ORIGINAL RESEARCH
published: 09 April 2020
doi: 10.3389/fmars.2020.00208
Edited by:
Michael Paul Jensen,
Southwest Fisheries Science Center
(NOAA), United States
Reviewed by:
Mariana M. P. B. Fuentes,
Florida State University, United States
Antonios D. Mazaris,
Aristotle University of Thessaloniki,
Greece
*Correspondence:
Autumn R. Iverson
ariverson@ucdavis.edu
Specialty section:
This article was submitted to
Marine Megafauna,
a section of the journal
Frontiers in Marine Science
Received: 22 November 2019
Accepted: 17 March 2020
Published: 09 April 2020
Citation:
Iverson AR, Benscoter AM,
Fujisaki I, Lamont MM and Hart KM
(2020) Migration Corridors
and Threats in the Gulf of Mexico
and Florida Straits for Loggerhead
Sea Turtles. Front. Mar. Sci. 7:208.
doi: 10.3389/fmars.2020.00208
Migration Corridors and Threats in
the Gulf of Mexico and Florida Straits
for Loggerhead Sea Turtles
Autumn R. Iverson1* , Allison M. Benscoter2, Ikuko Fujisaki3, Margaret M. Lamont4 and
Kristen M. Hart2
1 Cherokee Nation Technologies, Contracted to Wetland and Aquatic Research Center, U.S. Geological Survey, Davie, FL,
United States, 2 Wetland and Aquatic Research Center, U.S. Geological Survey, Davie, FL, United States, 3 Fort Lauderdale
Research and Education Center, University of Florida, Davie, FL, United States, 4 Wetland and Aquatic Research Center, U.S.
Geological Survey, Gainesville, FL, United States
Along migration corridors, animals can face natural and anthropogenic threats that differ
from those in breeding and non-breeding residence areas. Satellite telemetry can aid in
describing the timing and location of these migrations. We use this tool with switching
state-space modeling and line kernel density estimates to identify migration corridors
of post-nesting adult female loggerhead sea turtles (Caretta caretta, n = 89 tracks) that
nested at five beaches in the Gulf of Mexico. Turtles migrated in both neritic and oceanic
areas of the Gulf of Mexico with some exiting the Gulf. High-use migration corridors were
found in neritic areas to the west of Florida and also in the Florida Straits. Repeat tracking
of post-nesting migrations for eight turtles showed variability in track overlap, ranging
from ∼13 to 82% of tracks within 10 km of each other. Migration primarily occurred in
July and August. We document the longest known post-nesting migration to-date of a
wild adult female loggerhead of >4,300 km, along with an apparent stopover of about
1 month. Migration corridors overlaid on three spatially explicit anthropogenic threats
(shipping density, commercial line fishing, and shrimp trawling) showed hotspots in the
Florida Straits, off the northwest Florida coast and off the coast of Tampa Bay. Identifying
where and at what intensity multiple human activities and natural processes most likely
occur is a key goal of Cumulative Effects Assessments. Our results provide the scientific
information needed for designing management strategies for this threatened species.
Information about this loggerhead migration corridor can also be used to inform adaptive
management as threats shift over time.
Keywords: anthropogenic threats, Caretta caretta, Gulf of Mexico, loggerhead, migration corridors, satellite
tracking, sea turtle, switching state-space modeling
INTRODUCTION
Within migrating species there exists a large variety of migratory behavior. This can include
nomadic migration where species move long distances to take advantage of irregular or ephemeral
resources (e.g., banded stilt, Cladorhynchus leucocephalus; Pedler et al., 2014), one-way migration in
which there is no return to the starting point (e.g., European corn borer moth on pre-reproductive
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Loggerhead Migration Corridors and Threats
migration, Ostrinea nubilalis; Dingle,
2014) and multi-
generational migration where
the migratory path
takes
multiple generations to complete (e.g., monarch butterfly,
Danaus plexippus; Dingle, 2014). Perhaps the most well-known
type of migration is seasonal migration, in which species
travel seasonally between spatially separate breeding and non-
breeding grounds. Seasonal migration is thought to increase
fitness through the escape of deteriorating environmental
conditions such as temperature extremes or predators, and/or
a gain in energy or reproductive success (Alerstam et al., 2003;
Dingle, 2014).
Migration corridors are the predictable routes on which
seasonally migrating animals travel. Migratory corridors have
been delineated for various species, both terrestrial and marine,
around the world (e.g., Berger et al., 2006; Howard and Davis,
2009; Block et al., 2011). Determining migratory corridors
for marine species presents unique challenges as animals
may move across remote ocean basins for extended periods.
However, electronic tools such as GPS and satellite tags
have aided in research efforts to uncover these movements
(e.g., Block et al., 2011).
While for many species seasonal migration occurs annually,
for adult sea turtles migration occurs on average every 2–4 years
(Southwood and Avens, 2010). Sea turtles are not the only
reptile that migrates, but they are unique in the group as their
migration distances are larger than other reptile species by at
least an order of magnitude (Southwood and Avens, 2010).
They likely navigate these long distances using magnetic and
solar cues, as well as local cues, such as odor (Southwood and
Avens, 2010). For the Chelonian sea turtles that make round-
trip breeding migrations, these involve swimming both with and
against currents (Luschi et al., 2003).
During long-distance migration, species can face increased
metabolic and physiological challenges (Jenni-Eiermann and
Jenni, 2000; Southwood and Avens, 2010). They can also
experience a shifting predator assemblage and encounter storms
or other unsuitable climate conditions. Beyond this, they
may become exposed to potentially dangerous anthropogenic
activities such as energy development (Henkel et al., 2014;
Vander Zanden et al., 2016), direct or accidental harvesting (i.e.,
as bycatch, Hays et al., 2003), pollution (Henkel et al., 2012;
Keller, 2013), and ship strikes (Casale et al., 2010). Similar to
foraging and breeding habitats, migratory corridors represent an
important habitat for migrating species. Defining the location
and timing of these migratory corridors is a first step in
understanding where and how migrating populations may be
limited across space and time, and it offers an opportunity for
targeted conservation efforts.
Loggerhead sea turtles (Caretta caretta) typically migrate from
foraging areas to nesting beaches every 2–4 years (National
Marine Fisheries Service [NMFS] and United States Fish and
Wildlife Service [USFWS], 2008) sometimes moving thousands
of kilometers (Hays and Scott, 2013). Loggerheads in the
Gulf of Mexico (GoM) are part of the Northwest Atlantic
population, which is listed as threatened (NMFS and USFWS,
2008). Knowledge of the conditions and possible threats along
migration routes is important for conservation of the species, and
the Loggerhead Recovery Plan lists determining the migratory
pathways and management of these habitats as Recovery
Objectives/Actions (NMFS and USFWS, 2008). Previous studies
have identified migratory pathways for post-nesting loggerheads
in the GoM for 28 turtles that nested on the southwest
Florida coast (Girard et al., 2009) and 27 turtles from three
Florida nesting sites (Foley et al., 2013). These studies have
added important knowledge to our understanding of GoM
loggerhead migration. However, it is possible that loggerheads
nesting on other GoM beaches may use different migratory
pathways, so expanding our understanding of loggerhead
migration across spatially disparate beaches is important. Also,
identifying anthropogenic threats to migratory corridors is key
to conservation efforts for loggerheads and has yet to be assessed
for their migration corridors in the GoM. Further, no migratory
corridor has yet been designated in the GoM for loggerhead
critical habitat (NMFS and NOAA, 2014).
Here, we combine 48 previously published migration
tracks for post-nesting GoM loggerheads (Hart et al., 2012,
2014, 2015) with another 41 tracks, including some tagged
at a new study site at a nesting beach in Everglades
National Park, to discover high-use migration corridors in
the GoM. We use switching state-space modeling (SSM) to
identify these 89 migration routes from five nesting beaches
across 8 years (2008–2015) in the GoM, including nesting
beaches in both Florida and Alabama. We identify corridors,
summarize the peaks in migration timing, display repeat
migration patterns for individuals tracked more than once
from nesting grounds, and overlay anthropogenic threats during
those times onto the migration corridors to determine a
migration threat index.
MATERIALS AND METHODS
Turtle Capture and SSM
Turtle tagging occurred at five study sites in the GoM including
at Gulf Shores, Alabama, and four sites in Florida: Eglin Air
Force Base on Santa Rosa Island in northwest Florida, St. Joseph
Peninsula, Everglades National Park, and Dry Tortugas National
Park which included the nesting beaches of Loggerhead Key and
East Key (Table 1 and Figure 1).
We tagged and outfitted 81 loggerhead females (eight of these
were tagged twice for 89 tracks) with satellite transmitters after
they nested. All tagging followed established protocols (National
Marine Fisheries Service [NMFS]-Southeast Fisheries Science
Center [SEFSC], 2008) and methods in Hart et al. (2014). These
methods were approved by the United States Geological Survey-
Southeast Ecological Science Center-Institutional Animal Care
and Use Committee Protocol #2011-05. We fitted a platform
terminal transmitter to each turtle (SPOT5 or SPLASH10,
Wildlife Computers, Redmond, WA, United States). All tagged
turtles were released within 2 h at their capture location.
Satellite
data were
available
for download on
the
Wildlife Computers Portal.1 We used a hierarchical SSM
1www.wildlifecomputers.com
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FIGURE 1 | Migration paths (blue lines) taken by 81 adult female loggerhead sea turtles (Caretta caretta; 89 tracks) after being tagged at nesting beaches
throughout the Gulf of Mexico. Tagging locations (black squares) from top left moving clockwise: Gulf Shores, Eglin Air Force Base, St. Joseph Peninsula, Everglades
National Park, Dry Tortugas National Park. U.S. states are abbreviated: TX, Texas; LA, Louisiana; MS, Mississippi; AL, Alabama; FL, Florida. The 200 m bathymetric
contour is shown as a dashed line.
(Jonsen et al., 2003; Patterson et al., 2008) to characterize the
movements of all turtles, following our previous studies where
we determined foraging and inter-nesting periods for some
of these same turtles (Hart et al., 2012, 2013, 2014, 2015,
2018a,b). Specifically, we applied a model used by Breed et al.
(2009) that estimates model parameters by Markov Chain
TABLE 1 | Number of loggerhead sea turtle (Caretta caretta) tracks during
migration in the Gulf of Mexico after being tagged at various nesting beaches.
GS
EAFB
SJP
ENP
DTNP
Total
2008
3
3
2009
4
4
2010
4
2
6
2011
5
6
11
2012
5
2
5
9
21
2013
8
4
7
19
2014
2
2
7
11
2015
2
3
9
14
Total
22
2
13
5
47
89
GS, Gulf Shores, Alabama; EAFB, Eglin Air Force Base, Florida; SJP, St. Joseph
Peninsula, Florida; ENP, Everglades National Park, Florida; DTNP, Dry Tortugas
National Park, Florida.
Monte Carlo (MCMC) using WinBUGS via the software
program R. As input into the model, we used all tracking data
except for locations defined as Location Class Z, which are
considered invalid locations (CLS, 2011). We fit the model
to tracks of each individual turtle to estimate location and
behavioral mode every 6 or 8 h from two independent and
parallel chains of MCMC. Our samples from the posterior
distribution were based on 10,000 iterations after a burn-in
of 7,000 and were thinned by five. We defined binary turtle
behavioral modes based on SSM output as either “area-
restricted searching” or “transiting” as in earlier applications
(Jonsen et al., 2007).
After plotting the transiting locations, we further filtered
them to remove transit locations that represented movement
within inter-nesting or foraging periods. In this way, we included
only the turtle’s migration away from nesting beaches. The
transit locations constituting the migration were determined
by graphing the cumulative distance from the nesting beach,
which was defined as the graph’s rise after the last visit to
the nesting beach to the beginning of the asymptote signifying
the arrival at foraging grounds. Additionally, if a nesting
event (ground-truthed) fell within the migration period, we
classified the locations before the nest as “transit within the
inter-nesting period.”
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If the input locations to SSM have large temporal data gaps
or are highly imprecise, it can create SSM paths that deviate
from the true path (Jonsen et al., 2013). We inspected SSM main
migration output paths for 122 tracks as part of a larger project.
The turtles considered here (n = 89) remain after filtering out
SSM outputs that crossed large areas of land (n = 16), had no clear
migration away from nesting grounds (n = 7), had three or less
input locations during migration and/or had temporal gaps of a
week or greater during migration (n = 10). This ensured that the
SSM paths modeled the input locations as accurately as possible.
Migration Corridors
We visually identified two main migration corridors. To spatially
delineate the extent of each migration corridor, we determined
the line kernel density estimates (KDEs; Steiniger and Hunter,
2013) for each using the SSM migration lines in each corridor
(n = 37 tracks for the GoM, n = 27 tracks for the Florida
Straits). We used open source GIS software OpenJUMP (Steiniger
and Hay, 2009), with the OpenJUMPHoRAE toolbox (Steiniger
and Hunter, 2012) to calculate line KDEs and the 25, 50,
and 75% probability contours for each migration corridor
KDE. The probability contours are calculated from the KDEs,
whereby a density value represents a given probability that the
animal may be found in that cell, and polygons representing
probability of use can then be derived from the resulting
contours. The maximum percentage of 75% for the KDE
distribution was applied and represents a conservative estimate
of the migration corridor appropriate for assessing broad
movement patterns (Pendoley et al., 2014), which aids in
accounting for tracking bias (e.g., individuals tracked from the
same nesting site; Almpanidou et al., 2019). We determined
the line KDEs using a bandwidth of 42 km, the average
distance traveled per day for the turtles in the two identified
migration corridors (n = 64 tracks; Steiniger and Hunter, 2013)
and implemented a raster cell size of 10 km (in agreement
with other data layers). We chose 10 × 10 km grid cells to
balance the spatial error of most satellite locations received
(>1.5 km; CLS, 2011), the average daily distance the turtles
moved (42 km), and a reasonably precise area for planning
management actions.
Timing and Repeatability of Migration
Paths
To determine the timing of migration, we obtained the dates
(month and day, ignoring year) that each turtle migrated. We
then separately graphed the number of turtles migrating on any
given month/day for those that stayed within the GoM or went to
the Bahamas or Caribbean. To describe how similar paths were
for individual turtles tracked twice, we created 10 km buffers
around the first path for each turtle and then determined the
proportion of the second path that fell within the buffer. To
compare the median threat level between migration paths for
each turtle tracked twice, we extracted grid-cell threat values
(see below) along each path and ran Wilcoxon rank sum tests in
base R (R Core Team, 2020) for each individual.
Anthropogenic Threats
Previous work identified where foraging grounds for loggerheads
and Kemp’s ridleys (Lepidochelys kempii) in the GoM overlapped
with eight spatially explicit anthropogenic threats and found
that threats for turtles using the southwest coast of Florida
included commercial line fishing and harmful algal blooms
(HABs; Hart et al., 2018a). However, when we included only
threats during the peak migration time of July and August,
HABs – which occur usually between August and February2 –
were only present in a single year and in a relatively small
spatial location and so we did not include HABs in our analysis.
For turtles migrating through the Florida Straits, shipping lanes
are a concern (Hart et al., 2018a). Additionally, while shrimp
trawling effort is more concentrated in the northern GoM, trawls
can present a significant threat to turtles if exclusion devices
are not used and trawls are longer than 10 min (Sasso and
Epperly, 2006). Therefore, we overlay these three threats on the
migration corridors: commercial line fishing, shipping density,
and shrimp trawling.
For commercial line fishing we used data provided by the
National Oceanic and Atmospheric Administration (NOAA;
Wrege, pers. comm.) that displayed the number of fisher trips
that used line fishing in 2014 across the U.S. GoM. These trips
were reported by fishers using a 1-degree latitude-longitude grid.
For shipping density, we used Automatic Identification System
data that is collected by the U.S. Coast Guard and provided for
use by NOAA and the Bureau of Ocean Energy Management.3
These data provide the locations of large vessels [≥65 ft (20
m) in length or ≥26 ft (8 m) in length for towing vessels] in
monthly summaries. We downloaded the shipping density for
July and August for the latest year of tracking (2015). Files were
transformed into File Geodatabases using the Track Builder 3.1
tool on their website. Once points were obtained, we created
lines using the ArcMap 10.4 (ESRI, 2016). Points to Line tool
with separate lines for each vessel ordered by date and time.
We summed the number of lines in 10 km grid cells for the 2
months to show densities across peak migration time. Shrimp
trawling effort is reported by NOAA statistical zone, both of
which we obtained from NOAA (Nance et al., pers. comm.).
We mapped the effort as number of days fished during the
summer, defined and summarized by NOAA as May to August,
for years 2008 through 2015. We then averaged the effort
across these years.
The threats had different units and varying degrees of
intensity, so we standardized units while retaining weighted
values that represented relative levels, by dividing each threat
value by the maximum to get a proportion of threat level in
each grid cell. Only values that intersected the final grid area in
the eastern GoM were considered for obtaining the maximum
value. These proportions were added together to get a total threat
level value per grid cell. Any total threat level value >0 was
added to one to ensure that multiplication of the threat to the
turtle KDE value remained positive. In so doing, threats were
spatially weighted in relation to themselves but not weighted
2https://tidesandcurrents.noaa.gov
3https://marinecadastre.gov/ais
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Loggerhead Migration Corridors and Threats
across threats based on danger level to turtles (i.e., all threat types
were considered of equal danger). Once all threats were compiled,
we multiplied the threat values in each grid cell by 1 – the line
KDE contour value, which gave heavier weight to core areas of the
line KDEs. This provided a metric to identify potential hotspots
where differing levels of both migration and threats occur.
RESULTS
Turtles and SSM
We identified migration paths for 81 adult female loggerheads
(89 tracks) tagged after nesting in the GoM from 2008 to 2015
(Figure 1). Migration tracks ranged from 1 to 115 days for a
total of 1,341 days (mean ± SD: 15.1 ± 14.1 days). Most turtles
were tracked for more than a week after migration ended, but
five turtles stopped transmitting either during migration, or 1 day
after SSM indicated that migration ended. SSM input locations
during this time accounted for a total of 11,110 locations and
SSM output totaled 4,008 locations. The total distance moved
(successive distances between SSM locations per turtle) ranged
from 23 to 4,388 km (661.8 ± 595.1 km) for a total of 58,896 km
across all tracks (Table 2).
Migration Corridors
We identified two migration corridors for post-nesting adult
female loggerheads containing 72% of satellite tracks in this
study. One occurred in the eastern half of the GoM and the
other was through the Florida Straits out into the Bahamas
(Figure 1). There were a few exceptions: one turtle that nested in
Dry Tortugas National Park headed south across the Caribbean
Sea to waters off Nicaragua, a Gulf Shores-nesting turtle headed
west to Texas, and another turtle that nested at Gulf Shores
headed west toward Texas but then made a large loop back east
and south, swimming through both oceanic and neritic areas
eventually reaching Cuba. Oceanic areas (i.e., outside neritic
areas) generally had a lower number of paths, with the middle of
the GoM primarily having single, unique paths with a low degree
of clustering along specific routes. However, south of mainland
Florida, many tracks clustered through oceanic areas when turtles
crossed the Florida Current before reaching the neritic waters of
the Bahamas (Figure 1).
TABLE 2 | Tracking and switching state-space model (SSM) details for 81
loggerheads (Caretta caretta; 89 tracks).
Days tracked
in migration
SSM
input
SSM
output
TDM (km)
Speed
(km/h)
Range
1–115
7–1,221
3–345
22.5–4388.1
0–8.0
Mean
15.1
124.8
45.0
661.8
1.9
SD
14.1
149.5
42.3
595.1
1.1
Total
1341
11110
4008
58896.4
n/a
TDM, total distance moved during migration (cumulative distance in km between
successive SSM locations). Values summarized across 89 separate tracks. SSM
input and output values represent number of locations. Speed calculated for turtles
from SSM locations.
TABLE 3 | Migration details for loggerhead sea turtles (Caretta caretta) tracked
from nesting grounds twice.
Turtle Migration
year
Migration
dates (days)
TDM during
migration (km)
Diff in
TDM
% overlap
1
2013
7/13–7/28 (16)
768.9
28.0
2015
7/16–7/25 (10)
665.2
103.7
2
2009
7/24–7/31 (8)
490.5
82.1
2012
7/17–7/25 (9)
569.3
78.8
3
2011
8/5–8/22 (18)
843.7
47.5
2013
8/6–8/18 (13)
688.6
155.2
4
2010
8/17–8/23 (7)
438.2
24.7
2012
8/12–8/21 (10),
8/23–8/24 (2)
835.7
397.4
5
2010
8/19–8/31 (13),
9/2–9/8 (7)
937.4
13.5
2013
7/29–8/13 (16)
991.2
53.8
6
2013
8/8–9/2 (26)
1109.3
38.1
2015
7/25–8/12 (19)
993.4
115.9
7
2012
7/17–8/2 (17)
1090.7
19.6
2014
7/2–7/9 (8)
809.1
281.6
8
2011
7/21–7/26 (6)
171.8
35.0
2012
6/7–6/13 (7),
6/17–6/27 (11)
394.5
222.6
Mean
13.9
737.4
36.1
SD
6
269
21.5
Total
223
11, 798
All turtles were tagged in Dry Tortugas National Park, Florida except for turtle
8 which was tagged in Gulf Shores, Alabama. Turtles 4, 5, and 8 had non-
transiting locations during their migration, therefore migration dates were split up.
Migration dates are given as month/day. TDM, total distance moved (cumulative
distance between successive SSM locations); diff, difference. The % overlap is the
proportion of the second path within a 10 km buffer surrounding the first path.
The line KDE contours represent the probability that a
migrating turtle would be found in that area. The line KDE
created in the eastern GoM had an overlap of the 25 and 50%
contours, with a probability of a given turtle being found there at
62% (Figure 2). The 62 and 75% contours for this corridor were
relatively close in size and primarily covered neritic areas from
south of Alabama to the southern tip of western Florida. The 25
and 50% contours remained separate for the corridor extending
from the Florida Straits to the Bahamas, showing the core area of
migration lines in a funnel shape with the tip of the funnel in the
Florida Straits and the funnel opening around Andros Island, the
largest Bahamian island (Figure 2).
Timing and the Repeatability of
Migration Paths
Turtles migrated as early as 7 June and as late as 10 November (for
the turtle that headed to Nicaragua). However, the majority of
migration across all turtles occurred during July and August. This
peak was the same regardless of whether migration began in the
northern GoM or at the more southerly Dry Tortugas National
Park (Figure 3).
We tracked eight turtles twice during migration to their
foraging grounds (Table 3). One turtle was tagged in Gulf Shores,
Alabama and the other seven were tagged in Dry Tortugas
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FIGURE 2 | Migration line kernel density estimates (KDEs) for adult female loggerhead sea turtles (Caretta caretta; 64 tracks). Two migration corridors were
identified: in the eastern Gulf of Mexico and the Florida (FL) Straits into the Bahamas. The KDEs were based on multiple tracks in the Gulf of Mexico (n = 37) and FL
Straits (n = 27). Values represent probability that a migrating animal will be found in each contour. U.S. states are abbreviated: MS, Mississippi; AL, Alabama; GA,
Georgia; FL, Florida. The 200 m bathymetric contour is shown as a dashed line.
National Park, Florida. Paths taken by turtles were similar across
years (Figure 4). The percent of the second path that fell within
the 10 km buffer of the first path ranged from 13.5 to 82.1%,
with a grand mean of 36.1% (±21.5%). Threat levels along paths
were similar for turtles tracked twice; only one turtle (Turtle 8
in Table 3) showed a significant difference in threat levels, with
the second track moving through higher threats [median threat
level for track 1 = 1.23, median for track 2 = 1.36; Mann-Whitney
U = 329.00, n1 = 22, n2 = 43 P = 0.047; we did not include Turtle
5 (Table 3) in these comparisons because >80% of the track was
outside the threats grid]. This turtle migrated in the northern
GoM, and its second track took it close to the Chandeleur Islands
where threat levels were higher than off the coast of Alabama and
northern Florida.
Anthropogenic Threats
When threats were multiplied by the probability of turtle
presence as given by line KDE values, hotspots of high values
occurred around the northwest Florida coast, off of Tampa Bay,
and in the Florida Straits (Figure 5). Commercial line fishing was
present to some degree across the entire eastern GoM, with the
number of trips generally highest west and south of Florida, up
to a maximum of 5,462 trips per 1-degree latitude/longitude grid
cell (Supplementary Figure 1). Shipping density was highest in
a somewhat circular path from Louisiana to the Florida Straits,
where the number of vessels during the summer months reached
as high as 1,500 for a 10 km grid cell (Supplementary Figure 1).
Shrimp trawling was lower along the west coast of Florida than in
the water south of Louisiana, however over 2,000 effort hours of
shrimping during the summer months of 2011 (with an average
of ∼1,300 h across years) was reported for waters ∼18–55 m deep
in this area (Supplementary Figure 1).
DISCUSSION
We spatially defined areas where high-use loggerhead migration
paths overlap with sea turtle-specific anthropogenic threats in
the Gulf of Mexico. This is important as loggerheads are a
threatened species and the GoM has a high level of disturbance
and pollution. The GoM also has one of the highest levels
of species per unit area in the world, yet its biodiversity is
considered “most threatened” (Costello et al., 2010). Specifically,
we use 89 loggerhead migration tracks to identify high-use
corridors including turtles from Baldwin County, Alabama, and
Okaloosa, Gulf and Monroe Counties in Florida. This includes
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Loggerhead Migration Corridors and Threats
FIGURE 3 | The timing of migration paths taken by 81 adult female loggerhead sea turtles (Caretta caretta; 89 tracks) after being tagged at nesting beaches
throughout the Gulf of Mexico. The migrations are split by which area the turtle traveled through on migration: the Gulf of Mexico, the Florida (FL) Straits, or the
Caribbean. The long tail after September 6 is from the turtle that traveled to Nicaragua.
turtles tracked from a nesting beach in Everglades National Park
not included in previous summaries. Based on previous work,
loggerheads have been shown to use neritic waters west of Florida,
as well as oceanic waters in the middle of the GoM for their
migration. The corridors identified in this study align closely
with many of the previously published tracks (Girard et al., 2009;
Foley et al., 2013), indicating that these pathways are consistently
important for loggerheads nesting through the GoM. For
example, Foley et al. (2013) showed northern GoM turtles
migrating in similar areas: along western Florida, loggerheads
were located between 20 and 50 m bathymetry, and our core
migration KDE areas overlapped these depths. In the Florida
Straits, Girard et al. (2009) showed tracks in similar areas
for turtles moving from western Florida to the Bahamas and
Foley et al. (2013) showed many tracks along the same route
but moving in the opposite direction for turtles migrating
from eastern Florida into the GoM. Combined, these studies
support the importance of the areas identified in this study
as migratory corridors for loggerheads across years and for
traveling to and from nesting beaches. Identifying corridors helps
determine where management actions have potential to benefit
more migrating loggerheads.
Although turtles in this study used neritic and oceanic areas,
we found corridors were primarily located in neritic areas close
to the coast. In the mid-GoM oceanic areas, individual tracks
showed low degrees of overlap, consistent with other studies
tracking loggerheads through this area (Girard et al., 2009; Foley
et al., 2013). This may be attributed to changing currents and
eddies that make each path unique. Specifically, turtles migrating
in oceanic GoM waters may be influenced by eddies of the Loop
Current (Foley et al., 2013).
Of eight turtles tracked for two post-nesting migrations, we
found a relatively high degree of spatial similarity on their
paths across years. Given that sea turtles follow magnetic maps
(Southwood and Avens, 2010) and generally travel between the
same nesting beaches and foraging grounds across years, this is
expected. The repeat SSM paths were not exact replicates for
turtles, however, and this may be due in part to the limits of
location accuracy with satellite tags and/or variable model inputs
into the SSM (such as number of and location of input points)
that caused slightly different outputs. It is also possible that
shifting currents and/or shifting of local cues such as wind-borne
odor (Endres et al., 2016) could influence migration paths such
that we would not expect an exact overlap in space and time
across years. Theoretically, changes in course across years could
also be due to avoidance of the threats identified in this study,
however, we did not find evidence for this.
Despite the somewhat lower location accuracy of satellite
tags as compared to GPS tags, their battery longevity allows for
much longer tracking periods. As the tag technology improves,
researchers will be able to determine spatial consistency in
migration paths for a greater sample size of turtles, and during
remigration. One previously tracked loggerhead followed the
same migration path post-nesting and during its remigration
back to nesting grounds (Foley et al., 2013). Therefore, the
corridors we identified may also be important during remigration
to nesting grounds, which would occur at a different time
of year when threat levels may be different. Tracking turtles
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from foraging grounds to their nesting beaches would help
determine how consistently they use corridors for both directions
of migration. The cues that drive the phenology of migration
for sea turtles are not well understood, and more research on
the timing of migration from foraging grounds and associated
environmental conditions could help with understanding what
drives this behavior for individuals in some years and not others.
One turtle migrated out of the GoM south to Nicaragua, for
a total distance moved of 4,388 km. In 2017, a rehabilitated
loggerhead was released from South Africa and traveled for 2
years before reaching Australia, >10,000 km from its release site.4
For non-rehabilitated, wild turtles, the travel to Nicaragua from
the GoM represents the longest reported post-nesting loggerhead
migration to our knowledge. The next longest migration in this
study was 2,751 km, a difference of around 1,600 km. The
upper distance limit for adult Cheloniid sea turtles undertaking
breeding migrations is thought to be around 3,000 km, because of
limits on available fat stores (Hays and Scott, 2013). Interestingly,
the turtle migrating to Nicaragua paused along the coast of Cuba
from late August until early October after traveling for about
1,500 km. After this pause, the turtle resumed migrating, and
then when it had traveled ∼3,400 km from nesting grounds
it seemingly paused migration again to make a circular loop
about 50 km in diameter for 8 days in October. While the SSM
identified this time as migration, it is possible that this represents
a type of stopover, where the turtle may have been seeking
resources for refueling. These potential stopovers occurred in
the neritic waters of Cuba and then directly south of Cuba in
water >3,000 m deep.
There are anthropogenic threats to sea turtles which we
were not able to quantify and thus did not include in our
analysis, such as plastic pollution, effects from climate change,
and direct harvest. Plastic pollution presents a serious threat
to sea turtles, with over half of sea turtles in the world
predicted to have ingested plastic debris and a relatively
high-risk of ingestion predicted specifically in the GoM for
hard-shelled sea turtles (Schuyler et al., 2016), however, the
spatial extent of plastic available to turtles in the GoM is
not well-studied. Additionally, changes to ocean currents and
sea surface temperatures due to climate change were not
considered. Theoretically, sea turtles could be affected by
these changes during migration, as changing temperatures and
currents could alter the energetic costs of migration, however,
how these changes would affect sea turtle migration is not
well understood (Southwood and Avens, 2010). Direct harvest
is considered the third highest threat to sea turtles based
on expert opinion (Donlan et al., 2010), yet we did not
include this threat because of a lack of spatial information
on where direct harvest in the GoM may occur. Lastly,
based on the small percentage of HABs we found occurring
during the migration period, these blooms are likely of
higher concern for loggerheads on foraging grounds than
those migrating in the summer. However, these blooms can
be variable as demonstrated by the massive bloom that
started in the fall of 2017 and lasted long enough to affect
4www.aquarium.co.za
over 200 km of Florida’s west coast
in the summer of
2018,5 killing hundreds of sea turtles. Therefore, while this
may not be a primary, consistent threat to migrating sea
turtles of those we examined, it can still have important
impacts in some years.
For commercial line fishing, the data represents a minimum
estimate of possible impacts. This is because we included only
reported line fishing trips with known gear. We also did not
include trips where the gear was simply reported as combined
(multiple gear types). Commercial line fishing is known to have
cumulatively high sea turtle bycatch, but other forms of fishing
with nets may also have impacts (Lewison and Crowder, 2007).
Not all trips will have the same impact, as that depends on the
effort of each trip. Here we use only the number of trips as
a metric, assuming that more trips mean more impact. Even
with the threat from line fishing possibly being underestimated,
the potential threat to sea turtles appears relatively high across
most of the GoM, as many thousands of trips were reported for
just one summer.
Shipping density
is most problematic
for migrating
loggerheads that are traveling through the Florida Straits,
and a lower level of this threat occurs across all other areas
considered. We mapped this layer to demonstrate areas with
more potential for ship strikes, which have been shown as a
common cause of sea turtle mortality in the Mediterranean
(Casale et al., 2010). Lastly, shrimp trawling effort is not as
high across most of the western Florida shelf as in waters
surrounding Louisiana, however, this threat is persistent across
neritic areas of the GoM.
Here we show that shipping density, commercial line fishing,
and shrimp trawling can affect the mortality of loggerhead
sea turtles in the GoM. However, we did not weigh these
threats in relation to each other and were unable to consider
all possible threats, therefore we consider our threats index to
be a minimum estimate. Importantly, our identified corridor
in the GoM overlaps with migration areas for other species
of concern. Kemp’s ridleys migrate slightly earlier in the year,
with a peak in June, however they migrate through August
(Shaver et al., 2016), so for any traveling through these same
areas, they would be subject to these same threats. This corridor
also overlaps with Biologically Important Areas identified for
the resident population of Bryde’s whale (Baleanoptera edeni),
which are extremely rare, and represent the only year-round
baleen whale population in the northern GoM (LaBrecque
et al., 2015; Soldevilla et al., 2017). This whale occurs primarily
between depths of 100–300 m and is listed as endangered
(NMFS and NOAA, 2019).
Cumulative Effects Assessments (CEAs), sometimes referred
to as Cumulative Impact Assessments, are procedures that
identify and evaluate the collective impact of multiple human
activities and natural processes on the environment (Jones, 2016).
CEAs are considered critically important for informing effective
marine policy, however,
the use of CEAs in real-world
management processes remains a challenge largely due to the
wide variation in approaches. CEAs are complex and have
5https://coastalscience.noaa.gov
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Loggerhead Migration Corridors and Threats
FIGURE 4 | The repeatability of migration paths taken by eight adult female loggerhead sea turtles (Caretta caretta) after being tagged at Dry Tortugas National Park,
Florida (first seven panels) and Gulf Shores, Alabama (last panel). Parts of the track where the second post-nesting migration was within 10 km of the first
post-nesting migration are indicated in red. The 200 m bathymetric contour is shown as a dashed line.
been criticized for a lack of measurable and clearly defined
sustainability goals, being poorly aligned with institutional
frameworks, and a lack of objective criteria (Jones, 2016).
Nevertheless, attempts have been made to improve on CEAs by
re-evaluating the structure and intent, reducing ambiguity, and
orienting toward a common objective across regions (Willsteed
et al., 2018). By incorporating CEAs within a risk-based
framework that includes identification, analysis and evaluation,
it may be possible to simplify and streamline CEAs while
being transparent about uncertainty (Stelzenmüller et al., 2018).
Ideally, CEAs show where cumulative effects most likely occur
and at what intensity (Stelzenmüller et al., 2018). As we
did not weight threats with additional quantitative data on
mortalities and injuries caused, our analysis may be considered
a Cumulative Pressure Assessment (CPA), and a step toward a
fully parameterized CEA.
By overlaying anthropogenic threats onto the migration
corridors, we were able to identify that hotspots of high
values occurred around the northwest Florida coast, off
of Tampa Bay, and in the Florida Straits. In our study,
our conservation target is clear: the survival of migrating
adult female loggerheads. In the Loggerhead Recovery Plan,
managing migratory pathways and minimizing vessel strike
mortality are
listed as Recovery Objectives (NMFS and
USFWS, 2008), and therefore our results directly provide
scientific
information needed for designing management
strategies for this threatened species. In a risk-based framework,
management activities are monitored and evaluated, which may
lead to an understanding of thresholds for the cumulative
effects (Stelzenmüller et al., 2018).
In order to
inform
what threshold is acceptable for each threat, a future CEA
would benefit from an understanding of what mortality
level during migration is deemed sustainable for population
recovery. As new information becomes available on the
spatial intensity of threats, this estimated corridor can be
used to inform adaptive management of threats during the
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Loggerhead Migration Corridors and Threats
FIGURE 5 | Migration threat index for adult female loggerhead sea turtles (Caretta caretta; 64 tracks) after being tagged at nesting beaches throughout the Gulf of
Mexico. The 10 km grid cells are color-coded by the threat index value, which accounts for the line KDE contour value in the cell and the value of three possible
threats: commercial line fishing, shipping density, and shrimp trawling (see section Materials and Methods for more details). U.S. states are abbreviated: LA,
Louisiana; MS, Mississippi; AL, Alabama; FL, Florida. The 200 m bathymetric contour is shown as a dashed line.
migratory period. Overall,
it
is
imperative to understand
migration patterns and threats
for
these highly mobile
species, and our conservative estimate of threats provides
valuable information for the management and recovery of
loggerhead sea turtles.
DATA AVAILABILITY STATEMENT
The datasets generated for this study will not be made publicly
available. Restrictions apply to the datasets. Raw data is exempt
from publication due to the sensitivity of endangered species
location information. Requests to access the datasets should be
directed to the corresponding author. All other data used for
analyses are presented in the manuscript.
ETHICS STATEMENT
The animal study was reviewed and approved by the
United
States
Geological
Survey-Southeast
Ecological
Science Center-Institutional Animal Care and Use Committee
Protocol #2011-05.
AUTHOR CONTRIBUTIONS
AI and KH contributed to the conception and design of the study.
KH acquired funding. KH and ML managed tag deployment
and data collection. AI, AB, and IF organized the database and
performed analyses. AI wrote the first draft of the manuscript. All
authors contributed to manuscript revision, read and approved
the submitted version.
FUNDING
This work was supported by the Natural Resource Damage
Assessment (NRDA) to KH; and the U.S. Geological Survey
Priority Ecosystem Science Program (PES) to KH.
ACKNOWLEDGMENTS
We acknowledge assistance
from D. Ingram,
J.
Isaacs,
A. Lauritsen, S. MacPherson, and J. Phillips from the U.S. Fish
and Wildlife Service (USFWS). We are grateful to many “Share
the Beach” volunteers and M. Reynolds for field assistance in
Alabama. We also thank many U.S. Geological Survey volunteers
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Loggerhead Migration Corridors and Threats
and employees
for assistance
in
the field. We thank
the USFWS interns from Bon Secour National Wildlife
Refuge (NWR). Research activities were permitted under
Bon Secour NWR Special Use Permit 12-006S (issued to
KH), USFWS Permit TE206903-1 (issued to J. Phillips),
and the State of Florida Marine Turtle Permits 094, 118
and 176. We acknowledge the use of the satellite-tracking
and analysis tool (STAT) and telemetry data generated as
part of the Deepwater Horizon Natural Resource Damage
Assessment (publicly available from www.seaturtle.org). Any
use of trade, product, or firm names is for descriptive
purposes only and does not imply endorsement by the
U.S. Government.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at:
https://www.frontiersin.org/articles/10.3389/fmars.
2020.00208/full#supplementary-material
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Conflict of Interest: AI was employed by the company Cherokee Nation
Technologies.
The remaining authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a potential
conflict of interest.
Copyright © 2020 Iverson, Benscoter, Fujisaki, Lamont and Hart. This is an open-
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Frontiers in Marine Science | www.frontiersin.org
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April 2020 | Volume 7 | Article 208