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Satellite tracking and stable isotope analysis highlight differential recruitment among foraging areas in green turtles

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Identifying links between breeding and non-breeding sites in migratory animals is an important step in understanding their ecology. Recognising the relative importance of foraging areas and ascertaining site-specific levels of recruitment can provide fundamental and applied insights. Here, satellite telemetry and the stable isotope ratios (δ¹³C, δ¹⁵N and δ³⁴S) of 230 green turtles Chelonia mydas from a regionally important rookery in northern Cyprus were employed to evaluate the relative importance of 4 foraging areas. A preliminary analysis of stable isotope ratios suggested that a major foraging area had been missed through satellite telemetry as a large proportion of turtles had isotope ratios that did not correspond to sites previously identified. Stable isotope ratios were then employed to select 5 turtles to be fitted with platform terminal transmitters in 2015. All 5 turtles were subsequently tracked to the same location, Lake Bardawil in Egypt. Serially collected tissue samples from 45 females, ranging over 2 to 4 breeding seasons, suggested that foraging site fidelity was very common, with 82% of females exhibiting extremely high temporal consistency in isotope ratios. Quantifying fidelity allowed an evaluation of foraging area-specific contributions to each breeding cohort over the past 2 decades and demonstrated that recruitment was unequal among sites, and dynamic over time, with Egypt now currently the major contributor to the nesting aggregation. This work demonstrates the utility of stable isotope analysis to elucidate the spatial ecology of cryptic taxa and illustrates how more robust baselines can be assembled against which to measure the success of future marine conservation initiatives.
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MARINE ECOLOGY PROGRESS SERIES
Mar Ecol Prog Ser
Vol. 582: 201–214, 2017
https://doi.org/10.3354/meps12297 Published November 6
INTRODUCTION
Many species undertake migrations, including on -
to genetic shifts between successive life-stages (Bol ten
et al. 1998, Reich et al. 2007) or regular seasonal (Hob-
son & Schell 1998) and reproductive migrations
(Rubenstein & Hobson 2004). Philopatric species (ani-
mals that return to their natal region to breed) often
form genetically distinct populations (Greenwood
1980, Meylan et al. 1990), but not all individuals from
the breeding population necessarily migrate to the
same non-breeding site (Webster et al. 2002, Bolker et
al. 2007). Identifying these links be tween breeding
and non-breeding sites is a priority for species conser-
vation, but tracking migrating animals can be difficult.
Large terrestrial species can often be observed or
tracked using extrinsic markers (Rubenstein & Hobson
2004), although this is difficult with smaller, more
vagile species because detectability is low. Tracking
animals in the marine environment is especially chal-
lenging as animals can move across great distances.
Satellite telemetry has the ability to provide real-time
© The authors 2017. 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: b.j.godley@exeter.ac.uk
Satellite tracking and stable isotope analysis
highlight differential recruitment among
foraging areas in green turtles
Phil J. Bradshaw1, Annette C. Broderick1, Carlos Carreras1, 2, Richard Inger1, 3,
Wayne Fuller1,4, Robin Snape1, Kimberley L. Stokes1, Brendan J. Godley1,3,*
1Centre for Ecology and Conservation, University of Exeter, Cornwall Campus, Penryn, TR10 9FE, UK
2Department of Genetics, Microbiology and Statistics and IRBio, University of Barcelona, Av. Diagonal 643, 08028 Barcelona, Spain
3Environmental and Sustainability Institute, University of Exeter, Cornwall Campus, Penryn, TR10 9FE, UK
4Faculty of Veterinary Medicine, Near East University, Nicosia, Mersin 10, N. Cyprus
ABSTRACT: Identifying links between breeding and non-breeding sites in migratory animals is
an important step in understanding their ecology. Recognising the relative importance of foraging
areas and ascertaining site-specific levels of recruitment can provide fundamental and applied
insights. Here, satellite telemetry and the stable isotope ratios (δ13C, δ15N and δ34S) of 230 green
turtles Chelonia mydas from a regionally important rookery in northern Cyprus were employed to
evaluate the relative importance of 4 foraging areas. A preliminary analysis of stable isotope ratios
suggested that a major foraging area had been missed through satellite telemetry as a large pro-
portion of turtles had isotope ratios that did not correspond to sites previously identified. Stable
isotope ratios were then employed to select 5 turtles to be fitted with platform terminal transmit-
ters in 2015. All 5 turtles were subsequently tracked to the same location, Lake Bardawil in Egypt.
Serially collected tissue samples from 45 females, ranging over 2 to 4 breeding seasons, suggested
that foraging site fidelity was very common, with 82% of females exhibiting extremely high tem-
poral consistency in isotope ratios. Quantifying fidelity allowed an evaluation of foraging area -
specific contributions to each breeding cohort over the past 2 decades and demonstrated that
recruitment was unequal among sites, and dynamic over time, with Egypt now currently the major
contributor to the nesting aggregation. This work demonstrates the utility of stable isotope analy-
sis to elucidate the spatial ecology of cryptic taxa and illustrates how more robust baselines can be
assembled against which to measure the success of future marine conservation initiatives.
KEY WORDS: Migration · Foraging · Fidelity · Recruitment · Chelonia mydas · Marine turtles
O
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Mar Ecol Prog Ser 582: 201–214, 2017
insight into animal movements, including the large
seasonal migrations of marine megavertebrates (Hart
& Hyrenbach 2009, Block et al. 2011, Jeffers & Godley
2016), identify stock connectivity (Bonfil et al. 2005,
Heide-Jørgensen & Laidre 2006, Zerbini et al. 2006)
and guide the implementation of marine protected ar-
eas (Scott et al. 2012, Schofield et al. 2013, Revuelta et
al. 2015) and time-area closures (Shillinger et al. 2008).
Nevertheless, satellite telemetry is expensive and can
entail direct costs to the study animals, and therefore,
sample size is often limited (Wilson & McMahon 2006,
Godley et al. 2008). However, the satellite data from a
few individuals can be scaled-up to infer habitat use
at a population level through the use of forensic
chemical techniques such as stable isotope analysis
(Hobson 2007, Jaeger et al. 2010, Zbinden et al. 2011,
Robinson et al. 2016).
Stable isotope analysis (SIA) utilises the stable iso-
tope ratios in the tissue of an animal to evaluate its
resource use and migratory origin (Newsome et al.
2007, Hobson et al. 2010). The isotopic composition of
a consumer’s tissue reflects that of its diet after
undergoing a predictable trophic enrichment (Gra-
ham et al. 2010), providing a natural intrinsic tag that
can link an animal to a location (Hobson 2007). The
time period over which the diet is assimilated de -
pends on the tissue-specific turn-over rates, and
meta bolically active tissues can be selected depend-
ent upon the time frame to be studied (Reich et al.
2008, Hobson et al. 2010). Most studies to date have
employed the stable isotope ratios of carbon (13C:12C
or δ13C) and nitrogen (15N:14N or δ15N) as dietary trac-
ers because these elements are informative about
foraging site location and the trophic level of the con-
sumer (Peterson & Fry 1987, Hobson 1999). Carbon
stable isotopes of a consumer reflect those of the pri-
mary producer as little fractionation occurs through
successive trophic levels (~1‰) (DeNiro & Epstein
1978). More specifically to the marine environment,
δ13C can exhibit several strong spatial gradients re -
lating to mean temperature and salinity because
these factors influence primary production. In gen-
eral, δ13C values increase from higher to lower lati-
tudes, as well as from oceanic to neritic ecosystems,
and from pelagic to benthic food sources (Hobson
2007, Koch 2007). The δ15N of a primary producer can
be strongly influenced by the mode of nitrogen
cycling (Hobson et al. 2010), and substantial trophic
discrimination (~3.4‰) (DeNiro & Epstein 1981) en -
ables assumptions to be drawn concerning the con-
sumer’s trophic level (Hobson & Welch 1992, Godley
et al. 1998). Nitrogen cycling in coastal ecosystems is
strongly influenced by anthropogenic inputs of nitro-
gen. Sources such as agricultural fertilisers and ani-
mal or human waste can elevate nitrate levels, result-
ing in an increase in δ15N of particulate organic mat-
ter that is reflected within the food web (Vander
Zanden et al. 2005, Kendall et al. 2007).
Isotopic tracking at finer regional scales can be
confounded in situations where there is ambiguity in
source isotopic compositions (i.e. multiple geographi-
cally distinct areas share a similar isotopic profile), as
discrete isotopic differences may not exist (Hobson
2007). In these circumstances, an additional isotope
or trace element can be incorporated to supplement
the carbon and nitrogen isotopes and possibly estab-
lish discrete differences among sites. The stable iso-
tope ratio of sulphur (34S:32S or δ34S), for example, is
particularly useful in differentiating be tween inshore
and offshore feeding populations (Barros et al. 2010)
and ontogenetic dietary shifts associated with succes-
sional developmental habitats (Car dona et al. 2009).
Sulphur isotopes make ideal indicators for identifying
the source of primary production as very little trophic
discrimination occurs (Koch 2007). Sulphur is consid-
ered to truly discriminate between neritic and oceanic
ecosystems as phytoplankton and most macroalgae
assimilate marine sulphate and are characterised by
δ34S values of ~21‰ (Cardona et al. 2009). Conversely,
benthic primary producers such as seagrasses have a
lower and more variable δ34S value because 34S from
sulphide-rich sediments is oxidised back to a sulphate
within rhizospheres before being taken up by rooted
plants (Fry et al. 1982, Peterson & Fry 1987, Moncreiff
& Sullivan 2001).
There are some marine isoscapes (spatially explicit
predictions for baseline isotope values) available, but
these are generally of too coarse a scale to infer the
foraging site of a species at a regional level (Hobson
et al. 2010, Somes et al. 2010, McMahon et al. 2013).
Thus, isotopic approaches to infer foraging area are
often validated through the isotopic composition of
satellite-tracked individuals (e.g. Jaeger et al. 2010,
Zbinden et al. 2011, Seminoff et al. 2012) that can
then be used to create species-specific isoscapes,
such as those developed for the loggerhead turtle
Caretta caretta (Ceriani et al. 2014, Vander Zanden
et al. 2015). However, a primary caveat of integrating
SIA with satellite telemetry is the effective time lag
between these techniques; SIA records the isotopic
re gime prior to tissue sampling, whilst satellite tele -
metry tracks the animal after transmitter attachment
(Seminoff et al. 2012). Therefore, it is important to
assess the foraging site fidelity of the study species
before assuming that the isotopic composition of the
tracked animal was assimilated at the finally deter-
202
Bradshaw et al.: Differential recruitment of green turtles
mined foraging area (e.g. Vander Zanden et al. 2010,
Tucker et al. 2014).
Here, we set out to fully categorise the foraging
areas utilised by a green turtle Chelonia mydas pop-
ulation that has been the subject of long-term indi-
vidual-based research (Stokes et al. 2014). Extensive
satellite tracking has identified several key foraging
sites for this population (Godley et al. 2002, Stokes et
al. 2015), and repeat tracking of a small sample sug-
gested that they exhibit fidelity to these sites (Broder-
ick et al. 2007). We specifically set out to address 4
main research aims: (1) to infer the proportion of the
nesting population that forage at each identified site,
(2) to quantify foraging site fidelity among adult
females, (3) to assess recruitment from each foraging
area and (4) to evaluate the effectiveness of stable
isotope ratios in monitoring the relative importance
of foraging areas over time.
MATERIALS AND METHODS
Study site
Alagadi is a double coved beach that stretches
along approximately 2 km of shoreline and is cur-
rently the second largest green turtle nesting area
in Cyprus, and the fifth most important regionally
(Stokes et al. 2015). Long-term monitoring, including
saturation tagging and nest protection, was initiated
at Alagadi in 1992 with a mean ± SD of 16 ± 11.3
females nesting annually (range = 3–30, from 1993 to
2000). Since 2008, the number of females nesting
each year has in creased rapidly to 46.5 ± 2.4 (range =
23–86, 2008 to 2015).
Satellite telemetry
Between 1998 and 2011, 23 Platform Terminal
Transmitters (PTTs; see Supplement 1 at www. int-
res. com/ articles/ suppl/ m582 p201_ supp. pdf) were at -
tached to 21 female green turtles (Godley et al. 2002,
Broderick et al. 2007, Stokes et al. 2015) and 2 males
(Fig. 1; Wright et al. 2012). All PTTs were attached on
nesting beaches in northern Cyprus using standard
protocols set out in Godley et al. (2002), with satellite
data processing and conclusive endpoint destina-
tions as determined by Stokes et al. (2015). Satellite
tracking identified 4 distinct regions as important for-
aging areas for Mediterranean green turtles: (1) sev-
eral sites around Turkey and Cyprus (hereafter
Turkey-Cyprus); (2) the Gulf of Sirte and the Libya−
Tunisia border (hereafter West Libya); (3) the Gulf of
Bomba (in eastern Libya); and (4) Egypt (Fig. 1, Stokes
et al. (2015)). Subsequent to a preliminary analysis of
the stable isotope ratios, we targeted 5 specific
females during the 2015 breeding season for the
attachment of Wildlife Computer SPOT-293A tags
(see Supplement 1).
203
Fig. 1. Post-nesting green turtle satellite tracks from Cyprus to 4 broad-scale foraging areas: Turkey-Cyprus (TC), which
combines several foraging sites clustered around Turkey and Cyprus; West Libya (WL), which combines 2 sites (the Gulf of
Sirte and a site on the Libyan–Tunisian border); the Gulf of Bomba (Bo) in east Libya; and Egypt (Eg), which combines 2 sites
(Gulf of Arab and Lake Bardawil). Light grey tracks: individuals satellite tracked between 1998 and 2011 from Stokes et al.
(2015) and Wright et al. (2012); thick black dashed track: previously unpublished male tracked to southern Cyprus (PTT =
52818); black tracks: individuals satellite tracked in 2015 to Lake Bardawil, Egypt. Numbers indicate how many individuals
were satellite tracked to each foraging area. Pie charts segmented to represent the proportion of individuals assigned to each
foraging area based on their stable isotope composition from the 2015 analysis. The black section of each pie is equal to the
proportion of the 165 turtles assigned to that specific foraging area
Mar Ecol Prog Ser 582: 201–214, 2017
Tissue sample collection
A total of 323 tissue samples were collected from
230 green turtles on Alagadi beach in northern
Cyprus (35° 19’ 56.17” N; 33° 28’ 57.59” E) between
2006 and 2015. Tissue samples were collected from
post-nesting females during the breeding season
(mid-May until end of July), with the exception of 1
male encountered at Alagadi beach still coupled to
the emergent female (the other satellite-tracked
male was not tissue sampled; see Supplement 1). Tis-
sue samples comprising of a small epidermal biopsy
(<0.5 cm2) were taken from the trailing edge of the
fore flipper and stored in 96% ethanol until sample
preparation. All turtles were individually marked
using both external flipper tags and Passive Inte-
grated Transponder (PIT) tags (Stokes et al. 2014).
Stable isotope analysis
We analysed the stable isotopes of carbon, nitrogen
and sulphur from green turtle epidermal tissue sam-
ples (Seminoff et al. 2006, Reich et al. 2008) following
a standard protocol (Ceriani et al. 2014), with the
exception that samples were dried at 60°C for 48 to
72 h. Approximately 0.7 ± 0.1 mg of each sample was
weighed into a tin capsule, sealed and analysed for
carbon and nitrogen. Isotope analysis was performed
at the Stable Isotope Facility of the Environment and
Sustainability Institute (ESI; University of Exeter,
Penryn Campus) via a continuous flow isotope ratio
mass spectrometer (CF-IRMS) using a Sercon Inte-
gra2 stable isotope analyser. A greater sample mass
was required for sulphur isotope analysis, with approx-
imately 5 ± 0.5 mg of sample sealed into a tin capsule
together with a small amount (<1 mg) of vanadium
pentoxide to aid combustion of the larger sample quan-
tity. The analysis of sulphur isotopes was conducted
at Elemtex in Launceston, UK using an ANCA SL
attached to a Sercon 2020 CF-IRMS.
Stable isotope ratios are expressed using a conven-
tional notation as δvalues defined as parts per thou-
sand or per mil (‰) according to the following equa-
tion as per Bond & Hobson (2012):
δX= [(Rsample/Rstandard) − 1] (1)
where Xis 15N, 13Cor34S; Rsample is the corresponding
ratio of the heavier to lighter isotopes (15N:14N; 13C:12C
or 34S:32S); and Rstandard is relative to the international
standards of atmospheric nitrogen, Pee Dee Belemnite
and Vienna Cañon Diablo Trolite, respectively. The
standard deviations of the laboratory refer ence materi-
als among runs for δ15N were 0.18 ‰ for IAEA N1 (δ15N
= +0.4 ‰) and 0.25‰ for IAEA N2 (δ15N = + 0.25‰); for
δ13C they were 0.10 ‰ for IAEA CH6 (δ13C = −10.45‰),
0.16‰ for IAEA Isvec (δ13C = −46.6‰) and 0.19‰ for
IAEA nbs-18 (δ13C = −5.01‰); and for δ34S they were
0.32‰ for IAEA S1 (δ34S = −0.3‰), 0.29 ‰ for IAEA S2
(δ34S = +22.7‰), 0.42 ‰ for USGS 42 (δ34S=+7.8‰)
and 0.26 ‰ for USGS 43 (δ34S = +10.21 ‰).
Selecting samples
Tissue samples were available for some females
over multiple breeding seasons, and these were em-
ployed to quantify foraging site fidelity. However, to
avoid pseudoreplication when inferring foraging area
use at a population scale, we selected a single epider-
mal tissue sample for each turtle. A more de fined cri-
teria was employed for selecting tissue samples for
satellite-tracked turtles to ameliorate the time lag be-
tween satellite telemetry and SIA. For satellite-tracked
turtles, we selected tissue samples using the following
criteria in order of preference: (1) sample mass avail-
able to analyse all 3 isotopes; (2) sample collected dur-
ing the breeding season subsequent to satellite track-
ing, (3) sample collected during the PTT deployment
or (4) sample collected temporally closest to when the
turtle was satellite tracked (see Supplement 1). No tis-
sue samples were available for 4 satellite-tracked tur-
tles, and so these were omitted from this study (see
Supplement 1). When multiple tissue samples were
available for turtles that were not satellite tracked, we
selected the most recent sample available to minimise
any temporal variation in baseline isotopic values that
might occur over long time frames.
Control of possible methodological biases
To evaluate additional sources of variation, we ana -
lysed 20 paired samples to determine if lipid extraction
was necessary (Post et al. 2007). Paired t-tests were
conducted on lipid extracted and non-lipid extracted
samples (see Supplement 2) with no significant dif-
ferences found for δ15N values (paired t-test, t19 =
1.70, p = 0.11; Fig. S2a). Statistically significant dif-
ferences were detected between paired samples for
δ13C (paired t-test, t19 = −4.0, p < 0.001; Fig. S2b), but
the mean difference in δ13C due to lipid extraction
(mean = −0.2 ‰, range = −0.3 to 0.1‰) was judged
biologically irrelevant considering the mean differ-
ence in δ13C among sites (1.7‰). Thus, lipid extrac-
tion was deemed unnecessary.
204
Bradshaw et al.: Differential recruitment of green turtles 205
Some disparity exists within the literature concern-
ing the effect that a >70% ethanol concentration can
have on the isotopic values of stored tissue samples
(Hobson et al. 1997, Tillberg et al. 2006, Barrow et al.
2008, Kaufman et al. 2014). Therefore, as tissue sam-
ples for this study were stored in a 96% ethanol con-
centration, we conducted paired t-tests on tissue sam-
ples collected simultaneously from 33 individuals and
stored in 96% and 70% ethanol concentration for up
to 5 mo (see Supplement 3). We found no significant
differences between samples (δ15N, paired t-test, t32 =
0.67, p = 0.51, Fig. S3a; δ13C values, paired t-test, t32 =
−0.13, p = 0.90, Fig. S3b), meaning that no consistent
enrichment or depletion was observed. Possible sources
of variation in δ34S values were not investigated due
to limitations in tissue sample availability.
Assigning turtles to the foraging areas
Nominal assignment approaches are commonly
used to predict the foraging locations of a population
using stable isotope signatures calibrated from the
satellite telemetry of a subset of individuals (Wunder
2012). We broadly followed previously described
methods (Ceriani et al. 2012, Pajuelo et al. 2012, Van-
der Zanden et al. 2015) to predict foraging area using
a discriminant function analysis (see Supplement 4),
with a secondary classification method as per Zbin -
den et al. (2011) to distinguish between 2 sites that
were isotopically similar.
Investigating foraging site fidelity
Evidence for foraging site fidelity has already been
demonstrated for this population of green turtles
through the repeat satellite tracking of 3 individuals
(Broderick et al. 2007). To further investigate forag-
ing site fidelity among a broader sample, we used
serially collected samples from 45 females, with 33
sampled over 2 seasons, 9 sampled over 3 seasons
and 3 sampled over 4 seasons. Samples for 42 of the
females were collected from consecutive breeding
seasons including all females sampled for >2 sea-
sons. The other 3 females were sampled for 2 breed-
ing seasons, but these were not consecutive as they
were not sampled for a single intermediate breeding
season. To investigate fidelity, we used a repeatabil-
ity analysis to test the temporal consistency in isotope
ratios with the identity of the turtle as the grouping
factor and the predicted foraging area as a covariate
(see Supplement 5).
Evaluating foraging area-specific annual
contributions to the breeding cohort
If foraging site fidelity is typical within the Alagadi
population, then the individually based nesting data
collected at Alagadi since 1992 (Stokes et al. 2014)
can be used to retrospectively evaluate the foraging
area-specific contributions to each breeding cohort
based on the turtle’s unique identification. Although
the analysis is limited to nesting females that were
satellite tracked, or had their foraging area inferred
through SIA, some of these females have nested con-
sistently since 1992, and their unique identity can
therefore be used to gain an insight into foraging
area dynamics prior to the initiation of the tissue sam-
pling regime. For each year that a turtle nested, it
was included as a contributor from its respective for-
aging area, and therefore, some females are repre-
sented in multiple seasons.
RESULTS
A broad range in stable isotope values were found
(δ15N = +2.0‰ to +13.0‰, δ13C = −16.3‰ to −4.9
and δ34S = +0.2 ‰ to + 20.2‰; Fig. 2), and pairwise
comparisons showed all pairs of isotopes to be signif-
icantly correlated (Pearson’s product-moment corre-
lation coefficient, p < 0.001 in all cases, δ13C and
δ15N, r = −0.26; δ15N and δ34S, r = 0.23; δ13C and δ34S,
r = −0.75, see Supplement 6). Turtles tracked to
Bomba exhibited high δ13C values and low δ34S val-
ues, whereas the turtles tracked to Egypt had high
δ15N values compared to the other foraging areas.
Turtles tracked to Turkey-Cyprus and West Libya
were nearly isotopically indistinguishable in terms of
δ13C and δ15N, but individuals from Turkey-Cyprus
exhibited higher δ34S values compared to those from
West Libya, providing isotopic differentiation be -
tween these sites (Figs. 3 & 4).
Inferring foraging area use
The initial composition of the data, validated by the
19 turtles satellite tracked before 2015, strongly sug-
gested that the pre-defined foraging areas did not
fully characterise the isotope ratios of the turtle
population (Fig. 2). Thus, we hypothesised that a for-
aging area had been missed, or under-represented,
through previous satellite tracking effort.
To substantiate this hypothesis, we conducted a
preliminary discriminant analysis using δ13C and δ15N
Mar Ecol Prog Ser 582: 201–214, 2017
values to obtain prediction probabilities for turtles
that might forage in the area not previously charac-
terised. We selected 3 turtles with isotope ratios cor-
responding to this uncalibrated isospace, in addition
to the 19 satellite-tracked turtles, to calibrate a dis-
criminant analysis and predict the putative foraging
area for 181 turtles (see Table S2 in Supplement 4 &
Supplement 7). We then produced a list of 48 turtles
that were likely (at >80% probability) to forage in the
isotopically uncharacterised foraging area (Fig. 2).
This list of 48 turtles was subsequently used during
the 2015 breeding season to select 5 females for PTT
deployment based on their prediction probabilities.
Eight of those 48 turtles nested at Alagadi that sea-
son, of which 6 had a >90% probability of foraging in
the uncharacterised area and were specifically tar-
geted for PTT deployment. On their next successful
nesting attempt, 5 of these 6 turtles were fitted with
PTTs and tracked for 58 to 146 d (mean ± SD = 80.6 ±
37.13; Fig. S1). All 5 turtles were tracked to the same
foraging area, Lake Bardawil in Egypt (Fig. 1; Sup-
plement 1, Fig. S1) where the PTTs then failed, most
likely due to the hypersaline conditions within the
lake as this is a common occurrence among all turtles
tracked to this location (Nada et al. 2013, Kevin Ng
pers. comm.).
Subsequent to the 2015 breeding season, and with
the full isotopic composition of the turtle population
now validated through the satellite telemetry of 23
turtles, we conducted a second discriminant analysis
with the addition of a third stable isotope (δ13C, δ15N
and δ34S). Tissue samples collected in 2015 from not
206
Egypt
Bomba
Tur key Cy p rus
West Libya
Unidentified
G055
2.5
5.0
7.5
10.0
12.5
−10 − 8 6
13C(‰)
15N(‰)
Fig. 2. Bivariate plot of δ13C and δ15N values for green turtles
included in the preliminary discriminant analysis. Crosses
represent the mean ± SD of isotopic values for satellite-
tracked turtles used to calibrate each foraging area. Filled
shapes represent turtles satellite tracked to Bomba (n = 7; cir-
cles), Egypt (n = 2; triangles), Turkey-Cyprus (n = 3; squares)
and West Libya (n = 7; diamonds) along with individuals se-
lected to characterise the unidentified foraging area (n = 3; in-
verted triangles). Open circles: individualsof unknownforag-
ing area (n = 183). Note: One data point removed for greater
graph clarity (δ13C < −14‰). G055 highlighted as an isotopic
mismatch; satellite-tracked to West Libya but exhibited an
isotopic signature corresponding to Bomba
δ15N(‰)
8
12
16
20
10
δ34S (‰)
2468
Fig. 3. Classification of individuals to Turkey-Cyprus or
West Libya from the combined foraging area TCWL based
on the 95% CI of δ34S values of satellite-tracked turtles used
to calibrate Turkey-Cyprus (n = 3; squares) and West Libya
(n = 6; diamonds); dashed lines: upper and lower CI for
Turkey-Cyprus; dotted lines: upper and lower CI for West
Libya; open circles: turtles to be assigned. Individuals within
the grey overlapping region were unassigned (n = 15); indi-
viduals above the grey region were assigned to Turkey-
Cyprus (n = 11), and those below were assigned to West
Libya (n = 19)
5
10
15
20
510
δ
15
N(‰)
δ
34
S (‰)
Fig. 4. δ15N and δ34S values for green turtles predicted to
forage in Bomba (n = 22; filled circles), Egypt (n = 65; trian-
gles), Turkey-Cyprus (n = 11; squares), West Libya (n = 19;
diamonds), or an unassigned location (n = 48; open circles).
Ellipses set at 95% CI (total n = 165)
Bradshaw et al.: Differential recruitment of green turtles
previously sampled females (n = 27) and some alter-
nate tissue samples sourced for some females from
other years (n = 21) were incorporated within this
ana lysis, whilst some females (n = 42) from the pre-
liminary analysis were excluded, as the initial sample
mass was not always adequate to analyse the greater
quantity necessary for sulphur; this simultaneously
excluded 1 turtle (G044) satellite tracked to West
Libya (see Table S2 in Supplement 4). Discrete differ-
ences were found in the combined isotopic values
(MANOVA, Pillai’s trace test, F3, 19 = 6.54, p < 0.001),
yet multiple pairwise comparisons conducted with
Tukey’s honestly significant difference (HSD) still
failed to identify discrete differences among all for-
aging areas (Table 1). For this reason, we combined
the 2 foraging areas that were not discretely differen-
tiated (Turkey-Cyprus and West Libya; TCWL) to
establish discrete isotopic differences among 3 groups
(Bomba, Egypt and TCWL; Table 1). With normal dis-
tributions found for the 3 isotopes, and the variance
among foraging areas homo genous, we employed a
linear discriminant function analysis. We used non-
uniform priors using the number of turtles tracked to
each site from the satellite telemetry (Royle & Ruben-
stein 2004, Vander Zanden et al. 2015) and a poste-
rior probability of assign ment set at 80%. The discrim-
inant analysis was evaluated using the leave-one-out
cross validation method with 95.7% of turtles from
the training data correctly reclassified. The putative
foraging area was predicted for 132 of 165 turtles
(80%), with 45 (27%) assigned to the combined for-
aging area of TCWL.
The 45 turtles assigned to TCWL were then sub-
jected to a secondary classification method based on
their δ34S values because this discriminating criterion
showed the greatest statistical differences among
sites (Table 1). The pooled means and 95% confi-
dence intervals (CI) for the turtles satellite tracked to
each foraging area were used to create an overlap in
which turtles could not be reliably assigned to either
foraging area; these were then included with the tur-
tles unassigned from the discriminant analysis. Those
turtles with a δ34S value greater or lower than the
overlap created by the CI were assigned to Turkey-
Cyprus or West Libya, respectively (Fig. 3). The com-
bination of the 2 nominal assignment approaches
resulted in 11 turtles (7%, n = 165) predicted to for-
age in Turkey-Cyprus, 19 (12%) in West Libya, 65
(39%) in Egypt, 22 (13%) in Bomba and a total of 48
remaining unassigned (29%, 33 from the discrimi-
nant function analysis and 15 from the classification
method) (Figs. 1 & 4, Supplement 8).
Foraging site fidelity
The δ13C and δ15N values were remarkably consis-
tent over multiple seasons with highly significant
repeatability estimates (δ15N: R ± SE = 0.65 ± 0.09,
95% CI = 0.46 − 0.79, p = 0.001, Fig. 5a; δ13C: R =
0.74 ± 0.07, 95% CI = 0.58 − 0.84, p = 0.001, Fig. 5b).
The δ15N values were more variable than δ13C values,
with 65% of samples differing by <1 ‰ and 76.7% by
<1.5 ‰ in subsequent sampling, (always 2 yr apart)
within an overall range in δ15N among these females
of 8.8‰. In contrast, 91.7% of the δ13C values dif-
fered by <1 ‰ and 96.7% by <1.5 ‰ within an overall
range of 6.1 ‰. As carbon isotopes are a more accu-
rate predictor for the source of primary production
(Michener & Kaufman 2007), we contrasted the dif-
ference in δ13C among serially collected samples to
the mean difference in δ13C among sites (1.7 ‰). This
gave us a conservative estimate of 82% of females
(37 of the 45 females) remaining site-faithful as these
individuals did not exhibit a difference >1‰ in δ13C
among seasons. Thus, we assumed that foraging site
fidelity is extremely common within this population.
Only 2 females exhibited differences >1.5 ‰ in δ13C
among seasons (4.5%), suggesting that plasticity in
foraging site fidelity does exist, although it is rela-
tively rare (Fig. 5). An isotopic mismatch was also
noted for 1 turtle satellite tracked to West Libya in
2003 because it had isotopic values more suggestive
of the Gulf of Bomba when tissue was sampled dur-
ing the subsequent breeding season (Fig. 2; turtle
G055). However, this turtle appeared to remain faith-
ful to Bomba for the subsequent 2 interbreeding
intervals.
207
Foraging areas Nitrogen Carbon Sulphur
(a)
Bomba; Egypt < 0.001 0.01 < 0.001
Bomba; Turkey-Cyprus 0.56 <0.001 <0.001
Bomba; West Libya 0.66 <0.001 <0.001
Egypt; Turkey-Cyprus 0.1 0.32 0.04
Egypt; West Libya 0.011 0.36 0.31
Turkey-Cyprus; West Libya 0.98 0.98 0.5
(b)
Bomba; Egypt < 0.001 <0.001 <0.001
Bomba; TCWL 0.32 <0.001 <0.001
Egypt; TCWL < 0.001 0.12 0.045
Table 1. Tukey HSD results comparing stable isotope values
in green turtles among (a) the 4 foraging areas and (b) 3 for-
aging areas. TCWL: Turkey-Cyprus and West Libya com-
bined. Significant p-values (p < 0.05) adjusted for multiple
tests in bold
Mar Ecol Prog Ser 582: 201–214, 2017
Evaluating foraging area-specific annual
contributions to the breeding cohort
Foraging site fidelity being typical within this pop-
ulation, we were able to utilise the individually-
based nesting data collected at the breeding study
site since 1992 (Stokes et al. 2014) retrospectively to
evaluate foraging area specific contributions to each
breeding cohort. We found that the contributions to
the annual breeding cohort from each foraging area
were unequal among years (generalised linear
model, GLM, F3,84 = 8.91, p < 0.001) with a general
biannual variation characteristic among foraging
areas (Fig. 6, Supplement 9). The trends identified for
each foraging area suggest that Bomba was histori-
cally the major contributor to the breeding popula-
tion, but recent trends suggest that there has not
been any substantial increase in the number of
females from this site (Fig. 6a). Egypt, and in particu-
lar Lake Bardawil, may have only contributed a few
individuals to each breeding cohort until 2010, but
then the number of females significantly increased
from this site, with the result that Egypt is presently
the single most important foraging area for the Ala-
gadi rookery (Fig. 6b). The trends for Turkey-Cyprus
and West Libya (Fig. 6c & d, respectively) suggest
that these foraging areas only contribute a few indi-
viduals to each breeding cohort, which is in stark
contrast to the inferred importance of these sites from
the satellite telemetry (Fig. 1).
DISCUSSION
The stable isotope ratios of the study population
have significantly altered our perception of the rela-
tive importance of the 4 foraging areas identified
through satellite telemetry and allowed us to quan-
tify foraging site fidelity and investigate foraging
area dynamics. Based on our experience here, we
advocate the use of SIA prior to, and during, satellite
tracking campaigns and discuss in turn the major
insights that we have gained from the present study.
Selecting the elements for stable isotope analysis
Stable isotopes are now commonly used to track
animal migration across broad spatial scales for
both terrestrial and marine species (Rubenstein &
Hobson 2004, Michener & Kaufman 2007). However,
this study and others (e.g. Tucker et al. 2014) did
208
Fig. 6. Total number of females nesting at Alagadi (1992−
2015) (grey broken line) with the solid black line showing
foraging area-specific contributions to the breeding cohort
from (a) Bomba, (b) Egypt, (c) Turkey-Cyprus and (d) West
Libya
a
0
5
10
15
20
012
012
δ
15
N (‰) differe nces
5.0
7.5
10.0
12.5
b
0
10
20
30
δ
15
C (‰) differe nces
−10
−8
−6
Sample
δ15N (‰)δ13C (‰)
1234
Fig. 5. Temporal consistency of isotopic values for serially col-
lected tissue samples of green turtles over successive breed-
ing seasons (n = 45) for (a) δ15N and (b) δ13C values. Subplots
represent within individual absolute differences among seri-
ally collected samples using the first sample as a re ference.
Filled circles: Bomba; triangles: Egypt; squares: Turkey-
Cyprus; diamonds: West Libya; open circles: unassigned
Bradshaw et al.: Differential recruitment of green turtles 209
not find discrete isotopic differences among all non-
breeding sites at a regional level given that the 2
most geographically separated foraging areas
(Turkey-Cyprus and West Libya) were the most sim-
ilar for δ13C and δ15N. The predictable increase of
δ13C to wards the lower latitudes (Hobson 2007,
Koch 2007) was confounded as our foraging areas
were located on the north and south continental
faces. Without the addition of the sulphur isotope
ratios, we could not have reliably predicted the for-
aging area for a large proportion of turtles within
this population.
The strong negative correlation found between
δ13C and δ34S was previously undescribed among
sea grass habitats and resulted in sulphur being the
more informative for this study population. Sulphur
was specifically selected for this study because
green turtles are thought to feed predominantly on
seagrasses within the Mediterranean (Cardona et
al. 2010) that derive their nutrients from the marine
sediments as opposed to the open ocean environ-
ment. Benthic macroalgae and seagrasses therefore
can vary considerably among sites because the mar-
ine sedimentary cycle (reviewed by Thode 1991)
produces a wide range in δ34S values as the reduc-
tion of seawater sulphate to H2S in shallow sedi-
ments is influenced by rock type and accretion
rates. Thus, we considered that these factors should
produce variable δ34S values at a local level despite
the similarity in habitat type. However, strong intra-
site differences have been found in the δ34S values
of seagrasses attributed to the interaction of particu-
late organic matter and oxygen levels exuded by
seagrass roots (Oakes & Connolly 2004). Seagrass
samples taken only hundreds of metres apart can
have as great a difference in δ34S as samples taken
thousands of kilometres apart (Connolly et al. 2004).
Nevertheless, such variation over small geographic
scales is incorporated within large megavertebrates,
such as green turtles, that forage over tens of square
kilometres (Broderick et al. 2007, Christiansen et al.
2017), and the diet assimilated provided distinct dif-
ferences among distant foraging areas. In a similar
study, Tucker et al. (2014) did not find δ34S in log-
gerhead turtles Caretta caretta to be informative
because the intra-foraging site variation in δ34S val-
ues encompassed a much greater range (11 to 15
at several sites), effectively masking among-site dif-
ferences. However, loggerhead turtles consume a
broader diet over a greater range of depths than
green turtles, and, importantly, they do not neces-
sarily forage in food webs based on benthic primary
production.
Using stable isotope analysis to
target satellite tracking
The application of SIA validated by satellite tele -
metry is almost routine now when evaluating forag-
ing areas (Rubenstein & Hobson 2004, Hobson et al.
2010), but the SIA is commonly conducted subse-
quent to the satellite telemetry. The present study
has effectively demonstrated that SIA conducted
prior to, or during, satellite telemetry campaigns can
greatly augment the study by providing scientific
guidance to identify specific groups of individuals for
PTT attachment and infer the most likely number of
transmitters necessary to identify isotopically dis-
crete foraging sites.
Assigning turtles to their foraging area
The combined results of the nominal assignment
approaches predicted the foraging areas for 71 % of
the turtles sampled. This provided a sample size of
117 turtles, from the 165 analysed, to assess the rela-
tive importance of the foraging areas using SIA, and
this yielded substantially different results from those
inferred from satellite telemetry (Fig. 1). The satellite
telemetry conducted before 2015 inferred that 65%
of green turtles from northern Cyprus were foraging
in Libya (35% in West Libya and 30% in the Gulf of
Bomba), 22% in Turkey-Cyprus and 13% in Egypt
(Wright et al. 2012, Stokes et al. 2015, authors’ un -
publ. data). In contrast, SIA suggested that 25% for-
aged in Libya (12% in West Libya and 13% in the
Gulf of Bomba), 7% in Turkey-Cyprus and 39% in
Egypt but with 29% undetermined. The difference
be tween these 2 techniques arises from several fac-
tors, including the limited sample size associated
with satellite tracking relative to SIA sampling, inter-
annual variations in the relative contributions from
each foraging area and, most importantly in this case,
the recent demographic shift causing an increase in
turtles recruiting from Lake Bardawil. The observed
differences in results from these techniques under-
line the need to conduct SIA, in addition to satellite
tracking, over sufficient time frames to prevent erro-
neous conclusions because both the relative contri-
butions from foraging areas and baseline isotopic
values are dynamic. These techniques should com-
plement each other, as SIA will never be as accurate
as satellite telemetry but satellite telemetry will
rarely incorporate such robust sample sizes. Thus, a
sustained tissue sampling protocol should be sup-
ported by satellite telemetry as resources permit.
Mar Ecol Prog Ser 582: 201–214, 2017
210
Ascertaining foraging site fidelity
This study builds upon a growing body of evidence
that green turtles (Broderick et al. 2007, Vander Zan-
den et al. 2013, Shimada et al. 2014, 2016) and other
marine turtle species (Schofield et al. 2010, Thomson
et al. 2012, Tucker et al. 2014, Pajuelo et al. 2016)
show high levels of fidelity to non-breeding sites.
The ability to isotopically track some individuals for
up to 4 breeding seasons, with a temporal frame of
ap proxi mately 2 to 8 yr, presented clear evidence for
a high degree of fidelity to the pre-defined foraging
areas. We consider our estimate of 82% of females
exhibiting fidelity to be conservative as only 2 fe -
males (4.5%) exhibited substantial differences in
δ13C (>1.5 ‰), which would be more indicative of a
move over a broad spatial scale, considering the
mean difference in δ13C among our sites. However,
plasticity does exist, and Stokes et al. (2015) also
noted evidence from satellite telemetry of secondary
movements after turtles had taken up residency, but
these were also relative exceptions (4 individuals out
of a total of 29 tracked conclusively to foraging
grounds). These movements were generally between
neighbouring foraging sites, and in some cases only
temporary, but this suggests that foraging site fidelity
is not hard-wired and is most likely subject to exter-
nal variables such as resource availability.
Monitoring foraging site contribution over time
Significant temporal change in the number of indi-
viduals originating from foraging areas can be in -
formative of foraging area dynamics without the need
to conduct site-based surveys. In this case, the forag-
ing area-specific trends in the annual contribution to
each nesting cohort clearly demonstrate that the in-
crease in the number of females nesting at Alagadi is
primarily being driven by the recruitment of turtles
that forage in Egypt (Lake Bardawil). Several alterna-
tive and not mutually exclusive drivers could result in
such a foraging area-specific increase in re cruitment.
These include (1) an increase in the survival probabili-
ties of juveniles and sub-adults as industrial fisheries
are excluded (Casale 2011, Casale & Heppell 2016), (2)
greater foraging re sources reducing the age to sexual
maturity (Bjorndal et al. 2013), (3) temporal oscillations
in sea surface currents, such as those dictated by the
Cyprus eddy (Zodiatis et al. 2005), that can vary the
distribution of pelagic-stage juveniles and thus the
number of individuals recruiting to each foraging
area (Gaspar et al. 2012, Scott et al. 2014, 2017) or (4) a
shift in the ecological conditions within Lake Bardawil
so that this site now provides a more suitable foraging
resource (El-Bana et al. 2002, Abd Ellah & Hussein
2009, Nada et al. 2013). To expand on this latter hypo -
thesis, evidence suggests that the reopening and on-
going maintenance of the 2 man-made channels in
the western and central part of the lake have signifi-
cantly reduced salinity levels (1970: 100‰, 2012:
46.1‰) (Abd Ellah & Hussein 2009, Nada et al. 2013
and references therein) and allowed Cymo docea no-
dosa, the primary dietary item of the green turtle
within the Mediterranean (Cardona 2010), to colonise
and now dominate the shallow western basin (El-
Bana et al. 2002, Abd Ellah & Hussein 2009). Further
corroboration from satellite tele metry indicates that
the turtles predominately re main within the beds of C.
nodosa (Nada et al. 2013) which suggests that they
might not forage on the Ruppia Cirrhosa that forms
monospecific habitats within the eastern basin, and
the only seagrass re corded within the lake prior to the
reopening of the channels in 1988 (Lipkin 1977, El-
Bana et al. 2002, Abd Ellah & Hussein 2009). Therefore,
Lake Bardawil might provide a new foraging location
as high salinity levels and inadequate forage may
have previously precluded green turtles from this site.
The knowledge that a high proportion of recruits
are originating from a single site is a critical develop-
ment in our understanding of foraging area dynamics.
At present, the conservation efforts undertaken on
the beaches of northern Cyprus have been effective in
increasing the number of hatchlings reaching the wa-
ter (Stokes et al. 2014) with a possible rise in the num-
ber of juveniles reaching a reproductive age. How-
ever, ensuring that the current trends in recruitment
continue may largely depend on the adequate protec-
tion of the turtles foraging within Lake Barda wil, al-
though this might be challenging as some human
turtle conflict has been reported as turtle abundance
increases (Nada et al. 2013). Therefore, stakeholder
discussions and international co-operation are neces-
sary to protect turtles foraging in Egypt in addition to
those from the key recognised sites in Libya, namely
the Gulf of Bomba and the Gulf of Sirte (Casale 2011,
Stokes et al. 2015, Casale & Heppell 2016).
CONCLUSIONS
Through the analysis of stable isotopes calibrated
by satellite telemetry, we have answered several
important questions for the conservation of marine
turtles (see Hamann et al. 2010, Rees et al. 2016).
These include identifying and assessing the relative
Bradshaw et al.: Differential recruitment of green turtles
importance of all major foraging sites utilised by
green turtles nesting at Alagadi, quantifying forag-
ing site fidelity and gaining a critical insight into for-
aging area dynamics.
This work builds upon a detailed, long-term moni-
toring programme following a marked population
(e.g. Broderick et al. 2001, 2003, Stokes et al. 2014,
2015) that emphasises the true value that such indi-
vidual-based data can provide. The long-term nesting
data used to evaluate the annual contributions to the
rookery from each foraging area for >2 decades was
pivotal in identifying the substantial increase in the
number of turtles that forage in Lake Bardawil. In turn,
this insight emphasises the importance of Cymodocea
nodosa for this species and the necessity of protecting
these scarce habitats to ensure the long-term viability
of green turtles within the Mediterranean.
We stress the importance of having a balanced
satellite telemetry campaign, supported by long-term
SIA, as contributions from foraging areas to the breed-
ing cohort are unequal among years, and impor-
tantly, these proportions can shift dynamically over
time. These data can provide essential baseline evi-
dence to advise and monitor marine conservation
efforts such as establishing marine protected areas,
formulating site-specific management plans and in -
creasing international cooperation through the iden-
tification of important migratory links. A caveat to
this type of foraging area assessment is that males
are poorly represented. Evidence suggests that some
foraging areas can be highly female biased, reflect-
ing primary sex ratios (Jensen et al. 2016), and tar-
geted efforts are needed to collect more tissue sam-
ples from males. Future research will evaluate the
reasons for the substantial shift in the relative impor-
tance of these foraging areas and the root cause(s) for
the increase in recruitment from Lake Bardawil.
Acknowledgements. P.J.B. is funded by a National Environ-
ment Research Council grant 1353865. C.C. is supported by
the project CTM2013-48163 from Ministerio de Economía y
Competitividad. We thank the following for their support:
Marine Turtle Conservation Project (MTCP), Mediterranean
Association to Save the Sea Turtles (MEDASSSET), North
Cyprus Department of Environmental Protection, seaturtle.
org, Society for the Protection of Sea Turtles in North Cyprus
(SPOT); and for funding: Apache, British Chelonia Group,
BP Egypt, the British High Commission and British Residents
Society of North Cyprus, Darwin Initiative, Erwin Warth
Foundation, Friends of SPOT, INNPA, Kuzey Kıbrıs Turk-
cell, NERC, Marine Conservation Society Sea Turtle Conser-
vation Fund, and MEDASSET, UK. We also wish to thank E.
Duncan and L. Omeyer and the numerous MTCP volunteers
for their tireless efforts during fieldwork. Finally, we thank
the editor and 4 anonymous reviewers for their insightful
comments that have significantly improved this manuscript.
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Editorial responsibility: Keith Hobson,
London, Ontario, Canada
Submitted: February 14, 2017; Accepted: August 3, 2017
Proofs received from author(s): October 22, 2017
... Incidental capture is highly variable within the Mediterranean (Casale, 2011) and likely impacts nesting aggregations differently based on the genetic origin of individuals within each of the various fishing areas. For example, bycatch (Casale et al., 2010a;Casale, 2011;Nada and Casale, 2011;Turkozan et al., 2018) and intentional killing for meat (Nada and Casale, 2011) result in high sea turtle mortality in the Adriatic Sea, Egypt, the Tunisian Plateau and Turkey: all areas hosting foraging grounds for loggerhead (Bertuccio et al., 2019;Haywood et al., 2020b;Snape et al., 2016) and green turtles nesting in Cyprus (Bradshaw et al., 2017;Stokes et al., 2015) and for other populations and life stages . North Cyprus itself has high bycatch rates (Casale, 2011;Snape et al., 2016), and it appears that small juvenile green turtles, likely from mixed stocks, are heavily impacted in the local area. ...
... North Cyprus itself has high bycatch rates (Casale, 2011;Snape et al., 2016), and it appears that small juvenile green turtles, likely from mixed stocks, are heavily impacted in the local area. Only a few subadults and nonbreeding adults of this species are observed in the local small-scale fisheries (Snape et al., 2013) or use foraging sites in Cyprus despite their proximity (Bradshaw et al., 2017;Stokes et al., 2015). Differential bycatch among foraging areas may also explain differential rates of recruitment among foraging areas (Bradshaw et al., 2017). ...
... Only a few subadults and nonbreeding adults of this species are observed in the local small-scale fisheries (Snape et al., 2013) or use foraging sites in Cyprus despite their proximity (Bradshaw et al., 2017;Stokes et al., 2015). Differential bycatch among foraging areas may also explain differential rates of recruitment among foraging areas (Bradshaw et al., 2017). ...
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Estimating life‐history traits and understanding their variation underpins the management of long‐lived, migratory animals, while knowledge of recovery dynamics can inform the management of conservation‐dependent species. Using a combination of nest counts and individual‐based life‐history data collected since 1993, we explore the drivers underlying contrasting population recovery rates of sympatrically nesting loggerhead (Caretta caretta) and green (Chelonia mydas) turtles in North Cyprus. We found that nest counts of loggerhead and green turtles from 28 beaches across the island increased by 46% and 162%, respectively over the past 27 years. A Bayesian state‐space model revealed that, at our individual‐based monitoring site, nesting of green turtles increased annually at four times the rate of that of loggerhead turtles. Furthermore, we found that loggerhead turtles nesting at the individual‐based monitoring site had stable reproductive parameters and average adult survival for the species and are the smallest breeding adults globally. Based on results from multiple matrix model scenarios, we propose that higher mortality rates of individuals in all age classes (likely driven by differences in life history and interaction with fisheries), rather than low reproductive output, are impeding the recovery of this species. While the increase in green turtles is encouraging, the Mediterranean population is estimated to have around 3,400 adults and is restricted to the Eastern Basin. The recovery of loggerhead turtles is likely to be compromised until mortality rates in the region are adequately quantified and mitigated. As survival of immature individuals is a powerful driver for sea turtle population numbers, additional efforts should target management at pelagic and neritic foraging areas. Understanding threats faced by immature life stages is crucial to accurately parameterise population models and to target conservation actions for long‐lived marine vertebrates. This paper uses 27 years of data to investigate the long‐term population trends of green and loggerhead turtles in North Cyprus and explores drivers of the different recovery patterns observed for the two species. Nest counts of loggerhead and green turtles from 28 beaches increased by 46% and 162%, respectively. A Bayesian state space model revealed that, at the individual‐based monitoring site, the number of nesting green turtles increased annually at four times the rate of that of loggerhead turtles. Based on results from multiple matrix model scenarios, we propose that higher mortality rates of individuals in all age‐classes (likely driven by greater interaction with fisheries), rather than low reproductive output, are impeding the recovery of loggerhead turtles. The species’ recovery is likely to be compromised until mortality rates in the region are adequately quantified and mitigated. Photo credit: Tevfik Camgoz
... On the other hand, hawksbill turtles inhabiting the Eastern Pacific are confined to this area, and no foreign rookery is known to contribute to any of the coastal foraging grounds along the Eastern Pacific (Gaos et al. 2017a(Gaos et al. , b, 2018. Despite persistent migratory behavior, sea turtles demonstrate fidelity to foraging habitats established during early neritic recruitment stages (Shimada et al. 2016;Bradshaw et al. 2017;Conrad et al. 2018;Hancock et al. 2018). Therefore, quality and sustainability of foraging habitats may be related to future reproductive output and recovery of endangered sea turtles in the Eastern Pacific (Harrison et al. 2011). ...
... Further, these models inform on temporal dietary switching, from historical diets (Conrad et al. 2018) to ontogenetic shifts (Reich et al. 2007;Vander Zanden et al. 2013;Howell et al. 2016;Burgett et al. 2018;Ferreira et al. 2018) to annual or seasonal patterns (Páez-Rosas et al. 2021). Geographically, stable isotope analysis has allowed scientists to link diet and foraging to specific and sometimes undiscovered habitats important to sea turtles (Lemons et al. 2011;Bradshaw et al. 2017;Hancock et al. 2018;Tomaszewicz et al. 2018;Fukuoka et al. 2019;Piovano et al. 2020) in addition to relating diet to oceanographic parameters such as sea surface temperature (Esteban et al. 2020). Finally, controlled studies have investigated isotopic incorporation rates and discrimination values in green turtles Vander Zanden et al. 2012). ...
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We used stable isotopes to investigate isotopic niche size, overlap, and diet composition in green (black and yellow morphotype Chelonia mydas; 50.0 to 95.0 cm curved carapace length, CCL) and hawksbill turtles (Eretmochelys imbricata; 38.5 to 83.0 cm CCL) in a recently described foraging habitat in North Pacific Costa Rica. We measured whole blood stable carbon (δ¹³C) and nitrogen (δ¹⁵N) ratios in black (n = 39; mean ± SD, − 16.54 ± 0.66‰ and 14.39 ± 0.77‰), yellow (n = 13; − 15.74 ± 0.65‰ and 12.37 ± 0.55‰) and hawksbill turtles (n = 13; − 16.23 ± 1.34‰ and 12.63 ± 0.32‰) and skin δ¹³C and δ¹⁵N values in black (n = 36; − 15.32 ± 0.79‰ and 15.16 ± 0.72‰), yellow (n = 12; − 15.38 ± 0.91‰ and 13.78 ± 0.75‰) and hawksbill turtles (n = 10; − 14.33 ± 1.49‰ and 13.77 ± 0.29‰). Isotopic niche space revealed distinctly higher δ¹⁵N area in black turtles and significant overlap between yellow and hawksbill turtles, and a recent shift in diet in yellow turtles from omnivory to herbivory. In black turtles, isotopic niche suggests individual specialization during the non-upwelling season and generalization in diet during the upwelling season. Mixing model results suggest that black turtles forage at multiple trophic levels (fish: 34.8 ± 10.1% of diet and macroalgae: 51.8 ± 12.8% of diet), while yellow and hawksbill turtles primarily forage on macroalgae (85.0 ± 6.6% in yellow turtles and 85.1 ± 5.9% in hawksbill turtles). These results add to a growing understanding that diet in sea turtles is influenced by diet items present in the environment and suggest that black turtles are potential tertiary consumers.
... The sequences were aligned using the BioEdit 7.2.6 program ClustalW multiple alignments (Thompson et al. 1994). The mtSTR haplotypes were coded as the number of repeats of each of the four mtSTRs, as described in previous studies (Tikochinski et al. 2012(Tikochinski et al. , 2018Bradshaw et al. 2017). ...
... These shared STRs are not found in combination with the CM-A13.1 in the Atlantic, but the similarity of haplotypes (in a phylogeographic context) suggests that the Mediterranean populations were originated by Atlantic colonizers as happened with other species (e.g., Caretta caretta). The only CR haplotypes known to occur in both regions are CM-A13.1 and CM-A27.1 (Encalada et al. 1996;Bagda et al. 2012), which was attributed to a limited gene flow over an ecological time scale (Bradshaw et al. 2017). ...
Article
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The Mediterranean green turtle regional management unit is one of the 17 management units of green turtles considered a global conservation priority. However, previous studies using different genetic markers revealed very little diversity and differentiation across populations due to the overdominance of one haplotype (CM-A13) in the Mediterranean. We, therefore, used a more informative marker, mitochondrial short tandem repeats (mtSTRs), in 431 samples collected along the eastern Mediterranean coasts of Turkey and Northern Cyprus. In addition, we added the mtSTR haplotypes of previous studies and reached a total of 980 samples covering 12 nesting beaches (almost 100% of the populations in the region). We identified 42 haplotypes, 4 of which were recorded for the first time in the region. The species has a genetic diversity in the region higher than previously thought, ranging from 0.54 (Sugözü, Turkey) to 0.934 (Israel) and with the most common haplotypes being 6-8-8–4 (26.5%), 6-8-5-4 (17.3%), and 6-8-6-4 (14.9%). The analysis of a more extensive data set of mtSTRs supported recognizing at least three management units in the Mediterranean. Furthermore, we used the new data to assess the origin of the turtles foraging in Israel. We determined that Samandağ (Turkey) was the population of origin of most of the individuals. Overall, we show that mtSTRs highly improve the resolution to detect population structuring and source for this species and region.
... In addition to the potential effects of shifting juvenile growth rates, these changes in loggerhead nester size through time may reflect differences in foraging area inputs to the nesting population. Females leaving a rookery after nesting transition to different foraging locations (Seminoff et al. 2008, Zbinden et al. 2011, Vander Zanden et al. 2014, Ceriani et al. 2015, Bradshaw et al. 2017, Phillips et al. 2021; this variation in foraging site selection may influence resource availability and subsequent growth potential, with females from some foraging areas significantly larger than females from other areas occupying the same nesting habitat (Zbinden et al. 2011, Vander Zanden et al. 2014, Ceriani et al. 2015. Reproductively active females exhibit fidelity to foraging sites between breeding seasons (Broderick et al. 2007, Phillips et al. 2021. ...
... Changes at foraging areas may have lagged effects that significantly alter the composition of a nesting population. For example, Bradshaw et al. (2017) found a significant change in the contribution of different foraging areas to a Mediterranean green turtle nesting population over 23 yr. Nesting assemblages dispersing to a variety of foraging areas may experience a broader range of food quality and availability. ...
Article
Full-text available
For species reaching maturity at a range of ages or sizes, factors that influence juvenile growth and size at maturity may have lasting impacts on overall fitness. Assessing when animals reach maturity is especially challenging for species which are difficult to follow through time as a result of highly migratory behavior, long life spans, or both. We examined nesting female size in a reproductive assemblage of green turtles (Chelonia mydas) and loggerheads (Caretta caretta) on the east coast of Florida, USA. We used a long‐term dataset from 1982 to 2019 to estimate a minimum size at maturity interval on the basis of two standard deviations below mean female size for each species. The minimum size intervals for green turtles (81.4–89.3 cm) and loggerheads (68.1–79.1 cm) were lower than most previous estimates in the literature, many of which were simply the smallest individual ever observed. There was a significant decrease in the upper bound of the minimum size interval over the study period for both green turtles (1.6 cm) and loggerheads (4.1 cm). These shifts in size at maturity may be the result of changes in population demographics, habitat quality, and behavioral reactions to these changes. The development and periodic reassessment of robust estimators of maturity are an important part of programs centered around the monitoring and conservation of vulnerable wildlife populations.
... Nesting sites may host turtles that originate from multiple foraging areas (Limpus et al. 1992;Bjorndal et al. 2005), and foraging areas may be home to turtles from multiple nesting sites (Casale et al. 2008;Carreras et al. 2011). The linkages between foraging areas and nesting sites have been documented via genetic studies (Bjorndal et al. 2005;Bowen and Karl 2007), stable isotope studies (Bradshaw et al. 2017), and satellite tracking (Schofield et al 2013;Hays et al. 2014). Understanding this connectivity between nesting sites and foraging areas is a key component of conservation planning as it allows for the development of effective conservation agendas (Martin et al. 2007;Hamann et al. 2010;Hays et al. 2014;Dunn et al. 2019). ...
... Nesting sites can host sea turtles from multiple distant foraging areas (Luschi et al. 1996;Bolker et al. 2007;Bradshaw et al. 2017) and our findings, combined with past tracking efforts (Rees et al. 2012) and historical flipper tag returns (Salm et al. 1993), along with recent tracking of 45 foraging turtles from 2 foraging areas in the United Arab Emirates (Pilcher et al. 2020) reveal that Ras Al Hadd is a regionally important nesting site for green turtles. ...
Article
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There is limited information on postnesting dispersal of green turtles Chelonia mydas from nesting sites in the Arabian region. Understanding habitat connectivity can promote effective conservation programs across a wider range of critical sea turtle habitats. We present postnesting migration data for 9 green turtles departing from Ras Al Hadd in Oman, one of the largest and most important nesting sites for this species in the northwest Indian Ocean (NWIO). Turtles migrated to Eritrea (n = 1), India (n = 2), Oman (n = 4), and the United Arab Emirates (n = 2), demonstrating connectivity for this species across the NWIO and linkages to known green turtle foraging areas. Turtles used deep international waters of the NWIO, and coastal waters of Eritrea, India, Iran, Oman, Pakistan, Saudi Arabia, and Yemen. Alongside the potential for fisheries bycatch, ghost fishing, entanglement, and direct take in waters near nesting sites and at the dispersal destinations, these movement patterns reveal a need for coordinated efforts to address sea turtle mortality in fisheries at a regional level. These data enhance our knowledge of sea turtle distribution and connectivity in the Arabian region and will contribute to ongoing efforts to conserve sea turtles in the NWIO.
... While researchers have applied this method to understand many facets of an animal's trophic ecology, including interspecific competition and dietary response to pollutants , DNA metabarcoding is hindered by its inability to provide accurate estimates of prey count or biomass (Piñol et al. 2015;Jusino et al. 2019) due to the biases inherent to DNA amplification and sequencing . Therefore, when researchers require quantitative information about an animal's trophic ecology, they frequently turn to stable isotope analysis, a laboratory method which quantifies the ratio of elemental isotopes found in consumer tissues to determine many facets of an animal's trophic niche, such as their basal nutrient source (DeNiro and Epstein 1978), their trophic level (Wassenaar 2019), and even the areas where these animals forage (Bradshaw et al. 2017). Following the principle, "you are what you eat, plus a few per mille" (DeNiro 1976), researchers can also use stable isotope mixing models (Phillips 2012), which compare predator and prey isotopic values, to determine the relative contribution of prey-derived elements, and as a result, the relative contribution of prey to the diets of their consumers. ...
Article
The introduction of laboratory methods to animal dietary studies has allowed researchers to obtain results with accuracy and precision not possible with observational techniques. For example, DNA barcoding, or the identification of prey with taxon-specific DNA sequences, allows researchers to classify digested prey tissues to the species-level, while stable isotope analysis paired with Bayesian mixing models can quantify dietary contributions by comparing a consumer's isotopic values to those derived from their prey. However, DNA-based methods are currently only able to classify, but not quantify, the taxa present in a diet sample, while stable isotope analysis can only quantify dietary taxa that are identified a priori as prey isotopic values are a result of life history traits, not phylogenetic relatedness. Recently, researchers have begun to couple these techniques in dietary studies to capitalize on the reciprocal benefits and drawbacks offered by each approach, with some even integrating DNA-based results directly into Bayesian mixing models as informative priors. As the informative priors used in these models must represent known dietary compositions (e.g., percentages of prey biomasses), researchers have scaled the DNA-based frequency of occurrence of major prey groups so that their normalized frequency of occurrence sums to 100%. Unfortunately, such an approach is problematic as priors stemming from binomial, DNA-based data do not truly reflect quantitative information about the consumer's diet and may skew the posterior distribution of prey quantities as a result. Therefore, we present a novel approach to incorporate DNA-based dietary information into Bayesian stable isotope mixing models that preserves the binomial nature of DNA-based results. This approach uses community-wide frequency of occurrence or logistic regression-based estimates of prey occurrence to dictate the probability that each prey group is included in each mixing model iteration, and, in turn, the probability that each iteration's results are included in the posterior distribution of prey composition possibilities. Here, we demonstrate the utility of this method by using it to quantify the prey composition of nestling Louisiana waterthrush (Parkesia motacilla).
... physical contact between individuals)(Lavelle et al., 2012;Moll et al., 2007). Likewise, combining animal tracking with stable isotope or DNA metabarcoding data can help uncover how foraging decisions may influence patterns of movement and space useBradshaw et al., 2017;Votier et al., 2010). To date, studies of such sophistication have predominantly focused on individuals of a single species (see Section 9), but multi-species extensions represent an exciting avenue for future research. ...
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1. Individual decisions regarding how, why, and when organisms interact with one another and with their environment scale up to shape patterns and processes in communities. Recent evidence has firmly established the prevalence of intraspecific variation in nature and its relevance in community ecology, yet challenges associated with collecting data on large numbers of individual conspecifics and heterospecifics has hampered integration of individual variation into community ecology. 2. Nevertheless, recent technological and statistical advances in GPS‐tracking, remote sensing, and behavioral ecology offer a toolbox for integrating intraspecific variation into community processes. More than simply describing where organisms go, movement data provide unique information about interactions and environmental associations from which a true individual‐to‐community framework can be built. 3. By linking the movement paths of both conspecifics and heterospecifics with environmental data, ecologists can now simultaneously quantify intra‐ and interspecific variation regarding the Eltonian (biotic interactions) and Grinnellian (environmental conditions) factors underpinning community assemblage and dynamics, yet substantial logistical and analytical challenges must be addressed for these approaches to realize their full potential. 4. Across communities, empirical integration of Eltonian and Grinnellian factors can support conservation applications and reveal metacommunity dynamics via tracking‐based dispersal data. As the logistical and analytical challenges associated with multi‐species tracking are surmounted, we envision a future where individual movements and their ecological and environmental signatures will bring resolution to many enduring issues in community ecology.
... betweenness, centrality, closeness, graph theory, marine turtle, migratory, satellite telemetry, tracking collected with capture-mark-recapture, tracking, genetic, and stable isotope analytical methods (Bradshaw et al., 2017;Ceriani et al., 2017;Godley et al., 2010;Nishizawa et al., 2018;Rees et al., 2017). ...
Article
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Aim Understanding the spatial ecology of animal movements is a critical element in conserving long‐lived, highly mobile marine species. Analyzing networks developed from movements of six sea turtle species reveals marine connectivity and can help prioritize conservation efforts. Location Global. Methods We collated telemetry data from 1235 individuals and reviewed the literature to determine our dataset's representativeness. We used the telemetry data to develop spatial networks at different scales to examine areas, connections, and their geographic arrangement. We used graph theory metrics to compare networks across regions and species and to identify the role of important areas and connections. Results Relevant literature and citations for data used in this study had very little overlap. Network analysis showed that sampling effort influenced network structure, and the arrangement of areas and connections for most networks was complex. However, important areas and connections identified by graph theory metrics can be different than areas of high data density. For the global network, marine regions in the Mediterranean had high closeness, while links with high betweenness among marine regions in the South Atlantic were critical for maintaining connectivity. Comparisons among species‐specific networks showed that functional connectivity was related to movement ecology, resulting in networks composed of different areas and links. Main conclusions Network analysis identified the structure and functional connectivity of the sea turtles in our sample at multiple scales. These network characteristics could help guide the coordination of management strategies for wide‐ranging animals throughout their geographic extent. Most networks had complex structures that can contribute to greater robustness but may be more difficult to manage changes when compared to simpler forms. Area‐based conservation measures would benefit sea turtle populations when directed toward areas with high closeness dominating network function. Promoting seascape connectivity of links with high betweenness would decrease network vulnerability.
... In recent years, satellite tracking of marine turtle movements between their nesting sites and foraging areas has elucidated much of the previously unknown biology and habitat use (Godley et al., 2002(Godley et al., , 2008Luschi et al., 2003;Blumenthal et al., 2006;Hart et al., 2013Hart et al., , 2017Luschi and Casale, 2014;Pilcher et al., 2014Pilcher et al., , 2021aRees et al., 2016;Ferreira et al., 2020), including bi-directional movements (Limpus and Limpus, 2001;Pilcher et al., 2020). It is now well established that post-nesting turtles which used a particular nesting location return to several different foraging areas (Luschi et al., 1996;Bjorndal et al., 2005;Blumenthal et al., 2006;Bolker et al., 2007;Bradshaw et al., 2017;Shimada et al., 2020). Conversely, turtles foraging in one general habitat disperse to several different nesting locations (i.e., mixed stock) (Limpus et al., 1992;Dethmers et al., 2010;Carreras et al., 2011;Jensen et al., 2013;Read et al., 2015;Dutton et al., 2018). ...
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... Stable isotope studies of modern mediterranean sea turtles have provided information on which foraging grounds are utilised. By satellite tracking green (Bradshaw et al., 2017) and loggerhead (Haywood et al., 2020) turtles from the Levant; nitrogen, carbon and sulphur stable isotope profiles of several distinct foraging locations were identified, other turtles were then able to be assigned to these foraging areas using bayesian statistical methods. Stable isotopes of ancient remains could confirm if sea turtles are still foraging in the same areas, and therefore provide a tangible indication of how important specific areas are to sea turtle conservation currently and in the future. ...
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Stable isotope ratio variation in natural systems reflects the dynamics of Earth systems processes and imparts isotope labels to Earth materials. Carbon isotope ratios of atmospheric CO2 record exchange of carbon between the biosphere and the atmosphere; the incredible journeys of migrating monarchs is documented by hydrogen isotopes in their wings; and water carries an isotopic record of its source and history as it traverses the atmosphere and land surface. Through these and many other examples, improved understanding of spatio-temporal isotopic variation in Earth systems is leading to innovative new approaches to scientific problem-solving. This volume provides a comprehensive overview of the theory, methods, and applications that are enabling new disciplinary and cross-disciplinary advances through the study of "isoscapes": isotopic landscapes. "This impressive new volume shows scientists deciphering and using the natural isotope landscapes that subtly adorn our spaceship Earth." Brian Fry, Coastal Ecology Institute, Louisiana State University, USA "An excellent timely must read and must-have reference book for anybody interested or engaged in applying stable isotope signatures to questions in e.g. Anthropology, Biogeochemistry, Ecology, or Forensic Science regarding chronological and spatial movement, changes, or distribution relating to animals, humans, plants, or water." Wolfram Meier-Augenstein, Centre for Anatomy & Human Identification, University of Dundee, UK "Natural resources are being affected by global change, but exactly where, how, and at what pace? Isoscapes provide new and remarkably precise answers." John Hayes, Woods Hole Oceanographic Institution, USA "This exciting volume is shaping a new landscape in environmental sciences that is utilizing the remarkable advances in isotope research to enhance and extend the capabilities of the field." Dan Yakir, Weizmann Institute of Science, Israel.
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