Vol. 557: 247–259, 2016
doi: 10.3354/meps11874

MARINE ECOLOGY PROGRESS SERIESMar Ecol Prog Ser

Published September 28

OPEN
ACCESS

Spatial variation in directional swimming enables
juvenile sea turtles to reach and remain inproductive waters
F. Christiansen1, 2,*, N. F. Putman3, 4, R. Farman5, D. M. Parker6, M. R. Rice7,
J. J. Polovina8, G. H. Balazs8, G. C. Hays1
1

Deakin University, Geelong. Centre for Integrative Ecology, School of Life and Environmental Sciences,
Warrnambool Campus, VIC 3280, Australia
2
Cetacean Research Unit, School of Veterinary and Life Sciences, Murdoch University, Murdoch, WA 6150, Australia
3
Cooperative Institute for Marine and Atmospheric Studies, Rosenstiel School for Marine and Atmospheric Science,
University of Miami, 4600 Rickenbacker Causeway, Miami, FL 33149, USA
4
Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration,
4301 Rickenbacker Causeway, Miami, FL 33149, USA
5
Aquarium des Lagons, B.P. 8185, Nouméa 98807, Nouvelle-Calédonie
6
Joint Institute for Marine and Atmospheric Research, University of Hawaii, 2032 SE Oregon State University Drive,
Newport, Oregon 97365, USA
7
Hawaii Preparatory Academy, 65−1692 Kohala Mountain Road, Kamuela, Hawaii 96743, USA
8
Pacific Islands Fisheries Science Center, National Marine Fisheries Service,
National Oceanic and Atmospheric Administration, 2570 Dole Street, Honolulu, Hawaii 96822, USA

ABSTRACT: Ocean currents play an important role in the movement and distribution of organisms and for small animals it is often assumed that their movements in the ocean are determinedby passive drift. Here we challenge this assumption by conducting an experiment at the scale ofan entire ocean basin to test whether small (∼35 cm) juvenile loggerhead sea turtles Caretta
caretta move independently of ocean currents. By comparing the trajectories of 46 satellite
tracked turtles (11 502 positions, 12 850 tracking days) with Lagrangian drifters (3 716 303 positions, 927 529 tracking days) and virtual particles tracked within the Hybrid Coordinate Ocean
Model (HYCOM), we found that in certain areas turtles moved in a similar manner to ocean currents, but in other areas turtle movement was markedly different from ocean currents, with turtles
moving to areas thousands of kilometres from where they would have drifted passively. We further found that turtles were distributed in more-productive areas than would be expected if their
movement depended on passive transport only. These findings demonstrate that regional variation in directional swimming contributes to young sea turtles reaching more favourable developmental habitats and supports laboratory work suggesting that young turtles have a map sense todetermine their location in a seemingly featureless ocean.
KEY WORDS: Animal tracking · Caretta caretta · HYCOM · Lagrangian drifters · Movement
ecology · Ocean currents

INTRODUCTION
Many animal species show impressive long-distance movement (Wehner 2003, Hein et al. 2012,
Hays & Scott 2013), which allows them to make useof spatiotemporal variations in resource availability*Corresponding author: f.christiansen@murdoch.edu.au

to maximize fitness (Alerstam et al. 2003, Dingle &Drake 2007, Putman et al. 2014). When the movement of animals is affected by flows in the medium
through which they journey (e.g. water and air),many animal species can identify flow-related displacement and adjust their movement accordingly© The authors 2016. Open Access under Creative Commons by
Attribution Licence. Use, distribution and reproduction are unrestricted. Authors and original publication must be credited.
Publisher: Inter-Research · www.int-res.com

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(Alerstam et al. 2003, Åkesson & Hedenström 2007,
Dingle & Drake 2007, Chapman et al. 2011, Putman& Mansfield 2015). This ability often varies withontogeny, as the ratio between flow strength and theanimals’ own ability of movement changes with age
and/or size (Chapman et al. 2011). While adults ofmany species seem able to compensate partially or
fully for flows, younger age classes are thought to beless capable of doing so and environmentallyinduced drift is thought to primarily account for
directional movement (Hays et al. 2010, Chapman etal. 2011).
With the ability to undertake long-distance movement in open oceans (Luschi et al. 2003), sea turtles
represent a model species with which to study the
ontogeny of movement in relation to ocean flows.
Adult turtles possess remarkable navigational abilities, possibly using a combination of the earth’s geomagnetic field (Luschi et al. 2007) and olfactory cues
(Hays et al. 2003), to travel from hundreds to thousands of kilometres between breeding and foraging
sites (Luschi et al. 2003, Hays & Scott 2013, Hays etal. 2014). In contrast, it has long been thought thatyoung sea turtles, because of their limited swimmingand diving abilities coupled with positive buoyancy,
drift passively with ocean currents (Luschi et al. 2003,
Hays et al. 2010). A recent tracking experiment calls
this hypothesis into question, showing that small turtles significantly diverge from simultaneously deployed drifters (designed to track currents at theocean surface) in a matter of days (Putman & Mansfield 2015). Model simulations further show that
directional swimming is necessary for young sea
turtles (i.e. hatchlings) to remain within favourable
ocean currents (Scott et al. 2012), and for ensuring
self-recruitment in physically open marine systems(Wolanski 2016). Likewise, a growing number of laboratory experiments indicate that hatchling loggerhead sea turtles Caretta caretta perceive both latitudinal and longitudinal information from componentsof the Earth’s geomagnetic field and show directionalswimming that could help them reach and/or maintain their position in favourable ocean areas (Lohmann & Lohmann 1994, Lohmann et al. 2001, 2004,
Putman et al. 2011).
Whether an animal swims actively or drifts passively
in the ocean will be determined not only by its physiological abilities, but also by what potential benefits aparticular movement strategy will bring. Interestingly,
not all magnetic fields that exist within the oceanic
range of loggerheads elicit robust orientation responses. For example, hatchling turtles exposed tomagnetic fields that correspond to productive ocean

regions were randomly oriented (Putman et al. 2015).
Presumably, highly directional swimming in response
to magnetic fields in such regions would not be
strongly selected for, as quickly transiting throughsuch areas is unlikely to be beneficial (Putman et al.
2015). Simulating this spatially variable orientation
behaviour observed under laboratory conditions leads
turtles into productive areas favourable for development (Putman et al. 2012, 2015), however, whether or
not this occurs in the wild is not known. Further, whilesatellite-tracked juvenile turtles have been found toaggregate in more productive waters (Polovina et al.
2006, Abecassis et al. 2013), whether they actively
swim to these areas or are passively carried there byocean currents, is unknown.
The aim of this study is to extend our knowledge ofdirectional swimming in juvenile sea turtles in thewild, by investigating long-term (>1 yr) tracking dataof free-living juvenile loggerhead turtles across anentire ocean basin. To do this, we capitalize on theminiaturization of satellite tags that allow small
turtles to be tracked remotely (Kobayashi et al. 2014,
Scott et al. 2014). We also develop novel analyticalmethods to test the main hypotheses of juvenile turtle
movement derived from laboratory and modellingstudies: that directional movement by turtles (1)broadly varies by region and (2) facilitates movement
into more favourable ocean habitats than would be
expected if directional movement exclusively depended on ocean currents (Lohmann et al. 2012, Putman
et al. 2012, 2015, Mansfield & Putman 2013). We
address these questions by comparing actual tracks
of juvenile loggerheads with passive drift scenarios,
using Lagrangian drifters and the Hybrid Coordinate
Ocean Model (HYCOM).

MATERIALS AND METHODS
Turtle data
We satellite-tagged 46 juvenile loggerhead turtles
and released them into the open ocean 367 km southwest of New Caledonia (24.98° S, 163.03° E) in thewestern Pacific (Fig. 1a) on 21 September 2012 in
1250 m deep water with a temperature of 20.7°C(see Table S1 in Supplement 1 at www.int-res.com/
articles/suppl/m557p247_supp/). The turtles were
hatched and raised, for the purpose of this study, at
the Aquarium des Lagons in Noumea, New Caledonia, from eggs collected from multiple excavatednests from beaches in New Caledonia. The turtles
were 1 yr 7 mo old at the time of release. Upon

Christiansen et al: Directional swimming in juvenile turtles

release their mean ± SD straight carapace length was
34.8 ± 2.7 cm (min. = 31.0, max. = 41.0 cm) and theirweight 6.2 ± 1.2 kg (min. = 4.0, max. = 9.1 kg)(Table 1 in Supplement 1). The turtles were tracked
using Smart Position or Temperature (SPOT5) satellite transmitting nano-tags developed by Wildlife
Computers (wildlifecomputers.com). The SPOT5 tags
were attached to the turtle’s carapace using epoxy
resin and fiberglass cloth. The tags use the Argos
satellite network for geopositioning and were programmed with a 6/42 transmission duty cycle (6 h on,
42 h off, per 48 h interval). Each nano-tag weighed
48 g, which was between 0.5 and 1.2% of the weightof the turtles. The Argos system assigns each calculated position with a specified level of accuracy. We
reconstructed the tracks of the turtles using all
good-quality locations (location classes 1−3, errors<1500 m radius, see www.argos-system.org) while
excluding poor-quality locations (location classes A,
B, 0 and Z). We also processed the data through a
speed filter, which removed all positions that would
have required a swim speed > 5 m s−1 (n = 341 positions or 2.9% of all positions). This threshold waschosen based on the distribution of swim speeds.

Ocean currents
Movement tracks from passively moving Atlantic
Oceanographic & Meteorological Laboratory (AOML)
Lagrangian drifters were obtained from the NOAA
Global Drifter Program, which is freely available(www.aoml.noaa.gov/phod/dac/index.php). The drifters consist of a small surface buoy attached to a subsurface drogue (sea anchor) that is centred at 15 m
below the surface. With oceanic juvenile loggerhead
turtles spending most of their time (90%) in thisupper 15 m layer (Howell et al. 2010), the Lagrangian
drifter data should be representative of the ocean
currents that are most likely to influence turtle movement (Howell et al. 2010). To obtain a sufficiently
large drifter dataset to cover the area traversed by
the turtles (Fig. 1a), we downloaded drifter data from1980 to 2013 (data were available until June 30,2013). Although the drifters transmit their position
between 16 and 20 times per day, the drifter data areprovided in an interpolated format with a 6 h temporal resolution. To maximize spatial coverage in theregion where turtles were tracked and account forphysical processes that might influence turtle movement at the ocean surface (e.g. Stoke’s drift, windage), we also included data from Lagrangian drifters
which had lost their sea anchor.

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Modelled ocean velocity was based upon surface
currents of the Global Hybrid Coordinate Ocean
Model (HYCOM) (Bleck 2002). Global HYCOM has
output at 00:00 h GMT with a spatial resolution of
0.08° (~7 km grid-spacing at mid-latitudes). The
model is forced using wind stress, wind speed, heat
flux, precipitation, and river runoff. Hindcast output
assimilates satellite altimetry data, sea surface temperature, and in situ measurements from XBTs
(expendable bathythermographs), Argo floats, andmoored buoys (http://hycom.org). Thus, HYCOM
hindcasts accurately resolve mesoscale features suchas meandering currents, fronts, filaments and oceanic eddies (Bleck 2002, Chassignet et al. 2007) andrealistically characterize the movement of passively
drifting objects at the ocean surface (Putman & He2013). HYCOM output was taken for the period from21 September 2012 through 25 December 2013, thesame time-span over which turtles were tracked.

In silico trajectories
Model simulations were used to compare themovement of turtles, drifters and HYCOM particles.
The study area (Latmin = 60° S, Latmax = 0° S, Longmin =120° E, Longmax = 140° W) (Fig. 1a) was first divided
into 6000 grid cells of size 1° × 1°. A contingency
table (similar to van Sebille 2014), representing all
possible transitions (i.e. movements) between gridcells in the study area, was created. Each turtle track
was binned into an array of grid cells, and the number of transitions between different grid cells wasadded to the contingency table. From the resulting
contingency table we estimated the proportion oftime that turtles moved from a specific grid cell toanother in the study area (by scaling the values in arow so that they sum to 1). For grid cells that were
never visited by a turtle, all the transition values
were set to 0. To conform to the same temporal resolution as the Lagrangian drifter dataset, we first interpolated our turtle data to a 6 h temporal resolution
based on linear interpolation. Thus, each cell in theturtle transition matrix represented the probability ofa turtle moving from one grid cell to another over the
next 6 h. The same procedure was performed using
the tracks of the Lagrangian drifter buoys that passed
through the study area between 1980 and 2013. We
thus ended up with 2 transition probability matrices,
one for the turtles, and one for the drifters, with thesame temporal resolution.
Based on the estimated transition probability matrices, we simulated the trajectories of 1000 turtle par-250

Mar Ecol Prog Ser 557: 247–259, 2016

Fig. 1. Movement of juvenile loggerhead turtles Caretta caretta in relation to passive particles. (a) Interpolated real tracks of
46 juvenile loggerheads released southwest off New Caledonia on 21 September 2012. Black asterisk: release point (also inpanels c−e); black solid line: median track for all the animals with each 120 d period marked by a black node. Colour indicatesthe day of tracking. (b) Number of Lagrangian drifters (indicated by colour scale) passing through each grid cell in the study
area between 1980 and 2013. Red star: location of New Caledonia. (c−e) Cumulative distribution of 1000 simulated (c) turtle,(d) drifter and (e) HYCOM particles during one year of tracking. (f) Back-transformed fitted values of the distance between
simulated turtle and passive (drifters and HYCOM) particles as a function of time since release. Solid and dashed red lines:
average distance between simulated turtle and drifter particles, and simulated turtle and HYCOM particles, respectively; blue
line: average distance among drifter particles; purple line: average distance among HYCOM particles. (g) Density distributions of sea surface temperature (SST) encountered by simulated turtle (red), drifter (blue) and HYCOM (purple) particles over
1 yr of simulations. Black: density distribution of SST encountered by the real turtle tracks in panel (a). (h) Density distributionsof chl a concentration encountered by simulated turtle (red), drifter (blue) and HYCOM (purple) particles over 1 yr of simulations. Black: density distribution of chl a encountered by the real turtle tracks in panel (a)Christiansen et al: Directional swimming in juvenile turtles

ticles (using the turtle probability matrix), released at
the real turtle release site (24.98° S, 163.03° E) andtracked over one year. We refer to these as ‘simulated
turtle tracks’ as opposed to the ‘real turtle tracks’. The
simulated tracks were used in the subsequent testingof turtle movements against passive drift scenarios, sothat the same number of turtle and passive particles
could be released and compared. This approach further allowed particles to be released at several intervals along the trajectory of the real turtle track, toinfer regional differences in the degree of directionalswimming of juvenile turtles.
Along with the simulated turtle particles, 1000
drifter particles (based on the drifter probability matrix) were also released at the same location andtracked over the same time period. If a turtle or drifterparticle during this time ended up in a grid cell which
had no transition probabilities to other grid cells (anabsorbent cell), that trajectory was terminated.
ICHTHYOP v.2.21 particle-tracking software (Lett etal. 2008) was used to release 1000 virtual particles into
HYCOM (http://hycom.org/) output on 21 September
2012 within a 1° × 1° rectangle, centred on the releasesite of the turtles. For advection of particles through
HYCOM velocity fields, ICHTHYOP implemented a
Runge-Kutta 4th order time-stepping method whereby particle position was calculated each half-hour(Lett et al. 2008) and recorded every 6 h.
At the end of the simulations, the resulting turtle,
drifter and HYCOM trajectories were compared. The
spatial overlap between simulated turtle and passive
particles (drifter and HYCOM) was estimated, using
the area of intersection (AI) approach (Fieberg &Kochanny 2005), which provides a single measure ofoverlap between the 95% kernel utilization distributions of the turtle and the passive particles. AI ranges
between 0 (no overlap between the turtle and passive particles utilization distributions) and 1 (the turtle and passive particles have identical utilization
distributions). The in silico divergence rate between
the simulated turtle and passive (drifter and HYCOM) particles was investigated by estimating the
average distance between turtle and drifter particles,
and between turtle and HYCOM particles, as a function of time since release, using linear regression
models in R (R Core Team 2014). The relationshipbetween distance and time since release was nonlinear and was fitted after both variables were logtransformed. To be able to distinguish between
active (turtle driven) and passive (current driven)
divergence between particles, the average divergence rate between drifter particles (the drifter−
drifter distance as a function of time since release)251

and the average divergence rate between HYCOM
particles (the HYCOM−HYCOM distance as a function of time since release) were also calculated.
While the turtle transition matrix depicts the movement of turtles, which is a combination of physicalprocesses and the swimming behaviour of the turtles(Fossette et al. 2012), the drifter and HYCOM models
depict the movement attributable exclusively tophysical processes. Owing to the pooling of drifter
data across 30 yr, the drifters covered a wider rangeof oceanic conditions compared to those experienced
by the tracked turtles. In contrast, HYCOM depicts
ocean conditions during the same period in which
turtles were tracked. Therefore, the drifter data constitute a broad range of ocean transport scenarios(high dispersion), some of which may not be directly
relevant to the tracked turtles, whereas HYCOM will
display a smaller range of ocean transport scenarios(low dispersion), but presumably those directly relevant to the tracked turtles. However, HYCOM is necessarily a simplification of ocean circulation and does
not include all factors that contribute to the velocityof an object at the surface of the ocean (e.g. Stokes
drift, tides). Thus divergence between turtle movements and drifter movements might be due to spatiotemporal mismatch in oceanic conditions, whereas
divergence between turtle movements and HYCOM
might be due to model error. By comparing our turtle
tracks to both empirically derived and modelled passive particles that characterize long-term ocean
velocity in this region (i.e. drifters) and the ocean current conditions that were predicted to exist during
the tracking experiment (i.e. HYCOM) we were able
to cross-validate our models (Fossette et al. 2012) andbracket the range of oceanic conditions likely experienced by tracked turtles.

Spatial variations in directional movement
The optimal movement strategy for an animal may
change as the animal’s journey progresses and thedestination is approached (Chapman et al. 2011). To
investigate the degree of active directional movement versus passive drifting for the juvenile turtles
along their trajectory, simulated turtle and passive(drifter and HYCOM) particles were also released atregular intervals along the geographic median of thereal turtle tracks (Fig. 1a), and the resulting trajectories were compared 100 d after each release. Releasesites were positioned 120 d apart, starting from thereal release date (Day 0), with the last release site
being the median position of the real turtle tracks at

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Day 360. At each site, 100 turtle, 100 drifter and 100
HYCOM particles were released and tracked as
described above. Again, the divergence rates between simulated turtle and drifter particles, and between turtle and HYCOM particles, were estimatedfor each release site and compared to the divergence
rate between drifter particles and the divergence
rate between HYCOM particles, respectively.

Seasonal and yearly variations in ocean currents
To investigate the potential effects of seasonal variation in ocean currents on the spatial overlap anddivergence rate between simulated turtle and drifterparticles, the drifter data set was subdivided into the
4 austral seasons: summer (December−February),
autumn (March−May), winter (June−August) andspring (September−November) and the simulations
described above were repeated for each season. To
investigate yearly variations in ocean currents, thedrifter data set was similarly subdivided into 4 time
periods: Period 1 (1990−1995), Period 2 (1996−2001),
Period 3 (2002−2007) and Period 4 (2008−2013), andthe simulations were again repeated for each period.
Although a comparison of drifter movement between
individual years would have been more informative,
the low number of drifters passing through the study
area in a single year prevented this. Seasonal andyearly variations in ocean currents were not investigated for the HYCOM simulations as the reason forusing that model was to represent the ocean conditions that existed as turtles were moving throughthose oceanic regions at those particular times.

Sea-surface temperature and chlorophyll a
Sea turtles are ectotherms, meaning that their bodytemperature, and hence basal metabolic rate, isstrongly influenced by the surrounding sea temperature (Hochscheid et al. 2002, 2004, Southwood et al.
2003). Since metabolic rate influences growth andsurvival, remaining in a favourable temperature
range is likely to provide direct benefits for turtles.
The distribution of prey is another key factor influencing the movement of marine species (Sims et al.
2006). Oceanic juvenile loggerheads spend most of
their time (90%) in the upper 15 m of the water column (Howell et al. 2010) and are thought to be preying mostly on low trophic chain prey (neuston) thatare either at or near the water surface (Boyle & Limpus 2008). Since both primary and secondary produc-

tivity will be affected by sea surface temperature(SST) and chlorophyll a (chl a) concentration (Strömberg et al. 2009), these 2 variables are likely to influence sea turtle movement and distribution (Etnoyer
et al. 2006).
We compared the SST and chl a concentrations encountered by turtles and passive (drifter and HYCOM) particles in silico. SST data were obtained
from the National Oceanic and Atmospheric Administration’s (NOAA) National Climatic Data Center(NCDC) (www.ncdc.noaa.gov), at a temporal resolution of 1 d and a spatial grid resolution of 0.25°, covering the entire study area and tracking period. The
SST data were estimated by NOAA using infrared
satellite data from the Advanced Very High Resolution Radiometer (AVHRR) and in situ data from ships
and buoys, using the optimum interpolation method
described by Reynolds et al. (2007). Satellite-derived
surface chl a pigment concentration data were obtained from the Oregon State University Ocean Productivity reanalysis dataset (www.science.oregonstate.
edu/ocean.productivity/index.php), which is basedon MODIS chl a and temperature data, Sea-viewing
Wide Field-of-view Sensor (SeaWiFS) photosynthetically available radiation (PAR) (oceancolor.gsfc.nasa.
gov/cms/atbd/par), and estimates of euphotic zone
depth, using the Vertically Generalized Production
Model as the algorithm (Behrenfeld & Falkowski
1997). Chl a concentrations were obtained as 8 daverages and at a spatial grid resolution of 0.167°.
The distributions of SST and chl a values encountered by the simulated turtle, drifter and HYCOM
particles were compared using 2-sided KolmogorovSmirnov (K-S) tests. The critical value of the Dstatistic for the K-S test, DKS, was estimated based on
the sample size of the simulations (n = 1000) and alevel of significance of α = 0.05, which corresponded
to DKS = 0.043.

RESULTSIn situ trajectories
Location data of turtles were obtained from thetags for between 6 and 519 d (mean = 279, median =250), resulting in a total of 12 850 tracking days
between 2012 and 2014, during which the turtles
moved a cumulative distance of 196 to 10 201 km(mean = 5550, median = 5106) (Table S1 in Supplement 1). The mean and median time period between
2 Argos locations was 26.9 and 9.6 h, respectively.
After being released, the turtles moved rapidly southChristiansen et al: Directional swimming in juvenile turtles

towards the Tasman Sea area, located between New
Zealand and Australia (Fig. 1a, Animations S1 & S2at www.int-res.com/articles/suppl/m557p247_supp/).
After approximately 60 d the turtles reached the Tasman Sea, at which point their movement slowed
down and became more sinuous. The spread of theturtles also increased after reaching the Tasman Sea.
During the first half-year of tracking, the turtles werestill slowly moving south within the Tasman Sea,
however after this point their net movement shiftedslightly northwards before the tracks ended (Fig. 1a,
Animations S1 & S2).
To construct the turtle transition probability matrix
used for the model simulations, a total of 11 502 turtle
positions were used, covering 12 850 tracking days.
During this time, the turtles visited 566 (9.4%) of the6000 grid cells in the study area. From the grid cells
visited, the mean ± SD number of turtle positions was
20.3 ± 31.1 (min. = 1, max. = 402). To inform the transition probability matrix for the drifter data, 3 716 303
Lagrangian drifter positions were used, covering
927 529 tracking days in total. Between 1980 and2013 the drifters visited 5260 (87.7%) of the 6000 gridcells in the study area. From the grid cells visited,each cell was visited by (mean ± SD) 705 ± 604 drifter
buoys (min. = 1, max. = 3902). Hence, the drifter data
provided a good coverage of the study area (Fig. 1b).
The transition probability matrices showed thatmost transitions between grid cells occurred along the
diagonal of the matrix (Fig. S1 in Supplement 1). Thismeans that both turtles and drifters either stayed
within the same grid cell or moved to a grid cell directly adjacent to the current grid cell, or to one of its
diagonal neighbours, between time steps. No transitions were observed across 2 or more grid cells in asingle time step. While the drifter data set covered
transitions from all grid cells, the turtle transition matrix was limited to a fewer number of grid cells locatednear the centre of the study area (Fig. S1).

In silico trajectories
The simulated turtle and passive particles showeda clear difference in their trajectories (Fig. 1c−e, andAnimation S3). Consistent with the real turtle tracks(Fig. 1a & Animations S1 & S2), the simulated turtle
particles moved rapidly south towards the Tasman
Sea after being released (Fig. 1c & Animation S3). In
contrast, most of the drifter particles moved west andnorthwest towards the Australian east coast (Fig. 1d& Animation S3). The HYCOM particles were similarly distributed along the Australian east coast, as

253

well as south, but to a lesser extent compared to thedrifter particles (Fig. 1e & Animation S3). One year
into the simulations, the overlap of cumulative particle positions between the simulated turtles and thepassive particles was only 14.5 and 20.0% (area ofintersection), for drifter and HYCOM particles,
respectively. At this point, both the drifter andHYCOM particles had spread all along the Australian east coast, whereas most of the simulated turtle
particles were within the Tasman Sea area, withsome turtle particles having moved east off New
Zealand along the same latitude (Fig. 1c−e & Animation S3).
The rate of divergence between simulated turtle
and drifter particles (the slopes of the solid red line in
Fig. 1f & black line in Fig. S2a in Supplement 1) was
significantly higher than the divergence rate among
drifter particles (the slopes of the blue line in Fig. 1f &black line in Fig. S2b) (t = −35.8, p < 0.001, Table S2
in Supplement 1). The HYCOM simulations showedsimilar results, with the rate of divergence between
turtle and HYCOM particles (the slopes of thedashed red line in Fig. 1f & black line in Fig. S2c)
being significantly higher than the divergence rate
among HYCOM particles (the slope of the purple line
in Fig. 1f and black line in Fig. S2d) (t = 68.7, p <0.001, Table S2). The full model (Table S2) explained
55.0% (R2) of the variance in distance between particles. The divergence rate between particles (theslopes of the lines in Fig. 1f & Fig. S2) was always
higher at the beginning of the trajectories, but
decreased as the number of days since release
increased (Fig. 1f & Fig. S2).

Spatial variations in directional movement
The rate of divergence between simulated turtle
and passive particles (drifters and HYCOM) varied
across the trajectory of the turtles (Figs. 2 & 3). The
divergence rate was highest between turtle andpassive particles released at Day 0, as the simulated
turtles moved directly south from their real releasesite down towards the Tasman Sea area, in a direction more or less opposite to that of the ocean currents (i.e. drifters and HYCOM particles). Turtle
particles released at the other 3 simulated releasesites (Days 120, 240 and 360), all within the Tasman
Sea area, showed less divergence from the simulated passive particles (drifter and HYCOM); suggesting more similar movement of simulated turtles
and passive particles in this region (Figs. 2 & 3). In
support of this, the area of intersection between the

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simulated turtle and drifter particles after 100 d was
greatest for release sites in the Tasman Sea (AID0 =27.2%, AID120 = 56.8%, AID240 = 56.0%, AID360 =
44.9%). Likewise, the areas of intersection between
simulated turtle and HYCOM particles were also
greatest in the southern Tasman Sea, though insome cases distributions of HYCOM particles andsimulated turtles were quite different and swimmingby turtles in this region cannot entirely be ruled out(AID0 = 22.5%, AID120 = 21.8%, AID240 = 57.3%,
AID360 = 27.3%).

Seasonal and yearly variations in ocean currents
The in silico drifter particles showed small seasonalvariation in their trajectories (Fig. S3 in Supplement 1). Ocean currents were similar in summer,
autumn and spring, with drifters distributing themselves along most of the Australian east coast. In winter, however, the majority of drifters travelled offshore along the northeastern coast of Australia, with
only a few drifters travelling south (Fig. S3). Similarly, there was not much yearly variation in ocean

Fig. 2. Cumulative distributions of 100 simulated turtle (red), drifter (blue) and HYCOM (purple) particles released at different
locations (black asterisks) along the trajectory (every 120 d, at Days 0, 120, 240, 360) of the real turtle track (see Fig. 1a).
The duration of each simulated track was 100 d

Christiansen et al: Directional swimming in juvenile turtles

currents, with drifters from all time periods distributing themselves along most of the Australian east
coast (Fig. S4 in Supplement 1). The only devianceswere in Period 1, when most drifters ended up in thenortheastern part of Australia and none moved into
the Tasman Sea area, and in Period 2, when the255

drifters travelled mainly along the southeastern Australian coast and in the northern parts of the Tasman
Sea (Fig. S4). There was a small overlap between thesimulated turtles and drifters particles irrespective of
season (AIsummer = 13.8%, n = 940 263; AIautumn =10.9%, n = 966 648; AIwinter = 10.7%, n = 922 994;

Fig. 3. Differences in divergence rates, SST and chl a concentrations encountered between 100 simulated turtle and passive(drifters and HYCOM) particles released at different locations along the trajectory (every 120 d, at Days 0, 120, 240 and 360) of
the geographic median of real turtle tracks (see Fig. 1a). The duration of each simulated track was 100 d. (a) Back-transformed
fitted values of the distance between simulated turtle and passive (drifters and HYCOM) particles as a function of time sincerelease; solid and dashed red lines: average distance between simulated turtle and drifter particles, and simulated turtle andHYCOM particles, respectively; blue line: average distance among drifter particles; purple line: average distance among HYCOM particles. (b) Density distributions of sea surface temperature (SST) encountered by simulated turtle (red), drifter (blue)and HYCOM (purple) particles. (c) Density distributions of chl a concentrations encountered by simulated turtle (red), drifter(blue) and HYCOM (purple) particles

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Mar Ecol Prog Ser 557: 247–259, 2016

AIspring = 17.6%, n = 886 398) and time period(AIPeriod1 = 7.6%, n = 537 230; AIPeriod2 = 24.3%, n =725 319; AIPeriod3 = 11.2%, n = 1 204 671; AIPeriod4 =20.5%, n = 1 152 444). For all seasons and time periods, the rate of divergence (the slopes of the lines inFig. S5 in Supplement 1) between simulated turtle
and drifter particles was significantly higher thanbetween drifter particles (Fig. S5). Our results werehence robust to seasonal and yearly variations inocean currents.

Sea-surface temperature and chlorophyll a
The SSTs encountered by the simulated particles
differed significantly between turtles and drifters(DKS = 0.760, p < 0.001) and between turtles andHYCOM particles (DKS = 0.664, p < 0.001) (Fig. 1g).
Consistent with the SST encountered by the real
turtle tracks (18.5 ± 1.8°C, min. = 13.0, max. = 24.3,
Animation S1), most simulated turtle particles experienced relatively lower SST (17.8 ± 2.9°C, min. =9.0, max. = 27.0) compared to the drifter andHYCOM particles, which spread across a wider
range of latitudes and on average experienced
higher SST (Drifter: 24.2 ± 3.1°C, min. = 9.6, max. =31.2; HYCOM: 23.0 ± 3.1°C, min. = 9.0, max. = 30.5)(Fig. 1g).
The chl a concentrations encountered by the simulated particles also differed significantly between
turtles and drifters (DKS = 0.459, p < 0.001) and between turtles and HYCOM particles (DKS = 0.270, p <0.001) (Fig. 1h). Both the real (578 ± 244 mg C m−2
d−1, median = 543, min. = 115, max. = 4432, Animation S2) and simulated turtles (500 ± 223 mg C m−2
d−1, median = 453, min. = 23, max. = 7810) encountered higher chl a concentrations compared to thesimulated drifter (370 ± 286 mg C m−2 d−1, median =284, min. = 21, max. = 9250) and HYCOM particles(446 ± 342 mg C m−2 d−1, median = 360, min. = 27,
max. = 9250) (Fig. 1h).
The overlap in SST and chl a concentration encountered between the simulated turtle and passive(drifter and HYCOM) particles varied along the trajectory of the turtles (Fig. 3). Consistent with the fullyear simulations (Fig. 1), the simulated turtle particles released on Day 0 experienced lower SST andhigher chl a concentrations on average compared tothe passive particles. Simulated turtle particles released further down the trajectory of the real turtle
tracks (Days 120, 240 and 360) experienced similar
SST and chl a concentrations to the passive particles(Fig. 3).

DISCUSSION
This study shows, at the scale of an entire ocean
basin, that juvenile turtles display directional movement beyond what would be expected based on
ocean currents alone. Our conclusions are based on
the comparison of simulated juvenile turtle tracks(modelled from real turtle tracks) with 2 different
ocean current models to allow cross-validation. By
modelling the movement of turtles and ocean currents, we were able to simulate the release of turtles
at different sites within our study area and compare
their trajectories to simultaneously released passive
particles in order to assess spatial variations in directional movement of juvenile turtles. This would nothave been possible through a simple qualitative analysis of the raw turtle tracks.
The rate of divergence between simulated turtle
and passive particles varied across the trajectory of
the turtles, suggesting that directional swimmingby turtles varied regionally. After release, all of the
tracked turtles showed a strong directional movement south towards the Tasman Sea area. The consistency in movement between the real turtle tracks
and the high divergence rate between the simulated turtles and the passive particles suggest thatthe turtles were swimming actively against the currents to reach the Tasman Sea area. Within the
Tasman Sea, the divergence between simulated
turtles and passive particles was less pronounced,
which suggests that directional movement in thisregion might be largely attributable to ocean currents. This movement pattern is consistent withstranding records of young loggerhead sea turtles
in the Southwest Pacific, which shows that posthatchling loggerhead turtles of similar size do notoccur along the east Australian coastline (Boyle etal. 2009). Within the Tasman Sea area lies the Tasman Front, a major oceanographic feature and an
important foraging habitat for pelagic fish and larvae (Mullaney et al. 2014). Our tracking data andsimulations suggest that this area might also be asuitable foraging habitat for juvenile loggerheads,
with a reduction in directional swimming resulting
in the turtles moving in tandem with their prey (i.e.
neuston), and potential flotsam habitats, which drift
passively (Witherington et al. 2012, Mansfield et al.
2014). Consistent with this hypothesis, we foundthat simulated turtles were distributed in more productive, and also more pelagic, areas (higher chl a,appropriate temperature range) than what would
be expected if their movement depended on passive transport only. Hence, this apparent behav-

Christiansen et al: Directional swimming in juvenile turtles

ioural ‘switch’ is consistent with the hypothesis that
directional locomotion is reduced in favourable
oceanic areas (Putman et al. 2015). Had the turtles
been drifting passively, our models predict that ahigh proportion of them would have ended up ineast Australian coastal waters, which is not supported by stranding data in this region (Boyle et al.
2009). This study also adds support to the hypothesis that self-recruitment in marine systems that arephysically open for a species relies on the behaviour(i.e. directional swimming) of the species (Wolanski2016).
Although the tracked turtles in this study aggregated in the Tasman Sea, whether or not this area
constitutes a foraging ground for wild loggerheadsof similar size is unknown. While similar sized loggerhead turtles can be found off the coasts of Chileand Peru in the Southeast Pacific, this size class is
not commonly seen off the coast of east Australia,
west coast of New Zealand or in the Tasman Sea(Boyle et al. 2009). The same is true for the releasesite south of New Caledonia, where similar-sized
loggerhead turtles are seldom found. Further, thefew naturally occurring loggerhead turtles of similar
size found in this region did not originate from theSouthwestern Pacific genetic stock (C. J. Limpus
pers. comm.). The lack of similar sized loggerheads
in the region could also be caused by size differences between similarly aged captive and wild loggerheads. The captive reared turtles in this study
were only 1 yr 7 mo old at the time of release. Wild
loggerheads of similar size (∼35 cm straight carapace length) are more likely to be around 6 yr old(Zug et al. 1995). At this age the turtles could have
moved far from the Tasman Sea, potentially following prevailing ocean currents moving eastwards
towards South America (Naro-Maciel et al. 2014).
Hence, while our experimental design was appropriate to test our hypotheses relating to directional
movement in juvenile sea turtles, it was not intended to describe the movement of naturally occurring smaller post-hatchling turtles in this region. It
is also worth noting that the juvenile turtles in thisstudy represent a restricted size class (~35 cm),
which might not represent the behaviour of othersize classes of juvenile loggerhead turtles.
The growth and survival of sea turtles is likely
determined by prey availability and, as subtropicalectotherms, the temperature of the surrounding
waters (Mansfield & Putman 2013). Greater foodavailability (i.e. neuston) for turtles is likely correlated with greater chl a concentration (Etnoyer et al.
2006, Strömberg et al. 2009). Both the real and simu-

257

lated turtles encountered higher chl a concentrations
compared to the passive particles. This suggest thatthrough directional swimming, young sea turtles areable to reach more favourable developmental habitats, which are likely to benefit their growth and survival, and consequently their fitness. Directionalswimming also resulted in juvenile loggerheads ending up in relatively narrow temperature ranges ofcooler water. Whether the cooler SST encountered isa side effect of this movement pattern (turtles seeking more productive waters), or if it reduces metabolic costs of the turtles, remains to be investigated.
Although feeding rates of turtles generally decreaseat colder temperatures (Moon et al. 1997, Hochscheid
et al. 2004), the higher food availability (i.e. higher
chl a concentrations) might compensate for this.
Moreover, extended periods at the surface can significantly increase the temperatures experienced byyoung turtles beyond satellite-derived measures of
SST (Mansfield et al. 2014).
Some migrants moving in environments where
they can perceive fixed reference points (e.g. birds
flying over land) can detect lateral deflections causedby environmental flows (winds, currents) and compensate accordingly (Chapman et al. 2011). In contrast, sea turtles in the open ocean far from land are
not thought to be able to directly sense currents because of the absence of fixed reference points.
Instead, the most parsimonious explanation for our
finding that naïve juvenile loggerheads demonstrate
directional swimming, is that they are using some
sort of innate regional navigational markers (Lohmann et al. 2001) rather than responding to the current directly. Furthermore, the hypothesis for use of
the Earth’s geomagnetic field as a crude map, which
is founded in thorough experimental evidence (Lohmann & Lohmann 1994, Lohmann et al. 2001, 2004,
Luschi et al. 2007, Putman et al. 2011), is consistent
with the geographical extent of the inferred feeding
grounds being relatively large, since even low resolution geomagnetic navigation would be sufficientfor the animals to successfully locate these areas(Lohmann et al. 2004, Gould 2014).
In summary, we have shown that young loggerhead sea turtles on their first travels across an ocean
basin show regionally tuned directional swimming.
Our findings provide a valuable contribution to thegrowing amount of evidence of directional movement in young sea turtles (Lohmann & Lohmann
1994, Lohmann et al. 2001, 2004, Putman et al. 2011,
Putman & Mansfield 2015), and further shows that
this movement strategy can be used by juvenile sea
turtles to reach more favourable oceanic habitats.

Mar Ecol Prog Ser 557: 247–259, 2016

258

Acknowledgements. We thank E. van Sebille and D. R. Kobayashi for helpful discussions during analyses. We thank L.
Jim and the students from the Hawaii Preparatory Academy
for help in the field. We thank K. Frutchey and R. Clarke of
the Pacific Islands Regional Office, NMFS, NOAA for providing funding. Support for N.F.P. was provided by the
NOAA Atlantic Oceanographic & Meteorological Laboratory. The animals used in this study were raised in captivity
under permits from the New Caledonia government for the
conduction of this experiment (permit 3189-2010/ARR/
DENV issued 31 December 2010). All applicable government rules for proper sea turtle care and humane treatment
were followed (no animals were sacrificed). We thank C. J.
Limpus and 2 anonymous reviewers for their constructive
comments, which helped to improve this manuscript. G.C.H.
and F.C. formulated this study. G.H.B. conceived the turtle
tracking project and coordinated the fieldwork, M.R.R. coordinated and performed the field work and D.M.P. assembled
the turtle tracking data. F.C., N.F.P. and G.C.H. led the data
analysis and writing. Other authors contributed to manuscript drafts.

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Submitted: June 27, 2016; Accepted: August 15, 2016
Proofs received from author(s): September 12, 2016