AQUATIC BIOLOGY
Aquat Biol
Vol. 24: 185–199, 2016
doi: 10.3354/ab00647
Published online February 22
INTRODUCTION
Habitat selection in response to spatial hetero-
geneity is a common theme in landscape, population,
and evolutionary ecology, and is important for habi-
tat conservation (Morris 2003a,b). Both abiotic and
biotic factors can dictate habitat selection; the physi-
cal environment is considered a primary factor in -
fluencing resource availability, but species’ fitness is
also dependent on population density and inter -
actions, such as competition (Rosenzweig 1991). Ani-
mals may use information such as proximity to con-
specifics in both positive (attraction) and negative
(avoidance) ways (Dall et al. 2005), and understand-
ing how individuals position themselves relative to
others could improve our ability to conserve wildlife
populations (Ward & Schlossberg 2004).
Most conspecific habitat selection studies have
predominantly centered on terrestrial species (e.g.
birds by Ward & Schlossberg 2004; reptiles by
Stamps 1991), while a comparably small number of
studies exist in the marine realm (e.g. turtles by
Schofield et al. 2006, 2007; fish by Lecchini et al.
2007). This is likely due to the challenges involved in
observing individuals during experiments. Experi -
mental mani pulation is particularly difficult for highly
© The authors 2016. Open Access under Creative Commons by
Attribution Licence. Use, distribution and reproduction are un -
restricted. Authors and original publication must be credited.
Publisher: Inter-Research · www.int-res.com
*Corresponding author: ikuko@ufl.edu
Habitat selection by green turtles in a
spatially heterogeneous benthic landscape
in Dry Tortugas National Park, Florida
Ikuko Fujisaki1,*, Kristen M. Hart2, Autumn R. Sartain-Iverson3
1University of Florida, Ft. Lauderdale Research and Education Center, Davie, FL 33314, USA
2US Geological Survey, Wetland and Aquatic Research Center, Davie, FL 33314, USA
3Cherokee Nation Technologies, contracted to US Geological Survey, Wetland and Aquatic Research Center, Davie,
FL 33314, USA
ABSTRACT: 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 trans-
mitter 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 sta-
tion throughout the acoustic array was small (<0.1 in any year), and that of multiple turtle detec-
tions 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 tur-
tles 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 esti-
mates 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.
KEY WORDS: Acoustic telemetry · Co-occupancy · Habitat selection · Satellite telemetry
OPEN
ACCESS
Aquat Biol 24: 185–199, 2016
mobile and large-bodied marine vertebrates, such as
sea turtles. Sea turtles play key roles in coastal and
coral reef ecosystem function, with direct links
between green turtles Chelonia mydas and seagrass
productivity through herbivory (Thayer et al. 1984,
Moran & Bjorndal 2005) and indirect links for all spe-
cies of sea turtles to nutrient cycling through nesting
activities on beaches (Bouchard & Bjorndal 2000,
Bjorndal & Jackson 2003).
There is a small body of literature that has exam-
ined group activity, social structure, or conspecific
interactions, but sea turtles have generally been con-
sidered solitary animals. Lanyon et al. (1989) de -
scribed that green turtles do not form organized
groups; during courtship they use visual and chemi-
cal cues to find each other (Ernst et al. 1994, Spotila
2004). Bjorndal (1980) reported an absence of ag -
gressive behavior and an indication of a hierarchy for
some green turtle populations, whereas Schofield et
al. (2007) observed aggressive behavior between
female loggerheads Caretta caretta during courtship
and inter-nesting periods. During foraging periods,
habitat selection is influenced by environmental fac-
tors such as resource availability and water depth
(Seminoff et al. 2002, Senko et al. 2010), but little is
known about the effect of conspecific proximity. Pre-
vious satellite telemetry results have shown green
turtle core areas in close proximity and sometimes
overlapping (Seminoff et al. 2002, Hart et al. 2013).
However, potentially large location errors in satellite
data make it uncertain whether these turtles use
locations concurrently on a smaller scale. Studies on
loggerheads have shown consistent use of the same
foraging site over successive years (Broderick et al.
2007, Schofield et al. 2010) suggesting that some sea
turtles may establish distinct territories (Hart et al.
2015). Similarly, an acoustic telemetry study in Low
Isles, Australia, indicated that green turtles had
different core areas within the reef complex, which
implies individuals adopted unique optimal resource-
use patterns (Hazel et al. 2013). However, GPS track-
ing of pre- and internesting female loggerheads indi-
cated that turtles were found in closer proximity than
expected at random (Schofield et al. 2009). Here,
by combining satellite and acoustic telemetry data,
we investigate in-water habitat selection of subadult
and adult green turtles in a spatially heterogeneous
neritic zone.
Green turtles can migrate up to 1000s of km be -
tween foraging and nesting sites (Mortimer & Portier
1989, Plotkin 2003), but satellite tracking has shown
that not all green turtle populations migrate long dis-
tances (Hart et al. 2013). Short distances (2 km) post-
nesting migrations, indicating close proximity between
nesting and foraging sites, have also been observed
(Limpus et al. 1992). Studies using flipper tags and
satellite telemetry have shown multi-year use of for-
aging grounds by green turtles, suggesting high
long-term site fidelity to foraging areas (Limpus et
al. 1992, Broderick et al. 2007). Adult green turtles
are herbivorous and primarily consume seagrass and
algae, so their foraging grounds are frequently in
seagrass-dominated ecosystems (Bjorndal 1985, 1997,
Garnett et al. 1985, Bolten 2003). However, green
turtle diet can vary by foraging ground (Bjorndal
1985, 1997), life stage (Bolten 2003), and the avail-
ability of food sources (Garnett et al. 1985). Recent
studies using stable isotope analysis and animal-
borne videos suggested more variation in green tur-
tle diet than previously thought (Vander Zanden et
al. 2010, Burkholder et al. 2011).
Combining satellite telemetry and passive acoustic
telemetry at fixed locations, we quantified the likeli-
hood of (1) core area overlap and (2) co-occupancy by
multiple turtles. We defined co-occupancy as simul-
taneous detections of multiple turtles by the same
receiver in a unit of time. We also (3) tested the effect
environmental factors, including bathymetry, ben-
thic type, and proximity to capture site have on habi-
tat selection, and (4) examined daily and seasonal
patterns in co-occupancy.
MATERIALS AND METHODS
Study species
Our study species is the Endangered green turtle
Chelonia mydas (Hilton-Taylor 2000). Distributed
globally in tropical and subtropical waters (Hirth
1997), populations of green turtles have declined
worldwide due to various anthropogenic causes
(Groom bridge & Luxmoore 1989, Limpus 1995).The
IUCN (Hilton-Taylor 2000) highlights the need to
understand the foraging and movement ecology of
this species for conservation planning.
Study area
The study was conducted within Dry Tortugas
National Park (DRTO), the southwesternmost point
of Florida, USA. DRTO is within Florida Keys
National Marine Sanctuary (FKNMS), one of 14 mar-
ine protected areas of the National Marine Sanctuary
System. The area is also within the Northwest At -
186
Fujisaki et al.: Habitat selection by green turtles
lantic Regional Management Unit (RMU), one of 17
defined population RMUs for green turtles (Wallace
et al. 2010). The area is characterized by a subtropi-
cal climate and a variety of benthic habitat types
(Fig. 1). Whereas green turtle nesting activities occur
in DRTO (Reardon 2000), possible year-round resi-
dency of some adult females seen nesting locally
suggests this may also be their foraging site (Hart et
al. 2013). Within DRTO, an array of acoustic receivers
(Vemco VR2 and VR2W) was maintained to monitor
marine life. This study uses data from 7 active
receivers deployed (1.4−7.6 km apart) in water depth
4.8− 12.8 m in the northwest part of DRTO (Fig. 1). In
this area underwater visibility generally ranges from
3 to 30 m and there is some degree of human distur-
bance such as boating, fishing, and anchoring.
Deployment of satellite and acoustic tags
Satellite telemetry is an established method to de -
lineate habitats of free-ranging animals (Hays et al.
1991, Godley et al. 2008, Hart & Hyrenbach 2009).
Arrays of acoustic receivers at predetermined loca-
tions, however, can provide information on the use of
specific locations by individuals (How & de Lestang
2012). We tracked green turtles by satellite telemetry
and recorded detections at acoustic receivers de -
ployed at fixed locations between 2009 and 2012.
Because of the composition of the study site and
logistics, we concentrated our capturing efforts in the
northern portion of DRTO where we can reliably
work to capture green turtles (Fig. 1; K. M. Hart pers.
obs.).
Following the established protocols (NMFS SEFSC
2008), we caught turtles using the rodeo or turtle-
jumping technique (diving from a boat to capture
turtles; Ehrhart & Ogren 1999). Once we brought a
turtle aboard the boat, we took standard measure-
ments, including curved (CCL) and straight (SCL)
carapace lengths from the anterior notch to the last
posterior marginal scute, and took photographs to
document carapace and skin ano malies. Based on
external assessment of animal size and tail size, we
categorized them into subadult-sized (65−90 cm SCL;
Bresette et al. 2010) and adult-sized (>90 cm SCL)
187
Fig. 1. Dry Tortugas National Park (DRTO) with benthic type, turtle capture locations (n = 15), and locations of acoustic stations
(receiver locations A−G) shown with 200 m buffer (the approximate maximum distance to 100% detection probability; Kessel
et al. 2014) from the center (outer circle). Inset boxes show the location of DRTO in the State of Florida, USA (top left) and
seagrass cover type in DRTO (top right). SRV: submerged rooted vascular plants
Aquat Biol 24: 185–199, 2016
females or males; male turtles of which we took tail
measurements (n = 5) had cloaca-tip lengths ≥5.5 cm.
We tagged each animal with a passive integrated
transponder (PIT) in the right shoulder and affixed
an individually numbered flipper tag to each front
flipper.
We fitted a Wildlife Computers SPOT5 platform
terminal transmitter (PTT) to each turtle; each tag
(2.5xAA model, 71 mm long × 54 mm wide × 24 mm
high) had a saltwater switch. Tags had an output of
0.5 W and a mass of 115 g in air. Prior to transmitter
application with either Power-Fast™ or SuperBond™
2-part cool-setting marine epoxies, we removed
epibionts (e.g. barnacles, algae) from the carapace
and sanded and cleaned it with isopropyl alcohol. We
streamlined attachment materials and minimized the
epoxy footprint. Each tag had an anticipated battery
life of 1 yr and was programmed to operate continu-
ously. All turtles were released within 2 h at their
capture location.
For acoustic tags, we outfitted selected turtles with
Vemco V16-4L acoustic transmitters (25 g in air, 11 g
in water; 16 mm diameter × 68 mm length), pro-
grammed for a 90 s time interval, on the right rear
carapace marginal scute, using approx. half of a West
Marine epoxy putty stick, mixed immediately prior to
application. We streamlined the epoxy and mini-
mized its footprint. The drying time was 1 to 1.5 h,
depending on air temperature at the time of attach-
ment. The anticipated life of each tag was about 3 yr.
Home ranges and core areas
Site fidelity test and kernel density estimations
Argos Doppler data provides location error esti-
mates (location class: LC) with each point. Argos
assigns accuracy estimates of <250 m for LC 3, 250 to
<500 m for LC 2, 500 to <1500 m for LC 1, and
>1500 m for LC 0 (CLS 2011, also see Witt et al. 2010
for on-animal location estimates). The estimated
accuracy is unknown for LCs A and B (A and B differ
in number of received messages), and locations fail-
ing the Argos plausibility tests are tagged as class
LC Z (CLS 2011). Following Eckert (2006), we
applied the Douglas Argos-Filter for satellite data
keeping the highest 3 location classes (LC 0−2), and
using a speed filter of 3 km h−1 based on Dujon et
al. (2014), who assumed the average swim speed of
green turtles to be 2.5 km h−1. We used 15 for the
angle parameter setting (about 26°), and a maximum
radius of 2 km, and we also removed apparent erro-
neous locations (<1% of the data). Using the filtered
locations, we conducted a correlated random walk
(CRW) site-fidelity test within 65 km isobaths (this
bathymetry contour ranged 15 to >30 km from the
DRTO boundary). Following the mean positioning
method (Simpfen dorfer et al. 2002), we derived hourly
positions from acoustic data. With filtered satellite
and mean positioned acoustic locations, we derived
mean daily locations of each turtle to minimize corre-
lation. We created 50 and 95% kernel density esti-
mations (KDEs) for each turtle to represent the core
area of activity and the home range using the least
square cross-validation me thod. We used the adhabi-
tat package (Calenge 2006) for R (http://r-project.org)
for the CRW site fidelity test and KDE.
Benthic type, seagrass density, and bathymetry
in core areas
We used 3 ancillary data sets to characterize the
core area: vector data of benthic types released by
the National Park Service (http://science.nature.
nps.gov/im/units/sfcn/monitor/landscape/benthic_
mapping.cfm, accessed on November 5, 2014; Waara
2011), vector data of seagrass coverage obtained
from Fish and Wildlife Research Institute’s Marine
Resources Geographic Information System (MRGIS;
http://ocean.floridamarine.org/mrgis/Description_
Layers_Marine.htm, accessed January 31, 2014), and
a 1 m resolution bathymetry raster with 0.1 m accu-
racy derived from a combination of NOS sounding,
multi-beams, and light detection and ranging (lidar;
J. Luo unpubl. data). We combined all individual core
areas (50% KDEs) and calculated the number of 350
× 350 grid cells, which roughly corresponded to the
mean benthic patch size, for 13 benthic types and
3 seagrass types (continuous, discontinuous, none;
Fig. 1) within and outside of the combined core area.
We tested for differences in the probability of sea-
grass coverage on benthic types within and outside
of the combined core area using a chi-squared test.
In this analysis, 2 groups of compared areas were
within and outside of core areas with a sample unit of
350 × 350 m grid cells. We also compared grid cell
bathymetry within and outside of the combined core
area using a Wilcoxon rank sum test.
Core area overlap indices
Overlap indices are useful to quantitatively meas-
ure habitat sharing and the degree of interaction
188
Fujisaki et al.: Habitat selection by green turtles
among individuals (Fieberg & Kochanny 2005). For
every combination of satellite-tagged turtles in each
year, we calculated the core area overlap indices
including the proportion of animal i’s core area over-
lapped by animal j’s core area (HRi,j), the probability
of animal j being located in animal i’s core area
(PHRi,j), and the utilization distribution overlap index
(UDOI) (Fieberg & Kochanny 2005). HRi,j is a simple
measure to quantify static overlap whereas PHRi,j
considers relative probability of use; both range from
0 to 1 (Fieberg & Kochanny 2005). The UDOI, a gen-
eralization of Hurlbert’s (1978) E/Euniform statistic, is a
function of 2 utilization distributions and ranges from
0 (no overlap) to 1 (100% overlap). The UDOI is re -
commended (Fieberg & Kochanny 2005) to measure
space use sharing over other indices. Each turtle
combination was classified into one of the 3 possible
gender combinations: female−male (FM), female−
female (FF), and male−male (MM).
Analysis of acoustic detection data
Pedersen & Weng (2013) demonstrated that acous -
tic telemetry data at fixed locations are useful to esti-
mate detection probabilities of marine fish. In this
study, as the array does not cover the entire study
area, we considered each station as a point sample
location. Because acoustic receivers are passive
moni toring tools within a limited detection range (a
few hundred meters; Hazel et al. 2013), an absence of
observations (detections) implies that either (1) tur-
tles are not present within receiver detection range
but are present in the study area, (2) turtles are
within receiver detection range but there are imped-
iments to transmission, or (3) turtles left the study
area. To avoid non-detection caused by (3), we only
used acoustic detection data when all acoustically
tagged turtles were confirmed inside DRTO either by
satellite or acoustic data. In addition, because re -
ceiver batteries died or receivers were removed for
maintenance, we only analyzed data when all re -
ceivers were deployed and active to ensure non-
detections were not caused by receivers malfunction-
ing. The detection distance of acoustic receivers can
vary based on transmitter type, salinity and depth of
water, ambient noise, presence of pycnoclines and
thermoclines, and the behavior of the study animal
(Heupel et al. 2006). By reviewing studies mainly on
invertebrates and fish, Kessel et al. (2014) reported
the maximum distance for 100, 95, and 50% de -
tection probability was about 200, 350, and 550 m,
respectively, from the receiver. Range testing on
green turtles in a coral habitat showed that the de -
tection was near perfect up to 50 m and reduced to 50
and 11% at 300 and 300−400 m distances, respec-
tively (Hazel et al. 2013).
Detection probability at stations
We modeled the detection probability for each
year at a receiver, using the binomial distribution,
that is:
nijk ~ binomial (pijk, Nk)
(1)
and
(2)
where nijk is a number of detected days for station i,
turtle j, and year k, Nk is the number of days re -
ceivers are deployed in year k, and pijk is the detec-
tion prob ability. Bathymetryi and Benthici represent
bathymetry and benthic type at station i, Dij is the
distance between station i and the capture location
of turtle j, and B0, B1, B2, B3 are coefficients. Benthic
type is a variable with 3 categories: low profile
remnant (reef), continuous/discontinuous submerged
rooted vascular plants (SRV), and unconsolidated
sediment. Distance to the capture location was in -
cluded in the analysis because it may influence the
turtle occurrence if high site fidelity at the capture
location is true. Variability by acoustic station, turtle,
and year were accounted for as random effects, εi, εj,
and εk.
Detection probability of turtles
We modeled the probability of turtle detection in a
daily and seasonal (course of study period in each
year) scale for each year separately using the bino-
mial-logit model. Daily detection probability was
modeled as:
til ~ binomial (pil, T)
(3)
and
(4)
where til is the number of detected turtles and pil is
the detection probability in station i (i = 1, 2 … 7) at
day l (the range of l varies by year) and T is the num-
ber of acoustically tagged turtles in each year. The
conditional probability of detecting multiple turtles
log
1
Bathymetry
Benthic
0
1
2
3
p
p
B B
B
B D
ijk
ijk
i
i
ij
i
j
k
−
⎛
⎝⎜
⎞
⎠⎟ =
+
×
+
×
+
×
+ ε + ε + ε
p
p
B
il
il
i
l
−
⎛
⎝
⎞
⎠ = + ε + ε
log
1
189
Aquat Biol 24: 185–199, 2016
daily given at least one turtle detection was calcu-
lated for each year as:
(5)
where t is the number of detected turtles, T is the
number of tagged turtles in the modeled year, and
pt is the detection probability of t turtle(s).
Seasonal detection probability was modeled in a
similar way:
ti ~ binomial (pi, T)
(6)
and
(7)
where the notation for t, p, T and the subscript i are
the same as the daily model.
We assumed non-informative prior distributions for
the variance components, σstation, σyear, and σturtle
(Uniform (0, 10)), and estimated model parameters
using Gibbs sampling (20 000 draws obtained by
sampling 5 independent Markov chains — each run
for 40000 iterations after 10000 burn-ins and thinned
by 10 samples) using WinBUGS 1.4 (Spiegelhalter et
al. 2002) followed by Gelman-Rubin diagnostics to
confirm approximate convergence using the CODA
package in R (Brooks & Gelman 1998).
Gender combinations
We examined whether multiple turtle detections
occurred more frequently in particular gender com-
binations (i.e. FM, FF, MM). We compared the num-
ber of days each combination of turtles was detected
by gender combination. Because of unequal numbers
of each gender combination and the duration of
receiver deployments in each year, we compared the
detection data to a hypothetical detection scenario.
For the hypothetical numbers, we used the maximum
possible detection dates for all possible gender com-
binations. We then used a chi-squared test for differ-
ences in probabilities to compare the detection fre-
quency by gender combinations.
Temporal analysis
We calculated the time (in seconds) between con-
secutive detections (step-time) of different individu-
als within the range of the same receiver. Then we
examined whether the step time was different for
varying gender combinations using a Kruskal-Wallis
test.
RESULTS
Satellite and acoustic data
We deployed satellite tags on 15 turtles (9 males
[M1 to M9], 6 females [F1 to F6]) between 2009 and
2012 (6 in 2009, 2 in 2010, 3 in 2011, and 4 in 2012) of
which 13 turtles were also given acoustic tags (5 in
2009, 2 in 2010, 2 in 2011, and 4 in 2012; Table 1).
Based on SCL, turtles were subadult-sized (n = 5) and
adult-sized (n = 10). Tracking durations of individual
turtles ranged from 40 to 226 d (x¯ ± 1SD = 105.9 ±
57.5 d; Table 1). During this time, we re ceived 11389
total filtered locations across all turtles, ranging from
172 to 1411 per turtle. The number of daily satellite-
detected days ranged from 39 to 176 d per turtle. Of
15 satellite-tracked turtles, 14 tracks ended within
5 km of DRTO and 1 was detected beyond 5 km of
DRTO but within FKNMS.
We used acoustic detection data from June 14 to
July 20 in 2009 (36 d), June 6 to September 6 in 2010
(95 d), July 18 to September 3 in 2011 (52 d), and July
11 to August 27 in 2012 (47 d). The number of
acoustic detections was highly variable across the
stations and ranged from 0 to 4745 total detections,
occurring on 0 to 89 unique days (Table 2). The high-
est number of detections oc curred at station A and
there were no detections at station B. All acoustically
tagged turtles except one adult male (M5) were
detected near at least one station (Table 1). The total
number of detections per turtle ranged from 0 to 3622
(x¯ = 679.9, SD = 1002.7) and occurred over 0 to 51
unique days (x¯ = 21.9, SD = 18.7). The maximum
detection period was 135 d (F3).
Site fidelity, core areas, home ranges,
and overlap indices
All satellite-tagged turtles exhibited high site fide -
lity around DRTO; the results of CRW showed that all
randomly generated paths had greater R2 values than
true paths, suggesting that the turtle’s movements
were more spatially constrained rather than ran-
domly distributed. The size of core areas (50% KDE)
ranged from 1.3 to 18.9 km2 and home ranges (95%
KDE) ranged from 8.8 to 131.5 km2 (Fig. 2, Table 1).
All core areas and home ranges occurred around a
northeast portion of DRTO (Fig. 2).
There were 25 possible 2-turtle combinations of satel-
lite-tagged individuals, including 15 FM, 7 MM, and
3 FF combinations. At least one combination had inter-
secting core areas each year (Table 3). Core area inter-
log
1
p
p
B
i
l
l
−
⎛
⎝
⎞
⎠ = + ε
2|
1
2
1
p t
t
p
p
t
T
t
t
T
t
∑
∑
(
)
≥
≥
=
=
=
190
Fujisaki et al.: Habitat selection by green turtles
sections occurred between 9 FM,
1 MM, and 1 FF combination. The
static measure of overlap proportion,
HRi,j, was highly variable ranging
from 0 to 0.27, where as PHRi,j was
more conservative, ranging from 0 to
0.16. Overall, UDOI was small (0 to
0.003), suggesting a small degree of
space sharing between individuals.
Habitat type
The proportion of each benthic
type within and outside of the
areas encompassing all the core
areas in DRTO was significantly
different (χ2 = 128.5, p < 0.0001).
This difference appeared to be
attributed to a higher proportion of
SRV within the core area (Fig. 3).
In particular, continuous SRV was
the most common benthic type
with in the core area (30%), where -
as it covered only a minor portion
(11%) outside (Fig. 3). Similarly,
there was a higher proportion
of continuous and discontinuous
seagrass beds with in the core area
(37%) than out side (11%, χ2 =
178.5, p < 0.0001, Fig. 3). The area
within the combined core area
was characterized by significantly
(Wil co xon T = 16.0, p < 0.0001)
shallower bathymetry depth (x¯ =
−8.5 m, SD = 4.8 m) than outside
(x¯ = −14.1 m, SD = 6.2 m).
Detection probability by stations
The posteriors of model coeffi-
cients are summarized in Table 4.
A positive 95% credible interval
(CI) of B1 indicated a positive as -
sociation between detection prob-
abilities and bathymetry. Contrary
to this, a negative CI of B3 indi-
cated an inverse association be -
tween the detection probability
and the distance between the
acoustic station and capture loca-
tion. The broad CI of B2
that
191
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/2
00
9
46
15
(
A
)
2
(A
)
4.
2
22
.7
F
1
73
.8
S
u
b
ad
u
lt
6/
8/
20
09
10
/2
4/
20
09
8/
6/
20
09
11
1
1
(A
),
8
84
(
C
)
1
(A
),
2
3
(C
)
2.
3
16
.9
F
2
91
.2
A
d
u
lt
6/
9/
20
09
12
/2
9/
20
09
6/
9/
20
09
17
6
**
**
1.
8
14
.2
M
2
85
.2
A
d
u
lt
6/
9/
20
09
11
/1
1/
20
09
7/
4/
20
09
14
9
56
(
A
),
9
7
(C
),
2
(
E
)
4
(A
),
1
2
(C
),
1
(
E
)
1.
8
12
.1
F
3
83
.7
S
u
b
ad
u
lt
6/
9/
20
09
7/
20
/2
00
9
10
/2
1/
20
09
41
16
(
A
),
2
0
(D
)
5
(A
),
3
(
D
)
8.
4
40
.3
M
3
83
.2
S
u
b
ad
u
lt
8/
13
/2
00
9
11
/3
/2
00
9
*
79
*
*
1.
3
8.
8
20
10
M
4
95
.0
A
d
u
lt
6/
5/
20
10
9/
6/
20
10
7/
31
/2
01
0
78
12
1
(A
),
6
0
(C
),
1
94
(
F
),
9
(
G
)
12
(
A
),
7
(
C
),
1
9
(F
),
2
(
G
)
18
.9
13
1.
5
F
4
72
.4
S
u
b
ad
u
lt
6/
6/
20
10
9/
28
/2
01
0
8/
4/
20
10
11
5
27
6
(A
),
1
72
(
C
),
6
63
(
F
),
2
6
(G
)
22
(
A
),
1
6
(C
),
2
2
(F
),
1
(
G
)
9.
2
46
.2
20
11
M
5
96
.0
A
d
u
lt
5/
18
/2
01
1
9/
3/
20
11
**
65
**
**
10
.3
88
.7
F
5
10
3.
0
A
d
u
lt
5/
19
/2
01
1
9/
13
/2
01
1
5/
22
/2
01
1
36
13
4
(C
)
3
(C
)
16
.0
83
.8
M
6
98
.5
A
d
u
lt
8/
11
/2
01
1
3/
25
/2
01
2
*
39
*
*
5.
3
21
.7
20
12
M
7
93
.2
A
d
u
lt
7/
7/
20
12
8/
27
/2
01
2
10
/2
/2
01
2
91
25
(
D
),
1
06
3
(F
),
1
23
2
(G
)
1
(D
),
3
8
(F
),
1
0
(G
)
2.
7
16
.7
F
6
10
2.
6
A
d
u
lt
7/
7/
20
12
7/
15
/2
01
2
8/
28
/2
01
2
13
7
35
98
(
A
),
2
4
(C
)
44
(
A
),
1
(
C
)
2.
8
17
.8
M
8
83
.2
S
u
b
ad
u
lt
7/
8/
10
12
8/
29
/2
01
2
8/
6/
20
12
79
14
1
(A
),
4
(
C
),
1
32
(
E
)
14
(
A
),
1
(
C
),
1
(
E
)
2.
3
17
.3
M
9
85
.9
A
d
u
lt
7/
11
/2
01
2
8/
28
/2
01
2
9/
9/
20
12
90
72
1
(A
),
1
39
(
C
),
4
(
D
)
38
(
A
),
7
(
C
),
1
(
D
)
4.
1
22
.3
T
ab
le
1
. S
u
m
m
ar
y
of
g
re
en
t
u
rt
le
m
ea
su
re
m
en
ts
, t
el
em
et
ry
d
at
a
(s
at
el
li
te
a
n
d
a
co
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st
ic
),
a
n
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iz
es
o
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a
re
a
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om
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ra
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(5
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%
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es
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ro
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at
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te
d
at
a
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on
ly
f
or
t
h
e
p
er
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d
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D
ry
T
or
tu
g
as
N
at
io
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P
ar
k
(
w
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h
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5
k
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h
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g
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t
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t
h
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or
e
th
e
la
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ra
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re
t
h
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la
st
lo
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on
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t
h
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s
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N
u
m
b
er
o
f
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on
s
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ly
in
cl
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th
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p
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se
d
f
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al
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is
(
Ju
n
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14
−
Ju
ly
2
0
fo
r
20
09
, J
u
n
e
6−
S
ep
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6
f
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2
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ay
1
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3
f
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7
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r
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),
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w
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a
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t
h
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p
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se
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of
a
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t
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s
ta
g
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in
e
ac
h
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r
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ou
n
d
t
h
e
n
at
io
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p
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w
as
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rm
ed
. T
h
e
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tt
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b
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s
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at
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ac
ou
st
ic
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ta
ti
on
s
at
w
h
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h
t
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s
w
er
e
d
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te
d
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C
L
: s
tr
ai
g
h
t
ca
ra
p
ac
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n
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th
. *
A
co
u
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ev
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d
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ec
te
d
Aquat Biol 24: 185–199, 2016
ranged from negative to positive indicated that
the association between the detection probability
and benthic type was neither clearly negative nor
positive.
Daily and seasonal detection probability
The posterior probability of daily detection was
generally low; the estimated probability of no de tec -
tion was over 0.89 in all years (Table S1,
Fig. S1A in the Supplement at www.int-
res.com/articles/suppl/b024p185_supp.pdf).
The es ti mated prob ability of 1 turtle detec-
tion was <0.1, and that of multiple turtle
detections (>1 in dividual) was even lower,
(maximum of 0.13). However, the proba-
bility of multiple turtle detections was
much higher when it was conditioned on
at least one individual being detected, and
ranged from 0.0004 to 0.045 (Fig. S1C).
Also, within-season detection probability
was much higher (Fig. S1B); in 2009, the
year in which the largest number of turtles
were acoustically tagged (n = 5), sea-
sonal detection probabilities for 1 to 5 tur-
tles were 0.038, 0.006, 0.002, 0.001, and
<0.001. In 2012, when 4 turtles were
tagged, the seasonal de tection probabili-
ties for 1 to 4 turtles were 0.369, 0.256,
0.099 and 0.019.
Comparison by gender combination
Over all years, there were 52 days in
which unique FM combinations were de -
tected and 11 days when unique MM
combinations were detected on the same
day at the same station. Detection of FF
192
Year
Combinations
HR1,2
HR2,1
PHR1,2 PHR2,1 UDOI
1
2
2009
F1
F2
0.069
0.061
0.032
0.029
0.001
F1
F3
0.000
0.000
0.000
0.000
0.000
F2
F3
0.000
0.000
0.000
0.000
0.000
M1
M2
0.000
0.000
0.000
0.000
0.000
M1
M3
0.000
0.000
0.000
0.000
0.000
M2
M3
0.000
0.000
0.000
0.000
0.000
F1
M1
0.000
0.000
0.000
0.000
0.000
F1
M2
0.034
0.029
0.015
0.014 <0.001
F1
M3
0.000
0.000
0.000
0.000
0.000
F2
M1
0.060
0.074
0.033
0.031
0.001
F2
M2
0.000
0.000
0.000
0.000
0.000
F2
M3
0.000
0.000
0.000
0.000
0.000
F3
M1
0.016
0.037
0.018
0.010 <0.001
F3
M2
0.000
0.000
0.000
0.000
0.000
F3
M3
0.063
0.235
0.098
0.031
0.003
2010
F4
M4
0.090
0.025
0.010
0.045 <0.001
2011
F5
M5
0.216
0.142
0.085
0.111
0.009
F5
M6
0.000
0.000
0.000
0.000
0.000
M5
M6
0.012
0.083
0.056
0.006 <0.001
2012
F6
M7
0.000
0.000
0.000
0.000
0.000
F6
M8
0.082
0.273
0.162
0.027
0.004
F6
M9
0.034
0.167
0.081
0.010
0.001
M7
M8
0.000
0.000
0.000
0.000
0.000
M7
M9
0.000
0.000
0.000
0.000
0.000
M8
M9
0.091
0.091
0.086
0.040
0.004
Table 3. Indices of core area overlap for each combination of 2 satellite-
tracked green turtles in Dry Tortugas National Park from 2009 to 2012 in-
cluding proportion of animal i’s core area overlapped by animal j’s core
area (HRi,j), probability of animal j being located in animal i’s core area
(PHRi,j), and utilization distribution overlap index (UDOI)
Station
Depth
Benthic type
Duration
No. of detections
(m)
(mo/d/yr)
2009
2010
2011
2012
A
8.5
Low profile remnant
6/10/2009−11/7/2012
88 (11)
397 (31)
0 (0)
4240 (96)
B
4.6
Continuous SRV
8/9/2008−11/7/2012
0 (0)
0 (0)
0 (0)
0 (0)
C
9.4
Unconsolidated sediment
6/10/2009−5/8/2012
5/11/2012−11/7/2012
981 (27)
232 (20)
134 (3)
167 (9)
D
12.8
Unconsolidated sediment
6/6/2009−2/1/2011
7/18/2011−11/7/2012
20 (3)
9 (0)
na
29 (2)
E
4.9
Low profile remnant
6/10/2009−11/27/2012
2 (1)
0 (0)
na
332 (1)
F
9.4
Discontinuous SRV
6/6/2009−2/7/2012
7/5/2012−11/7/2012
0 (0)
857 (40)
0 (0)
1063 (38)
G
11
Unconsolidated sediment
6/14/2009−11/7/2012
0 (0)
35 (3)
0 (0)
232 (10)
Table 2. Summary of bathymetry depth, benthic type of acoustic stations, and detections of green turtles. Number of detections
are only for the period used for analysis (June 14−July 20 for 2009, June 6−September 6 for 2010, May 19−September 3 for 2011,
and July 11−August 27 for 2012), when all receivers were deployed and the presence of all turtles tagged in each year around
the Park was confirmed. Numbers in parentheses are the number of days with detections. Detection data from receivers D and E
were not used for analysis due to long undeployed periods. SRV: submerged rooted vascular plants; na: not available
Fujisaki et al.: Habitat selection by green turtles
193
combinations was possible in 2009 be cause multiple
females were acoustically tagged, but it did not
occur. When we tes ted the proportion of detected
days for same gender combinations (FF and MM
combinations, 17.5%) and different gender combi-
nations (FM, 82.5%) against the maximum possible
detection rate (37% for FF and MM combinations
and 63% for FM combinations), the result was sig-
nificant (χ2 = 10.32, p = 0.023): the FM combinations
were de tected on a disproportionally larger number
of days.
Temporal analysis
There were 415 observations in which 6 unique tur-
tle combinations (Fig. 4A) were consecutively detec -
ted at the same station in a single day. The step-time
was highly variable, ranging from 4 s to 13.9 h, and
was skewed (Fig. 4B): 40% of 2 turtle detections oc-
curred within 1 min, 71% of them occurred within
5 min and the fre quency of 2 turtle detections de-
clined along increasing time intervals. The Kruskal-
Wallis test indicated that step-time was different
Fig. 2. Estimated core areas
and home ranges (50 and
95% kernel density estima-
tions, KDE) and mean daily
lo cations of green turtles
tracked by satellite and
acoustic telemetries in the
northeast portion of Dry
Tortugas National Park in
2009 (n = 6), 2010 (n = 2),
2011 (n = 3), and 2012 (n = 4)
Aquat Biol 24: 185–199, 2016
among turtle combinations (χ2 = 13.04, p = 0.023,
Fig. 4B), but we did not observe a consistent trend of
step-times differing between same and different gen-
der combinations.
DISCUSSION
Tropical and subtropical neritic zones are produc-
tive areas maintaining a high biodiversity of marine
life. While a variety of marine species use the neritic
zone, knowledge of spatial−temporal variation in
habitat selection by highly mobile large vertebrates
within this ecologically important area is limited.
Satellite and acoustic tracking of sea turtles in a spa-
tially heterogeneous habitat allowed us to observe
habitat selection in this zone at different scales. Our
findings include habitat use by green turtles in rela-
tion to resource availability and conspecifics, and
temporal details of specific habitat use.
Site fidelity and core area size
During tracking periods the majority of turtles
stayed within 5 km of the DRTO boundary and none
demonstrated migratory behavior. This observation
aligns with our previous study of nesting green tur-
tles Chelonia mydas in DRTO which
found that the majority of satellite-
tracked turtles were year-round resi-
dents within DRTO and FKNMS (Hart
et al. 2013). Studies in other areas
have also suggested high site fidelity
of green turtles between successive
breeding seasons (Limpus et al. 1992,
Broderick et al. 2007, Stokes et al.
2015). Therefore, it is possible these
turtles are long-term residents within
DRTO and FKNMS; however, no tur-
tle was acoustically detected in sub-
sequent years after tagging. The last
observations within DRTO occurred
194
Fig. 3. Area by (A) ben-
thic type and (B) sea-
grass category in and out
of combined core areas
(50% kernel density esti-
mations) from satellite-
tagged green turtles (n =
15) in Dry Tortugas Na-
tional Park. Note differ-
ence in scale between A
and B. SRV: submerged
rooted vegetation
Parameter
Mean
SD
2.5% Median
97.5%
B0
−16.3400 21.3100 −51.6500 −21.8800 26.1400
B1
1.3050
0.6507
0.1761
1.2700
2.5890
B2 (low profile remnant)
4.3620 19.1700 −35.6600
9.2350 30.6700
B2 (continuous/discontinuous SRV)
1.6910 19.6100 −37.9600
4.0580 31.6500
B2 (unconsolidated sediment)
−0.3214 18.2600 −36.4100
1.1490 28.6700
B3
−0.9265
0.2302
−1.3720
−0.9280 −0.4768
Table 4. Posterior summary (mean, SD, median, and 95% credible interval) of
parameters for the model of daily detection probability at acoustic stations es-
timated by acoustic detection data. B0, B1, B2, B3 are intercept and coefficients
for bathymetry depth, benthic type (low profile remnant, continuous/discon-
tinuous SRV, and unconsolidated sediment) and distance to the capture loca-
tion, respectively. SRV: submerged rooted vascular plants
Fujisaki et al.: Habitat selection by green turtles
more often with satellite tele metry than acoustic
telemetry; of 13 turtles tracked by both methods, only
3 were acoustically detected later than satellite
detections (Table 1). This implies that turtles eventu-
ally left DRTO or moved into another area within
DRTO, or that the acoustic tag detection/retention
duration was much shorter than we expected.
As demonstrated by previous studies (see reviews
by Godley et al. 2008, Hart & Hyrenbach 2009), this
study also shows that satellite data are useful for
delineating individual core areas and home ranges.
Core areas of all 15 turtles satellite-tracked in this
study were concentrated in the seagrass-rich north-
ern part of DRTO, which is similar to previously iden-
tified core areas for other nesting green turtles in
DRTO (Hart et al. 2013). However, because our tur-
tles were caught in the same general area in DRTO,
the distribution of core areas for the entire green tur-
tle population in the park is unclear. Tracking turtles
caught in different parts of DRTO would help to bet-
ter understand the use of benthic resources by the
green turtle population in the park.
The estimated home-range size of tagged turtles in
this study (8.8−131.5 km2) was comparable to that of
turtles foraging in the Gulf of California (4.1−
32.3 km2, Seminoff et al. 2002) and of juveniles in the
Big Sable Creek Complex in southwest coastal Ever-
glades National Park, Florida (24.6−371.0 km2, Hart
& Fuji saki 2010), but was much smaller than esti-
mates from foraging sites off the coast of Tamaulipas,
Mexico and Texas (406.3−4944.4 km2; Shaver et al.
2013). This difference may reflect varied spatial
arrangements of suitable habitat among study sites
(Scho field et al. 2010).
Core area selection
The higher proportion of seagrass cover within
core areas as compared to the rest of DRTO suggests
that our turtles consistently inhabit areas with high
resource availability. The seagrass area is generally
shallow, surrounded by coral-reef platforms, and
only covers a small portion of the park (about 15% of
DRTO when continuous and discontinuous seagrass
habitat is combined; Fig. 1). This small area coupled
with the high concentration of core areas there
underscores the potential issue of habitat degrada-
tion due to overgrazing; this has been a problem at
other locations such as Indonesian marine protected
areas in the Indian Ocean (Agatti Lagoon and Lak-
shadweep Islands; Lal et al. 2010, Kelkar et al. 2013,
Christianen et al. 2014). An aerial survey reported
high abundance of green turtles (ca. 600000) in
a small geographic area in Torres Strait, Australia
(Fuentes et al. 2015). However, currently there are no
abundance estimates for green turtles in DRTO and
therefore it is uncertain whether there are sufficient
resources to sustain the population. Given the re -
ported increases of green turtles due to conservation
efforts elsewhere (Christianen et al. 2014), a future
increase in green turtles at DRTO as a result of
current conservation efforts is possible. Our results
highlight the importance of maintaining the seagrass
195
Fig. 4. (A) Box-plots of observed step-time (time interval in minutes between consecutive detections of different green turtles
at a single acoustic station in the same day) by turtle combinations and the (B) frequency of the observed step-times with all
combinations based on detections by acoustic receivers deployed in Dry Tortugas National Park from 2009−2012 with
13 acoustically tagged turtles
Aquat Biol 24: 185–199, 2016
habitat as well as obtaining population estimates of
green turtles in DRTO.
Core areas from multiple turtles overlapped each
year, which indicates some level of habitat sharing,
but overall UDOI was small (<0.01; Table 3), sug-
gesting a restricted degree of habitat sharing. It is
unclear whether the observed habitat sharing was
simply an aggregation to resources or if turtles used
the presence of others in habitat selection. Because
we caught the turtles in water, the nesting status of
the females was uncertain; however, much of the
tracking period coincided with known breeding
periods and thus courtship behavior was possible,
as we observed mating pairs of green turtles in the
study area during these years (K. M. Hart pers. obs).
Although in this study we did not have enough
satellite-tracked subadult turtles or data outside
of the nesting season, future comparisons of over-
lapped core areas by size class and season could
illuminate the effect of mating activities on habitat
selection.
Detection probability by acoustic telemetry
Analysis with the receiver detection model showed
that use of specific locations monitored by acoustic
telemetry is dictated by spatial factors, especially
proximity to capture sites and water depth. Our
receivers were deployed within depth classes that
green turtles use the most extensively within a
coastal foraging area (0−10 m and 10−20 m; Seminoff
et al. 2002). As stated above, satellite data indicated
that core areas were concentrated in generally shal-
low water in DRTO, but within this shallow range
where receivers were deployed (4.6−12.8 m) the
detection probability increased in deeper water. This
could imply both (1) that turtles occupied areas only
a few meters deep less frequently and (2) that detec-
tion range varied by water depth, especially in shal-
low water (Kessel et al. 2014). The model also indi-
cated that the detection probability was higher when
it was closer to capture sites, which supports high site
fidelity. Because all turtles in this study were caught
in the northeast of DRTO within a 2 km range (Fig. 2),
the inverse association between detection probability
and capture site in dicates that this is a central activity
area for our turtles.
On a daily basis, the detection probability of a tur-
tle at a station was slim in any study year and the
probability of detecting multiple turtles at the same
station was even smaller. The conditional probabil-
ity of multiple turtle detections — the probability of
de tection given at least one turtle was detected —
was much larger. The overall small probability esti-
mates of multiple turtle detections do not support
conspecific attraction, but estimates of conditional
prob abilities were not small enough to support
conspecific avoidance, particularly given the small
sample size. Notably, the estimated conditional
probability of multiple turtle detection tended to be
larger in years when more turtles were acoustically
tagged. In fact, we have many sightings of green
turtles in close proximity, sometimes even within
1 m (K. M. Hart pers. obs). Therefore, multiple turtle
detections likely occur more frequently throughout
the entire population of green turtles in DRTO.
Again, understanding the population size of green
turtles in DRTO would provide more insight into
co-occupancy patterns.
The temporal analysis showed a clear pattern that
multiple turtle detections occurred within a short
period of time, even within a few seconds. This ob -
servation further supports that multiple turtles share
habitat on smaller spatial and temporal scales. On a
seasonal scale, the detection probability of both sin-
gle and multiple turtles was much higher compared
to the daily detection probability. For example, the
posterior mean of multiple turtle detections in 2012
was about 0.37 during the course of the season. This
result suggests that multiple turtles more likely use a
common location over a longer temporal span, as
would be expected based on the overlapping core
areas observed by the satellite data.
The acoustic data also showed gender effects on
specific habitat use, possibly reflecting courtship be -
havior (Lanyon et al. 1989). We observed a dispropor-
tionally higher amount of FM combination detections
as compared to same gender combinations. We did
not detect any FF combinations even though it was
possible. This result is potentially because of female-
female aggression as reported for loggerheads
(Schofield et al. 2007); however, the temporal analy-
sis did not show step-time differences by gender
combination, in part due to the large variability and
the small number of combinations detected in the
same day.
The acoustic data results show increased con -
ditional detection probabilities of multiple turtles
and co-occupancy at a receiver within a short
period of time. This, combined with the satellite
results showing overlapping core areas, indicate
that these turtles are not necessarily territorial,
despite the fact that each turtle establishes a
unique core area and home range and small
degree of habitat sharing.
196
Fujisaki et al.: Habitat selection by green turtles
Combining telemetry technologies
Sea turtle habitat selection was not well un -
derstood until various automatic tracking techniques
emerged, due to the high mobility and aquatic nature
of the species. Novel uses of technology and the inte-
gration of data have become increasingly important
to answer remaining questions on their ecology
(Hazen et al. 2012). Combining satellite and acoustic
telemetries is an effective method for defining and
characterizing the habitats of marine species on mul-
tiple spatial and temporal scales, as shown by this
study and in our previous DRTO study of hawksbills
Eretmochelys imbricata (Hart et al. 2012). A recent
study also showed that the combination of animal-
borne acoustic and GPS tracking was useful to exam-
ine the pattern of predator−prey interactions in water
(Lidgard et al. 2014). There are a few critical issues
we encountered in using data from satellite and
acoustic tracking, though combining the 2 techniques
mitigated the drawbacks associated with each tech-
nique. Although active detection with satellite
telemetry enables continuous monitoring and lower
costs compared to GPS telemetry, it comes with a
large uncertainty in location accuracy (Vincent et al.
2002). This potentially large location error constrains
conducting analyses on factors that may influence
habitat selection in a highly heterogeneous environ-
ment. Acoustic telemetry can address this shortfall
by providing more spatially explicit information on
habitat use while being a cost-effective tracking
method for species that establish a home range (Zeh
et al. 2015). One clear disadvantage with acoustic
telemetry, however, is the uncertainty in obtaining
detection records, as data collection depends on
array size and animal movements. We obtained a
vast number of acoustic detections, but this was dra-
matically reduced when we removed duplications for
daily analysis. Further, 1 tagged turtle was never
acoustically detected, although satellite data con-
firmed this turtle was in DRTO, and there was 1 sta-
tion with no detections despite its close proximity to
the capture site. Additionally, all turtles had shorter
than expected acoustic tracking periods, which may
be due to the attachment method. An other concern
with acoustic telemetry is tag/receiver failure. To
avoid the effect of false absence on our detection
probability estimates, we had to exclude records
from 2 receivers during a time of battery failure.
These uncertainties associated with acoustic teleme-
try may be mitigated by (1) ensuring the array of re-
ceivers covers the area of interest, (2) tagging a large
number of study animals, (3) servicing equipment
every few months to ensure receiver recording status
and detect false absences, and (4) use of sentinel tags
for range detection and receiver testing. We also note
that although we selected satellite and acoustic
telemetry due to logistical goals and constraints, GPS
telemetry is another technique potentially effective
to both quantify the core area and co-occupancy with
high location accuracy (Witt et al. 2010).
CONCLUSIONS
Our study provided numerical estimates of core
area overlap, co-occupancy probabilities, and habitat
characterization for green turtles Chelonia mydas.
Our results suggest that our turtles established
unique and partially overlapping core areas, and co-
occupancy, although it was observed, was relatively
rare. A high concentration of core areas highlights
the importance of obtaining population estimates to
understand resources needed to sustain the popula-
tion in the face of declining seagrass ecosystems
(Orth et al. 2006) and better inform the estimated co-
occupancy probability. The results also suggest that
habitat selection may in part be mediated by gender.
Population estimates through recapture studies, in
conjunction with acoustic telemetry, may reveal how
much habitat overlap occurs in the entire green turtle
population within the study area.
Acknowledgements. We acknowledge assistance from Na -
tional Park Service employees Kayla Nimmo, Tree Gottshall,
Tracy Ziegler, and Captains John Spade, Janie Douglass,
and Blue Douglass. We are grateful to USGS volunteers and
employees K. Ludwig, B.J. Reynolds, M. Cherkiss, T. Selby,
B. Smith, M. Denton, B. Jeffrey, J. Beauchamp, and others.
All research on green turtles was conducted according to
institutional and animal care protocols (USGS-SESC-
IACUC-2011-05), and was authorized by Dry Tortugas Sci-
entific Research Permits DRTO-2008 -SCI-0008, DRTO-2010
-SCI-0009, DRTO-2012-SCI-0008, State of Florida Marine
Turtle Permit 176 (issued to K. Hart), and National Oceanic
and Atmospheric Administration National Marine Fisheries
Endangered Species permit 13307 (issued to K. Hart). Any
use of trade, product, or firm names is for descriptive
purposes only and does not imply endorsement by the US
Government.
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