Ecology, 95(10), 2014, pp. 2840–2850
Ó 2014 by the Ecological Society of America
Ontogeny of long distance migration
REBECCA SCOTT,1,2,5 ROBERT MARSH,3
AND
GRAEME C. HAYS1,4
1
Department of Biosciences, College of Science, Swansea University, Swansea SA2 8PP United Kingdom
Future Ocean, Department of Evolutionary Ecology of Marine Fishes, GEOMAR, Helmholtz Center for Ocean Research,
Dusternbrookerweg 20, Kiel 24105 Germany
3
Ocean and Earth Science, National Oceanography Centre, University of Southampton, Southampton SO14 3ZH United Kingdom
4
Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Warrnambool,
Victoria 3280 Australia
2
Abstract. The movements of some long-distance migrants are driven by innate compass
headings that they follow on their first migrations (e.g., some birds and insects), while the
movements of other first-time migrants are learned by following more experienced conspecifics
(e.g., baleen whales). However, the overall roles of innate, learned, and social behaviors in
driving migration goals in many taxa are poorly understood. To look for evidence of whether
migration routes are innate or learned for sea turtles, here for 42 sites around the world we
compare the migration routes of .400 satellite-tracked adults of multiple species of sea turtle
with ;45 000 Lagrangian hatchling turtle drift scenarios. In so doing, we show that the
migration routes of adult turtles are strongly related to hatchling drift patterns, implying that
adult migration goals are learned through their past experiences dispersing with ocean
currents. The diverse migration destinations of adults consistently reflected the diversity in
sites they would have encountered as drifting hatchlings. Our findings reveal how a simple
mechanism, juvenile passive drift, can explain the ontogeny of some of the longest migrations
in the animal kingdom and ensure that adults find suitable foraging sites.
Key words: ARIANE particle tracking software; animal movement; biotelemetry; dispersal; habitat
selection; NEMO ocean model; ocean currents; particle tracking; surface drifter buoys.
INTRODUCTION
In the animal kingdom, regular to-and-fro migrations
between breeding and foraging habitats are widespread
and may span many thousands of kilometers (e.g., Hein
et al. 2012). Individuals often show high fidelity to their
habitats (e.g., Bowen and Karl 2007, Broderick et al.
2007, Baracho-Neto et al. 2012), and consequently the
drivers of these movements and the cues/behaviors used
to optimize travel between distant sites have received a
lot of attention in recent years (e.g., Alexander 1998,
Alerstam et al. 2003, Chapman et al. 2010, Liedvogel et
al. 2011, Mueller et al. 2013, Putman et al. 2013). For
some species, migrations appear to evolve through social
learning. For example, baleen whale calves (e.g.,
Megaptera novaeangliae) follow their mothers on their
first migrations between tropical calving and highlatitude feeding areas and later return independently to
these same sites (Weinrich 1998). Social learning is also
commonly observed in many bird species (e.g., Columba
livia and Grus americana), with individuals altering their
routes when flying with more experienced conspecifics
(Mueller et al. 2013, Pettit et al. 2013). For central place
foragers, like some insect species, various cues (e.g.,
Manuscript received 25 November 2013; revised 19 March
2014; accepted 27 March 2014. Corresponding Editor: M. C.
Wimberly.
5 E-mail: beckyscott130@hotmail.com
familiar landmarks) and path-integration techniques (in
the absence of such cues) can drive the evolution of
movement pathways to feeding areas (e.g., Schatz et al.
1999, Müller and Wehner 2010). For other species, key
migratory decisions appear to evolve through innate
processes; indeed, both the decision to migrate and
migration directions have been shown to be genetically
predetermined in some bird and insect species (e.g.,
Berthold and Helbig 1992, Mouritsen et al. 2013).
However, for many groups, the processes that shape
both migration routes and foraging destinations remain
enigmatic despite the wide availability of techniques for
recording extended animal movements. For example,
Lagrangian analyses of ocean currents and winds (e.g.,
from surface drifter buoys and ocean/atmospheric
models) have gained great application for studying the
movements of smaller organisms not amenable to large
tracking devices, such as airborne insects, and drifting
marine organisms such as early life-stage fish larvae and
hatchling sea turtles (Chapman et al. 2012, Scott et al.
2012a, Baltazar-Soares et al. 2014). For larger mobile
organisms such as sea turtles, large fish, marine
mammals, and birds, satellite tracking has proved
instrumental in detailing their migratory feats (e.g.,
Block et al. 2011).
Adult sea turtles (which are philopatric to their natal
areas; Bowen and Karl 2007) have been particularly well
studied through the use of satellite tracking technology
(e.g., Godley et al. 2008). Synthesis of tracking data sets
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INNATE VS. LEARNED MIGRATION BEHAVIORS
has highlighted a range of different post-breeding
migratory strategies across populations of adult turtles:
(1) oceanic and coastal movements to fixed neritic
foraging grounds, (2) coastal shuttling between fixed or
seasonal neritic sites, (3) local residence, and (4) pelagic
foraging (Godley et al. 2008). Adult hard-shelled
cheloniid turtles (e.g., Chelonia mydas, Caretta caretta,
and Eretmochelys imbricata: family Cheloniidae) typically migrate across expanses of open ocean to discrete
neritic foraging habitats. The tendency of cheloniid
turtles to fast during oceanic crossings appears to place
an upper migration limit of ;3000 km on the distance
an adult cheloniid turtle can travel from their breeding
ground to their foraging ground (Hays and Scott 2013).
However, migration strategies can vary both between
and within different cheloniid turtle populations (e.g.,
Godley et al. 2008, Hays et al. 2010). Leatherback turtles
(Dermochelys coriacea), the only species of soft-shelled
turtle (Dermochelyiidae family), do not migrate to
discrete habitats and instead forage pelagically. This
foraging strategy frees this species from an upper
migration limit, enabling the exploitation of very distant
(.11 000 km) foraging habitats (e.g., Benson et al.
2011). Thus, while satellite tracking studies have
revealed that a range of post-breeding migration
strategies is evident within the sea turtles, understanding
the drivers that underpin the movement patterns and
foraging habitat selections of adult turtles has remained
enigmatic and highlighted the need for more quantitative and novel interdisciplinary approaches (e.g., Hays et
al. 2010).
As hatchling turtles have relatively weak swimming
abilities, ocean currents are thought to drive their broadscale dispersion into oceanic areas where the juveniles
then grow for several years before recruiting to coastal
subadult foraging sites closer to their natal area (e.g.,
Scott et al. 2012a). Upon reaching maturity, postbreeding sea turtles can perform open ocean crossings to
the same fixed neritic foraging grounds year after year
(e.g., Broderick et al. 2007). However, understanding
how they select their particular foraging grounds has
proved elusive. Recently a new paradigm was suggested,
that this ontogenetic development of sea turtle migrations may be driven by ocean currents (Hays et al. 2010).
In this study, the north/south dichotomy in the postbreeding migrations/foraging sites of adult turtles
tracked from a breeding ground in the Mediterranean
was hypothesized to reflect the north/south dichotomy
in the local ocean circulation system (and hence
hatchling dispersion patterns). Here, by combining
global satellite tracking data sets to identify the
movements of adult turtles, and using a global ocean
model to identify the movements of hatchling sea turtles,
we globally assessed the ontogeny of long-distance
movements for sea turtles that may initially drift
passively but then subsequently move more actively as
they grow into large, powerful swimmers. In so doing,
we provide compelling global support that ocean
2841
currents drive the ontogeny of cheloniid sea turtle
migrations through two main mechanisms. Cheloniid
turtles either migrated to neritic foraging sites they
would have passively drifted to as hatchlings (albeit,
typically on more direct active migration routes), or
when hatchlings drifted to unsuitable adult neritic
foraging sites, turtles performed less typical migration
strategies: either coastal shuttling to fixed/seasonal
habitats, local residence, or oceanic foraging. The
movements of leatherback turtles (the only non-cheloniid species) are more directly shaped by ocean currents
that drive the distribution of their drifting pelagic food
sources. Hence, while many small organisms and
juvenile life stages of larger organisms are reliant on
current flows for long-distance dispersal (Chapman et al.
2011), here we provide the first compelling evidence that
ocean current flows also drive (both directly and
indirectly) the evolution of active migrations for large
mobile adult sea turtles.
MATERIALS
AND
METHODS
We combined published satellite tracking data of
.400 adult sea turtles (families Cheloniidae and
Dermochelyidae) from 42 nesting sites with .40 000
Lagrangian-derived drift trajectories generated from (1)
a 30-year database of surface drifter buoy tracks and (2)
seven years of ocean model particle tracking simulations. Details on the post-breeding migrations of
satellite-tracked adult turtles were obtained from published maps following the methodology of Hays and
Scott (2013). Due to the wealth of satellite tracking data,
details of the individual satellite tracking studies are
included in the supplemental material (Appendix A:
Figs. A1–A4). Hatchling drift scenarios were assessed
using Lagrangian surface drifter buoys from the Global
Drifter Program (Global Drifter Program data available
online)6 that passed within 150 km of nesting sites.
Simulated particle tracks were generated using ARIANE
particle tracking software (program available online).7
Also used was the global eddy-permitting (1/48 resolution)
NEMO ocean model (Madec 2008, Scott et al. 2012b) that
has been run in hindcast mode from 1958 to 2007. From
each nesting location, 1000 ARIANE particles were
released 10–60 km offshore and assigned a start date by
randomly selecting a year between 2000 and 2006 and a
day of the year during the population’s peak hatchling
season (typically a 2–3 month window). Fixing the
number of released particles at 1000 per nesting location
was a pragmatic decision to help ensure data sets were
sufficiently large for our subsequent statistical analysis but
at the same time not too computationally demanding for
model simulations over multiple sites. By sampling a wide
range of possible trajectories per nesting location, we
account for both seasonal and interannual variability, and
6
7
http://www.aoml.noaa.gov/envids/gld/
http://stockage.univ-brest.fr/;grima/Ariane/
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REBECCA SCOTT ET AL.
chaotic mesoscale (eddy) variability of ocean currents in
the NEMO hindcast.
Most cheloniid turtle populations migrate through
coastal and oceanic waters to reach discrete neritic
foraging sites. For these populations, travel bearings
were taken when adult migration and hatchling dispersal
paths reached distances of 500 km from nesting beaches.
This distance was selected as it best captured the general
travel directions of both drifters and adult turtles as: (1)
coastal processes often made it difficult to identify the
initial (or general) travel direction of buoy/particle
trajectories until they had established their course
beyond the coastal realm, and (2) 500 km captured the
general travel directions of turtles that performed
migrations of this magnitude, while ensuring a maximum number of buoys/particles were still drifting at this
distance. Turtles traveling within 158 of drifters were
considered to be traveling in agreement with ocean
current flows. A window of 158 was a pragmatic choice,
due to the chaotic influence of mesoscale variability on
drift. For each breeding area, we generated 1000 sets of
turtle bearings, assuming a random departure direction
from the nesting beach. For example, if 12 adults had
been satellite tracked .500 km from a beach, we
generated 1000 sets of 12 random departure directions
500 km from the nesting beach. This provided a null
model against which to compare the adult tracking and
particle tracking results. We then assessed the proportion of the randomly generated sets of adult travel
bearings that were within 158 of one of the 1000 particle
tracking or buoy bearings. For example, in the case just
mentioned, for each of the 1000 simulations of 12
randomly selected departure directions, we assessed the
proportion of those 12 random bearings that were
within 158 of one of the 1000 particle tracking or buoy
bearings. Likewise, we compared the actual proportion
of tracked turtles that were within 158 of one of the 1000
particle tracking or buoy bearings. This analysis was
conducted to assess if significantly more turtles were
observed to travel in the same direction (within 158 of
drift bearings) as ocean current flows than expected by
chance, based on the randomly generated simulated
travel bearings. For cheloniid turtle populations where
all turtles were nonmigratory or all turtles performed
entirely coastal shuttling migrations, these simulations
were not carried out, as migration directions were not
relevant or constrained to two directions along the
coast. For populations where all turtles foraged oceanically (e.g., all leatherback turtle populations), the
relationships between broad-scale population movement
patterns and ocean circulation patterns were examined.
RESULTS
Published satellite tracking data were obtained from
42 nesting sites where 3 to .100 turtles had been tracked
on their post nesting migrations (see Appendix A for
Figs. A1–4 and satellite tracking references). Data were
available for 243 cheloniid turtles; 78 loggerhead turtles
Ecology, Vol. 95, No. 10
(Caretta caretta), 26 green turtles (Chelonia mydas), 17
hawksbill turtles (Eretmochelys imbricata), and 21 olive
ridley turtles (Lepidochelys olivacea). No data were
available for two cheloniid species; the flatback turtle
(Natator depressus) and Kemp’s ridley turtle (Lepidochelys kempii ). Data on the general movements of
leatherback turtles were based on satellite tracking
deployments on .200 leatherback turtles (Dermochelys
coriacea).
A total of 1398 Lagrangian drifter buoys passed
within 150 km of nesting sites, enabling 1794 1-yr-long
Lagrangian hatchling drift trajectories to be derived (as
some buoys passed the vicinity of .1 nesting sites; Fig.
1). Using ARIANE with the NEMO hindcast data, a
total of 42 000 1-yr-long Lagrangian trajectories were
computed, which produced .3 million modeled particle
locations (see Appendix A: Fig. A1). While drifter buoys
provided empirical observations of ocean currents to
verify model simulations, analysis of the NEMO
hindcast enabled more detailed investigation into ocean
currents experienced by hatchlings, with 1000 Lagrangian trajectories computed during the peak hatchling
season at each of the 42 nesting sites. Model (in silico)
Lagrangian trajectories and in situ Lagrangian buoy
trajectories revealed the same large-scale ocean circulation patterns (see Appendix B: supplementary video).
Hard shelled sea turtles (Cheloniidae family)
While adult cheloniid turtles do not generally drift
with ocean currents, our evidence suggests that for all
cheloniid turtle species and populations, ocean currents
drive the development of an individual’s post-breeding
migration through two main mechanisms.
Mechanism one.—Adult cheloniid turtle populations
typically perform directed migrations, which include
open ocean crossings to coastal foraging habitats several
hundreds to ;3000 km from their breeding beaches
(Hays and Scott 2013). For these populations, individual
turtles from all cheloniid species consistently migrated to
foraging habitats that they would have encountered as
hatchlings. At breeding grounds where ocean currents
showed strong directionality (low circular SD of drift
direction bearings; Fig. 2; black symbols), turtle
migration routes were also observed to follow strong
and overlapping directionality (Fig. 3 and Appendix A:
Fig. A2a, b). At breeding beaches where ocean currents
were more dispersed and variable (higher circular SD of
drift direction bearings; Fig. 2; white symbols), the
migration routes of cheloniid turtles were also observed
to either be more dispersed, or turtles migrated directly
to a subset of potential sites they would have encountered (albeit, often along more convoluted indirect drift
routes; Fig. 4a, b, Appendix A: Fig. A2c–o).
Mechanism two.—At breeding sites where the potential ‘‘downstream’’ coastal foraging sites that cheloniid
hatchlings would drift to exceeded 3000 km (Fig. 2; gray
symbols), cheloniid adult turtles performed less typical
migration patterns. Instead of embarking on directed
October 2014
INNATE VS. LEARNED MIGRATION BEHAVIORS
2843
FIG. 1. Examples of hatchling drift scenarios from 42 nesting sites (white stars). Colored lines show 1794 1-yr-long trajectories
derived from surface drifter buoys (spanning 1981–2011). Colors differentiate the trajectories. The large-scale circulation depicted
from these buoys is broadly similar to that observed in particle tracking model outputs (see Appendix A: Fig. A1 and Appendix B):
within 6 158 of the equator, flows are predominantly westward, incorporating some Ekman divergence about the equator itself; in
the subtropics, drifts follow the major western boundary currents, most conspicuously the Gulf Stream, the Kuroshio, the Agulhas,
and the East Australian Currents; elsewhere in the subtropics, flows are sluggish and less organized.
migrations to discrete neritic habitats, turtles were
observed to adopt one of three alternative strategies;
coastal shuttling, local residence near their breeding
areas, or oceanic foraging (Fig. 4c–e and Appendix A:
Fig. A3a–c).
The two mechanisms through which local ocean
circulation patterns were shown to drive the ontogeny
of migration for adult cheloniid turtles were population
and not species specific. The migrations of all cheloniid
turtle species could be explained by these two mechanisms, with the same species (and indeed individuals
from the same nesting populations) adopting different
strategies based on the local conditions they are
predicted to have encountered while dispersing as
hatchlings from their breeding grounds.
Support for the first mechanism was strongest from
breeding sites with strong directionality in ocean
currents. At these sites, adult turtles showed strong
directionality in their post-breeding movements, which
were significantly oriented with the direction of ocean
current flows. For example, at Ascension Island
(equatorial Atlantic), all of the 20 tracked green turtles
and Lagrangian drifters traveled broadly west toward
the coast of Brazil (Fig. 3a). From another four sites;
Zakynthos (Mediterranean; Fig. 3b), China (Guangdong province; Fig. 3c), Taiwan (South China Sea; Fig.
3d and Appendix A: Fig. A2a), and Puerto Rico
(Caribbean; Fig. 3e and Appendix A: Fig. A2b) all
turtles and drifters traveled broadly either (1) north and
south or (2) east and west in line with strong bifurcation
in current flows at the respective breeding beaches. At all
these sites, significantly more turtles than expected by
chance migrated in the same direction as ocean current
flows (P , 0.002 in all cases; Fig. 3).
Further support for the first mechanism came from
breeding sites where ocean currents were more variable
and dispersed. Here, turtle migrations were also widely
dispersed or directed toward a subset of a range of
habitats encountered along passive dispersal routes. For
example, .30 green and olive ridley turtles tracked from
three different nesting sites in northern Australia
migrated to a range of different foraging habitats along
a subset of a broad range of drift trajectories (Fig. 4a,
Appendix A: Fig. A2c, d). All drifters from Tortuguero
National Park (on the Caribbean coast of Costa Rica)
initially drifted east along the coast of Panama toward
Columbia in a large standing eddy before looping back
broadly northwest toward Nicaragua, Honduras, Belize,
and Mexico. All 12 green and hawksbill turtles tracked
from this site migrated broadly northwest along more
direct routes, and opposing the initial eastward flowing
currents, to foraging grounds in Nicaragua, Honduras,
and Belize that they would have encountered while
drifting (Fig. 4b). Hence at these sites, while turtles do
not necessarily migrate in the same direction as ocean
currents, turtles still all migrated to sites that they are
expected to have encountered while drifting (see also
Appendix A: Fig. A2e–o).
All sites where the nearest potential adult coastal
foraging areas downstream (as determined by ocean
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REBECCA SCOTT ET AL.
Ecology, Vol. 95, No. 10
entirely coastal shuttling migrations (avoiding any open
sea crossings). For example, loggerhead turtles from
North Carolina shuttled north and south along the coast
to seasonal neritic habitats along the eastern U.S. coast
(Fig. 4e). Furthermore, distances of the nearest potential
adult cheloniid neritic foraging grounds downstream of
prevailing ocean current flows were significantly further
at those sites supporting mechanism two vs. mechanism
one (Mann-Whitney Wilcoxon test, W ¼ 351; P , 0.001,
mean 5781 km; SD ¼ 1525 km and mean 600 km; SD ¼
555 km respectively).
Leatherback turtles (Dermochelyidae family)
FIG. 2. Dispersion of (a) simulated model and (b) empirical
surface buoy drift trajectories (based on the spread-circular SD
of drift trajectories, and downstream distance to nearest land
mass) vs. the corresponding migration strategy designated for
that cheloniid turtle breeding population. Black circles correspond to turtle populations (e.g., Ascension Island and
Zakynthos) where adult migrations and hatchling drift trajectories were designated as showing strong and overlapping
directionality. Open circles correspond to populations (e.g., in
northern Australia) where adult migrations and hatchling drift
trajectories were designated as more dispersed and variable.
Gray circles correspond to populations (e.g., in North Carolina,
USA and the Cocos [Keeling] Islands) where ocean circulation
patterns transported hatchlings to sites too far away (.3000
km; dashed line) to return from as adults.
current flows) were ;4000–8000 km from the natal area
(and thus exceeding the upper observed/predicted limit
on adult migration distances; Hays and Scott 2013)
provided support for the second mechanism. At these
sites, turtles adopted one of the less typical postbreeding migration strategies (Fig. 4c–e, Appendix A:
Fig. A3a–c). At island archipelago nesting rookeries,
turtles either remained locally resident, as was the case
for the nonmigratory green turtles breeding in the
Indian Ocean’s Cocos (Keeling) Islands (Fig. 4d), or
foraged relatively nearby in oceanic waters, as was the
case for some loggerhead turtles tracked from the
eastern Atlantic Cape Verde Islands (Fig. 4c). At
mainland nesting rookeries, turtles tended to perform
Leatherback turtle movements seem to be shaped by
ocean currents through more direct processes, whereby
ocean currents directly influence their prey distributions
and thus their broad-scale movement patterns are in
accordance with the broad-scale patterns in ocean
circulation. For example, the foraging movements of
leatherback turtles tracked from South Africa were in
close association with the southward-flowing Agulhas
current and areas of high eddy activity, which would
shape the dispersal of hatchling leatherback turtles and
the distribution of the pelagic prey items consumed by
this species (Fig. 4f ). In the North Atlantic, migrating
adult leatherback turtles and drifting hatchling turtles
from four nesting regions all dispersed widely throughout this ocean basin in agreement with the patterns of
the surface ocean current systems (Appendix A: Fig.
A4a). This was also apparent in other ocean basins, e.g.,
from nesting rookeries in the Indo-Pacific region, adult
turtles migrations were again consistent with the general
patterns in the surface ocean circulation and hatchling
drift (Appendix A: Fig. A4b–d).
DISCUSSION
Our study provides an advance in the study of animal
migration, by providing strong evidence for the hypothesis that the passive drift experiences of early life stages
may shape adult migration routes for a well known
group of migrators, the sea turtles. This pattern
contrasts with the processes that shape migration and
movement routes to foraging sites in other taxa, where
social learning or innate behaviors (e.g., compass
bearings) are often important. While the hatchling
dispersal phase is the least understood sea turtle lifehistory stage, we highlight that the importance of
studying hatchling dispersal extends beyond the direct
implications for the early life-history stages, to implications for turtles throughout adulthood.
The foraging location of adult turtles can be defined in
terms of (1) the direction from the nesting area and (2)
the distance. Our evidence highlights the interplay
between distance and direction and the fact that ocean
currents drive the ontogenetic development of both these
components of migration, developing arguments made
in a previous study focusing on an individual breeding
site (Hays et al. 2010). For adult cheloniid sea turtles,
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2845
FIG. 3. Sites where adult turtle tracks and drifter routes showed strong and significantly overlapping directionality (mechanism one
populations). Maps depict adult turtle foraging locations (black squares) and adult turtle locations at 500 km (blue circles; used to
derive travel bearings) from their nesting sites (white stars). Colored lines depict the range of drift trajectories at each site. Histograms
depict the number of tracked turtles that traveled .500 km that were observed to travel in the same direction (within 158) as drift
trajectories and the expected proportion traveling in the same direction as drift trajectories based on 1000 sets of randomly generated
turtle travel bearings (see Methods) . Significantly more turtles were observed (indicated by solid arrows in each histogram) to travel in
the same direction as drift trajectories than could be expected by chance alone (P , 0.002 in all cases). (a) Green turtles (n ¼ 20) tracked
from Ascension Island (Papi et al. 2000, Hays et al. 2002). All turtles and drifters traveled west toward the coast of Brazil. (b)
Loggerhead turtles (n¼17) tracked from Zakynthos (see Hays et al. 2010). Twelve turtles migrated .500 km, the majority of turtles and
drifters (.90%) traveled north to the Adriatic, and the remaining three turtles that migrated .500 km and drifters traveled broadly
southeast or southwest. (c) Green turtles (n ¼ 4) tracked from China (Song et al. 2002, Chan et al. 2003). Three turtles (and 76% of
drifters) migrated west-southwest to south China, while one turtle (and 14% of drifters) traveled northeast toward Okinawa Island,
Japan. (d) Green turtles (n ¼ 8) tracked from Taiwan (Cheng 2000; see also Appendix A: Fig. A2a). Five turtles migrated .500 km, all
turtles migrated broadly northeast or southwest toward Japan and China in accordance with the bifurcation of drifters. (e) Hawksbill
turtles (n ¼ 15) tracked from Mona Island, Puerto Rico (Van Dam et al. 2008; see also Fig. A2b). Eight turtles foraged around Puerto
Rico, four migrated .500 km broadly east or northwest toward other Caribbean islands, and three traveled broadly west to the
Dominican Republic, Nicaragua, and Honduras. Lagrangian drifters traveled broadly east, north, and northwest (;60%), the rest
traveled broadly west along the southern coast of the Dominican Republic, and toward Nicaragua, Honduras, Belize, and Mexico.
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REBECCA SCOTT ET AL.
the upper ceiling on migration distance between
breeding and foraging areas seems to be around 3000
km, with this ceiling set by the maximum fat store
available to sustain fasting during oceanic crossings
(Hays and Scott 2013). Our evidence suggests that this
ceiling seems to drive the migration behavior of adult
turtles. For example, for some breeding sites, hatchlings
would drift to mainland sites .3000 km away, such as
the Cocos (Keeling) Islands (Indian Ocean) where green
turtles nest (Whiting et al. 2008). These very distant
foraging sites (e.g., the coast of Africa and Madagascar
for hatchlings drifting from the Cocos Islands) may
contain good adult foraging areas (e.g., see Hughes
1974), but exceed the ceiling for their regular breeding
migrations. Hence in these cases, instead of the more
typical adult cheloniid post-breeding migrations through
oceanic waters to discrete neritic foraging grounds
(shaped by mechanism one of the hypotheses), we found
that adult turtles tended to adopt one of the less typical
migration strategies such as pelagic foraging (e.g.,
Hawkes et al. 2006) or residence at the breeding grounds
from more isolated island rookeries (Whiting et al. 2008)
or coastal shuttling to fixed/seasonal habitats from
mainland nesting rookeries (e.g., Hawkes et al. 2007).
For turtles adopting these less typical strategies (consistent with mechanism two of our drift hypothesis), the
movements and foraging areas of these turtles are likely
to be driven by previous experiences after the juvenile
oceanic development phase is over and turtles start
recruiting back to subadult coastal development habitats
close to their natal areas. Indeed, some juvenile turtles
have been directly tracked recruiting back to foraging
sites close to their breeding grounds (Nichols et al. 2000,
Eckert et al. 2008, Peckham et al. 2011). This upper limit
on migration distance in sea turtles contrasts with that
of other taxa. For example, birds can migrate much
further than swimming migrants (e.g., Hein et al. 2012,
Hays and Scott 2013) due to the higher travel speeds
they can attain (e.g., Alexander 1998). Indeed, this
enables birds such as Sooty Shearwaters (Puffinus
griseus) and Arctic Terns (Sterna paradisaea) to perform
annual migrations that can span .17 000 km between
the Arctic and Antarctic, to take advantage of an endless
summer, and to exploit seasonally available resources
(Shaffer et al. 2006, Egevang et al. 2010).
For cheloniid turtle populations, the two variants of
the hatchling drift mechanism could be used to explain
all the different adult post-nesting migration strategies.
However, leatherback turtles have a fundamentally
different pattern of movement and foraging ecology.
In contrast to adult cheloniid turtles, leatherbacks do
not maintain close fidelity to foraging grounds, but
instead are great ocean wanderers/pelagic foragers. They
consequently forage in transit and have a pattern of
broad-scale pan-oceanic movements that can extend up
to 11 000 km from their breeding grounds (e.g., Benson
et al. 2011). Nevertheless, even for these ocean
wanderers, hatchling drift patterns can explain their
Ecology, Vol. 95, No. 10
broad patterns of movement, such as the tendency for
leatherbacks nesting in the North Atlantic region to
remain in that ocean basin rather than traveling to the
South Atlantic and vice versa. Furthermore, ocean
currents directly influence their drifting prey distributions, which are concentrated by oceanographic features
such as mesoscale eddies, convergences, and upwellings
(e.g., Lambardi et al. 2008). Indeed, a previous, more
detailed analysis into the tracks of nine leatherback
turtles tracked from South Africa revealed that the
movements of these turtles were virtually indistinguishable from those of passive Lagrangian drifters, with the
variability in ocean currents and eddy activity explaining
their route variability and foraging hotspots (Lambardi
et al. 2008). The movements of adult leatherback turtles
are thus likely to be shaped by the interplay between
local prey distributions and their past experiences as
drifting hatchlings.
Given the high mortality rates of hatchling sea turtles,
which have a protracted and highly dispersive juvenile
life stage, one would expect a strong selective pressure
for turtles that survive to maturity to imprint on (and
return to) foraging sites that they had encountered along
successful drift routes. Indeed, the ability to return to
habitats is well documented in breeding sea turtles,
which can be highly philopatric to their natal areas and
also show high fidelity to their foraging habitats (Bowen
and Karl 2007, Broderick et al. 2007). Other marine
species with juvenile dispersal phases, such as salmon,
also imprint on natal sites as juveniles and later return as
adults (Putman et al. 2013), and there is increasing
evidence that a range of species, including sea turtles and
salmon, are able to use information from the Earth’s
magnetic field to return to target sites (Lohmann et al.
2008, Putman et al. 2013). Interestingly, our evidence for
imprinting on sites encountered by passive drift provides
an explanation for the conundrum that adult sea turtles
often travel extended distances from their breeding sites
to their foraging grounds, when suitable foraging
grounds exist much closer.
The processes that drive differences in the ontogeny of
migration across taxa are enigmatic, e.g., social learning
vs. innate behaviors vs. passive drift. Social learning
(e.g., in whales) can arise, for example, when there is
extended parental care of the offspring, so by necessity
the offspring travel with their parents for long periods
and hence learn migration routes (Weinrich 2008). In
contrast, in sea turtles there is no parental care: after egg
laying, adult sea turtles have no further interactions with
their developing eggs or hatchlings. In this situation, one
possibility is that turtles might have an innate tendency
to travel to certain sites, as has been shown in some bird
and insect species (e.g., Berthold and Helbig 1992,
Mouritsen et al. 2013). However, our evidence that
locations encountered by passive drift provide a good
explanation of subsequent adult foraging sites makes an
argument against innate selection of foraging locations
in sea turtles. In birds, the innate behavior seen in first-
October 2014
INNATE VS. LEARNED MIGRATION BEHAVIORS
2847
FIG. 4. Sites where (a–c) drift trajectories and adult cheloniid turtles migrations were more dispersed (mechanism one), or (c–e)
where the nearest land mass downstream of currents was too far for adult cheloniid turtles to return to (mechanism two), or (f )
where broad-scale patterns in ocean circulation reflect broad-scale movements of oceanic foraging leatherback turtles. (a) Olive
ridley turtles (n ¼ 8) tracked from the Tiwi Islands, North Australia (Whiting et al. 2007). Adult turtles migrated along a subset of a
range of potential drift routes to their foraging habitats. (b) Green and hawksbill (n ¼ 12) turtles tracked from Tortuguero, Costa
Rica (Troëng et al. 2005a, b). All drift trajectories were initially entrained in an area of high eddy activity, drifting broadly toward
Panama and Columbia before looping back and drifting northwest toward Nicaragua, Honduras, and Belize. All adult turtles
migrated along more direct routes broadly northwest to Nicaragua, Honduras, and Belize. (c) Oceanic vs. neritic foraging
loggerhead turtles (n ¼ 10) from the Cape Verde Islands (Hawkes et al. 2006). Seven turtles foraged in oceanic waters, while three
turtles migrated southeast to the coast of Sierra Leone; the only land mass encountered by drifters within 3000 km (dashed line) of
the natal area. Prevailing drift routes transported hatchlings into the open ocean and to very distant (.4000 km) land masses. Thus,
this site provides support for both mechanisms one and two. (d) Nonmigratory green turtles (n ¼ 6) from the Cocos Islands
(Whiting et al. 2008). Drifters traveled broadly west and south and did not encounter land until crossing the Indian Ocean and
reaching the coasts of Madagascar and East Africa (.5000 km away). Only two drifters passed close to other land masses within a
3000 km buffer of the natal area (dashed line), drifting near Indonesia and into the Bay of Bengal. (e) Coastal shuttling loggerhead
turtles (n ¼ 12) from North Carolina (Hawkes et al. 2007). Drifters traveled in the North Atlantic gyre to eastern Atlantic coastal
habitats .5000 km away. Turtles traveled north or south along the coast to fixed/seasonal coastal habitats rather than migrating
away from the mainland. (f ) Leatherback (n ¼ 9) and loggerhead (n ¼ 3) turtles from South Africa (Luschi et al. 2006). The foraging
movements of leatherback turtles were in close association with the southward flowing Agulhas Current and areas of high eddy
activity. However, three loggerhead turtles migrated north along the coast. Drifters entered both the Indian and South Atlantic
Oceans, transporting cheloniid hatchlings too far away from their natal area for turtles to return as adults. Symbols follow those
detailed in Fig. 3, however black circles (f ) correspond to high-use areas (occupied by 1 leatherback turtles).
2848
REBECCA SCOTT ET AL.
time migrants is essentially to follow a certain compass
bearing, and the efficiency of this system may be
updated in subsequent trips through learned behaviors
(e.g., Perdeck 1958, Berthold and Helbig 1992, Pettit et
al. 2013). This innate behavior in birds is likely
operating successfully in a fairly simple broad-scale
landscape of suitable foraging environments, so a
straightforward rule for first-time migrants (e.g., ‘‘head
west-southwest,’’ as in the case of starlings in Europe;
Perdeck 1958) may suffice. It may be that such a simple
system in sea turtles, i.e., for adults to simply follow an
innate compass heading on their first post-nesting
migration, is less reliable than returning to suitable
foraging grounds they have previously encountered.
While it is widely accepted that ocean currents drive
the general dispersion of hatchlings from their natal
beaches, and as suggested here, the ontogeny of their
subsequent breeding migrations, there is growing
evidence that small amounts of active directional
swimming based on perception of the local geomagnetic
field at favorable range limits (Lohmann et al. 2001) can
help reduce the probability of advection to unfavorable
areas (e.g., Scott et al. 2012b). Since hatchlings have not
been observed to show any geomagnetic directional
swimming responses within favorable range limits
(Lohmann et al. 2001), there is no evidence for any
innate behavior that may alter the general dispersal
pathways of turtles drifting along safe trajectories.
Essentially, directional swimming may reduce the
proportion of drift scenarios that are unsuccessful, in
that they lead to hatchling death (e.g., through cold
stunning at high latitude waters) or drift to very distant
mainland sites that adults do not subsequently return to.
However, even if hatchlings embark on periods of
directional swimming, predominant drift patterns will be
dominated by the ambient current flows (Putman et al.
2012, Scott et al. 2012a, b). Consequently, any assumptions of directional swimming by hatchlings will not
undermine our central conclusions that adult foraging
sites reflect hatchling drift experiences. Furthermore, we
looked at the first year of drift only, because as turtles
develop, their dispersal becomes less passive with
increasing size and swimming strength. While assumptions of current-induced hatchling drift are reasonable,
extreme weather events like large storms can displace
hatchling sea turtles thousands of kilometers along
aberrant dispersal routes not well sampled by surface
drifter buoy data or fully represented in model
simulations (Monzón-Argüello et al. 2012). With
increased storm activity predicted under future climate
change scenarios (Webster et al. 2005), if hatchling
displacements are favorable for survival, storms may
play an increasing role in shaping the ontogeny of sea
turtle migrations.
By analyzing current flows across years, our analysis
will have captured aspects of both intra- and interannual
variability in prevailing ocean circulation patterns; the
primary determinant of hatchling dispersal. Reassuring-
Ecology, Vol. 95, No. 10
ly, modeled Lagrangian trajectories and in-situ Lagrangian buoy trajectories revealed the same large-scale
ocean circulation patterns that were consistent with the
circulation patterns generated by other site-specific
studies where higher resolution regional models and/or
more detailed analysis into intra/interannual variation in
ocean currents have been carried out (e.g., Hays et al.
2010). While data from surface drifter buoy data sets are
limited in spatial and temporal coverage, the increasing
resolution and accuracy of ocean models that can be
used to generate millions of Lagrangian drift trajectories
(Baltazar-Soares et al. 2014) has great utility for
assessing spatially and temporally relevant dispersal
patterns of drifting organisms. Lagrangian analyses of
ocean currents thus provides the potential to aid the
understanding of the movements and migrations of
other marine species with passive dispersal life stages
such as juvenile fish and plankton (Chapman et al. 2011,
Baltazar-Soares et al. 2014) and, hence, will allow the
applicability of our conclusions that early-life drift
patterns shape adult migration routes to be tested across
other taxa. There may be several reasons why foraging
site selection based on previous drift has adaptive benefit
to sea turtles. Not only may this be an effective way to
help ensure that adults find suitable foraging sites, but
additionally it may allow new foraging sites to emerge as
conditions change. Furthermore, where hatchlings
disperse widely to suitable foraging sites, this mechanism
may allow many different foraging sites to emerge,
helping to reduce the impacts of density-dependent
reduced foraging habitat quality and also helping ensure
population survival if some foraging sites are quickly
degraded. In addition, our evidence suggests that new
foraging sites for sea turtles will emerge as a consequence of any changes in ocean circulation impacting
hatchling drift scenarios. In short, our results highlight
the intimate link between the dispersion patterns for
hatchling turtles and the movement of adults, and we
suggest that assessing this link between juvenile and
adult stages of other marine species may be a fruitful
and timely area of research given the wide availability of
ocean current modeling techniques and animal movement data sets.
ACKNOWLEDGMENTS
R. Scott was funded by a Natural Environment Research
Council (NERC) PhD studentship supervised by G. C. Hays
and R. Marsh, and is currently funded by The Future Ocean.
We thank the huge number of people who have been involved
in sea turtle tracking studies that have made this review
possible. We are also grateful to Jeff Blundell for assistance in
the local implementation of the ARIANE trajectory software,
and to Andrew Coward and Beverly de Cuevas (National
Oceanography Centre; NOC) for making model output from
NEMO freely available. We thank Bruno Blanke and Nicolas
Grima for freely providing ARIANE to the oceanographic
community. NEMO is a state-of-the-art, portable modeling
framework developed by a consortium of European institutions, namely the National Centre for Scientific Research
(CNRS), Paris, the UK Met Office (UKMO), Mercator-Ocean,
and the UK National Environment Research Council (NERC).
October 2014
INNATE VS. LEARNED MIGRATION BEHAVIORS
The latter development was funded under the NERC ‘‘Oceans
2025’’ program. Andrew Yool (NOC) assisted in the animation
of particle trajectories shown in the supplementary video.
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SUPPLEMENTAL MATERIAL
Appendix A
Figures showing further hatchling drift scenarios, adult turtle migrations, and associated satellite tracking references (Ecological
Archives E095-246-A1).
Appendix B
A movie showing particle tracking results from 42 turtle nesting sites showing 1-yr-long drift scenarios for hatchlings (Ecological
Archives E095-246-A2).