trade-offs among key activities within these fluctuating environments while apparently minimizing exposure to predators and other threats through their movement behavior . There is an increasing body of evidence suggesting that many sirenian populations predominantly forage at night, plausibly as an adaptation to reduce risk of falling victim to hunters or, possibly, boat strikes. Sexual selection has also shaped the behavioral ecology of sirenian movements, as mature males are frequently on the move in search of estrous females during the breeding season . Mating and parturition can alter female movements and habitat selection for brief periods, but otherwise reproductive status does not appear to strongly affect female movement behavior over large or small scales. Further research is warranted on most sirenian populations to confirm these conclusions. Continued technological and analytical advancements promise to reveal more secrets of these fascinating and cryptic creatures.KeywordsBehavioral thermoregulationBiologgerCentral place foragingDiel movementsHome rangeMovement rateSatellite trackingSeasonal rangeSex differencesSireniansSite fidelitySmall-scaleTravel corridors
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1. Agricultural intensification has modified grassland habitats, causing serious declines in farmland biodiversity including breeding birds. Until now, it has been difficult to objectively evaluate the link between agricultural land-use intensity and range requirements of wild populations at the landscape scale. 2. In this study of Black-tailed Godwits Limosa limosa, we examined habitat selection and home range size during the breeding phase in relation to land-use intensity , at the scale of the entire Netherlands. From 2013 to 2019, 57 breeding godwits were tracked with solar-Platform Transmitter Terminals (26-216 locations [mean: 80] per bird per breeding phase) and used to estimate their core (50%) and home ranges (90%). Of these, 37 individuals were instrumented in Iberia and therefore unbiased toward eventual breeding locations. The tracks were used to analyse habitat selection by comparing the mean, median and standard deviation of land-use intensity of core and home ranges with matching iterated random samples of increasing radii, that is, 500 m (local), 5 km (neigh-bourhood), 50 km (region) and the whole of The Netherlands. 3. Land-use intensities of the core and home ranges selected by godwits were similar to those at the local and neighbourhood scales but were significantly lower and less variable than those of the region and the entire country. Thus, at the landscape scale, godwits were selected for low-intensity agricultural land. 4. The core range size of godwits increased with increasing land-use intensity, indicating high agricultural land-use intensity necessitating godwits to use larger areas. 5. This is consistent with the idea that habitat quality declines with increasing land-use intensity. This study is novel as it examines nationwide habitat selection and space use of a farmland bird subspecies tracked independently of breeding locations. Dutch breeding godwits selected areas with lower land-use intensity than what was generally available. The majority of the Dutch agricultural grassland (94%) is managed at high land-use intensity, which heavily restricts the viability of breeding possibilities for ground-nesting birds. The remote sensing methodology 26888319, 2023, 1, Downloaded from
To assign conservation status to a population, its size, trends, and distribution must be estimated. The Mediterranean green turtle population has shown signs of recovering over the past decade, likely in response to nest protection, but satellite tracking suggests adult foraging remains largely restricted to only a few key sites in the eastern Mediterranean. Previous research suggested that the majority of green turtles nesting at an important rookery in Cyprus, forage in Lake Bardawil, Egypt making an observed population increase dependent on this important site, which is under a high degree of anthropogenic maintenance. Here we provide new data that further demonstrates the importance of Lake Bardawil to green turtles that nest at other major rookeries on Cyprus, in the Karpaz Peninsula, with 74% of satellite tracked females (n=19) migrating to this key site. We also report on the first systematic nest counts for this area in over two decades and identify the inter-nesting habitat used by females nesting at these important beaches on the north and south coasts of the Peninsula. Comparing the oldest available 3-year nest count averages (1993-1995), with nest counts undertaken as part of this study (2017-2019), mean annual nest numbers increased from 186 to 554, an increase of 198%. Our data confirm the continued importance of these beaches for the Mediterranean green turtle population and underscore the reliance of this endangered population on a manmade lagoon for recent increases in clutch counts at monitored beaches. The results highlight the utility of satellite telemetry to inform conservation status assessments and establishing conservation at both nesting and foraging sites across the population.
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The accuracy of global positioning system (GPS) locations obtained from study animals tagged with GPS monitoring devices has been a concern as to the degree it influences assessments of movement patterns, space use, and resource selection estimates. Many methods have been proposed for screening data to retain the most accurate positions for analysis, based on dilution of precision (DOP) measures, and whether the position is a two dimensional or three dimensional fix. Here we further explore the utility of these measures, by testing a Telonics GEN3 GPS collar's positional accuracy across a wide range of environmental conditions. We found the relationship between location error and fix dimension and DOP metrics extremely weak (r²adj ∼ 0.01) in our study area. Environmental factors such as topographic exposure, canopy cover, and vegetation height explained more of the variance (r²adj = 15.08%). Our field testing covered sites where sky-view was so limited it affected GPS performance to the degree fix attempts failed frequently (fix success rates ranged 0.00-100.00% over 67 sites). Screening data using PDOP did not effectively reduce the location error in the remaining dataset. Removing two dimensional fixes reduced the mean location error by 10.95 meters, but also resulted in a 54.50% data reduction. Therefore screening data under the range of conditions sampled here would reduce information on animal movement with minor improvements in accuracy and potentially introduce bias towards more open terrain and vegetation.
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Estimating the absolute number of individuals in populations and their fecundity is central to understanding the ecosystem role of species and their population dynamics as well as allowing informed conservation management for endangered species. Estimates of abundance and fecundity are often difficult to obtain for rare or cryptic species. Yet, in addition, here we show for a charismatic group, sea turtles, that are neither cryptic nor rare and whose nesting is easy to observe, that the traditional approach of direct observations of nesting has likely led to a gross overestimation of the number of individuals in populations and underestimation of their fecundity. We use high-resolution GPS satellite tags to track female green turtles throughout their nesting season in the Chagos Archipelago (Indian Ocean) and assess when and where they nested. For individual turtles, nest locations were often spread over several tens of kilometres of coastline. Assessed by satellite observations, a mean of 6.0 clutches (range 2-9, s.d. = 2.2) was laid by individuals, about twice as many as previously assumed, a finding also reported in other species and ocean basins. Taken together, these findings suggest that the actual number of nesting turtles may be almost 50% less than previously assumed. © 2017 The Author(s) Published by the Royal Society. All rights reserved.
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An animal’s home range is driven by a range of factors including top-down (predation risk) and bottom-up (habitat quality) processes, which often vary in both space and time. We assessed the role of these processes in driving spatiotemporal patterns in the home range of the green turtle (Chelonia mydas), an important marine megaherbivore. We satellite tracked adult green turtles using Fastloc-GPS telemetry in the Chagos Archipelago and tracked their fine-scale movement in different foraging areas in the Indian Ocean. Using this extensive data set (5081 locations over 1675 tracking days for 8 individuals), we showed that green turtles exhibit both diel and seasonal patterns in activity and home range size. At night, turtles had smaller home ranges and lower activity levels, suggesting they were resting. In the daytime, home ranges were larger and activity levels higher, indicating that turtles were actively feeding. The transit distance between diurnal and nocturnal sites varied considerably between individuals. Further, some turtles changed resting and foraging sites seasonally. These structured movements indicate that turtles had a good understanding of their foraging grounds in regard to suitable areas for foraging and sheltered areas for resting. The clear diel patterns and the restricted size of nocturnal sites could be caused by spatiotemporal variations in predation risk, although other factors (e.g. depth, tides and currents) could also be important. The diurnal and seasonal pattern in home range sizes could similarly be driven by spatiotemporal variations in habitat (e.g. seagrass or algae) quality, although this could not be confirmed.
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It is a golden age for animal movement studies and so an opportune time to assess priorities for future work. We assembled 40 experts to identify key questions in this field, focussing on marine megafauna, which include a broad range of birds, mammals, reptiles, and fish. Research on these taxa has both underpinned many of the recent technical developments and led to fundamental discoveries in the field. We show that the questions have broad applicability to other taxa, including terrestrial animals, flying insects, and swimming invertebrates, and, as such, this exercise provides a useful roadmap for targeted deployments and data syntheses that should advance the field of movement ecology.
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We examined habitat selection by green turtles Chelonia mydas at Dry Tortugas National Park, Florida, USA. We tracked 15 turtles (6 females and 9 males) using platform transmitter terminals (PTTs); 13 of these turtles were equipped with additional acoustic transmitters. Location data by PTTs comprised periods of 40 to 226 d in varying months from 2009 to 2012. Core areas were concentrated in shallow water (mean bathymetry depth of 7.7 m) with a comparably dense coverage of seagrass; however, the utilization distribution overlap index indicated a low degree of habitat sharing. The probability of detecting a turtle on an acoustic receiver was inversely associated with the distance from the receiver to turtle capture sites and was lower in shallower water. The estimated daily detection probability of a single turtle at a given acoustic station throughout the acoustic array was small (<0.1 in any year), and that of multiple turtle detections was even smaller. However, the conditional probability of multiple turtle detections, given at least one turtle de tection at a receiver, was much higher despite the small number of tagged turtles in each year (n = 1 to 5). Also, multiple detections of different turtles at a receiver frequently occurred within a few minutes (40%, or 164 of 415, occurred within 1 min). Our numerical estimates of core area overlap, co-occupancy probabilities, and habitat characterization for green turtles could be used to guide conservation of the area to sustain the population of this species.
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Moving animals connect our world, spreading pollen, seeds, nutrients, and parasites as they go about the their daily lives. Recent integration of high-resolution Global Positioning System and other sensors into miniaturized tracking tags has dramatically improved our ability to describe animal movement. This has created opportunities and challenges that parallel big data transformations in other fields and has rapidly advanced animal ecology and physiology. New analytical approaches, combined with remotely sensed or modeled environmental information, have opened up a host of new questions on the causes of movement and its consequences for individuals, populations, and ecosystems. Simultaneous tracking of multiple animals is leading to new insights on species interactions and, scaled up, may enable distributed monitoring of both animals and our changing environment. Copyright © 2015, American Association for the Advancement of Science.
For many marine species, locations of migratory pathways are not well defined. We used satellite telemetry and switching state-space modeling (SSM) to define the migratory corridor used by Kemp's ridley turtles (Lepidochelys kempii) in the Gulf of Mexico. The turtles were tagged after nesting at Padre Island National Seashore, Texas, USA from 1997 to 2014 (PAIS; n=80); Rancho Nuevo, Tamaulipas, Mexico from 2010 to 2011 (RN; n=14); Tecolutla, Veracruz, Mexico from 2012 to 2013 (VC; n=13); and Gulf Shores, Alabama, USA during 2012 (GS; n=1). The migratory corridor lies in nearshore Gulf of Mexico waters in the USA and Mexico with mean water depth of 26m and a mean distance of 20km from the nearest mainland coast. Migration from the nesting beach is a short phenomenon that occurs from late-May through August, with a peak in June. There was spatial similarity of post-nesting migratory pathways for different turtles over a 16year period. Thus, our results indicate that these nearshore Gulf waters represent a critical migratory habitat for this species. However, there is a gap in our understanding of the migratory pathways used by this and other species to return from foraging grounds to nesting beaches. Therefore, our results highlight the need for tracking reproductive individuals from foraging grounds to nesting beaches. Continued tracking of adult females from PAIS, RN, and VC nesting beaches will allow further study of environmental and bathymetric components of migratory habitat and threats occurring within our defined corridor. Furthermore, the existence of this migratory corridor in nearshore waters of both the USA and Mexico demonstrates that international cooperation is necessary to protect essential migratory habitat for this imperiled species.
Conventional Very High Frequency (VHF) transmitters encased in a float were attached by a lanyard to ten adult green turtles foraging in Repulse Bay, central Queensland, Australia. Short term movements of 4-25 km and foraging ranges between 84-850 ha were recorded during attachment times of 4-29 days. Recaptures of tagged turtles in this area support radio tracking data. These are the largest movements reported by green turtles in a foraging ground and the only foraging movement data published on adult green turtles. Large foraging movements are attributed to the low-average above ground seagrass biomass in southern Repulse Bay. The seagrass community comprised Zostera capricorni, Halodule uninervis and Halophila ovata. Lavage samples revealed green turtles in Repulse Bay selected for Z. capricorni.