Hart et al. Movement Ecology (2021) 9:1
https://doi.org/10.1186/s40462-020-00237-3
RESEARCH
Open Access

Drivers of realized satellite tracking

duration in marine turtles

Kristen M. Hart1*
, Jacquelyn C. Guzy1 and Brian J. Smith2





Abstract
Background: Satellite tags have revolutionized our understanding of marine animal movements. However, tags
may stop transmitting for many reasons and little research has rigorously examined tag failure. Using a long-term,
large-scale, multi-species dataset, we evaluated factors influencing tracking duration of satellite tags to inform study
design for future tracking studies.
Methods: We leveraged data on battery status transmitted with location data, recapture events, and number of
transmission days to probabilistically quantify multiple potential causes of failure (i.e., battery failure, premature
detachment, and tag damage/fouling). We used a combination of logistic regressions and an ordinary linear model
including several predictor variables (i.e., tag type, battery life, species, sex, size, and foraging region).
Results: We examined subsets of data from 360 satellite tags encompassing 86,889 tracking days deployed on four
species of marine turtles throughout the Gulf of Mexico, Caribbean, and Bahamas from 2008 to 2019. Only 4.1% of
batteries died before failure due to other causes. We observed species-specific variation in how long tags remain
attached: hawksbills retained 50% of their tags for 1649 days (95% CI 995–1800), loggerheads for 584 days (95% CI
400–690), and green turtles for 294 days (95% CI 198–450). Estimated tracking duration varied by foraging region
(Caribbean: 385 days; Bahamas: 356; southern Gulf of Mexico [SGOM]: 276, northern Gulf of Mexico [NGOM]: 177).
Additionally, we documented species-specific variation in estimated tracking duration among foraging regions.
Based on sensor data, within the Gulf of Mexico, across species, we estimated that 50% of tags began to foul after
83 95% CI (70–120) days.
Conclusions: The main factor that limited tracking duration was tag damage (i.e., fouling and/or antenna
breakage). Turtles that spent most of their time in the Gulf of Mexico had shorter tracking durations than those in
the Bahamas and Caribbean, with shortest durations observed in the NGOM. Additionally, tracking duration varied
by species, likely as a result of behaviors that damage tags. This information will help researchers, tag companies,
permitting agencies, and funders better predict expected tracking durations, improving study designs for imperiled
marine turtles. Our results highlight the heterogeneity in telemetry device longevity, and we provide a framework
for researchers to evaluate telemetry devices with respect to their study objectives.
Keywords: Biologging, Telemetry, Platform terminal transmitter


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* Correspondence: kristen_hart@usgs.gov
1U.S. Geological Survey, Wetland and Aquatic Research Center, 3321 College
Avenue, Davie, FL 33314, USA
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Hart et al. Movement Ecology (2021) 9:1
Page 2 of 14
Introduction
The use of satellite telemetry has become a standard
practice in field of marine vertebrate ecology to track
movements and habitat use of animals at sea [1, 2], in-
cluding marine turtles (see reviews by [3–6]). These
tracking datasets provide important spatio-temporal data
for understanding both nearshore and ocean-basin scale
movements of individuals in the marine environment
[7]. Current technological advances in biologging tools
allow for an increase in the scope and scale of our un-
derstanding of marine animal movements (e.g., horizon-
tal and vertical movements over time [8];). However,
finer-scale tracking data does not necessarily advance
understanding of animal ecology, as there are tradeoffs
between tag costs, sample size, tag failure rates [9], and
study length, which can span days to years. Project goals
and budgets typically dictate the type of satellite tag
used, with researchers weighing expected battery life
against size of tags to select the smallest tag with highest
expected battery life and thus reduce the burden on the
animal [10]. Few guidelines exist for
‘best in practice’
but creative tests of available tags have recently emerged
(e.g., drag estimates [11]).
Ultimately, many satellite tracking studies seek to in-
form conservation and management strategies (e.g., [12])
thus, careful examination of factors influencing instru-
mented animals (i.e., [10, 11, 13, 14], including drivers of
tag failure, are
imperative. Yet despite exponential
growth in tracking studies worldwide [8], translating
these data into useful conservation messages is challen-
ging [15] as is tracking their impact on policy [16]. Be-
yond identifying probable causes of failure in specific
studies (e.g., ocean sunfish [17]), limited research to date
has rigorously examined satellite tag failure. Instead, stud-
ies typically report various tag performance metrics. For
example, causes of signal loss from transmitters routinely
attached to birds, marine turtles, and marine mammals
has been attributed to battery failure, salt-water switch
failure, antenna breakage, tag fouling by marine algae or
barnacles, animal mortality or predation, and premature
detachment of tags ([18]; e.g., penguins [19], migratory
birds [20], sharks [21]). However, because remotely
sensed data (e.g., sensor wet/dry status) and auxiliary
information (e.g., battery voltage) are now regularly
relayed with locations, rather than reporting perform-
ance metrics, researchers can conduct rigorous quan-
titative assessments to uncover when and why tags
fail [18], and ascertain what conditions seem to drive
the variation in those tag failure rates. Here, we use
and extend the procedures previously outlined (i.e.,
[18]). Our aim was to synthesize data available in our
own tracking projects to assess factors influencing
satellite-derived transmissions and help improve the
conservation value of these tracking studies.

Here, we focused on determining drivers of realized
satellite tracking durations for several species of hard-
shelled marine turtles tagged at both nesting beaches
and in-water sites, and tracked for over 12 years in the
Gulf of Mexico, Caribbean, and Bahamas. We examined
four main causes influencing tracking duration: battery
failure, premature detachment, tag fouling, or tag dam-
age, and incorporated the influence of species, gender,
turtle size, tag model, and resident foraging location on
tracking duration. Our results inform study design for
future research by providing information on realistic
tracking durations for proposed projects on marine tur-
tles, but also provide a framework for evaluating the di-
verse causes of tag failure that can be applied to a wide
variety of terrestrial and marine taxa.
Methods
We deployed satellite tags on four species of imperiled
marine turtles (green turtles Chelonia mydas, logger-
heads Caretta caretta, hawksbills Eretmochelys imbri-
cata, and Kemp’s ridleys Lepidochelys kempii) captured
throughout the Gulf of Mexico and Caribbean from May
2008 through July 2019 (Fig. 1). Tagging primarily oc-
curred at several locations within the Gulf of Mexico, in-
cluding Louisiana (Ship Shoal, Port Fourchon/Belle Pass,
and Chandeleur Islands), Mississippi (Pascagoula), Ala-
bama (Gulf Shores), Florida (Dry Tortugas, Biscayne,
and Everglades National Parks), and the U.S. Virgin
Islands (Buck Island Reef National Monument). See Hart
et al. [22–25] for more details on study sites and tagging
locations.
Capture, marking, and satellite tag attachment
We captured turtles using both land-based interception of
nesting females after nesting and non-nesting emergences,
whereupon we restrained them with a portable corral
(96.5 cm wide × 67.3 cm height). We also captured turtles
using several in-water methods (i.e., hand captures via
snorkeling [26], rodeo or turtle-jumping [27, 28]; trawling,
and dipnet [24, 29]). Upon capture each new turtle was
given a passive integrated transponder (PIT) tag in the
shoulder or front flipper (Biomark, Boise, ID; models 12
mm tag = BIO12.B.01 V2 PL.SY and 8mm tag =
BIO8.B.03 V1 PL.SY) and individually numbered Inconel
flipper tags (National Band and Tag, Newport, KY; model
681) following established protocols (NMFS-SEFSC 2008).
For all turtles we took standard measurements including
curved (CCL) and straight (SCL) carapace lengths.
Turtles were outfitted with satellite transmitters on
their anterior carapace using established protocols [30]
and we limited the epoxy (Superbond™) footprint to
minimize drag to turtles [11]. Refer to Additional file 1
for detailed information on tag attachment protocol.
Most tags were not coated with anti-fouling paint until

Fig. 1 Map indicating location of marine turtles (black dots, n = 333) within designated foraging areas in the northern Gulf of Mexico (n = 113
turtles), southern Gulf of Mexico (n = 134), Bahamas (n = 40), and Caribbean (n = 46). For clarity species are not depicted [loggerhead (n = 186),
green turtle (n = 72), hawksbill (n = 42) and Kemp’s ridley (n = 33)]. Two turtles off the of east coast of Florida (black dots enclosed in brackets)
forage within the Atlantic and are not included in analyses. Blue dots indicate locations where turtles have primarily been tagged: Louisiana (Ship
Shoal, Port Fourchon-Belle Pass, Chandeleur Islands), Mississippi (Pascagoula), Alabama (Gulf Shores), Florida (Dry Tortugas, Biscayne and
Everglades National Parks), and the U.S. Virgin Islands (Buck Island Reef National Monument-BIRNM)
Hart et al. Movement Ecology (2021) 9:1
Page 3 of 14
recent years. We used various models of satellite tags
from Wildlife Computers (Redmond, WA, USA; SPOT,
SPLASH, and Fastloc GPS tags; Additional file 2]. We
programmed tags to send location data daily for all
Kemp’s ridleys and all green turtles tagged in the Carib-
bean. Likewise, from 2008 to 2010 we programmed tags
to transmit daily for loggerheads and hawksbills, and to
conserve battery life we duty-cycled these beginning in
2011. More specifically, in 2011 we programmed tags on
nesting female loggerheads to transmit every 3rd day
(from 1 October – 31 March) and in 2012, tags on nest-
ing hawksbills were set to every 3rd day (from 1
December-30 April). To further conserve battery life, we
also imposed daily transmission limits of 200–500 per
day based on manufacturer recommendations. We in-
cluded expected tag battery life according to these pa-
rameters as a covariate in the tracking duration models.
All tagged turtles were released within 2 h at their cap-
ture location.
Resident foraging regions
We assigned individual turtles to a foraging region that
was defined based on prevailing currents within the Gulf
of Mexico (i.e., Loop Current, Florida Current, Gulf
Stream [31] and established differences in water quality

between Gulf of Mexico [32] and the relatively clear,
tropical waters of the Caribbean ([33]; Fig. 1). More spe-
cifically, we delineated waters between Texas and north
of Naples, Florida, as the Northern Gulf of Mexico
(NGOM); likewise, waters between Mexico and south of
Naples, FL, were the Southern Gulf of Mexico (SGOM;
Fig. 1). We delineated waters south of the Florida Straits,
east of Puerto Rico, and south of Cuba as Caribbean.
We delineated waters east of the Gulf Stream and north
of Cuba as the Bahamas.
To assign turtles to a foraging region we examined sat-
ellite tracking data by plotting cumulative distance trav-
eled over time (e.g., [34–36]) and examining plots to
identify which date individuals reached an “asymptote”,
or the point in time where distance traveled began to
level out, such as a female departing a nesting beach and
arriving at a foraging region (Additional file 4 a,b). We
extracted the corresponding Argos location for these
dates and assigned a turtle to one of the four foraging
regions defined above. For turtles not reaching an
asymptote (i.e., in-water captured turtles tagged at their
foraging grounds that remained in the vicinity of their
capture location during tracking period; Additional file 4
c-d), we selected the Argos location at the median date
of tracking as the foraging site. Finally, for turtles with

Hart et al. Movement Ecology (2021) 9:1
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an increase in cumulative distance from tagging sites followed by
a return to the tagging site (e.g., an in-water captured adult male
turtle departing foraging grounds to a nesting beach, then return-
ing to the foraging ground; Additional file 4 e-f), we identified the
inflection point of cumulative distance from tagging site, and
using data prior to that point, extracted location at the median
date of that tracking period to represent the foraging site.
Analyses
All analyses and figures were constructed in RStudio using R
version 3.6.0 [37] and species-specific foraging regions (Fig. 1)
were plotted using ArcGIS 10.0 (Environmental Systems Re-
search Institute, Redlands, California, USA). Refer to Hart
et al. ([38]) for data used in analyses (below). Data exploration
was carried out following the protocol described in [39] and
model assumptions were verified by plotting residuals versus
fitted values against all covariates. After fitting all models, we
reported either pseudo-R2 (GLMs) or R2 (ordinary linear
model) statistics as a measure of the model’s internal validity.
Battery life
To estimate battery status (i.e., working vs. expired), we
examined the “status.csv” file provided from the most re-
cent data download for each satellite tag. This status file
Fig. 2 Potential causes for satellite tag failure on marine turtles and our me
sources of information including recaptures (epoxy failure) and auxiliary tel
this paper are available from the USGS ScienceBase repository: https://doi.o
provides non-location tag status information, such as
battery voltage and readings from auxiliary sensors dur-
ing each tag transmission. Full battery voltages are
around 3.4 V, and tags fail when their battery voltages
drop to around 3.0 V so cannot perform to capacity
(Kevin Ng, Wildlife Computers, oral pers. comm., 14
February 2018). We plotted voltage of each tag over time
and visually examined each plot to determine whether
the battery dropped and remained below 3.0 V (Fig. 2)
and categorized them as Still working/Expired. We used
logistic regression with a binomial likelihood and a logit
link function to estimate the percentage of tags that
never had their battery expire. This was an intercept-
only model (i.e., no covariates, ‘Status ~ 1’). We used the
profile likelihood method (which is unbiased when par-
ameter estimates are close to zero or 1) to generate a
95% confidence interval [40] using the generic R func-
tion ‘confint’.
Epoxy duration
We estimated the amount of time a tag remained epox-
ied to a turtle by examining physical recaptures of
satellite-tagged turtles, where we noted the length of
time between recaptures and whether a tag was still
thods for estimating each one. Our approach leveraged multiple
emetry data (battery failure and sensor fouling). The data analyzed in
rg/10.5066/P9OXCKYI
Table 1 Count of the fouling status of satellite tags on marine
turtles assigned to foraging areas including the Bahamas,
Caribbean, southern Gulf of Mexico (SGOM), and northern Gulf
of Mexico (NGOM)
Not Fouled
Fouled
Total
NGOM
34
17
51
SGOM
3
11
14
Caribbean*
5
2
7
Bahamas*
1
0
1
Total
43
30
Grand Total
73
Data for NGOM and SGOM were pooled into one category, ‘Gulf of Mexico’ for
analysis (Fig. 4). Asterisk (*) denotes tags excluded from fouling analyses
because of small sample size
Hart et al. Movement Ecology (2021) 9:1
Page 5 of 14
attached. We constructed a logistic regression with a bi-
nomial likelihood and a logit link function where the
presence of a tag depended upon an interaction between
the number of days since it was applied and the species
(i.e., ‘Status ~ NumDays * Species’). Rather than report-
ing the slopes estimated by this model, we reported the
mean number of days that 50% of the tags remained at-
tached, as this is a more intuitive metric of tag retention.
Tag fouling
To determine fouling status, we examined the “sta-
tus.csv” file for a subset of tags that contained data de-
scribing fouling of tag sensors. More specifically, for
each tag we plotted values from the wet-dry sensor (i.e.,
MinWetDry and MaxWetDry values), together over time
(e.g., Fig. 2); when these values approach each other, this
can indicate fouling [41]. Where these two values were
consistent and maintained a large distance (i.e., Min-
WetDry values of ~ 20, MaxWetDry values of ~ 255) for
the tag’s duration, we assigned tags as “Not Fouled” [41].
Conversely, tags that contained values for MinWetDry
which increased to within ~ 50–75 units of MaxWetDry
at some point during the tag’s deployment or that
showed these values converging toward the end of the
deployment, were classified as “Fouled”. We then con-
structed a logistic regression with a binomial likelihood
and logit link function where the fouling status of a tag
depended upon the number of days since it was applied
(i.e.,
‘Status ~ NumDays’). As with the epoxy duration
analysis, rather that reporting the slope of this line, we
instead reported the mean number of days that 50% of
the tags remained unfouled.
Realized tracking duration
To estimate realized tracking duration we constructed
an ordinary linear model where the length of time (days)
a tag transmitted depended on the following predictors:
tag model group (SPOT-location only, SPLASH-location
plus depth, Fastloc GPS-location, depth and GPS), tag
battery lifespan, turtle species, sex, size, and foraging re-
gion (i.e.,
‘NumDays ~ Foraging_Area + species + tag
type + battery life + size + sex’). Age class was incorpo-
rated in the ‘sex’ variable (i.e., female, male, immature).
Expected battery life was determined based on estimates
provided by the satellite tag manufacturer after taking
into consideration tag model, battery size, the program-
ming schedule and transmission limits ([38]; data avail-
able: https://doi.org/10.5066/P9OXCKYI).
Because turtle species in this study exhibit variation in
geographic range, some species do not occur in high
numbers within some foraging regions, leading to an un-
balanced dataset. For example, foraging regions for log-
gerheads tagged in our study sites were primarily within
the NGOM, SGOM, and Bahamas, whereas foraging

regions for Kemp’s ridleys were primarily in the NGOM.
Therefore, to explicitly evaluate species within their re-
spective foraging regions, we built a separate ordinary
linear model with tracking days as the response variable
and foraging region plus species as the predictor
variables.

Results
From 2008 to 2019, we deployed 360 satellite tags on
four species of marine turtles: loggerheads (n = 186; fe-
male n = 172, male n = 8, immature n = 6); green turtles
(n = 90; female n = 51, male n = 25, immature n = 14);
hawksbills (n = 42; female n = 36, male n = 1, immature
n = 5); and Kemp’s ridleys (n = 42; female n = 34, male
n = 6, immature n = 2). A subset of these tags was used
for each analysis based on different criteria. Specifically,
for the Battery Failure analysis, 342 tags contained data
on voltage and were included in the analysis. For the
Fouling analysis, 65 tags contained sensor data (i.e., Min-
WetDry, MaxWetDry) and could be assigned to a for-
aging region,
thus were
included
in the analysis
(Table 1). For the Epoxy Duration analysis, we included
turtles with more than one capture event (n = 118). Fi-
nally, for the Tracking Duration analysis, we included
the 333 turtles with satellite tags for which we could as-
sign foraging regions (Table 2).

Battery life
Analysis of battery voltage during satellite tag transmis-
sions indicated that 14 tags out of 342 (4.1%) expired
(i.e., were drained) while still attached to turtles. Thus,
we interpreted this as 96% of our tags failed for some
other reason before the battery died; we estimated that
the probability of satellite
tags remaining charged
enough to successfully transmit turtle locations was
96.0% (95% CI: 93.5–97.7%).

Table 2 Number of marine turtles with satellite tags assigned to foraging areas including the Bahamas, Caribbean, Southern Gulf of
Mexico (SGOM), and Northern Gulf of Mexico (NGOM)
Species
Foraging Region
Bahamas
Caribbean
SGOM
NGOM
Atlantic*
Total
Loggerhead
36
2
74
74
2
188
Green turtle
11
57
4
72
Hawksbill
3
34
5
42
Kemp’s ridley
1
32
33
Total
39
47
137
110
2
Grand Total
335
Bold values indicate data for which species-foraging region comparisons were made (Figs. 5, 6). Asterisk (*) denotes tags excluded from analyses because of small
sample size
Hart et al. Movement Ecology (2021) 9:1
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Epoxy duration
A subset of 118 satellite tagged turtles were captured
more than once (loggerheads n = 62, green turtles n = 27,
hawksbills n = 29) and either had their tag still affixed or
we noted it was not present. This dataset is primarily re-
stricted to adult female turtles where recapture data is
generated during nesting events; however, three in-water
recaptures of immature turtles are also included. Across
species,
47
tags
(36%)
remained attached
for a
Fig. 3 Predicted proportion of tags still attached over time (days) from the
n = 88; and hawksbill, n = 69. Solid lines represent the mean response and
line indicates the values where 50% of tags remain attached; loggerheads
294 days (95% CI 198–450), and hawksbills for 1649 days (95% CI 995–1800
confidence intervals are wide after this time; however, 95% of tags remain
considerably short time-frame of less than 70 days. Com-
bining initial tagging events with subsequent recaptures
resulted in 371 data points with which to fit a logistic re-
gression (loggerheads n = 214, green turtles n = 88,
hawksbills n = 69; pseudo R2 = 0.57). Results indicated
that loggerheads retained 50% of their tags for 584 days
(95% CI 400–690; Fig. 3). We estimated that green tur-
tles retained 50% of their tags for 294 days (95% CI 198–
450; Fig. 3). Finally, we estimated that hawksbills
epoxy failure analysis (loggerhead, n data points = 214; green turtle,
shading represents 95% confidence intervals. Horizontal dashed grey
retain 50% of their tags for 584 days (95% CI 400–690), green turtles for
). For hawksbills, we have few recaptures over 1000 days, and
attached for 600 days
Hart et al. Movement Ecology (2021) 9:1
Page 7 of 14
retained 50% of their tags for 1649 days (95% CI 995–
1800; Fig. 3).
Fouling
Of 73 satellite tags with available data for analysis (Table 1),
the majority were attached to turtles foraging in NGOM
(n = 51) and SGOM (n = 14) rather than the Caribbean or
Bahamas, therefore we restricted our comparison to the
Gulf of Mexico (n = 65 tags). Within the Gulf of Mexico, 28
of 65 satellite tags became fouled (43%); such fouling did
not always result in the complete loss of transmissions and
derived locations. We estimated that in the Gulf of Mexico,
across species, after 83 days, 50% of satellite tags (95% CI:
70–120) became fouled; pseudo R2 = 0.44, Fig. 4, Additional
file 3). Examination of the raw data indicated that of tags
that fouled, 18 were on loggerheads, 8 were on green tur-
tles, and 2 were on Kemp’s ridleys.
Realized tracking duration
Our realized tracking duration model included 333 tur-
tles (loggerheads = 186, green turtles = 72, hawksbills =
42, Kemp’s ridleys = 33, Table 2; sex:
female = 284,
male = 33, immature = 16) with a total of 86,889 tracking
days. Based on model estimates, the length of tracking
duration was strongly influenced by foraging region and
species
(Table 3, Fig. 5; R2 = 0.20). The shortest
Fig. 4 Predicted proportion of unfouled tags over time (days) in the Gulf o
n = 28). Solid line represents the mean response and gray shading represen
durations were observed in the NGOM, followed by the
SGOM, Bahamas, and then the Caribbean. Model-
estimated tracking durations, using loggerheads as the
reference category, were as follows: Bahamas (356 days,
95% CI 271–440), Caribbean (385 days, 95% CI 267–
504), SGOM (276 days, 95% CI 212–340), and NGOM
(177 days, 95% CI 102–253; Figs. 5 and 6). Using SGOM
as the reference category, the shortest durations were
observed from Kemp’s ridleys (NGOM: 137 days, 95% CI
5–269), followed by green turtles (SGOM: 164 days, 95%
CI 79–248),
loggerheads (NGOM: 177 days, 95% CI
102–253), and then hawksbills (Caribbean: 427 days, 95%
CI 350–504; Fig. 6).
Of 333 satellite tags, most (75%) were SPOT models
(n = 250) compared to SPLASH tags (n = 71) or Fastloc
GPS tags (n = 12; Additional file 2). Across all tag types,
21 tags of 333 (6.3%) transmitted longer than expected
(i.e., exceeded expected battery life). On average, 38.1%
of SPOT tags reached their expected battery life and
similarly, 44.7% of SPLASH and 32.2% of GPS tags
reached their expected tag life. Satellite-tag specific
manufacturer estimates of battery life (accounting for
tag model and programming schedules) varied from 320
to 914 days for SPOT tags, from 165 to 485 for SPLASH
tags, and 485 to 1007 days for GPS tags. However, esti-
mated battery life did not have a significant effect on
f Mexico from the fouling analysis (n = 65 tags: unfouled n = 37, fouled
ts the 95% confidence interval
Table 3 Model structure and rankings examining predicted number of days satellite tags are predicted to transmit for marine turtles
Estimate
Std. Error
t-statistic
p-value
Intercept
238.29
208.99
1.14
0.255
Foraging Region (Caribbean)
29.90
62.58
0.48
0.633
Foraging Region (NGOM)
−178.07
50.26
−3.54
0.000
Foraging Region (SGOM)
−79.54
42.43
−1.88
0.062
Species (C. mydas)
−112.20
39.00
−2.88
0.004
Species (E. imbricata)
41.49
60.29
0.69
0.492
Species (L. kempii)
−40.62
61.33
−0.66
0.508
Tag Model (SPLASH)
−23.72
74.89
−0.32
0.752
Tag Model (SPOT)
−21.27
89.84
−0.24
0.813
Battery Capacity
0.09
0.16
0.56
0.576
Size
0.76
1.70
0.45
0.654
Sex (Males)
64.97
43.36
1.50
0.135
Sex (Immature)
190.02
73.60
2.58
0.010
Dataset is female-biased and sample size for immature turtles is small (females = 284, males = 33, immature = 16); refer to Tables 1–2 for sample size among other
categorical variables. The intercept (i.e., reference category) is comprised of female loggerheads in the Bahamas with GPS tags
Fig. 5 Predicted length of time (days) that satellite tags attached to marine turtles will transmit within foraging regions (Caribbean, Bahamas,
Southern Gulf of Mexico (SGOM) and Northern Gulf of Mexico (NGOM) based on length of time each tag (n = 333) transmitted. Solid black circles
represent mean estimates and lines represent 95% confidence intervals
Hart et al. Movement Ecology (2021) 9:1
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Fig. 6 Predicted length of time (days) that satellite tags transmit on marine turtles within foraging regions. Solid black circles represent mean
estimates and lines represent 95% confidence intervals. The southern and northern Gulf of Mexico are abbreviated as SGOM and NGOM,
respectively. Some species are not prevalent in all foraging regions and thus we make no predictions there (e.g., hawksbills in the SGOM). Our
findings show that both species and location are important in determining realized tracking durations
Hart et al. Movement Ecology (2021) 9:1
Page 9 of 14
realized tracking duration, corroborating our findings
from the previous sections (β = 0.09, p = 0.576; Table 3).
Similarly, tag model did not have a significant effect on
tracking duration. With GPS tags as the reference cat-
egory, neither SPOT (β = − 21.27, p = 0.81; Table 3) nor
SPLASH (β = − 23.72, p = 0.75; Table 3) tags differed in
tracking duration. More specifically, tag model types
transmitted for a similar duration of time, and on aver-
age, SPOT tags transmitted for 276 days (95% CI 212–
340), SPLASH tags transmitted 274 days (113–434), and
GPS tags transmitted for 297 days (144–451; Table 3,
Fig. 7). Likewise, size (β = 0.76, p = 0.65) did not have a
significant effect on tracking duration of adult turtles,
and sex was important (β = 190.02, p = 0.01; Table 3),
with females tending to transmit for shorter periods; the
patterns for juveniles was likely driven by small sample
size of the immature age class (Fig. 7).
Discussion
Here, we provided a robust analysis of a long-term,
large-scale, multi-species
satellite
tracking dataset

focused on marine turtles to decipher what influenced
tracking duration and identify likely causes of tag and
transmission failures. In a rare exploration of what influ-
ences satellite tag functionality, we found that tag battery
life was not the limiting factor, rather it was the environ-
ment. Our findings suggest a mechanism for tag failure
(i.e., tag damage) that will inform future research ques-
tions of the biologging community. Specifically, we ob-
served a pattern among foraging regions, where tracking
duration varied latitudinally, decreasing in order from
the Caribbean and Bahamas (~ 356–385 days) to the
SGOM (~ 276 days), and NGOM (~ 177 days). Average
battery failure rate for tags was ~ 4%, indicating that
96% of tags failed for another reason before batteries
drained. For all species, tags tended to remain attached
for at least 1 year. Notably, the estimated rate of tag
fouling reached 50% after 83 days in the Gulf of Mexico,
and despite tags transmitting from sites in the Bahamas
and Caribbean, few became fouled in these regions. We
suggest that because the incidence of battery failure was so
low, and recapture
information
indicated that
tags

Fig. 7 Predicted length of time (days) that satellite tags transmit on a) marine turtles b) by size, c) by tag model type, and d) by sex. Solid black
circles represent the mean and lines represent 95% confidence intervals; dashed lines in c) represent mean expected battery life for tag types,
from manufacturer calculations. Our findings indicate that adult size, tag type, and sex are not important in determining realized tracking
durations; instead only species and foraging region (Fig. 6) drive observed patterns in tracking durations
Hart et al. Movement Ecology (2021) 9:1
Page 10 of 14
remained affixed to turtles for at least 1 year, that most tag
failures were caused by tag damage (sensor fouling and/or
antenna breakage) rather than battery or epoxy failure.
Foraging regions drive ultimate tracking duration
patterns
We found that the number of days satellite tags transmit-
ted varied by foraging region, where marine turtles spend
most of their life. Further, in the Gulf of Mexico foraging
region, 50% of tags fouled rapidly (i.e., within 3 months),
consistent with previous research indicating that barnacle
settling rates in the NGOM are elevated [42].
We suggest variation in tag transmissions across for-
aging regions was in part driven by regional differences
in water quality, directly related to the Gulf of Mexico’s
Loop Current that is part of the Gulf Stream system of
the Atlantic Ocean. Characteristics of the Loop Current
and its anticyclonic eddies have been studied with
satellite-tracked drifters and remote sensing, and indi-
cate the powerful influence of this current on water cir-
culation in the Gulf of Mexico [43]. Briefly, the Loop
Current enters the Gulf of Mexico northward from the

Yucatan Peninsula, moves clockwise, and exits through
the Florida Straits between Cuba and Key West, Florida
[31]. This pattern of water flow creates several different
“Ecoregions” in the Gulf of Mexico, whereby the NGOM
Ecoregion is distinct from the SGOM with respect to spe-
cific habitat types, bathymetry, eutrophication, hypersalin-
ity [44] and anthropogenic influences [45]. Anecdotally,
we tracked two loggerheads foraging in the Atlantic and
compared them to turtles foraging within the NGOM.
The Atlantic foraging region is at approximately the same
latitude as NGOM foraging grounds (Fig. 1), yet these tags
transmitted longer (208 and 479 days) than the average for
NGOM tags (~ 177 days [38];), likely because the Atlantic
does not have the same eutrophic water conditions as the
NGOM. Although we have not determined if fouling was
extensive enough to stop tag transmissions, we suggest
that nutrient-rich waters may be responsible for fouling of
tags in our study and is likely responsible for the shorter
tracking durations we observed in the Gulf of Mexico.
Studies investigating the degree to which biofouling influ-
ences transmission signal strength could inform and im-
prove future tracking work.

Hart et al. Movement Ecology (2021) 9:1
Page 11 of 14
Ultimately, our results indicate that tags transmit for a
shorter duration in the NGOM foraging region because
of both fouling of tags and potential species-specific
variation in behavioral aspects (e.g., hiding under struc-
tures). We suggest that where turtles live and take up
residence affects aspects of tag damage, both physical
(i.e., antenna breakage) and ecological (i.e., tag fouling;
see Additional file 3). Frick et al. [46, 47] documented
significant loads of epibionts living on turtles, thus anti-
fouling paint is one solution for minimizing rates of
fouling in tracking studies. We have recently begun ap-
plying anti-fouling paint to all tags across all projects in
order to deter growth of marine organisms ([6]; see
Additional file 1). Future studies to quantify the effect of
antifouling paint on tracking durations would be valu-
able. However, in some areas barnacle settling rates are
naturally elevated (e.g., Ship Shoal, LA [42]). Thus, im-
provements in anti-fouling technology beyond paint are
particularly important for tags deployed in the NGOM.
Notably, attempts during this study to modify tags de-
ployed in NGOM by increasing the 3-dimenstional as-
pect (i.e., having cone shaped washer/sensor on tags) to
facilitate easier scraping of epibionts by turtles have not
prevented rapid fouling rates on tags transmitting in the
NGOM (mean number of transmission days for tags
with a cone was 149 days (n = 83) and tags without a
cone transmitted an average of 185 days (n = 27). Devel-
opment of additional physical
‘windshield washers’ on
sensors may reduce epibiont loads, and creation of mini-
ature sensors for antennas to relay data on their status
(i.e., broken, fouled) may further improve satellite tag
function.
Species-specific variation in tracking duration and tag
attachment
Our findings are consistent with that of previous acous-
tic tagging research on marine turtles that showed
shorter tag retention times for green turtles compared to
hawksbills of similar (juvenile) sizes. Specifically, green
turtle tag retention rates in a Caribbean study were 1/
7th that of hawksbills [48]. Although the attachment
methods are different for acoustic versus satellite tags, this
comparison underscores the difficulty of obtaining long
tracking durations on green turtles with external epoxy at-
tachments. Notably, we observed long tracking durations
on species with sometimes significant epibiont loads (i.e.,
loggerheads) compared to green turtles (Fig. 6), under-
scoring the importance of foraging region on fouling and
realized tracking durations. Perhaps contributing to in-
creased epibiont loads on loggerheads are their more sed-
entary behavioral tendencies [49, 50].
These results underscore that there can be species-
specific differences in tag performance, even within taxo-
nomic families. Studies that examine tag fix rates (the

proportion of attempted location fixes that are success-
ful) are commonly concerned with bias driven by habitat
conditions [51]. This implies that fix rates are habitat-
dependent and typically ignores the role that species-
specific traits (e.g., behavior, shell hardness) may play.
For example, Smith et al. [52] studied the fix rates of
GPS tags implanted in Burmese pythons (Python bivitta-
tus) and assumed that their conclusions were applicable
to all large constricting snakes (families Boidae and
Pythonidae). Other studies have compared the impact of
tag attachment on different species; the same GPS at-
tachment method that was appropriate for Black-backed
Gulls (Larus fuscus) greatly reduced apparent survival of
Great Skuas (Stercorarius skua) [53]. As we have shown
here, tag performance can vary greatly within a taxon,
and in some cases species-specific tagging protocols may
be necessary. Furthermore, comparative study designs
seeking ecological inference from the spatial patterns of
different closely related species (e.g., [54, 55]) should
take differential tag performance into consideration to
avoid biasing their conclusions.
Implications for future study design
Establishment of more precise estimates of mean tracking
duration specific to a particular study site or species is
helpful for guiding expectations for the amount of return
data. Further, considerations of tag model choice and tim-
ing of tagging are important and carefully matching study
objectives to tag choice is critical. Thus, if a research pri-
ority is to track a nesting turtle from breeding to foraging
regions, tags must function for at least several months to
collect enough data to separate error in location points
with actual movement (i.e., migration).
Our results also answer the call of Jones et al. [11] to
consider critically our attachment techniques; we found
that it does not take much adhesive (Additional file 1) to
obtain long tracking durations for any of our low-profile,
low-drag tags. To fill key knowledge gaps, studies must
match study design and realistic expectations of tag per-
formance. Our dataset included tags that remained at-
tached for as little as 30 days to those lasting as long as
1653 days, and included data on turtles across life stages
and across several species for which there is limited
tracking data globally (i.e., males and immature turtles,
see [3, 5, 56, 57]). Thus, our results contribute towards
filling data gaps for imperiled sea turtles captured in
both developmental and foraging areas. Further, we hope
that our expected tracking durations and predicted tag
attachment duration estimates (Fig. 3), along with our
attachment protocol, will be useful for both permitting
agencies and funding parties in matching expected pro-
ject results to what is feasible in field studies, ultimately
achieving more conservation dividends for these im-
periled marine turtles [15]. In particular, the type of

Hart et al. Movement Ecology (2021) 9:1
Page 12 of 14
information we present here can inform managers
charged with decisions on the tradeoffs of collecting
more data versus time to collect it [58] and the value of
animal movement for management planning [59]. With-
out a clear picture of what to expect for actual tracking
durations from various tags, researchers may mismatch
research questions and study design, thus rendering
them unable to effectively translate their data into useful
conservation approaches.

Summary
Biologging tools continue to play a key role in determin-
ing marine animal movement patterns, including data
on timing of migrations, spatial extent of corridors used,
and locations where animals concentrate their home
ranges [6, 60]. Thorough examination of robust tracking
datasets is critical to improve data provided by these
tools (e.g., satellite tags). Improving data quality is par-
ticularly important because these tools are frequently
used on imperiled species, some of which (like marine
turtles) may breed only every 1–3 years. Most tag fail-
ures across all marine turtle species in our study were
caused by tag damage, either consisting of sensor fouling
or antenna breakage, which are currently difficult to
tease apart. However, sensor data are available and fu-
ture sensor development distinguishing between types of
tag damage would improve the quality of tracking data.
Specifically, increasing regular messaging of diagnostic
data, improving design and placement of sensors, and
creating more physical protection for robust antennas
could improve tracking science.
Tag manufacturers make their best estimates for tag
‘life’ based on battery capacity. But real-world factors such
as programming schedules, animal behavior, and environ-
mental conditions affect the realized tracking durations
across species. Our results indicate that provided tags re-
main attached to animals and intact, they have enough
battery to meet or exceed manufacturer recommenda-
tions. Future improvements to sensors to send additional
data reflecting presence and condition of tags and anten-
nas, in conjunction with innovations such as miniaturized
batteries,
longer-logging accelerometers, and on-board
data-processing algorithms can improve our understand-
ing of animal movement patterns and what drives them,
and ultimately help researchers explore life in the wild
when animals are not directly observable.

Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s40462-020-00237-3.
Additional file 1. Satellite tag attachment protocol for hard-shelled sea
turtles.

Additional file 2. Number of satellite tags deployed at different tagging
sites during each year. Refer to main text Fig. 1 for generalized tagging
locations. Size of bubble scaled by sample size, and abbreviations for
tagging sites are as follows: Belle Pass, Louisiana = BPLA; Gulf Shores,
Alabama = AL; various northern Gulf of Mexico in-water sites in Louisiana,
Mississippi, northwest Florida = NGOM; Dry Tortugas National Park =
DRTO; Everglades National Park/Biscayne National Park = ENP; Buck Island
Reef National Monument = BIRNM. Satellite tag models were comprised
of SPOT (n = 250; models 244A, 293A, and 375A), SPLASH (n = 71; models
284A, 296F, 297F, 309A, 238A) and GPS (n = 12; models 296F, 344E, 238A,
385A).
Additional file 3. Example of SPOT tag fouling by marine organisms on
a loggerhead turtle (Caretta caretta) in the northern Gulf of Mexico. Tag
was attached a) June 13th and b) 25 days later (July 8th, 2013) the tag is
covered primarily with barnacles, although the antenna is visible.
Photograph by the U.S. Geological Survey.
Additional file 4. Methodology used to assign marine turtles to
foraging regions by plotting satellite tag location data (panels a, c, e) and
plotting cumulative distance traveled over time for each individual
(panels b, d, f). Visualizing these data can indicate the point in time
where distance traveled begins to level out/reach an asymptote (i.e., red
dashed lines). We designated foraging sites near the asymptote.
Examples include a) a nesting female departing the Dry Tortugas and
arriving at a foraging site in the Bahamas, c) an in-water captured male
resident of the Dry Tortugas, who made some looping movements away
from, and then back to, the Dry Tortugas, and e) a male migrating from
the waters offshore of Cancun, Mexico presumably to breed, then exhibit-
ing return migration to the foraging site at the Dry Tortugas. Grey shaded
boxes in panels b and f indicate migration intervals; no migration is oc-
curring in panel d for this resident turtle.
Abbreviations
Fig.: Figure; NGOM: Northern Gulf of Mexico; SGOM: Southern Gulf of Mexico;
GPS: Global positioning system; PIT: Passive integrated transponder;
PTT: Platform terminal transmitter
Acknowledgements
We thank the following individuals for assistance with fieldwork that made
this project possible: Mike Cherkiss, Andrew Crowder, David Roche, Mat
Denton, Andre Daniels, Mandy Tumlin, Peter Iacono, Veronica Winter, Megan
Arias, Devon Nemire-Pepe, Hayley Crowell, Thomas Selby, Dave Seay, Au-
tumn Iverson, Elissa Connolly-Randazzo, Ashley Meade, Scott Eanes, Jim
Grimes, Bill Ackourey, Sara Jarossy, Derek Burkholder, Kim Sonderman, Whit-
ney Crowder, Harold Crowder, Mike Barnette, Taylor Hart, Ian Bartoszek, Jeff
Beauchamp, Gareth Blakemore and others. We thank many National Park Ser-
vice colleagues for project support including Tracy Ziegler, Tree Gottshall,
Glenn Simpson, Meaghan Johnson, Kayla Nimmo, Allen Zamrock, Janie
Douglass, Clay “Blue” Douglass, John Spade, Mikey Kent, Clayton Pollock, Na-
thaniel Holloway-Adkins, Zandy Hillis-Starr, Tylan Dean, Dave Hallac and nu-
merous interns. Mike Cherkiss, Megan Arias, Veronica Winter, and Andrew
Crowder assisted with data management and assistance collating plots for
foraging sites, battery life, and tag fouling. Any use of trade, firm, or product
names is for descriptive purposes only and does not imply endorsement by
the U.S. Government.
Authors’ contributions
KH secured funding, collected the data, and conceived of the original idea.
BS and JG performed the data analysis and created the figures. KH, JG, and
BS wrote the manuscript. All authors contributed to the design of the study,
interpretation of the data, the final version of the manuscript, and approved
the final manuscript.
Funding
We acknowledge funding for various aspects of the tagging portion of this
project from the U.S. Geological Survey (USGS) Ecosystems Wildlife program,
the USGS Priority Ecosystems Science Program, the USGS Coastal and Marine
Geology Program, the USGS Natural Resource Protection Program, Natural
Resource Damage Assessment for the Deepwater Horizon Oil Spill, the
National Park Service, the National Fish and Wildlife Fund, the U.S.

Hart et al. Movement Ecology (2021) 9:1
Page 13 of 14
Department of Interior Bureau of Ocean Energy Management, Marine
Minerals and Studies programs.
Availability of data and materials
The data analyzed in this paper are available from the USGS ScienceBase
repository: https://doi.org/10.5066/P9OXCKYI.
Ethics approval and consent to participate
This study meets the legal requirements of capturing, handling, and
attaching satellite tags to sea turtles in the United States under Endangered
Species Act permits issued to K. Hart including: MTP176; NMFS permits
20315, 17381, 13307, 16146, 22281; NPS permits EVER-2018-SCI-0023, EVER-
2016-SCI-0032, EVER-2014-SCI-0031, DRTO-2018-SCI-0007, DRTO-2016-SCI-
0008, DRTO-2014-SCI-0004, DRTO-2012-SCI-0008, DRTO-2010-SCI-0009, DRTO-
2008-SCI-0008, BUIS-2016-SCI-0009, BUIS-2015-SCI-0012, BUIS-2014-SCI-0009,
BUIS-2012-SCI-0002, BUIS-2011-SCI-0003, BISC-2019-SCI-0008,BISC-2018-SCI-
0015; Federal Fish and Wildlife Territorial Permits STX036–11 and STX042–12
(St. Croix, U.S. Virgin Islands); Federal United States Fish and Wildlife Permit
#TE98424B-1 and #TE98424B-0 (Baldwin County, Alabama); Bon Secour Na-
tional Wildlife Refuge Special Use Permit #16-005S, 12-006S; and Louisiana
Department of Wildlife and Fisheries Scientific Collecting Permits #WDP-19-
006, LNHP-18-006, LNHP-17-001, LNHP-15-085. Work was also performed
under a USFWS permit issued to J. Philips: TE206903–1. Sampling was ap-
proved under Institutional Animal Care and Use protocols USGS-SESC 2011–
05, USGS SESC 2014–03, SER-BISC-BUIS-DRTO-EVER-Hart-Sea Turtles-Terrapins-
2018-A2.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1U.S. Geological Survey, Wetland and Aquatic Research Center, 3321 College
Avenue, Davie, FL 33314, USA. 2Department of Wildland Resources, Utah
State University, Logan, UT 84322, USA.
Received: 25 August 2020 Accepted: 16 December 2020
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