# Swirling in the ocean: Immature loggerhead turtles seasonally target old anticyclonic eddies at the fringe of the North Atlantic gyre

Philippine Chambault, Alberto Baudena, Karen Bjorndal, Marco A.R. Santos, Alan Bolten, Frederic Vandeperre

<figure><img src="image_1.png" /></figure>

## To cite this version:

Philippine Chambault, Alberto Baudena, Karen Bjorndal, Marco A.R. Santos, Alan Bolten, et al. Swirling in the ocean: Immature loggerhead turtles seasonally target old anticyclonic eddies at the fringe of the North Atlantic gyre. Progress in Oceanography, 2019, 175, pp.345-358. 10.1016/j.pocean.2019.05.005. hal-02519228

HAL Id: hal-02519228

https://hal.science/hal-02519228v1

Submitted on 11 Aug 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

Swirling in the ocean: immature loggerhead turtles seasonally
target old anticyclonic eddies at the fringe of the North Atlantic
gyre

Philippine Chambault¹,²*, Alberto Baudena³, Karen A. Bjorndal⁴, Marco AR Santos⁵, Alan B. Bolten⁴, Frederic Vandeperre¹,²,⁶

¹ IMAR – Instituto do Mar, Departamento de Oceanografia e Pescas, Universidade dos Açores, 9901-862 Horta, Portugal

² OKEANOS - Universidade dos Açores, Departamento de Oceanografia e Pescas, 9901-862 Horta, Portugal

³ CNES/Sorbonne Universités, UPMC Université Paris 06, LOCEAN, 4 Place Jussieu, F-75005 Paris, France

⁴ Archie Carr Center for Sea Turtle Research and Department of Biology, University of Florida, PO Box 118525, Gainesville, FL 32611, USA

⁵ DRAM, Secretaria Regional do Mar, Ciência e Tecnologia; Colónia Alemã, 9901-862, Horta, Portugal

⁶ MARE – Marine and Environmental Sciences Centre, Universidade dos Açores, 9901-862 Horta, Portugal

<footnote>*philippine.chambault@gmail.com</footnote>
30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

**Abstract**

In a highly heterogeneous open ocean, swirling oceanographic structures such as eddies drive ocean productivity and aggregate many predators, including oceanic sea turtles. During early life, juvenile loggerhead turtles can spend more than a decade feeding on gelatinous zooplankton in the open ocean, but the way they use mesoscale eddies is still poorly understood. Here, we investigated the relationships between (1) the distribution and (2) the diving behaviour of immature loggerhead turtles of the North-East Atlantic and mesoscale eddies. For this purpose, 28 turtles were satellite tracked from the Azores archipelago. Using the Residence Time (RT) analysis as a proxy to identify high-use areas, the tracks and dive data of the turtles as well as drifter trajectories were analysed in relation to eddy characteristics, which include eddy radius, amplitude, type (cyclonic vs. anticyclonic), lifetime and region (inner core, outer core, periphery). The turtles dispersed widely using many distinct high-use areas. Although there were always more cyclones than anticyclones over the study region, the individuals seasonally associated more with the inner cores of old anticyclonic eddies, likely due to the higher productivity of decaying anticyclones. The comparison between passive drifters and turtles' movements showed an active swimming behaviour from the turtles rather than a passive advection through currents. Three dive types were identified, and the one associated with the highest RT was characterized by dives of medium duration and depths in the inner cores of eddies. This study is the first to highlight strong affinities of oceanic loggerhead turtles for old anticyclonic eddies around the Azores, suggesting a greater complexity of these warm-core eddies that appear to be much more productive than cyclones.

Keywords: Azores archipelago, mesoscale eddies, eddy cores, diving behaviour, *Caretta caretta*, Residence Time
61

# 1. Introduction

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

Mesoscale eddies are large oceanographic features (~100 km) that drive ocean productivity by aggregating phytoplankton through horizontal advection, vertical flux or stratification (Gaube et al., 2014). The enhancement of primary productivity generated by mesoscale eddies affects the entire marine food chain through bottom-up processes in both cyclonic and anticyclonic eddies (Lévy et al., 2001). Given the patchiness of ocean productivity, such mesoscale features are key foraging habitats for many pelagic predators (Scales et al., 2014). Some associations exist between mesoscale eddies and the movements of numerous megafauna species such as cetaceans (Bauer et al., 2015; Davis et al., 2002; Garcia-Rojas et al., 2018; Vandeperre et al., 2014), pinnipeds (Bailleul et al., 2010; Campagna et al., 2006; Della Penna et al., 2015; Dragon et al., 2010; Massie et al., 2016; Nordstrom et al., 2013), seabirds (Cotté et al., 2007; Tew Kai and Marsac, 2010; Weimerskirch et al., 2004), fishes (Gaube et al., 2018; Jaine et al., 2014), marine mammals and sea turtles (Chambault et al., 2016; Mansfield et al., 2014; Polovina et al., 2006).

The most common life history pattern in sea turtles is the Oceanic-Neritic pattern adopted by four of the seven sea turtle species. This consists in the use of oceanic zones by early juveniles, whereas larger juveniles and adults primarily limit their movements to neritic habitats (Bolten, 2003; Carr, 1987; Reich et al., 2007). The best-known example of this Oceanic-Neritic pattern is the loggerhead turtle, for which the early life stage has received particular attention (Bolten, 2003; Carr, 1987). During this period, oceanic juvenile loggerhead turtles can spend more than a decade (Bjorndal et al., 2003, 2000) feeding on gelatinous zooplankton in the open ocean (Frick et al., 2009). In the North Pacific, despite the use of both cyclonic and anticyclonic eddies, juvenile loggerhead turtles tend to spend less time in cold-core cyclonic eddies (Howell et al., 2010). Similarly, between Taiwan and China, Kobayashi et al. (2011) have shown that loggerhead turtles within this hotspot targeted strong cyclonic eddy edges. More recently, a study conducted in the South Atlantic Ocean has shown affinities of juvenile loggerheads for inner cores of anticyclonic eddies (Gaube et al., 2017).

In the present study, we focus on the Azores archipelago, located in the North-East Atlantic, which has been identified as a foraging hotspot for juvenile loggerhead turtles originating primarily from the south-eastern United States (Bolten et al., 1998; Monzón-Argüello et al., 2009). The Azores archipelago is characterized by a high oceanographic instability, resulting in numerous cyclonic and anticyclonic eddies regularly detaching from the Azores current (AzC), and lasting from 200 to 490 days (Alves et al., 2002; Alves and
Colin de Verdière, 1999; Gould, 1985). Located at the eastern edge of the North Atlantic 94
subtropical gyre, the AzC forms a southern branch of the Gulf Stream at about 50°W, and 95
then passes south of the Azores archipelago (Johnson and Stevens, 2000). North of the AzC, 96
the Azores Counter Current flows westward (Onken, 1993), propagating anticyclonally 97
(Alves and Colin de Verdière, 1999). The AzC is also characterized by a pronounced 98
subtropical front which separates the warmer and more saline subtropical waters from the 99
colder and less saline Eastern Atlantic Central waters (Pingree et al., 1999). 100

In the past two decades, the oceanographic and biological processes of this particular 101
region have been intensively studied. However, despite the high number of immature 102
loggerhead turtles inhabiting this Azorean region, the relationship between the movements of 103
these loggerhead turtles and mesoscale eddies has not been investigated. Given the proximity 104
of the nutrient-poor subtropical gyre (Stramma and Siedler, 1988), we expect strong 105
associations between loggerhead movements and mesoscale eddies, particularly with the inner 106
cores of anticyclones as demonstrated by Gaube et al. (2017). 107

Recent advances in satellite oceanography have enabled the automated identification 108
of mesoscale eddies at the global scale (Chelton et al., 2011), providing valuable information 109
on eddy characteristics. Complementary features such as eddy lifetime derived from the 110
Retention time index (d'Ovidio et al., 2013) together with Finite-Size Lyapunov Exponents 111
(FSLE, D'Ovidio et al., 2004) were used in this study to investigate the relationships between 112
mesoscale eddies and loggerhead (i) Residence Time (e.g. favourable habitats) and (ii) diving 113
behaviour. Using the Residence Time (RT) analysis as a proxy to identify high-use areas, the 114
tracks and dive data of 28 immature loggerhead turtles satellite tracked from the Azores were 115
analysed in relation to eddy characteristics (i.e. distance to eddy centre, radius, velocity, 116
amplitude, type, lifetime and eddy region) and passive Argo drifters. 117

118

119

120

## 2. Materials and Methods

### 2.1 *Data collection*

Between 1994 and 2000, 31 juvenile loggerhead turtles were captured around the Azores (Fig. 1a). The tagging periods occurred from summer (mid-June) to autumn (early December). Turtles were either dipnetted or caught as bycatch in the longline fishery. Depending on the injuries caused by the hooks to the turtles, individuals were classified in two categories: 123
124
125
126
controls (with no injuries, n = 25) and light hooked (light injuries, n = 6). Body size was
measured as minimum straight carapace length (SCL) from the anterior point at midline
(nuchal scute) to the posterior notch at midline between the supracaudals (Bolten, 1999).
Turtles were fitted with satellite-linked time-depth recorders (SLTDR) from Wildlife
Computers (Redmond, Washington USA) and Telonics (Mesa, Arizona USA).

Diving behaviour data recorded by the tags describe specific diving parameters,
namely maximum dive depths, dive durations and Time at Depth (TAD), binned as 6-hour
period histograms. Maximum depths were collected in the following bins: 5, 10, 15, 20, 25,
30, 40, 50, 60, 70, 80, 100, 150 and >150 m. Similarly, maximum dive durations were stored
as follows: 2, 5, 10, 20, 30, 45, 60, 75, 90, 120, 180, 240, 250 min. Time at Depth was stored
in the following bins: 1, 3, 5, 10, 15, 25, 35, 50, 60, 75, 100, 125, 150 and >150 m.

138

## 2.2 Pre-filtering analysis of the turtles' tracking data

The 31 satellite tags provided geographic locations for each individual through the Argos
system (www.argos-system.org). Argos locations associated with a speed over 10 km.h⁻¹ were
discarded, as well as “type Z” locations, considered insufficiently accurate using the *sdafilter*
function from the *argosfilter* package on R (Freitas et al., 2008). The *crwLE* function from the
*crawl* package on R (Fit Continuous Correlated Random Walk Models to Animal Movement
data, Johnson, 2017a) was then applied to each of the 31 tracks to estimate a daily location per
turtle and improve Argos estimates using a state-space modelling approach taking into
account Argos location errors. Three individuals were removed due to a limited number of
locations (#24190, #25363 and #22273, n < 20).

149

## 2.3 Drifter data

To assess if the turtles were being passively advected by ocean currents, surface drifter data
was extracted from NOAA's Atlantic Oceanographic and Meteorological Laboratory
(http://osmc.noaa.gov/erddap/tabledap/gdp_interpolated_drifter.html). In total, 383 drifter
trajectories were available over the study region and for the tracking period (1994-2001).

155

## 2.4 Residence Time calculation

A Residence Time (RT expressed in hours) analysis (Barraquand and Benhamou, 2008;
Benhamou and Riotte-Lambert, 2012) was applied to the remaining 28 turtle tracks in order to
identify High Residence Time (HRTs) areas (locations associated with highest values of RT),
presumably favourable habitats. The RT for a given location indicates the time spent within a circle centred on this location with a given radius *r*. It is therefore computed as the difference between forward and backward first passage times at distance *r* from the centre, plus possible additional backward and/or forward times spent within the circle provided that the animal does not leave the circle for more than a given time (set to 8 h in the present study), before returning within the circle. This approach provides a more contrasted and less noisy time series (with respect to simpler analyses based on first passage times). RT time series were computed with a radius *r* ranging between 10 and 80 km, by 10 km to explore different scales for each individual. Finally, for each turtle, the radius providing the best contrast (i.e. absolute difference between high vs. low values of RT rather than a noisy signal) was retained for the analysis (see SI, Fig. 1). For visual purpose and in order to be comparable across individuals, RT (initially expressed in days) has been scaled between 0 (low RT) and 100% (high RT) by setting to 100% the maximum RT values of each individual.

In order to compare the drifters' movements to turtle's movements, a similar RT analysis was conducted on the 383 drifters extracted from NOAA over the study region and during the turtles tracking period. Due to the large sample size, a visual inspection of each radius over the 383 drifters could not be done. Assuming a similar behaviour for all passive drifters (based on same characteristics), the same radius for all drifters was set. We then performed a sensitivity analysis by testing all possible radii from 10 to 80 km every 10 km and comparing the RT patterns over eddy type/region and seasons for each radius.

180

## 2.5 Seasonal movements

To assess seasonal patterns, one density map per season was generated from tracking data by accumulating the number of days each individual had passed through each grid cell of 0.5x0.5 decimal degrees. Seasons were defined as: spring from April to June, summer from July to September, autumn from October to December, and winter from January to March. The *tripGrid* function from the *trip* package was used to generate the density maps (Sumner, 2016).

188

## 2.6 Environmental data

To describe the habitat used by the loggerhead turtles in HRT areas, a series of environmental variables were extracted at each turtle/drifter/eddy location from two different databases:

191
1) **Copernicus-MyOcean**: ocean currents (scalsars *u* and *v*) and Sea Level Anomalies (SLA) from the *Global Ocean Gridded L4 Sea Surface Heights and Derived Variables reprocesses* product (SEALEVEL-GLO-PHY-L4-008-047) were extracted daily at 0.25° resolution. The ocean current velocities were then used to compute two Lagrangian diagnostics: Finite-Size Lyapunov Exponents (FSLE, d'Ovidio et al., 2004), and the Retention Time of eddies (hereafter called eddy lifetime, d'Ovidio et al., 2013), both computed at a resolution of 0.1°. The latter gives a proxy of the eddy age expressed in days, and was extracted every four days. Sea Surface Temperature (SST) and Mixed Layer Depth (MLD) were also extracted daily at eddy centres from the *Global Ocean Physics Reanalysis Glorys2v4* product (PHY-001-025) at a 0.25° resolution (http://marine.copernicus.eu/services-portfolio/access-to-products/).

2) **Aviso**: the *Mesoscale Eddy Trajectory Atlas* product was used to extract daily information on eddies present in the study region: location of the eddy centre, eddy radius (in km), eddy amplitude (in cm) and eddy type (cyclonic vs. anticyclonic) (https://www.aviso.altimetry.fr/en/data/products/value-added-products/global-mesoscale-eddy-trajectory-product.html).

After downloading these variables over the tracking period and study region, the value of each variable was extracted at each turtle location using the function *extract.data* from the *SDMTools* package on R (VanDerWal et al., 2014). The chosen spatial resolution enabled to reduce the potential bias related to Argos location errors. Only the FSLE needed to be extracted at a finer resolution (~0.08°) to ensure the detection of filaments, but several coarser resolutions (0.15°, 0.25°, 0.5°) were tested in the analysis to ensure the robustness of the results.

To assess the habitat available for the turtles, and especially the number of cyclones in relation to the number of anticyclones, these environmental variables (*u*, *v*, SST, SLA and MLD) were also extracted at each eddy centre and for each tracking day from the *Mesoscale Eddy Trajectory Atlas* to cover the entire study region. The total number of cyclones and anticyclones was then calculated for each tracking day over the whole study region (SI, Fig. 1).

To compare turtles' movements to passive drifters' movements, the turtle's swimming speed corrected for ocean currents was first calculated for each individual. Then, the heading of the turtles' swimming and of ocean currents at each turtle's location was derived, and circular diagrams of these headings were compared to passive drifters' headings using the `clifro` package on R.
To test if the turtles selected particular types of eddies (cyclonic vs. anticyclonic), the distance between each turtle location and the closest eddy centre was calculated. Based on the radius *r* of the closest eddy to each turtle location, we then estimated if the individual was located inside or outside the eddy. Three categories were therefore defined: outside (O), inside cyclonic eddy (C) and inside anticyclonic eddy (A). Similarly, we estimated where in the eddy each turtle was located: in the inner core (IC) if the distance to the eddy centre was <1/2 *r*, in the outer core (OC) if the distance *d* to the eddy centre was 1/2 *r* < *d* < *r*, at the eddy periphery (P) if the distance to the eddy centre was *r* + (3/4 *r*).

## 2.7 Habitat modelling

To investigate the relationships between loggerhead HRT areas and their habitat, we constructed a series of Generalized Additive Models (GAMs) using the *mgcv* package in R (Wood, 2006). One model per season was constructed as follows:

$$ \text{RT}_{\text{percent}} \sim \text{s}(\text{sla})+\text{s}(\text{fsle})+\text{s}(\text{lifetime})+\text{s}(\text{radius})+\text{s}(\text{amplitude})+\text{s}(\text{lon, lat})+\text{s}(\text{ID,bs="re"}) $$

The RT<sub>percent</sub> (expressed in percentage to be comparable between all individuals) was used as a response variable and an inverse Gaussian distribution was applied to the models. To account for the inter-individual variability, turtle ID was added as a smooth component using the term *s*(ID, bs="re") to account for a random effect (Woods, 2012). To reduce autocorrelation, the dataset was subsampled every two to three locations for each season, and autocorrelation was then tested using the *acf* function in R. In parallel, spatial coordinates (longitude and latitude) were included in each model as an explanatory variable (Dormann et al., 2007). Based on the best predictors to depict mesoscale features and specifically eddies characteristics, five variables were chosen to have a potential influence on the movements of loggerhead turtles: SLA, FSLE, eddy lifetime, eddy amplitude and eddy radius. Eddy region and eddy type were also added as factor covariates. The predictors were first tested for collinearity using the Variance Inflation Factor (below four) (Hair et al., 2011). To assess a potential seasonal variability, we ran one GAM per season. Models with all possible combinations were then computed, and the models were compared based on the Akaike Information Criterion (AIC). The number of knots for each variable was first not restricted. Depending on the complexity of the splines, the degree of freedom were then reduced if needed to help the biological interpretation of the relationships.

## 2.8 Diving behaviour analysis
258 Environmental variables were matched with diving parameters to assess any diving pattern
259 according to eddy type and eddy region. Dive depth, dive duration and TAD were first
260 merged for each bin (6-hour record) of each individual. Environmental variables as well as the
261 RT were then extracted at the exact location and time of each dive bin. To classify diving
262 behaviours, a clustering analysis was then performed on each bin of depth, duration and TAD
263 for each 6-hour record using the *HCPC* function from the R package *FactoMineR* (Husson et
264 al., 2015). Following the same procedure described in part 2.7, we constructed a series of
265 GAMs to relate the RT (expressed in percentage) associated with the dive data to eddy
266 characteristics. One model per dive cluster was constructed using the following predictors:
267 SLA, FSLE, eddy lifetime, amplitude, radius, region and type.
268

269

270

271

## 3. Results

272

### 3.1 Tag performance, travelled distance and speed

273 The 28 tracked turtles retained for the analysis measured between 36.3 cm (#25317) and 61.1
274 cm SCL (#23657) in straight carapace length (Table 1, mean±SD: 44.4±4.2 cm SCL). The
275 number of daily locations estimated by the state space model was on average 149±967 per
turtle (range: 23-337), for a tracking duration ranging from 22 to 336 d (#22531 and #20012,
276 respectively). The total distance travelled varied between 212 km (#22531) and 5676 km
277 (#22130, mean±SD: 2290±967 km), and the average travel speed was 0.61±0.10 km.h⁻¹
278 (Table 1).
279

280

281

### 3.2 Dispersal and Residence Time

282 The turtles dispersed widely around the Azores archipelago (between 50 and 20°W and
283 between 18 and 46°N), which is a region characterized by numerous oceanic eddies, both
284 anticyclonic and cyclonic (Fig. 1a,b). Among the 4174 locations analysed, 72% were located
285 outside eddies and 28% inside eddies. Forty-five percent of the locations inside eddies were
286 associated with anticyclones and 55% with cyclones. Turtle dispersal varied throughout the
287 year as the latitudes occupied by turtles differed significantly according to season (Kruskal-
288 Wallis rank sum test, $\chi^2 = 211, p < 0.001$) - see Fig. 1d. The loggerheads had a wider dispersal
289 during spring and winter, whereas they limited their latitudinal range from summer to autumn.
290
291
292
293
294
295

This seasonal pattern was confirmed in Figure 2, as the individuals tended to aggregate around the Azores in summer and autumn and disperse more widely in spring and further south during winter. The radius providing the better contrast to identify high use areas varied between individuals from 10 to 70 km (SI, Fig. 1) and highlighted numerous HRT areas that were heterogeneously spread in the study region rather than shared by several turtles (Fig. 1c).

296
297
298
299
300
301
302
303
304
305

### 3.3 Turtles' headings vs. passive drifters' headings

The comparison between the movements of turtles and passive drifters is illustrated in Figure 3. Similar to drifters, turtles' real heading (corrected for ocean currents) was mainly westward (Fig. 3a), whereas ocean currents flowed mainly south-south-eastward (Fig. 3b). The track of the individual #20015 confirmed the differences in terms of swimming and currents headings (Fig. 3d,e). Globally, the tracked turtles swam therefore actively rather than passively, and at a higher velocity than ocean currents. This result was confirmed by the heading of passive surface drifters that headed mainly south-eastward and westward, but at higher velocities (Fig. 3c).

306
307
308
309
310
311
312
313
314
315
316
317

### 3.4 Eddy characteristics over the entire study region

Over the *entire* area (from 10 to 60°N and from 60 to 0°W) and during the tracking period (November 1994 to June 2001), the total number of cyclones recorded for each day was higher than the number of anticyclones (mean: 73 vs. 67). The same result was obtained when the mean was taken for each season (SI, Fig. 2a). The number of eddies globally decreased in late autumn and winter, and the SLA increased from April to October, being positive for anticyclones and negative for cyclones. The MLD was significantly deeper in anticyclonic eddies, regardless of the month (SI, Fig. 2c). Simultaneously, the MLD got shallower in spring and summer and reached its deepest value in January. Except in September, the SST differed significantly between cyclones and anticyclones for all months, being slightly warmer in anticyclones (SI, Fig. 2d).

318
319
320
321
322

### 3.5 Residence Time according to eddy type and region

Turtles' RT varied according to season, eddy type and eddy region (Figs. 4 and 5). Turtles used both cyclones and anticyclones while in HRT areas, while lower RTs were observed outside eddies. Higher RTs were observed in anticyclones during spring, whereas RT was higher in cyclones in summer and winter (Fig. 5a). RT was also higher in the inner core (IC)
323

of eddies during all the seasons (Fig. 5c). Unlike turtles' RT, drifters' RT did not vary according to eddy type or region (Fig. 5b,d).

324

325

326

## 3.6 Habitat modelling

327

Five common variables were selected for the four seasonal models, i.e. SLA, FSLE, eddy amplitude, eddy radius and eddy lifetime (Fig. 6).

328

329

In *spring*, the most parsimonious model contained five variables (SLA, FSLE, amplitude, radius and lifetime). The final model explained 82% of the deviance, and all variables were significant (*p* < 0.01). RT increased for both low (i.e. in cyclones) and high values of SLA (i.e. in anticyclones) whereas it was at its minimum for SLA around 0 (i.e. outside eddies). RT was however maximum for positive SLAs and high values of FSLE (i.e. ocean fronts). RT reached an optimum for eddy amplitude around 3 and 7 cm and for long eddy lifetime around 80 d.

330

331

332

333

334

335

336

In *summer*, the most parsimonious model contained two variables (SLA and radius), and the final model explained 50% of the deviance, and all variables were significant (*p* < 0.01). RT was maximum for positive SLAs with an optimum reached at 0.17 m. RT was also maximum for eddy radii of 60 and 90 km.

337

338

339

340

In *autumn*, the most parsimonious model contained four variables (SLA, amplitude, lifetime and radius). The final model explained 56% of the deviance, and all variables were significant (*p* < 0.05). RT was maximum for positive SLAs (i.e. in anticyclones), old eddies (long lifetime > 60 d) of medium amplitudes (7-10 cm) and small to medium radius (40-80 km).

341

342

343

344

345

In *winter*, the most parsimonious model contained three variables (SLA, amplitude and radius), and the final model explained 63% of the deviance, and all variables were significant (*p* < 0.001). RT was maximum for negative SLAs (i.e. in cyclones), low and high amplitudes (5 and 13 cm) and old eddies (75 and 120 d).

346

347

348

349

350

## 3.7 Diving behaviour

351

Eighteen individuals were retained for the analysis, which included a total of 3326 diving records of 6-hour. Each of them contained dive depth, dive duration and TAD. Most of the dives were shallow (≤5 m) and short (≤2 min) – see Fig. 7a,b,c. The clustering analysis identified three diving behaviours:

352

353

354
355

- C1: infrequent shallow (Fig. 7a, i.e. long periods at the surface) and short dives (10% of the dives ≤2 m), with turtles remaining close to the surface (94% of the time spent <3m, Fig. 7c).

356

357

- C2: dives of medium depth (Fig. 7a) and medium to long duration (up to 240 min, Fig. 7b), with turtles remaining most of the time at depth between 10 and 25 m (69% of the dives ≤35 m, Fig. 7c).

358

359

360

- C3: active shallow dives (Fig. 7a) of short duration (≤2 min, Fig. 7b), with turtles remaining close to the surface (86% of the time spent <3m, Fig. 7c).

361

362

When looking at dives deeper than 5 m and according to eddy type (Fig. 7d-f), a slightly higher number of dives in cyclones and outside eddies was performed at shallow depths (Fig. 7d). While no obvious difference was observed in terms of dive duration (Fig. 7e), turtles spent a slightly higher proportion of the time at depths between 5-10 m and 15-25 m (Fig. 7f). In cyclonic eddies, TAD was slightly higher for depths over 60 m (Fig. 7f). Finally, some differences were observed according to eddy region (Fig. 7g-i), with a higher proportion of dives performed in the inner cores were recorded between 15-25 m (Fig. 7i). Dives deeper than 60 m were mainly performed in summer and autumn, whereas shallow dives occurred mostly in spring and winter (Fig. 7j,l). Dives longer than 45 min occurred only in autumn and winter while short dives were mostly performed during summer (Fig. 7k).

363

364

365

366

367

368

369

370

371

372

The RT differed slightly according to dive cluster as it was globally higher for dives C2 (39%) compared to dives C1 (36%) and C3 (37%). The RT was always higher when dives were performed in the inner cores of eddies, regardless of the dive cluster, but no preference was observed according to eddy type (Fig. 8b). In *dives C1*, the most parsimonious model contained four variables (SLA, amplitude, lifetime and radius), and the final model explained 44% of the deviance (Fig. 9). RT was maximum for positive and negative SLAs. RT was also maximum for eddy with low amplitude, medium radius (60-100 km) and medium lifetime of ~40 d. In *dives C2*, the most parsimonious model contained two variables (SLA and FSLE), and the final model explained 49% of the deviance (Fig. 9). RT was maximum for both positive and negative SLAs and for low FSLE (<0.1 d⁻²). In *dives C3*, the most parsimonious model contained two variables (SLA and amplitude), and the final model explained 57% of the deviance (Fig. 9). RT was maximum for eddy of low amplitude (5 cm).

373

374

375

376

377

378

379

380

381

382

383

384

385

386
While the early part of the oceanic migration of loggerhead turtles has recently been documented via the tracking of early juvenile turtles (SCL: 11-18 cm) in the north-western Atlantic (Mansfield et al., 2014), the habitat affinities of older immature individuals found later on in the North-East Atlantic were still poorly documented (Freitas et al., 2018; McCarthy et al., 2010; Varo-Cruz et al., 2016). Our study is therefore the first to describe (i) the seasonal patterns, (ii) the habitat use in relation to eddies and (iii) the diving behaviour of 28 immature loggerhead turtles satellite tracked from the Azores during their late oceanic migration around the Azores archipelago.

In agreement with previous studies conducted on immature loggerhead turtles in the Pacific (Howell et al., 2010; Polovina et al., 2006, 2000), Indian Ocean (Dalleau et al., 2014) and Atlantic Ocean (Barceló et al., 2013; do Canhoto, 2014; Freitas et al., 2018; Gaube et al., 2017; McCarthy et al., 2010; Varo-Cruz et al., 2016, 2013), the turtles tracked in our study dispersed widely (30° in longitude and 27° in latitude). Our findings indicate a seasonal pattern with a wider dispersal during winter and spring whereas in summer and autumn the turtles tended to concentrate close to the Azores archipelago. Although this seasonal migration might be partly related to the tagging period occurring between May and December off the Azores, this pattern is similar to the one observed around the Canary Islands, where individuals of the same size (mean SCL: 47.7 cm) moved further north in summer (Varo-Cruz et al., 2016). Such migration might be related to the seasonality of the ocean productivity, especially for turtles inhabiting the Azorean waters, which are influenced by the proximity to the nutrient-poor subtropical gyre. Indeed, oceanic gyres are considered as ocean deserts (Tomczak and Godfrey, 2013) due to their scant productivity (Marañón et al., 2000). In this context, it is interesting to note that a strong relationship between the movements of adult leatherback turtles and the chlorophyll *a* concentration has recently been shown in the North Atlantic, where the leatherbacks moved further south during winter following the decrease in size of the subtropical gyre (Chambault et al., 2017). Azorean loggerhead turtles might follow the same pattern in response to the variable extent of the subtropical gyre.

The observed latitudinal displacement might also be related to a thermal preference. SST is an important variable driving the distribution of this species, especially during its early
420

stage (Mansfield et al., 2009; Varo-Cruz et al., 2016). In the north-western Atlantic, Mansfield et al. (2009) have shown that the movements of juvenile loggerheads were limited by the 10-15°C isotherm. Given that SST decreases during winter in the northern hemisphere, warmer temperatures are found further south of the Azores. This may explain the southern movements of these loggerheads during the colder months.

421

422

423

424

425

Unlike the 27 other individuals, one turtle (#23662) headed south-westward, maintaining a straight course during nearly 4000 km. Given that most immature turtles found around the Azores originate from nesting aggregations in the south-eastern U.S. (Bolten et al., 1998), this individual might have started its migration to neritic feeding grounds located on the eastern North American coast (Bjorndal et al., 2003, 2000; Bolten, 2003).

426

427

428

429

430

431

## 4.2 Affinities for the inner cores of old anticyclones

432

The Residence Time (RT) analysis indicated the use of numerous high-use areas spread over the entire study region, and not necessarily shared by several individuals. Although the proportion of tracking days spent outside eddies was higher than inside eddies for all seasons, the high-use areas were mostly located inside eddies, and particularly in anticyclones.

433

434

435

436

Furthermore, despite the higher number of cyclonic eddies present in the entire study region (Barbosa Aguiar et al., 2011), the tracked turtles associated more with anticyclones, which is in accordance with previous studies conducted on immature loggerhead turtles in the North Pacific (Howell et al., 2010) and South Atlantic Oceans (Gaube et al., 2017). This result is surprising as, in most oceanic regions, anticyclones are associated with negative chlorophyll *a* anomalies at the sea surface (Gaube et al., 2014). Given their domed permanent thermocline, cold cyclones associated with negative Sea Level Anomalies (SLAs) are commonly more productive than warm anticyclones associated with positive SLAs (McGillicuddy et al., 1998; Vaillancourt et al., 2003). However, among the two categories of anticyclones ('regular' with depression of thermocline vs. 'mode-water' with shallower thermocline), the mode-water eddies also show high productivity and phytoplankton biomass (McGillicuddy et al., 2007; Sweeney et al., 2003). Furthermore, under certain conditions, regular anticyclonic eddies can also become productive, especially due to the modulation of winter mixing (Dufois et al., 2016) or during eddy decays (McGillicuddy, 2016; Nelson et al., 1989). This is in line with our result showing a preference for older anticyclones. Additionally, there could be some lag between an eddy position and the beginning of the enhanced productivity despite the detection of restricted search behaviour by the turtles. Such

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452
453

temporal lag might lead to the identification of restricted search behaviour in areas with no
eddies. However, the RT analysis conducted on the passive drifters extracted from the NOAA
showed no affinities for a particular eddy type, confirming therefore that the association
between the turtles and anticyclones was not induced by passive drifting but rather by an
active swimming.

454

455

456

457

458

When looking at the region of the eddy targeted, our findings show affinities for the
inner cores of anticyclones throughout the year. Despite such trend being similar to the one
obtained for passive drifters, the RT differences were much higher for the turtles, suggesting a
behavioural response rather than a passive drift. Indeed, several studies have shown that the
chlorophyll a can be greatly enhanced inside anticyclones compared to surrounding waters
(Brannigan, 2016; Crawford et al., 2007; Gaube et al., 2013; Godø et al., 2012; Hansen et al.,
2010; Lehahn et al., 2011), suggesting potential upwelling of nutrient-rich waters within the
eddy induced by Ekman pumping (Martin and Richards, 2001; McGillicuddy and Robinson,
1997). Despite using a different methodology to delineate eddy regions, Gaube et al. (2018,
2017) found similar associations with the interiors of warm-core anticyclones for both
immature loggerheads of the south-west Atlantic and mature white sharks of the north-west
Atlantic. The authors propose that either prey aggregation or a metabolic advantage of being
in warmer waters in anticyclones may enhance foraging success or reduce the physiological
costs of thermoregulation in cold waters. However, around the Azores, the SST difference
between cyclones and anticyclones is low, suggesting that the affinities for anticyclones are
probably not based on thermal preferences. Alternatively, the association with warm-core
eddies may be related to specific foraging opportunities. However, so far, no studies have
established a link between these mesoscale features and the preferential prey of sea turtles
such as gelatinous zooplankton. The preference for older eddies, which has been related to
higher primary productivity during decay (McGillicuddy, 2016; Nelson et al., 1989), may
indicate differential foraging opportunities as a driver.

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

Surprisingly, immature loggerhead turtles seemed to target slow circulation areas
inside eddies rather than the periphery that are generally associated with productive oceanic
fronts or filaments of high current velocity. This is in accordance with a recent study
conducted on juvenile loggerhead turtles (Freitas et al., 2018), using a methodology (First
Passage Time: FPT) similar to the one used in our study. Despite higher values of RT were
found for high FSLE in spring, such relationship was not observed for the other seasons. Such
affinities for low turbulent areas contrasts with those of many other marine predators that
commonly use high turbulent frontal zones to forage (Bon et al., 2015; Bradshaw et al., 2004;

480

481

482

483

484

485

486
487

De Monte et al., 2012; Della Penna et al., 2015; Murase et al., 2014). Although Azorean turtles spent most of their time outside eddies, the RT in such turbulent areas was always low. This might be partly due to the fact that the RT refers to fixed locations, while the frontal features typical of turbulent areas generally move during their lifetime, resulting in low RTs.

488

489

490

491

Due to a temporal mismatch between the ocean colour data provided by the Seawifs satellite (from 1998 onwards) and our tracking data (starting in 1994), the relationships between chlorophyll a and eddy types could not be investigated in the present study. A further study focusing on this relationship would therefore be beneficial to better understand the biological characteristics of both cyclones and anticyclones around the Azores.

492

493

494

495

496

497

## 4.3 Diving behaviour and Residence Time

498

499

500

501

502

503

504

505

506

507

508

509

510

511

512

Most of the dives were short (≤2 min) and shallow (≤5 m), which is typical of the loggerhead turtle during migrations in the Mediterranean Sea (Álvarez de Quevedo et al., 2013), the Indian Ocean (Dalleau et al., 2014), the North Pacific (Howell et al., 2010; Polovina et al., 2003) and the North Atlantic (Freitas et al., 2018; Varo-Cruz et al., 2016). The dives of class C2 were associated with slightly higher RT. A recent study using cameras deployed on juvenile loggerheads demonstrated that foraging events occurred at mean depth of 19.7±9.6 m and were recorded during either deep dives (mean: 34.9 m, 16 min) or shallower dives of medium duration (mean: 18.4 m, 18 min, Narazaki et al., 2013). Narazaki et al. (2013) also confirmed that mid-water foraging events were associated with slow-moving gelatinous prey (i.e. jellyfish and siphonophores). Azorean loggerheads may therefore forage during deep and long dives. However, no affinities for either cyclones or anticyclones were observed during dives. Higher RT values were also observed in the inner cores of eddies, confirming the affinities of this species for the interior of such mesoscale features. Nevertheless, no consistent difference in diving behaviour was recorded as a function of the eddy type and eddy region.

513

514

515

516

517

518

519

520

As highlighted by Narazaki et al. (2015) in the North Pacific, we did not find a strong seasonal pattern in diving behaviour, such as the occurrence of deeper dives (> 60 m) in only in summer and autumn. Given that the thermocline plays a crucial role in the vertical distribution of pelagic prey (Benoit-Bird et al., 2013; Hakoyama et al., 1994), its location beneath a thin and often depleted mixed layer during these seasons could explain the deeper dives performed during this period. In contrast, shallow dives occurred mostly in spring and winter, probably due to the rich and thick mixed layer that deepens during the cold months, providing potential prey in the upper layer for loggerhead turtles. Similar findings were also
521

obtained for the loggerhead turtles of the Canary Islands (Varo-Cruz et al., 2016). Long dives
occurred also during winter, confirming that immature loggerheads either forage during this
period (Narazaki et al., 2015) or may rest under lower temperatures.

522

523

524

525

4.4 *Perspectives*

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542

By combining the satellite tracks of 28 turtles to ocean models providing information on eddy
characteristics, the present study provides important insights on a poorly known early life
stage of immature loggerhead turtles during their oceanic migration. Despite technological
advances in biologging in the past decades, one limitation of the tracking devices is their
limited life-span, not exceeding a few years and therefore not recording the complete early
stage of oceanic juveniles that can last more than 10 years in loggerhead turtles (Bjorndal et
al., 2003, 2000). In that context, numerical models are increasingly used to simulate the
dispersal of hatchlings and juveniles during their first years at sea (Gaspar et al., 2006; Gaspar
and Lalire, 2017; Lalire and Gaspar, 2019; Putman et al., 2014, 2012). Such numerical models
are commonly compared to observations, but they are rarely calibrated using tracking data.
The combination of recent active dispersal models and tracking data from immature
loggerhead turtles tracked from the Azores (present study) and other areas (e.g. Freitas et al.,
2018; Varo-Cruz et al., 2016) would provide unprecedented and reliable information on the
dispersal of this species during its early life.

543

544

545

546

547

548

549

550

551

Acknowledgements.

552

553

This manuscript is dedicated to the memory of Brian Riewald whose research focused on the movement patterns of loggerheads in the oceanic realm around the Azores. Funding was provided to the ACCSTR by Honolulu Laboratory, Southwest Fisheries Science Center, National Marine Fisheries Service (NOAA); Disney Conservation Fund; and Office of Protected Species, National Marine Fisheries Service (NOAA). This work could not have been accomplished without the assistance of Helen R. Martins, Joseph Franck, and Stephen Connett. FV was supported by a grant from the Fundação para a Ciência e a Tecnologia (SFRH/BPD/110294/2015). PC received funding through the European project INDICIT (grant agreement n°11.0661/2016/748064/SUB/ENV.C2).
603 Bolten, A.B., Bjorndal, K.A., Martins, H.R., Dellinger, T., Biscoito, M.J., Encalada, S.E.,
604 Bowen, B.W., 1998a. TRANSATLANTIC DEVELOPMENTAL MIGRATIONS OF
605 LOGGERHEAD SEA TURTLES DEMONSTRATED BY mtDNA SEQUENCE
606 ANALYSIS. Ecological Applications 8, 1-7. https://doi.org/10.1890/1051-
607 0761(1998)008[0001:TDMOLS]2.0.CO;2

608 Bolten, A.B., Bjorndal, K.A., Martins, H.R., Dellinger, T., Biscoito, M.J., Encalada, S.E.,
609 Bowen, B.W., 1998b. Atlantic developmental migrations of loggerhead turtles
demonstrated by mtDNA sequence analysis. Ecological Applications 8, 1-7.
610
611 https://doi.org/10.1890/1051-0761(1998)008[0001:TDMOLS]2.0.CO;2

612 Bon, C., Della Penna, A., d'Ovidio, F., Y.P. Arnould, J., Poupart, T., Bost, C.-A., 2015.
613 Influence of oceanographic structures on foraging strategies: Macaroni penguins at
614 Crozet Islands. Movement Ecology 3, 32. https://doi.org/10.1186/s40462-015-0057-2

615 Bradshaw, C.J.A., Hindell, M.A., Sumner, M.D., Michael, K.J., 2004. Loyalty pays: potential
616 life history consequences of fidelity to marine foraging regions by southern elephant
617 seals. Animal Behaviour 68, 1349-1360.
618 https://doi.org/10.1016/j.anbehav.2003.12.013

619 Brannigan, L., 2016. Intense submesoscale upwelling in anticyclonic eddies. Geophysical
620 Research Letters 43, 3360-3369. https://doi.org/10.1002/2016GL067926

621 Campagna, C., Piola, A.R., Rosa Marin, M., Lewis, M., Fernández, T., 2006. Southern
622 elephant seal trajectories, fronts and eddies in the Brazil/Malvinas Confluence. Deep
623 Sea Research Part I: Oceanographic Research Papers 53, 1907-1924.
624 https://doi.org/10.1016/j.dsr.2006.08.015

625 Carr, A., 1987. New perspectives on the pelagic stage of sea turtle development. Conservation
626 Biology 1, 103-121.

627 Chambault, P., de Thoisy, B., Heerah, K., Conchon, A., Barrioz, S., Dos Reis, V., Berzins, R.,
628 Kelle, L., Picard, B., Roquet, F., Le Maho, Y., Chevallier, D., 2016. The influence of
629 oceanographic features on the foraging behavior of the olive ridley sea turtle
630 *Lepidochelys olivacea* along the Guiana coast. Progress in Oceanography 142, 58-71.
631 https://doi.org/10.1016/j-pocean.2016.01.006

632 Chambault, P., Roquet, F., Benhamou, S., Baudena, A., Pauthenet, E., de Thoisy, B., Bonola,
633 M., Dos Reis, V., Crasson, R., Brucker, M., Le Maho, Y., Chevallier, D., 2017. The
634 Gulf Stream frontal system: A key oceanographic feature in the habitat selection of the
635 leatherback turtle? Deep Sea Research Part I: Oceanographic Research Papers 123,
636 35-47. https://doi.org/10.1016/j.dsr.2017.03.003

637 Chelton, D.B., Schlax, M.G., Samelson, R.M., 2011. Global observations of nonlinear
638 mesoscale eddies. Progress in Oceanography 91, 167-216.
639 https://doi.org/10.1016/j-pocean.2011.01.002

640 Cotté, C., Park, Y.-H., Guinet, C., Bost, C.-A., 2007. Movements of foraging king penguins
641 through marine mesoscale eddies. Proceedings of the Royal Society of London B:
642 Biological Sciences 274, 2385-2391. https://doi.org/10.1098/rspb.2007.0775

643 Crawford, W.R., Brickley, P.J., Thomas, A.C., 2007. Mesoscale eddies dominate surface
644 phytoplankton in northern Gulf of Alaska. Progress in Oceanography, Time Series of
645 the Northeast Pacific 75, 287-303. https://doi.org/10.1016/j-pocean.2007.08.016

646 d'Ovidio, F., Fernández, V., Hernández-García, E., López, C., 2004. Mixing structures in the
647 Mediterranean Sea from finite-size Lyapunov exponents. Geophys. Res. Lett. 31,
648 L17203. https://doi.org/10.1029/2004GL020328

649 d'Ovidio, F., Monte, S.D., Penna, A.D., Cotté, C., Guinet, C., 2013. Ecological implications
650 of eddy retention in the open ocean: a Lagrangian approach. J. Phys. A: Math. Theor.
651 46, 254023. https://doi.org/10.1088/1751-8113/46/25/254023
652 Dalleau, M., Benhamou, S., Sudre, J., Ciccione, S., Bourjea, J., 2014. The spatial ecology of juvenile loggerhead turtles (*Caretta caretta*) in the Indian Ocean sheds light on the "lost years" mystery. Mar Biol 161, 1835-1849. https://doi.org/10.1007/s00227-014-2465-z

656 Davis, R.W., Ortega-Ortiz, J.G., Ribic, C.A., Evans, W.E., Biggs, D.C., Ressler, P.H., Cady, R.B., Leben, R.R., Mullin, K.D., Würsig, B., 2002. Cetacean habitat in the northern oceanic Gulf of Mexico. Deep Sea Research Part I: Oceanographic Research Papers 49, 121-142. https://doi.org/10.1016/S0967-0637(01)00035-8

660 De Monte, S., Cotté, C., d'Ovidio, F., Lévy, M., Corre, M.L., Weimerskirch, H., 2012. Frigatebird behaviour at the ocean-atmosphere interface: integrating animal behaviour with multi-satellite data. Journal of The Royal Society Interface 9, 3351-3358. https://doi.org/10.1098/rsif.2012.0509

664 Della Penna, A., De Monte, S., Kestenare, E., Guinet, C., d'Ovidio, F., 2015. Quasi-planktonic behavior of foraging top marine predators. Scientific Reports 5, 18063. https://doi.org/10.1038/srep18063

667 do Canhoto, B., 2014. Foraging areas and associated environmental preferences of juvenile loggerhead turtles (*Caretta caretta*).

669 Dormann, C.F., McPherson, J.M., Araújo, M.B., Bivand, R., Bolliger, J., Carl, G., Davies, R.G., Hirzel, A., Jetz, W., Kissling, W.D., Kühn, I., Ohlemüller, R., Peres-Neto, P.R., Reineking, B., Schröder, B., Schurr, F.M., Wilson, R., 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30, 609-628. https://doi.org/10.1111/j.2007.0906-7590.05171.x

674 Dragon, A.-C., Monestiez, P., Bar-Hen, A., Guinet, C., 2010. Linking foraging behaviour to physical oceanographic structures: Southern elephant seals and mesoscale eddies east of Kerguelen Islands. Progress in Oceanography, 3rd GLOBEC OSM: From ecosystem function to ecosystem prediction 87, 61-71. https://doi.org/10.1016/j.pocean.2010.09.025

679 Dufois, F., Hardman-Mountford, N.J., Greenwood, J., Richardson, A.J., Feng, M., Matear, R.J., 2016. Anticyclonic eddies are more productive than cyclonic eddies in subtropical gyres because of winter mixing. Sci Adv 2. https://doi.org/10.1126/sciadv.1600282

683 Freitas, C., Caldeira, R., Reis, J., Dellinger, T., 2018. Foraging behavior of juvenile loggerhead sea turtles in the open ocean: from Lévy exploration to area-restricted search. Marine Ecology Progress Series 595, 203-215.

686 Freitas, C., Lydersen, C., Fedak, M.A., Kovacs, K.M., 2008. A simple new algorithm to filter marine mammal Argos locations. Marine Mammal Science 24, 315-325. https://doi.org/10.1111/j.1748-7692.2007.00180.x

689 Frick, M.G., Williams, K.L., Bolten, A.B., Bjorndal, K.A., Martins, H.R., 2009. Foraging ecology of oceanic-stage loggerhead turtles *Caretta caretta*. Endangered Species Research 9, 91-97. https://doi.org/10.3354/esr00227

692 Garcia-Rojas, M.I., Jenner, K.C.S., Gill, P.C., Jenner, M.-N.M., Sutton, A.L., McCauley, R.D., 2018. Environmental evidence for a pygmy blue whale aggregation area in the Subtropical Convergence Zone south of Australia. Marine Mammal Science. https://doi.org/10.1111/mms.12494

696 Gaspar, P., Georges, J.-Y., Fossette, S., Lenoble, A., Ferraroli, S., Maho, Y.L., 2006. Marine animal behaviour: neglecting ocean currents can lead us up the wrong track. Proc. R. Soc. B 273, 2697-2702. https://doi.org/10.1098/rspb.2006.3623

699 Gaspar, P., Lalire, M., 2017. A model for simulating the active dispersal of juvenile sea turtles with a case study on western Pacific leatherback turtles. PLOS ONE 12, e0181595. https://doi.org/10.1371/journal.pone.0181595
702 Gaube, P., Barceló, C., Jr, D.J.M., Domingo, A., Miller, P., Giffoni, B., Marcovaldi, N.,
703 Swimmer, Y., 2017. The use of mesoscale eddies by juvenile loggerhead sea turtles
704 (*Caretta caretta*) in the southwestern Atlantic. PLOS ONE 12, e0172839.
705 https://doi.org/10.1371/journal.pone.0172839

706 Gaube, P., Braun, C.D., Lawson, G.L., McGillicuddy, D.J., Penna, A.D., Skomal, G.B.,
707 Fischer, C., Thorrold, S.R., 2018. Mesoscale eddies influence the movements of
708 mature female white sharks in the Gulf Stream and Sargasso Sea. Scientific Reports 8,
709 7363. https://doi.org/10.1038/s41598-018-25565-8

710 Gaube, P., Chelton, D.B., Samelson, R.M., Schlax, M.G., O'Neill, L.W., 2013. Satellite
711 Observations of Mesoscale Eddy-Induced Ekman Pumping. J. Phys. Oceanogr. 45,
712 104-132. https://doi.org/10.1175/JPO-D-14-0032.1

713 Gaube, P., McGillicuddy, D.J., Chelton, D.B., Behrenfeld, M.J., Strutton, P.G., 2014.
714 Regional variations in the influence of mesoscale eddies on near-surface chlorophyll.
715 Journal of Geophysical Research: Oceans 119, 8195-8220.
716 https://doi.org/10.1002/2014JC010111

717 Godø, O.R., Samuelsen, A., Macaulay, G.J., Patel, R., Hjøllo, S.S., Horne, J., Kaartvedt, S.,
718 Johannessen, J.A., 2012. Mesoscale Eddies Are Oases for Higher Trophic Marine
719 Life. PLOS ONE 7, e30161. https://doi.org/10.1371/journal.pone.0030161

720 Gould, W.J., 1985. Physical oceanography of the Azores front. Progress in Oceanography 14,
721 167-190. https://doi.org/10.1016/0079-6611(85)90010-2

722 Hair, J.F., Ringle, C.M., Sarstedt, M., 2011. PLS-SEM: Indeed a Silver Bullet. Journal of
723 Marketing Theory and Practice 19, 139-152. https://doi.org/10.2753/MTP1069-
724 6679190202

725 Hakoyama, H., Boeuf, B.J.L., Naito, Y., Sakamoto, W., 1994. Diving behavior in relation to
726 ambient water temperature in northern elephant seals. Can. J. Zool. 72, 643-651.
727 https://doi.org/10.1139/z94-087

728 Hansen, C., Kvaleberg, E., Samuelsen, A., 2010. Anticyclonic eddies in the Norwegian Sea;
729 their generation, evolution and impact on primary production. Deep Sea Research Part
730 I: Oceanographic Research Papers 57, 1079-1091.
731 https://doi.org/10.1016/j.dsr.2010.05.013

732 Howell, E.A., Dutton, P.H., Polovina, J.J., Bailey, H., Parker, D.M., Balazs, G.H., 2010.
733 Oceanographic influences on the dive behavior of juvenile loggerhead turtles (*Caretta
734 caretta*) in the North Pacific Ocean. Mar Biol 157, 1011-1026.
735 https://doi.org/10.1007/s00227-009-1381-0

736 Husson, F., Josse, J., Mazet, L.S., 2015. FactoMineR: multivariate exploratory data analysis
737 and data mining. R package Version 1313 [WWW Document]. URL https://cran.r-project.org/web/packages/FactoMineR/

739 Jaine, F.R.A., Rohner, C.A., Weeks, S.J., Couturier, L.I.E., Bennett, M.B., Townsend, K.A.,
740 Richardson, A.J., 2014. Movements and habitat use of reef manta rays off eastern
741 Australia: offshore excursions, deep diving and eddy affinity revealed by satellite
742 telemetry. Marine Ecology Progress Series 510, 73-86.
743 https://doi.org/10.3354/meps10910

744 Johnson, D.S., 2017. crawl: Fit Continuous-Time Correlated Random Walk Models to Animal
745 Movement Data, R package version 2.1.1 [WWW Document]. URL https://CRAN.R-project.org/package=crawl

747 Johnson, J., Stevens, I., 2000. A fine resolution model of the eastern North Atlantic between
748 the Azores, the Canary Islands and the Gibraltar Strait. Deep Sea Research Part I:
749 Oceanographic Research Papers 47, 875-899. https://doi.org/10.1016/S0967-0637(99)00073-4
750
751 Kobayashi, D.R., Cheng, I.-J., Parker, D.M., Polovina, J.J., Kamezaki, N., Balazs, G.H., 2011.

752 Loggerhead turtle (*Caretta caretta*) movement off the coast of Taiwan:

753 characterization of a hotspot in the East China Sea and investigation of mesoscale

754 eddies. ICES J Mar Sci 68, 707–718. https://doi.org/10.1093/icesjms/fsq185

755 Lalire, M., Gaspar, P., 2019. Modeling the active dispersal of juvenile leatherback turtles in

756 the North Atlantic Ocean | Movement Ecology | Full Text. Movement Ecology 7, 7.

757 Lehahn, Y., d'Ovidio, F., Lévy, M., Amitai, Y., Heifetz, E., 2011. Long range transport of a

758 quasi isolated chlorophyll patch by an Agulhas ring. Geophysical Research Letters 38.

759 https://doi.org/10.1029/2011GL048588

760 Lévy, M., Klein, P., Treguier, A.-M., 2001. Impact of sub-mesoscale physics on production

761 and subduction of phytoplankton in an oligotrophic regime [WWW Document].

762 https://doi.org/info:doi/10.1357/002224001762842181

763 Mansfield, K.L., Saba, V.S., Keinath, J.A., Musick, J.A., 2009. Satellite tracking reveals a

764 dichotomy in migration strategies among juvenile loggerhead turtles in the Northwest

765 Atlantic. Mar Biol 156, 2555. https://doi.org/10.1007/s00227-009-1279-x

766 Mansfield, K.L., Wyneken, J., Porter, W.P., Luo, J., 2014. First satellite tracks of neonate sea

767 turtles redefine the ‘lost years’ oceanic niche. Proceedings of the Royal Society of

768 London B: Biological Sciences 281, 20133039.

769 https://doi.org/10.1098/rspb.2013.3039

770 Marañón, E., Holligan, P.M., Varela, M., Mouriño, B., Bale, A.J., 2000. Basin-scale

771 variability of phytoplankton biomass, production and growth in the Atlantic Ocean.

772 Deep Sea Research Part I: Oceanographic Research Papers 47, 825–857.

773 https://doi.org/10.1016/S0967-0637(99)00087-4

774 Martin, A.P., Richards, K.J., 2001. Mechanisms for vertical nutrient transport within a North

775 Atlantic mesoscale eddy. Deep Sea Research Part II: Topical Studies in

776 Oceanography, The Biological Oceanography of the north-east Atlantic-the PRIME

777 study 48, 757–773. https://doi.org/10.1016/S0967-0645(00)00096-5

778 Massie, P.P., McIntyre, T., Ryan, P.G., Bester, M.N., Bornemann, H., Ansorge, I.J., 2016.

779 The role of eddies in the diving behaviour of female southern elephant seals. Polar

780 Biol 39, 297–307. https://doi.org/10.1007/s00300-015-1782-0

781 McCarthy, A.L., Heppell, S., Royer, F., Freitas, C., Dellinger, T., 2010. Identification of

782 likely foraging habitat of pelagic loggerhead sea turtles (*Caretta caretta*) in the North

783 Atlantic through analysis of telemetry track sinuosity. Progress in Oceanography,

784 CLimate Impacts on Oceanic TOp Predators (CLIOTOP) 86, 224–231.

785 https://doi.org/10.1016/j-pocean.2010.04.009

786 McGillicuddy, D., Robinson, A.R., Siegel, D.A., Jannasch, H.W., Johnson, R., Dickey, T.D.,

787 McNeil, J., Michaels, A.F., Knap, A.H., 1998. Influence of mesoscale eddies on new

788 production in the Sargasso Sea. Nature 394, 263–266. https://doi.org/10.1038/28367

789 McGillicuddy, D.J., 2016. Mechanisms of Physical-Biological-Biogeochemical Interaction at

790 the Oceanic Mesoscale. Annual Review of Marine Science 8, 125–159.

791 https://doi.org/10.1146/annurev-marine-010814-015606

792 McGillicuddy, D.J., Anderson, L.A., Bates, N.R., Bibby, T., Buesseler, K.O., Carlson, C.A.,

793 Davis, C.S., Ewart, C., Falkowski, P.G., Goldthwait, S.A., Hansell, D.A., Jenkins,

794 W.J., Johnson, R., Kosnyrev, V.K., Ledwell, J.R., Li, Q.P., Siegel, D.A., Steinberg,

795 D.K., 2007. Eddy/Wind Interactions Stimulate Extraordinary Mid-Ocean Plankton

796 Blooms. Science 316, 1021–1026. https://doi.org/10.1126/science.1136256

797 McGillicuddy, D.J., Robinson, A.R., 1997. Eddy-induced nutrient supply and new production

798 in the Sargasso Sea. Deep Sea Research Part I: Oceanographic Research Papers 44,

799 1427–1450. https://doi.org/10.1016/S0967-0637(97)00024-1
800 Monzón-Argüello, C., Rico, C., Carreras, C., Calabuig, P., Marco, A., López-Jurado, L.F.,

801 2009. Variation in spatial distribution of juvenile loggerhead turtles in the eastern

802 Atlantic and western Mediterranean Sea. Journal of Experimental Marine Biology and

803 Ecology 373, 79–86. https://doi.org/10.1016/j.jembe.2009.03.007

804 Murase, H., Hakamada, T., Matsuoka, K., Nishiwaki, S., Inagake, D., Okazaki, M., Tojo, N.,

805 Kitakado, T., 2014. Distribution of sei whales (*Balaenoptera borealis*) in the

806 subarctic–subtropical transition area of the western North Pacific in relation to oceanic

807 fronts. Deep Sea Research Part II: Topical Studies in Oceanography, Fronts, Fish and

808 Top Predators 107, 22–28. https://doi.org/10.1016/j.dsr2.2014.05.002

809 Narazaki, T., Sato, K., Abernathy, K.J., Marshall, G.J., Miyazaki, N., 2013. Loggerhead

810 Turtles (*Caretta caretta*) Use Vision to Forage on Gelatinous Prey in Mid-Water.

811 PLOS ONE 8, e66043. https://doi.org/10.1371/journal.pone.0066043

812 Narazaki, T., Sato, K., Miyazaki, N., 2015. Summer migration to temperate foraging habitats

813 and active winter diving of juvenile loggerhead turtles *Caretta caretta* in the western

814 North Pacific. Mar Biol 162, 1251–1263. https://doi.org/10.1007/s00227-015-2666-0

815 Nelson, D.M., McCarthy, J.J., Joyce, T.M., Ducklow, H.W., 1989. Enhanced near-surface

816 nutrient availability and new production resulting from the frictional decay of a Gulf

817 Stream warm-core ring. Deep Sea Research Part A. Oceanographic Research Papers

818 36, 705–714. https://doi.org/10.1016/0198-0149(89)90146-5

819 Nordstrom, C.A., Battaile, B.C., Cotté, C., Trites, A.W., 2013. Foraging habitats of lactating

820 northern fur seals are structured by thermocline depths and submesoscale fronts in the

821 eastern Bering Sea. Deep Sea Research Part II: Topical Studies in Oceanography,

822 Fourth International Symposium on Bio-logging Science 88–89, 78–96.

823 https://doi.org/10.1016/j.dsr2.2012.07.010

824 Onken, R., 1993. The Azores Countercurrent. J. Phys. Oceanogr. 23, 1638–1646.

825 https://doi.org/10.1175/1520-0485(1993)023<1638:TAC>2.0.CO;2

826 Pingree, R.D., Garcia-Soto, C., Sinha, B., 1999. Position and structure of the

827 Subtropical/Azores Front region from combined Lagrangian and remote sensing

828 (IR/altimeter/SeaWiFS) measurements. Journal of the Marine Biological Association

829 of the United Kingdom 79, 769–792.

830 Polovina, J., Howell, E.A., Parker, D., Balazs, G.H., 2003. Dive-depth distribution of

831 loggerhead (*Caretta caretta*) and olive ridley (*Lepidochelys olivacea*) sea turtles in the

832 central North Pacific: might deep longline sets catch fewer turtles? Fishery Bulletin

833 101, 189–193.

834 Polovina, J., Uchida, I., Balazs, G., Howell, E.A., Parker, D., Dutton, P., 2006. The Kuroshio

835 Extension Bifurcation Region: A pelagic hotspot for juvenile loggerhead sea turtles.

836 Deep Sea Research Part II: Topical Studies in Oceanography, Top predator “hot spots”

837 in the North Pacific 53, 326–339. https://doi.org/10.1016/j.dsr2.2006.01.006

838 Polovina, J.J., Kobayashi, D.R., Parker, D.M., Seki, M.P., Balazs, G.H., 2000. Turtles on the

839 edge: movement of loggerhead turtles (*Caretta caretta*) along oceanic fronts, spanning

840 longline fishing grounds in the central North Pacific, 1997–1998. Fisheries

841 Oceanography 9, 71–82. https://doi.org/10.1046/j.1365-2419.2000.00123.x

842 Putman, N.F., Abreu-Grobois, F.A., Broderick, A.C., Ciofi, C., Formia, A., Godley, B.J.,

843 Stroud, S., Pelembe, T., Verley, P., Williams, N., 2014. Numerical dispersal

844 simulations and genetics help explain the origin of hawksbill sea turtles in Ascension

845 Island. Journal of Experimental Marine Biology and Ecology, Charismatic marine

846 mega-fauna 450, 98–108. https://doi.org/10.1016/j.jembe.2013.10.026

847 Putman, N.F., Verley, P., Shay, T.J., Lohmann, K.J., 2012. Simulating transoceanic

848 migrations of young loggerhead sea turtles: merging magnetic navigation behavior
849 with an ocean circulation model. Journal of Experimental Biology 215, 1863–1870.

850 https://doi.org/10.1242/jeb.067587

851 Reich, K.J., Bjorndal, K.A., Bolten, A.B., 2007. The ‘lost years’ of green turtles: using stable isotopes to study cryptic lifestages. Biology Letters 3, 712–714.

853 https://doi.org/10.1098/rsbl.2007.0394

854 Scales, K.L., Miller, P.I., Hawkes, L.A., Ingram, S.N., Sims, D.W., Votier, S.C., 2014. On the Front Line: frontal zones as priority at-sea conservation areas for mobile marine vertebrates. J Appl Ecol 51, 1575–1583. https://doi.org/10.1111/1365-2664.12330

857 Stramma, L., Siedler, G., 1988. Seasonal changes in the North Atlantic subtropical gyre. Journal of Geophysical Research: Oceans 93, 8111–8118.

859 https://doi.org/10.1029/JC093iC07p08111

860 Sumner, M.D., 2016. trip: Tools for the Analysis of Animal Track Data [WWW Document].

861 R package version 1.5.0. URL https://CRAN.R-project.org/package=trip

862 Sweeney, E.N., McGillicuddy, D.J., Buesseler, K.O., 2003. Biogeochemical impacts due to mesoscale eddy activity in the Sargasso Sea as measured at the Bermuda Atlantic Time-series Study (BATS). Deep Sea Research Part II: Topical Studies in Oceanography, The US JGOFS Synthesis and Modeling Project: Phase II 50, 3017–3039. https://doi.org/10.1016/j.dsr2.2003.07.008

867 Tew Kai, E., Marsac, F., 2010. Influence of mesoscale eddies on spatial structuring of top predators’ communities in the Mozambique Channel. Progress in Oceanography, CLimate Impacts on Oceanic TOp Predators (CLIOTOP) 86, 214–223.

870 https://doi.org/10.1016/j.pocean.2010.04.010

871 Tomczak, M., Godfrey, J.S., 2013. Regional Oceanography: An Introduction. Elsevier.

872 Vaillancourt, R.D., Marra, J., Seki, M.P., Parsons, M.L., Bidigare, R.R., 2003. Impact of a cyclonic eddy on phytoplankton community structure and photosynthetic competency in the subtropical North Pacific Ocean. Deep Sea Research Part I: Oceanographic Research Papers 50, 829–847. https://doi.org/10.1016/S0967-0637(03)00059-1

876 Vandeperre, F., Aires-da-Silva, A., Santos, M., Ferreira, R., Bolten, A.B., Serrao Santos, R., Afonso, P., 2014. Demography and ecology of blue shark (*Prionace glauca*) in the central North Atlantic. Fisheries Research 153, 89–102.

879 https://doi.org/10.1016/j.fishres.2014.01.006

880 VanDerWal, J., Falconi, L., Januchowski, S., Shoo, L., Storlie, C., 2014. SDMTools: Species Distribution Modelling Tools: Tools for processing data associated with species distribution modelling exercises. R package version 1 1–221.

883 Varo-Cruz, N., Bermejo, J.A., Calabuig, P., Cejudo, D., Godley, B.J., López-Jurado, L.F., Pikesley, S.K., Witt, M.J., Hawkes, L.A., 2016. New findings about the spatial and temporal use of the Eastern Atlantic Ocean by large juvenile loggerhead turtles. Diversity and Distributions 22, 481–492. https://doi.org/10.1111/ddi.12413

887 Varo-Cruz, N., Hawkes, L.A., Cejudo, D., López, P., Coyne, M.S., Godley, B.J., López-Jurado, L.F., 2013. Satellite tracking derived insights into migration and foraging strategies of male loggerhead turtles in the eastern Atlantic. Journal of Experimental Marine Biology and Ecology 443, 134–140.

891 https://doi.org/10.1016/j.jembe.2013.02.046

892 Weimerskirch, H., Le Corre, M., Jaquemet, S., Potier, M., Marsac, F., 2004. Foraging strategy of a top predator in tropical waters: great frigatebirds in the Mozambique Channel. Marine Ecology Progress Series 275, 297–308.

895 Wood, S., 2006. Generalized Additive Models: An Introduction with R. CRC Press.

896 Woods, S., 2012. Random effects in GAM.

897

898
**Figure captions**

**Figure 1.** (a) Locations of the 28 loggerhead turtles according to (a) the area tag deployment, (b) the type of eddy associated with each position: anticyclonic (in red), cyclonic (in blue) and outside eddy (in grey), (c) the Residence Time and (d) the boxplot of the latitudinal range of all individuals according to seasons. In (c), the RT (initially expressed in hours) has been scaled between 0 (low RT) and 100% (high RT) to be comparable across individuals. The magenta dots inside the black box in (a) refer to the location of each turtle capture and the black dots in (d) to the means.

**Figure 2.** Loggerhead turtle density maps in (a) spring, (b) summer, (c) autumn and (d) winter. Colour scale is indicative of the number of cumulative days the turtles passed through each grid cell of 0.5x0.5 decimal degree. The number of locations recorded per season is noted at the bottom left of each panel.

**Figure 3.** Circular diagrams of (a) the 28 turtles' headings and (b) currents' headings extracted at all turtles' locations. (c) Circular diagram of a passive drifter (#18674) extracted over the study region. Trajectory of an Argos-tracked turtle (#20015) with (d) superimposed swimming vectors (corrected for currents) and (e) surface current vectors. (f) Trajectory of a passive drifter with superimposed drifter vectors. The blue squares (in d,e,f) refer to the starting points and the red triangle to the last locations.

**Figure 4.** Maps of eddy locations (from Aviso, red for anticyclonic and blue for cyclonic) and daily SLA (in cm, from Copernicus) for individuals (a,e) #23659, (b,f) #20012, (c,g) #23660 and (d,h) #22131. The track of each turtle was superimposed on each map with the proportion of RT (in %) calculated along the track and represented by black dots varying in size according to RT values. The four maps at the bottom correspond to enlarged portions of the tracks shown in grey rectangles at the top. The location corresponding to each of the four dates is noted with pink empty dots along the tracks. Dotted lines in the bottom maps delineate inner cores of eddies. IC and OC stand for inner core and outer cores.
931

**Figure 5.** Seasonal means (±SE) of the Residence Time (in %) for all turtles (a,b) and passive drifters (c,d) according to eddy type (A: anticyclonic, C: cyclonic and O: outside eddy) and eddy region (IC: inner core, OC: outer core, P: periphery and O: outside eddy).

932

933

934

935

**Figure 6.** Relationships between loggerhead Residence Time (in percentage) and eddy characteristics obtained from the four seasonal GAMs. The solid black line in each plot is the smooth function estimate and the shaded regions refer to the approximate 95% confidence intervals. The Y axis represents the response variable (RT) expressed in log scale. Positive values on the Y axis indicate a high probably of foraging activity, and conversely. The horizontal dotted lines indicate no effect of the environmental variable.

936

937

938

939

940

941

942

**Figure 7.** Histograms of dive depth (a,d,g,j), dive duration (b,e,h,k) and TAD (c,f,i,l) for all individuals according to dive cluster (a,b,c), eddy type (d,e,f; A: anticyclonic, C: cyclonic, O: outside eddy), eddy region (g,h,i; IC: inner core, OC: outer core, P: periphery, O: outside eddy) and season (j,k,l). In Fig. 6d-l, depth class above 5 m were removed to get a better contrast.

943

944

945

946

947

948

**Figure 8.** Means (±SE) of the Residence Time (in %) for all turtles according to dive cluster and (a) eddy type (A: anticyclonic, C: cyclonic and O: outside eddy) and (b) eddy region (IC: inner core, OC: outer core, P: periphery and O: outside eddy). The black dots refer to the means.

949

950

951

952

953

**Figure 9.** Relationships between loggerhead Residence Time (in percentage) and eddy characteristics obtained from the three GAMs performed for each dive cluster. The solid black line in each plot is the smooth function estimate and the shaded regions refer to the approximate 95% confidence intervals. The Y axis represents the response variable (RT) expressed in log scale. Positive values on the Y axis indicate a high probably of foraging activity, and conversely. The horizontal dotted lines indicate no effect of the environmental variable.

954

955

956

957

958

959
<table><caption>Table 1. Summary of the horizontal movements of the 28 tracked loggerhead turtles. Numbers at the bottom in italics are means±SD.</caption><thead><tr><th rowspan="2">Turtle ID</th><th rowspan="2">Start date</th><th rowspan="2">End date</th><th rowspan="2">Nloc</th><th>Distance</th><th>Duration</th><th>Speed</th><th>SCL</th></tr><tr><th>(km)</th><th>(d)</th><th>(km.h<sup>-1</sup>)</th><th>(cm)</th></tr></thead><tbody><tr><td>22131</td><td>09/09/1994</td><td>08/06/1995</td><td>270</td><td>3887</td><td>272</td><td>0.60±0.39</td><td>40.3</td></tr><tr><td>22130</td><td>15/09/1994</td><td>19/07/1995</td><td>301</td><td>5676</td><td>307</td><td>0.78±0.45</td><td>47</td></tr><tr><td>24185</td><td>15/09/1995</td><td>26/03/1996</td><td>188</td><td>3553</td><td>193</td><td>0.76±0.36</td><td>45.9</td></tr><tr><td>24187</td><td>22/09/1995</td><td>31/12/1995</td><td>101</td><td>1340</td><td>100</td><td>0.55±0.33</td><td>49.9</td></tr><tr><td>24186</td><td>23/09/1995</td><td>27/02/1996</td><td>155</td><td>2290</td><td>157</td><td>0.61±0.28</td><td>49.7</td></tr><tr><td>24188</td><td>23/09/1995</td><td>31/05/1996</td><td>246</td><td>3332</td><td>251</td><td>0.56±0.31</td><td>50.5</td></tr><tr><td>23662</td><td>18/09/1998</td><td>04/03/1999</td><td>164</td><td>3971</td><td>167</td><td>0.99±0.40</td><td>40.8</td></tr><tr><td>23660</td><td>21/09/1998</td><td>13/03/1999</td><td>171</td><td>2250</td><td>173</td><td>0.54±0.30</td><td>42.2</td></tr><tr><td>22531</td><td>29/09/1998</td><td>21/10/1998</td><td>23</td><td>212</td><td>22</td><td>0.38±0.24</td><td>37</td></tr><tr><td>23658</td><td>17/10/1998</td><td>07/03/1999</td><td>132</td><td>1513</td><td>141</td><td>0.48±0.34</td><td>42</td></tr><tr><td>23661</td><td>21/10/1998</td><td>06/03/1999</td><td>135</td><td>1965</td><td>136</td><td>0.61±0.33</td><td>36.4</td></tr><tr><td>23657</td><td>02/11/1998</td><td>24/03/1999</td><td>140</td><td>2448</td><td>142</td><td>0.71±0.39</td><td>61.1</td></tr><tr><td>22530</td><td>17/11/1998</td><td>08/06/1999</td><td>197</td><td>2874</td><td>203</td><td>0.59±0.32</td><td>47.4</td></tr><tr><td>23659</td><td>18/12/1998</td><td>03/05/1999</td><td>130</td><td>2244</td><td>136</td><td>0.70±0.30</td><td>43.8</td></tr><tr><td>24179</td><td>16/09/1999</td><td>25/02/2000</td><td>160</td><td>1640</td><td>162</td><td>0.43±0.28</td><td>40</td></tr><tr><td>25317</td><td>30/09/1999</td><td>16/11/1999</td><td>47</td><td>594</td><td>47</td><td>0.51±0.19</td><td>36.3</td></tr><tr><td>25362</td><td>01/10/1999</td><td>03/02/2000</td><td>123</td><td>1567</td><td>125</td><td>0.52±0.28</td><td>48.4</td></tr><tr><td>20011</td><td>14/06/2000</td><td>29/09/2000</td><td>108</td><td>2378</td><td>107</td><td>0.92±0.68</td><td>42</td></tr><tr><td>20016</td><td>16/07/2000</td><td>22/10/2000</td><td>97</td><td>1338</td><td>98</td><td>0.57±0.26</td><td>47.8</td></tr><tr><td>20017</td><td>18/07/2000</td><td>24/11/2000</td><td>127</td><td>1681</td><td>129</td><td>0.54±0.33</td><td>44.1</td></tr><tr><td>20015</td><td>19/07/2000</td><td>21/11/2000</td><td>126</td><td>2289</td><td>125</td><td>0.76±0.38</td><td>42.3</td></tr><tr><td>22274</td><td>19/07/2000</td><td>24/08/2000</td><td>35</td><td>581</td><td>36</td><td>0.66±0.33</td><td>42.5</td></tr><tr><td>22275</td><td>19/07/2000</td><td>19/02/2001</td><td>213</td><td>3142</td><td>215</td><td>0.61±0.33</td><td>48.9</td></tr><tr><td>20012</td><td>20/07/2000</td><td>21/06/2001</td><td>337</td><td>5192</td><td>336</td><td>0.64±0.35</td><td>51</td></tr><tr><td>20014</td><td>23/07/2000</td><td>30/11/2000</td><td>131</td><td>1790</td><td>130</td><td>0.57±0.37</td><td>40.7</td></tr><tr><td>20013</td><td>26/07/2000</td><td>25/11/2000</td><td>123</td><td>1262</td><td>122</td><td>0.43±0.20</td><td>40.5</td></tr><tr><td>22209</td><td>27/07/2000</td><td>15/10/2000</td><td>81</td><td>1167</td><td>80</td><td>0.60±0.36</td><td>45</td></tr><tr><td>20018</td><td>02/08/2000</td><td>25/11/2000</td><td>113</td><td>1963</td><td>115</td><td>0.70±0.38</td><td>40.2</td></tr></tbody><tfoot><tr><td></td><td></td><td></td><td>149±967</td><td>2290±967</td><td>151±55</td><td>0.61±0.10</td><td>44.4±4.2</td></tr></tfoot></table>
a) Locations of tags deployment

<figure><img src="image_3.png" /></figure>

b) Loggerhead locations

<figure><img src="image_4.png" /></figure>

c) Residence Time

<figure><img src="image_5.png" /></figure>

d) Latitudinal range

<figure><img src="image_6.png" /></figure>
a) Spring (Apr-Jun)

<figure><img src="image_7.png" /></figure>

b) Summer (Jul-Sep)

<figure><img src="image_8.png" /></figure>

c) Autumn (Oct-Dec)

<figure><img src="image_9.png" /></figure>

d) Winter (Jan-Mar)

<figure><img src="image_10.png" /></figure>
a) Turtles

<figure><img src="image_11.png" /></figure>

Swimming speed (m/s) [0,0.05] = [0.05,0.1] = [0.1,0.3] [0.3,1.04]

b) Currents

<figure><img src="image_12.png" /></figure>

Current speed (m/s) [0,0.05] = [0.05,0.1] = [0.1,0.3] [0.3,0.71]

c) Drifters

<figure><img src="image_13.png" /></figure>

Drifter speed (m/s) [0,0.05] = [0.05,0.1] = [0.1,0.3] [0.3,1.67]

d) Turtle (#20015)

<figure><img src="image_14.png" /></figure>

e) Currents (#20015)

<figure><img src="image_15.png" /></figure>

f) Drifter (#18674)

<figure><img src="image_16.png" /></figure>
a) #23659: 1999-04-08 (Spring)

<figure><img src="image_17.png" /></figure>

b) #20012: 2000-09-12 (Summer)

<figure><img src="image_18.png" /></figure>

c) #23660: 1998-12-03 (Autumn)

<figure><img src="image_19.png" /></figure>

d) #22131: 1995-03-13 (Winter)

<figure><img src="image_20.png" /></figure>

e) #23659: 1999-04-08 (Spring)

<figure><img src="image_21.png" /></figure>

f) #2000-09-12 (Summer)

<figure><img src="image_22.png" /></figure>

g) #23660: 1998-12-03 (Autumn)

<figure><img src="image_23.png" /></figure>

h) #22131: 1995-03-13 (Winter)

<figure><img src="image_24.png" /></figure>
<figure><img src="image_25.png" /><figcaption>b) Eddy type: drifters</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>c) Eddy region: turtles</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>d) Eddy region: drifters</figcaption></figure>

<figure><img src="image_28.png" /></figure>
<table><thead><tr><th></th><th>SPRING (82%)</th><th>FSLE (d-2)</th><th>Eddy amplitude (cm)</th><th>Eddy lifetime (d)</th><th>Eddy radius (km)</th></tr></thead><tbody><tr><th>Spring (82%)</th><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Summer (50%)</th><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Autumn (56%)</th><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Winter (63%)</th><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
a) Dive depth

(m)

<figure><img src="image_29.png" /></figure>

b) Dive duration

(min)

<figure><img src="image_30.png" /></figure>

c) TAD

(m)

<figure><img src="image_31.png" /></figure>

d) Dive depth

(m)

<figure><img src="image_32.png" /></figure>

e) Dive duration

(min)

<figure><img src="image_33.png" /></figure>

f) TAD

(m)

<figure><img src="image_34.png" /></figure>

g) Dive depth

(m)

<figure><img src="image_35.png" /></figure>

h) Dive duration

(min)

<figure><img src="image_36.png" /></figure>

i) TAD

(m)

<figure><img src="image_37.png" /></figure>

j) Dive depth

(m)

<figure><img src="image_38.png" /></figure>

k) Dive duration

(min)

<figure><img src="image_39.png" /></figure>

l) TAD

(m)

<figure><img src="image_40.png" /></figure>
a) Eddy type

<figure><img src="image_41.png" /></figure>

b) Eddy region

<figure><img src="image_42.png" /></figure>
<table><thead><tr><th></th><th>C1 (44%)</th><th>Eddy amplitude (cm)</th><th>Eddy lifetime (d)</th><th>Eddy radius (km)</th></tr></thead><tbody><tr><th>C2 (49%)</th><td>SLA (m)</td><td>log(RT)</td><td>FSLE (d-2)</td><td></td></tr><tr><th>C3 (57%)</th><td>SLA (m)</td><td>log(RT)</td><td>Eddy amplitude (cm)</td><td></td></tr></tbody></table>