Sabrina Fossette

Jean-Yves Georges
email: jean-yves.georges@c-strasbourg.fr

Hideji Tanaka

Yan Ropert-Coudert

Sandra Ferraroli

Nobuaki Arai

Katsufumi Sato

Yasuhiko Naito

Yvon Le Maho

Marine Ecology Progress Series, in

Keywords: Benthic behaviour, Dermochelys coriacea, Diving behaviour, Foraging, Guiana's continental shelf, Satellite tracking

We present the first combined analysis of diving behaviour and dispersal patterns in gravid leatherback turtles during 3 consecutive nesting seasons in French Guiana. In total 23 turtles were fitted with Argos satellite transmitters and 16 individuals (including 6 concurrently satellite-tracked) were equipped with an electronic time-depth recorder for single inter-nesting intervals, i.e. between two consecutive ovi-positions. The leatherbacks dispersed over the continental shelf, ranging from the coastal zone to the shelf break and moved over 546.2 ± 154.1 km (mean ± SD) in waters of French Guiana and neighbouring Surinam. They mostly performed shallow (9.4 ± 9.2 m) and short (4.4 ± 3.4 min) dives with a slight diurnal pattern.

They dived deeper as they moved away from the coast suggesting that they were predominantly following the seabed. Inter-nesting intervals could be divided into two phases: during the first 75% of the time turtles spent at sea, they dived on average 47 min h -1 before showing a lower and more variable diving effort as they came back to the shore. The extended movements of leatherbacks and the fine analysis of dive shapes suggest that in French Guiana leatherbacks may feed during the inter-nesting interval, probably to compensate for the energy costs associated with reproduction. This results in this endangered species being exposed to high risks of interactions with local fisheries throughout the continental shelf.

INTRODUCTION 2

Understanding how wild animals face trade-offs between survival and reproduction is important in species with high reproductive effort, particularly in critically endangered species where adult mortality may be high enough to result in extinction. In sea turtles, reproduction takes place over 2 months during which females lay 1 to 14 clutches of 50 to 130 eggs each, depending on the species [START_REF] Miller | Reproduction in sea turtles[END_REF]. Accordingly, sea turtles (IUCN 2004) have high reproductive energy costs. Among sea turtles, critically-endangered leatherback turtles (Dermochelys coriacea) show the highest reproductive output [START_REF] Miller | Reproduction in sea turtles[END_REF] as they may lay the equivalent of 20% of their body mass (Georges et al. unpublished data) and supposedly do not feed over the nesting season [START_REF] Miller | Reproduction in sea turtles[END_REF][START_REF] Rivalan | Trade-off between current reproductive effort and delay to next reproduction in the leatherback sea turtle[END_REF].

Consequently, during the inter-nesting intervals, leatherbacks might minimise energy expenditure to maximize the amount of energy allocated to ovi-position and egg production [START_REF] Reina | Respiratory frequency, dive behavior and social interactions of leatherback turtles, Dermochelys coriacea, during the inter-nesting interval[END_REF][START_REF] Wallace | Bioenergetics and diving activity of interesting leatherback turtles Dermochelys coriacea at Parque Nacional Marino Las Baulas, Costa Rica[END_REF]. This seems to be the case in the gravid leatherbacks nesting in Pacific coasts of Costa Rica, which have been reported to move very slowly near the seabed [START_REF] Reina | Respiratory frequency, dive behavior and social interactions of leatherback turtles, Dermochelys coriacea, during the inter-nesting interval[END_REF] and display an extremely low metabolic rate [START_REF] Wallace | Bioenergetics and diving activity of interesting leatherback turtles Dermochelys coriacea at Parque Nacional Marino Las Baulas, Costa Rica[END_REF] during the nesting season.

In contrast, the gravid leatherbacks nesting on both sides of the Atlantic cover great distances at sea (Georges et al. in press) and swim at high speed during inter-nesting intervals [START_REF] Eckert | Diving and foraging behavior of Leatherback sea turtles (Dermochelys coriacea)[END_REF][START_REF] Eckert | Swim speed and movement patterns of gravid Leatherback sea turtles (Dermochelys coriacea) at St Croix, US Virgin Island[END_REF]. Atlantic leatherbacks nesting in the Caribbean perform nocturnal shallow, and diurnal deep, dives [START_REF] Eckert | Diving and foraging behavior of Leatherback sea turtles (Dermochelys coriacea)[END_REF]) consistent with the vertical migration of their main prey (i.e. gelatinous plankton, [START_REF] Hays | A review of the adaptive significance and ecosystem consequences of zooplankton diel vertical migrations[END_REF], auguring for nocturnal foraging activity (Myers & Hays in press). In other words, between two consecutive nesting events, leatherback turtles may compensate for high reproductive costs either by reducing their activity, or by feeding, as suggested for the Pacific and Atlantic populations, respectively.

The diving behaviour of marine animals has been studied in several different ways, such as by analysing depth profiles concurrently with, by example, swim speed (e.g. [START_REF] Ropert-Coudert | Preliminary investigations of prey pursuit and capture by king penguins at sea[END_REF][START_REF] Eckert | Swim speed and movement patterns of gravid Leatherback sea turtles (Dermochelys coriacea) at St Croix, US Virgin Island[END_REF], three-dimensional compass data (e.g. [START_REF] Mitani | A method for reconstructing three-dimensional dive profiles of marine mammals using geomagnetic intensity data: results from two lactating Weddell seals[END_REF] or underwater video (e.g. [START_REF] Reina | Respiratory frequency, dive behavior and social interactions of leatherback turtles, Dermochelys coriacea, during the inter-nesting interval[END_REF][START_REF] Watanabe | Seal-mounted cameras detect invertebrate fauna on underside of Antarctic ice shelf[END_REF]. Concurrent study of diving and dispersal behaviour [START_REF] Georges | Satellite tracking of seabirds: interpretation of activity pattern from the frequency of satellite locations[END_REF]) provides important information regarding the areas where particular behaviours occurred, and defines the oceanic zones where individuals tend to congregate. Such information is crucially needed for protected species whose distribution overlaps with areas of natural and/or anthropogenic threats (e.g. Georges et al. in press).

Here we present the first combined analysis of diving and dispersal patterns of the critically-endangered leatherback turtle during their inter-nesting intervals over 3 consecutive nesting seasons in French Guiana. Following the extended dispersal recently reported in this population (Georges et al. in press), we predict that leatherbacks in French Guiana do not reduce their activity as suggested in the Pacific ocean [START_REF] Reina | Respiratory frequency, dive behavior and social interactions of leatherback turtles, Dermochelys coriacea, during the inter-nesting interval[END_REF], but may rather dive consistently and display feeding activity, as suggested in the Caribbean Sea (Eckert et al. 1989, Myers & Hays in press). In addition, since leatherbacks face lethal interactions with industrial fisheries in French Guiana while dispersing widely over the continental shelf (Delamare 2005, Georges et al. in press), our study aims to identify those areas and depths where these interactions are more likely to occur.

MATERIALS AND METHODS

The study was carried out during the nesting seasons 2001-2003 at Awala-Yalimapo beach (5.7°N -53.9°W), French Guiana, on the border with Surinam, South America (Figure 1). Horizontal movements. During the study period, a total of 23 females was equipped with satellite platform transmitter terminals (PTTs, Kiwisat 101 AA-cell, Sirtrack, New-Zealand, weight 150g, cross section: 4cm²). PTTs were held in place on the carapace using a customised harness attached during ovi-position in 2001 (see [START_REF] Eckert | Shallow water diving by Leatherback turtles in the South China Sea[END_REF]) and were directly fixed on the central ridge of the turtle's carapace in 2002 and 2003 (see [START_REF] Southwood | Heart rates and diving behaviour of Leatherback sea turtles in the eastern Pacific Ocean[END_REF]. Harnesses were automatically released from the animals after several months during post-nesting migrations due to a corrodible link in the attachment system [START_REF] Eckert | Shallow water diving by Leatherback turtles in the South China Sea[END_REF].

PTTs directly fixed on the carapace were removed as soon as turtles came back to the nesting beach after at least one inter-nesting interval.

At-sea movements were reconstructed using the Argos system (www.cls.fr). Each Argos location was provided with a class of accuracy, with classes 1, 2 and 3 having nominal standard deviations around the true position of 1000 m, 350 m and 150 m, respectively, whereas location classes A, B and 0 have no designed accuracy. We analysed all locations of all accuracies, excluding locations that were on land, locations separated by less than one hour and locations that implied travel rates > 10km/h [START_REF] Eckert | Swim speed and movement patterns of gravid Leatherback sea turtles (Dermochelys coriacea) at St Croix, US Virgin Island[END_REF][START_REF] Gaspar | Neglecting ocean currents can lead us up the wrong track[END_REF]) by filtering out the least accurate locations.

For individual turtles tracked during more than one inter-nesting interval, we only considered the first track to avoid pseudo-replication. For each individual track, we calculated the time spent at sea, the overall distance travelled and the distance to the furthest point from the beach (dispersal range). Each track was divided into phases according to the distance the turtles moved relative to the beach. Outbound/inbound phases corresponded to the period when turtles moved away/back from/to the beach, respectively. For some individuals an intermediate phase between outbound and inbound phases was identified, when the distance to the beach remained close to its maximum value.

Vertical movements.

Sixteen females fitted with a PTT were concurrently equipped with an electronic Time-Depth Recorder (TDR, Little Leonardo, Japan, weight: 54g, cross section: 3.5cm², length: 116mm), directly fixed on the central ridge of the turtle's carapace for a single inter-nesting interval. Each logger included a pressure sensor measuring depth (range: 0-100 m, ±0.05m) every second. Following recapture, loggers were removed and data downloaded into a laptop computer. Data were analysed using IGOR Pro software (WaveMetrics Inc., Oregon, USA). All dives > 0.5 m and < 2 m (n = 48220 dives) occurred exclusively during the first 1.5h and the last 6h of each individual inter-nesting interval, probably reflecting travelling from/to the nesting beach, and were excluded from the analysis to allow us to focus on other diving patterns. We recorded the start and end time of each dive, the maximum depth reached, the duration of descent/bottom/ascent phases, the rates of descent and ascent and the duration of the preceding and the subsequent post-dive surface interval. The bottom phase was defined as the period during which depth was deeper than 90% of the maximum depth of a given dive. For each bottom phase, we calculated the depth amplitude and number of rapid, up-and-down undulations observed in the depth profile (hereafter termed as wiggles, see [START_REF] Wilson | Foraging ecology[END_REF][START_REF] Houghton | Diving behaviour during the interesting interval for loggerhead turtles Caretta caretta nesting in Cyprus[END_REF].

In order to classify dive profiles, a Principal Component Analysis (PCA) was performed on all 20607 dives > 2 m considering the above-mentioned parameters. As the total number of dives recorded varied among individuals (from 679 to 3539 dives), relationships between dive parameters were performed considering a random sample of 600 dives per individual. This avoided pseudo-replication while taking inter-individual variability into account [START_REF] Cherel | Diving behaviour of female northern rockhopper penguins, Eudyptes chrysocome moseleyi, during the brooding period at Amsterdam Island (Southern Indian Ocean)[END_REF]. Diurnal patterns in terms of number of dives, dive depth and dive duration were investigated considering the nautical definition of dawn and dusk (i.e. when the sun was 12° below the horizon at Awala-Yalimapo beach; http://aa.usno.navy.mil/). Diving effort was analysed by considering hourly dive frequency and time spent diving per hour. Changes in hourly diving effort were investigated throughout the inter-nesting interval in 12-hour increments centred on midnight and midday, excluding 12-hours blocks that were incomplete (i.e. for the first and last hours of the trip). Statistical analyses were carried out using Minitab statistical software. Values are given as means ± SD, differences being considered as statistically significant when P < 0.05.

All turtles were measured during ovi-position using a flexible measuring tape (±0.5cm) following Georges & Fossette (2006). Standard curvilinear carapace length was measured on the midline of the shell, from the nape notch of the carapace to the end of the caudal peduncle.

Curvilinear carapace width was measured at the level of maximum width back of the foreflippers (Georges & Fossette 2006).

RESULTS

Among the 23 females equipped with ARGOS transmitters, we obtained data from 11 individuals (7 in 2001, 2 in 2002 and 2 in 2003) over at least one complete inter-nesting interval (Table 1). Among the 16 individuals that were fitted with TDRs, 10 were successfully recaptured with their tag, within which 7 (3 in 2001, 2 in 2002 and 2 in 2003; Table 2) were monitored over one complete inter-nesting interval. Four individuals were concurrently monitored with Argos and TDR devices over one complete inter-nesting interval.

Horizontal movements

Most locations obtained from Argos were of poor quality, with locations 3, 2, 1, 0, and below contributing to 4, 8, 14, 13 and 60% of the total number of locations received, respectively. The 11 turtles spent on average 10.2 ± 0.9 days (range: 8.3-11.8 days) at sea between two consecutive nesting events (Table 1). Turtles dispersed within a short range from the coast (90.4 ± 47.7 km, range: 37.1-176.0 km), remaining on the shallow continental shelf (<200m deep), although they moved over hundreds of kilometres (546.2 ± 154.1 km, range: 375.7-846.6 km) in waters of French Guiana and of neighbouring Surinam (Figure 1 and Table 1). Four turtles remained within 50 km of Awala-Yalimapo beach, where they moved erratically in shallow waters (approximately 20 m deep). Four other turtles remained within 100 km from the beach, reaching waters approximately 50 m deep, while the last three individuals reached the edge of the continental shelf where depth is about 100 m. Among the 7 turtles that moved over significant distances, two headed West into Surinam waters where they tending to move anticlockwise before swimming along the coast towards the nesting beach. The five other turtles remained in French Guiana waters East from Awala-Yalimapo beach, tending to move clockwise until they crossed their initial path off the Maroni River.

They then reached Surinam waters where they moved anticlockwise before swimming along the coast towards the nesting beach.

There was no significant relationship between turtle biometry (standard curvilinear carapace length and curvilinear carapace width) and trip duration (Spearman rank correlation, r S = -0.19, p = 0.65; r S = 0.12, p = 0.77, for carapace length and width, respectively, n = 8 turtles; 3 of the 11 turtles were not measured), dispersal range (r S = -0.40, p = 0.32; r S = 0.17, p = 0.69, for length and width, respectively) and total distance travelled (r S = -0.12, p = 0.78; r S = 0.37, p = 0.37, for length and width, respectively).

Vertical movements

General characteristics

A total of 20607 dives > 2 m was recorded from the 10 turtles, the longest dive being 28.2 min for a maximum depth of 63.9 m while the deepest dive was 83.8 m for a duration of 17.9 min (Table 2). Turtles performed mostly shallow (9.4 ± 9.2 m) andshort (4.4 ± 3.4 min) dives with substantial variation among individuals (Table 2). Dives shallower than 8 m and 25 m represented 50% and 90% of the 6000 randomly sampled dives, respectively (Figure 2a). Dives shorter than 4 min and 10 min represented 50% and 90% of the 6000 randomly sampled dives, respectively (Figure 2b). Dives deeper than 40 m (n = 92) were performed by one single individual (#200101) and lasted on average 15.3 ± 3.1 min and did not significantly increase in duration with increasing depth (from 40m to 85m, ANOVA, F 8,91 = 2.02, p = 0.06). Within a dive, the time spent at the bottom lasted a mean of 1.6 ± 1.8 min, corresponding to 32.8 ± 16.0% (ranging from 1 to 90%) of the total dive duration (Table 2).

Wiggles occurred at the bottom of most of the dives, as 50% and 90% of the dives showed less than 12, and less than 40 wiggles, respectively, with a mean of 16.3 ± 17.5 wiggles per dive (Figure 3). Mean surface interval was 1.4 ± 1.9 min (Table 2).

Due to dive depth distribution, the relationships between dive depth and other dive parameters were investigated considering dive depth ranging from 2 m to 40m with 5-m increments (individual relations were calculated with 600 random dives per turtle except #200101, n= 508 dives because of 92 dives > 40m deep; n tot = 5908 dives, Figure 4). Mean dive duration increased significantly with increasing depth class when considering either each turtle individually (Spearman rank correlation, p < 0.05 in all cases) or when considering all turtles together (Spearman rank correlation between the grand mean and depth classes, r S = 0.97, n = 10, p < 0.001). There was no significant relationship between mean bottom time and depth class when considering either each turtle individually (Spearman rank correlation, p > 0.05 in all cases) or all turtles (r S = 0.24, n = 10, p = 0.57). When considering all individuals together, both rates of descent and ascent were significantly related to dive depth (r S = 0.95, n = 10, p < 0.001; and r S = 0.90, n = 10, p < 0.01, respectively, Figure 4). However these relationships did not occur for several turtles, when considered individually (descent rate: #200103, #200202, #200203, #200301,#200303 and #200304; ascent rate: #200101, #200103, #200203, #200301, #200302, #20303 and #200304). Turtles descended and ascended at about 0.1 m.s -1 for dives < 15m and regularly increased their rate of vertical travel for deeper depths with a mean maximum rate of 0.26 ± 0.06 m s -1 (n=5908 dives; Figure 4). There were significant relationships between preceding surface interval and dive duration when considering each turtle individually (Pearson rank correlation, p < 0.05 in all cases, n = 600 dives per turtle) except for 4 individuals (#200102, #200103, #200201, #200302, Pearson rank correlation, p > 0.2 in all cases, n = 600 dives per turtle). However, this relationship did not occur if all turtles are considered together (r²= 0.07, n= 6000 dives, p = 0.23). Similarly, there were significant relationships between post-dive surface interval and dive duration if each turtle was considered individually (Pearson rank correlation, p < 0.05 in all cases, n = 600 dives per turtle) except for 3 individuals (#200201, #200301, #200302, Pearson rank correlation, p > 0.1 in all cases, n = 600 dives per turtle). However, this relationship did not occur when considering all turtles (r²= 0.09, n= 6000 dives, p = 0.21).

Dive types

The PCA performed on dive parameters for the 20607 dives > 2 m identified 2 factors explaining 34% and 20% of the observed variance, respectively. Factor 1 and factor 2 were predominantly associated with dive duration and bottom time, respectively and enabled us to distinguish 4 main dive types (Figures 3 and5 and Table 3). Type 1 dives were the shallowest and the shortest dives, whereas Type 3 dives were the deepest and the longest.

Type 2 dives and Type 4 dives had intermediate mean maximum dive depths and mean dive durations but differed in their bottom time, with Type 2 having the shortest and Type 4 having the longest bottom time (Figures 3 and5 andTable 3). In addition, Type 2 dives showed the slowest ascent rate of all dives (Table 3).

Diurnal pattern

There was no significant difference between the number of dives performed during daytime and night time over the entire inter-nesting interval (nday = 10296 dives versus nnight = 10311 dives, χ ² 1 = 0.005, p > 0.05). Dives performed during daytime were significantly deeper and correspondingly longer (9.9 ± 9.2 m; 4.6 ± 3.3 min) than those performed at night (8.9 ± 9.1 m, T-test, t = 7.41, p < 0.01; 4.2 ± 3.4 min, T-test, t = 7.80, p < 0.01; Figure 6).

Turtles performed on average 10.7 ± 3.6 dives per hour (hourly dive frequency) corresponding to a mean time spent diving of 45.0 ± 4.1 min h -1 (Table 2). There was no significant difference between day and night either in terms of hourly dive frequency (Mann-Whitney, Z = 107.0, n = 10, p = 0.9) or in terms of time spent diving per hour (Mann-Whitney, Z = 123.0, n = 10, p = 0.19).

The four dive types were not equally distributed between day and night (χ ² 3 = 374.6, n = 20607, p < 0.001; Figure 7). Shallow Type 1-dives predominantly occurred during night time (χ ² 23 = 134.1, n = 11733, p < 0.001 followed by a contrast test; Figure 7) whereas Type 4-dives occurred mostly during the daytime (χ ² 23 = 338.7, n = 3186, p < 0.001 followed by a contrast test; Figure 7). Type 2 and Type 3 were equally distributed throughout the 24-hour cycle (χ ² 23 = 13.2, n = 5105, p > 0.05, χ ² 23 = 8.9, n = 583, p > 0.05, respectively; Figure 7).

Integrating vertical and horizontal movements

The 7 satellite-tracked turtles for which dive records were complete throughout the inter-nesting interval spent a mean of 9.8 ± 1.3 days at sea between two consecutive nesting events (Table 2). This is similar to the 10.3 ± 1.4 days spent at sea by the 5 individuals only fitted with PTT (Mann-Whitney, Z = 40, n = 12, p = 0.42; Table 1).

All 7 turtles dived continuously throughout their inter-nesting interval but with substantial changes in dive parameters through time (Figure 8a and Appendix). Among these 7 turtles with complete dive records, 4 individuals were also successfully tracked by Argos during the entire inter-nesting interval (Table 2). For these 4 turtles, maximum depth and mean depth increased with distance to the beach (Spearman rank correlation, p < 0.05 in the 4 cases; Figure 8a and8b).

Similarly both indices of hourly diving effort varied significantly throughout the 7 inter-nesting intervals (Kruskal-Wallis, p < 0.05 in all cases; Figure 8c andAppendix Indeed, the time spent diving per hour was significantly higher during phase 1 (47.3 ± 3.0 min h -1 ) than during phase 2 (31.3 ± 6.7 min h -1 , Mann-Whitney, Z = 77.0, n = 7 turtles, p < 0.01; Figure 8c andAppendix). Yet the hourly dive frequency did not differ significantly between phase 1 (9.7 ± 3.0 dives h -1 ) and phase 2 (12.4 ± 2.6 dives h -1 , Mann-Whitney, Z = 39.0, n = 7 turtles, p = 0.09; Figure 8c andAppendix).

Phase 1 lasted a mean of 7.2 ± 1.3 days (i.e about 75% of the inter-nesting interval), with significant differences among individuals in the time spent diving per hour (47.2 ± 2.9 min h -1 , ANOVA, F 6,104 = 14.0, n = 7 turtles, p < 0.001) due to a particularly low value for one individual (#200201, 40.9 ± 4.4 min h -1 , post-hoc Tuckey test) compared to the 6 others which did not differ (48.3 ± 1.4 min h -1 , ANOVA, F 5,86 = 1.6, n = 6 turtles, p = 0.17).

Similarly, during that first phase, the hourly dive frequency showed substantial differences among individuals (9.7 ± 3.0 dives h -1 , ANOVA, F 6, 104 = 12.4, n = 7 turtles, p < 0.001).

Turtles performed deeper, but fewer, dives as they moved away from the coast (Figure 8b, 8c and Appendix). Turtles which remained within 50 km of the coast (#200103, #200301) performed 13.0 ± 1.9 dives h -1 , mostly short and shallow Type 1-dives (47.0 ± 18.8% of their recorded dives). Turtle #200102, which dispersed between 50-100 km from the beach, performed 8.8 ± 2.4 dives h -1 , predominantly dives of intermediate depth of Type 2 and Type 4 (40.5 ± 20.3% and 31.9 ± 20.8% of her recorded dives, respectively). Finally, #200101 moved over more than 100 km from the beach and performed the fewest dives (5.0 ± 3.0 dives h -1 ) but most of them (69.1 ± 55.5%) were long and deep Type 3-dives.

Phase 2 lasted a mean of 2.6 ± 1.8 days (i.e about 25% of the inter-nesting interval).

For each turtle, this phase was highly variable in terms of hourly diving effort (Figure 8c and Appendix). However, the time spent diving per hour and the hourly dive frequency did not differ significantly among turtles and averaged 31.3 ± 6.7 min h -1 and 12.5 ± 2.6 dives h -1 respectively (Kruskal-Wallis, H 6,37 = 6.7, p = 0.35, and H 6,37 = 9.18, p = 0.16, respectively, n = 7 turtles). Phase 2 was predominantly associated with short and shallow Type 1-dives suggesting that the turtles were generally swimming at the surface when moving back to the nesting beach (Figure 8b) but also with a non-negligible proportion (13.1 ± 3.9%) of Type 4dives.

DISCUSSION

The diving behaviour of leatherback turtles during the nesting season has been widely studied [START_REF] Eckert | Diving pattern of two Leatherback sea turtles (Dermochelys coriacea) during internesting intervals at Sandy Point, St Croix, U.S. Virgin Island[END_REF][START_REF] Eckert | Diving and foraging behavior of Leatherback sea turtles (Dermochelys coriacea)[END_REF][START_REF] Eckert | Shallow water diving by Leatherback turtles in the South China Sea[END_REF](Eckert et al. , 2002, in press;, in press;Keinath & Musik 1993[START_REF] Southwood | Heart rates and diving behaviour of Leatherback sea turtles in the eastern Pacific Ocean[END_REF], Hays et al. 2004a[START_REF] Reina | Respiratory frequency, dive behavior and social interactions of leatherback turtles, Dermochelys coriacea, during the inter-nesting interval[END_REF], Wallace et al. 2005, Myers & Hays in press), but to date, only one study proposed the concurrent analysis of diving behaviour with dispersal patterns assessed by satellite telemetry in this species during the nesting season (Eckert et al. in press). Our study shows that leatherback turtles nesting in French Guiana have a wide range of dispersal over the continental shelf, moving over hundreds of kilometres in waters of French Guiana and neighbouring Surinam and show a bathymetry-constrained dive pattern.

General dispersal patterns

In French Guiana, leatherbacks explore three main zones of the continental shelf, ranging from the costal zone (within 50 km from the coast), the neritic zone (within 100 km from the coast) to the edge of the continental shelf. There are important individual differences in the distance travelled and in the diving pattern during the inter-nesting interval, yet, leatherbacks spend a similar time at sea regardless of their dispersal effort. Note here that our individuals show inter-nesting intervals of similar duration and dispersal range to those of leatherbacks in other Atlantic nesting sites, whether equipped or not with externally-attached instruments (Miller 1997, Georges et al. in press). This indicates that the instruments have a negligible effect on that aspect of the turtles' behaviour (see Fossette et al. submitted). The observed individual variations are apparently not related to the individual size. Since sea turtles grow continuously throughout their life-span [START_REF] Chaloupka | Age, and Population Dynamics[END_REF] this suggests that the duration of the inter-nesting interval and the dispersal pattern are not agerelated. It might rather be related to individual body condition or to oceanographic conditions (see [START_REF] Gaspar | Neglecting ocean currents can lead us up the wrong track[END_REF]) but identifying the actual causes of the inter-individual variability was beyond the scope of this study.

General diving patterns

In French Guiana, leatherback diving behaviour appears to be restricted by the bathymetry of the continental shelf, with 90% of the dives being shallower than 25 m and shorter than 10 min. Dive duration, thus, never exceeds the Aerobic Dive Limit (ADL) estimated between 33 and 67 min [START_REF] Southwood | Heart rates and diving behaviour of Leatherback sea turtles in the eastern Pacific Ocean[END_REF], Hays et al. 2004a[START_REF] Wallace | Bioenergetics and diving activity of interesting leatherback turtles Dermochelys coriacea at Parque Nacional Marino Las Baulas, Costa Rica[END_REF]. This aerobic diving behaviour is supported by the weak or absent relationship between post-dive surface interval and dive duration. Similarly, leatherbacks do not appear to anticipate the duration of their dive as suggested by the weak or absent relationship between preceding surface interval and dive duration. Such a short and shallow diving activity is similar to that reported for turtles of the Eastern Pacific [START_REF] Southwood | Heart rates and diving behaviour of Leatherback sea turtles in the eastern Pacific Ocean[END_REF][START_REF] Reina | Respiratory frequency, dive behavior and social interactions of leatherback turtles, Dermochelys coriacea, during the inter-nesting interval[END_REF][START_REF] Wallace | Bioenergetics and diving activity of interesting leatherback turtles Dermochelys coriacea at Parque Nacional Marino Las Baulas, Costa Rica[END_REF]) and the China Sea [START_REF] Eckert | Shallow water diving by Leatherback turtles in the South China Sea[END_REF] but differs from the deep pelagic dives performed by leatherbacks in the Caribbean Sea [START_REF] Eckert | Diving and foraging behavior of Leatherback sea turtles (Dermochelys coriacea)[END_REF][START_REF] Eckert | Swim speed and movement patterns of gravid Leatherback sea turtles (Dermochelys coriacea) at St Croix, US Virgin Island[END_REF], Hays et al. 2004a). Descent and ascent rates are comparable, yet lower, in French Guiana (0.13 ± 0.09 m s -1 and 0.11 ± 0.11 m s -1 , respectively) than in the Eastern Pacific (descent rate: 0.15 ± 0.06 m s -1 , ascent rate: 0.20 ± 0.11 m s -1 estimated from [START_REF] Reina | Respiratory frequency, dive behavior and social interactions of leatherback turtles, Dermochelys coriacea, during the inter-nesting interval[END_REF] and China Sea (descent rate: 0.20 ± 0.05 m s -1 , ascent rate: 0.20 ± 0.04 m s -1 estimated from [START_REF] Eckert | Shallow water diving by Leatherback turtles in the South China Sea[END_REF] but much lower than those estimated in the Caribbean Sea (descent rate: 0.41 ± 0.18 m s -1 , ascent rate: 0.31 ± 0.01 m s -1 , estimated from [START_REF] Eckert | Swim speed and movement patterns of gravid Leatherback sea turtles (Dermochelys coriacea) at St Croix, US Virgin Island[END_REF]. As in Costa Rica [START_REF] Reina | Respiratory frequency, dive behavior and social interactions of leatherback turtles, Dermochelys coriacea, during the inter-nesting interval[END_REF], French Guiana leatherbacks seem to stroke continuously when moving vertically as suggested by the similar vertical rates during descent and ascent.

However, in Costa Rica leatherback turtles maintain very low energy expenditure during the inter-nesting interval [START_REF] Wallace | Bioenergetics and diving activity of interesting leatherback turtles Dermochelys coriacea at Parque Nacional Marino Las Baulas, Costa Rica[END_REF]) by laying motionless on the seafloor [START_REF] Reina | Respiratory frequency, dive behavior and social interactions of leatherback turtles, Dermochelys coriacea, during the inter-nesting interval[END_REF]. In contrast, leatherbacks from French Guiana spend almost one third of their time at the bottom of the dive, where they perform numerous, substantial wiggles, suggesting that they actively swim throughout the dive. This is supported by direct measurements of the actual swim speed in leatherbacks from French Guiana (H.T. Pers. Comm.). Additionally, when swimming back to the nesting beach, leatherbacks from French Guiana move to the proximity of the shore in the last days of the inter-nesting interval, presumably in anticipation of egg laying. Such patterns may also allow gravid turtles to cope with potential early egglaying. The observation of a similar behaviour in leatherback turtles nesting in Gabon (Georges et al. in press), on the other side of the Atlantic basin, is consistent with the fact that the duration of the inter-nesting interval is restricted by the timing of egg-maturation. In short, leatherbacks appear to adopt at least two strategies during their inter-nesting intervals according to their nesting site: the "Pacific strategy" consists in reducing swim activity to limit energy expenditure between two consecutive ovi-positions, and the "Atlantic strategy" where leatherbacks dive and swim almost continuously throughout the inter-nesting interval while dispersing extensively, probably for feeding (Eckert et al. 1989, in press, Myers & Hays in press, this study). These two strategies may be linked with the local oceanographic conditions, with local food availability probably shaping the behaviour in the different nesting sites, as also reported in green turtles [START_REF] Hays | Behavioural plasticity in a large marine herbivore: contrasting patterns of depth utilisation between two green turtle (Chelonia mydas) populations[END_REF].

Combined analysis

The bathymetry of the Guiana's continental shelf is not precisely known because of the continual influence of Amazon-derived mud banks on its morphology (Anthony & Dolique 2004) but in a general sense, depth increases gradually with distance from the coast (Figure 1). In our study, leatherback turtles dived deeper as they moved away from the coast, implying that they tend to follow the topography of the seabed as has been suggested for their relatives from the China Sea [START_REF] Eckert | Shallow water diving by Leatherback turtles in the South China Sea[END_REF]. This is supported by occasional, direct observations of well-adhered, but fresh, mud on the carapace of nesting leatherbacks in French Guiana (personal observations), suggesting that the mud was acquired some appreciable time before hauling out.

One striking finding of our study is that, despite the high inter-individual variability in their dispersal effort, all leatherback individuals spend the same amount of time diving (80% of their time spent at sea). Indeed, we found that during the first 75% of the inter-nesting interval leatherbacks dived on average 47 min per hour with a striking consistency among individuals, whereas they showed a lower and more variable diving effort thereafter when returning to the coast. Similarly [START_REF] Southwood | Heart rates and diving behaviour of Leatherback sea turtles in the eastern Pacific Ocean[END_REF] identified two phases in the diving behaviour of leatherbacks in Costa Rica, with a first phase showing relatively deep and long dives compared to the rest of the inter-nesting interval. Additionally, we found that the shapes of the dives changed according to the two phases defined above and from the distance to the nesting beach.

Close to the nesting beach

Immediately after leaving the beach and when swimming back to their nesting site, leatherbacks mainly perform shallow (5m-deep) Type 1-dives. These dives are the most abundant in all inter-nesting intervals. Comparable dive types have been reported for green (Chelonia mydas) and loggerhead (Caretta caretta) turtles [START_REF] Minamikawa | Buoyancy control in diving behaviour of the loggerhead turtle, Caretta caretta[END_REF][START_REF] Hochscheid | Reptilian diving: highly variable dive patterns in the green turtle Chelonia mydas[END_REF][START_REF] Georges | The diving behaviour of green turtles undertaking oceanic migration to and from Ascension Island: dive duration, dive profiles and depth distribution[END_REF][START_REF] Houghton | Diving behaviour during the interesting interval for loggerhead turtles Caretta caretta nesting in Cyprus[END_REF] as well as for leatherbacks [START_REF] Eckert | Shallow water diving by Leatherback turtles in the South China Sea[END_REF][START_REF] Southwood | Heart rates and diving behaviour of Leatherback sea turtles in the eastern Pacific Ocean[END_REF][START_REF] Eckert | Swim speed and movement patterns of gravid Leatherback sea turtles (Dermochelys coriacea) at St Croix, US Virgin Island[END_REF][START_REF] Reina | Respiratory frequency, dive behavior and social interactions of leatherback turtles, Dermochelys coriacea, during the inter-nesting interval[END_REF][START_REF] Wallace | Bioenergetics and diving activity of interesting leatherback turtles Dermochelys coriacea at Parque Nacional Marino Las Baulas, Costa Rica[END_REF], and have been interpreted as travelling dives (Papi et al. 1997[START_REF] Eckert | Swim speed and movement patterns of gravid Leatherback sea turtles (Dermochelys coriacea) at St Croix, US Virgin Island[END_REF][START_REF] Reina | Respiratory frequency, dive behavior and social interactions of leatherback turtles, Dermochelys coriacea, during the inter-nesting interval[END_REF]. Type 1-dives may contribute to optimal travelling behaviour, as animals avoid unstable conditions at the sea surface (e.g pitching and rolling due to wave action) as well as reducing surface drag so that transport costs are minimized (Minamikawa et al. 2000). Type 1-dives mostly appeared at night with peaks at the beginning of the night (07:00 pm -09:00 pm) and later, around 02:00 am suggesting that leatherbacks may travel more at these times.

By contrast, [START_REF] Eckert | Swim speed and movement patterns of gravid Leatherback sea turtles (Dermochelys coriacea) at St Croix, US Virgin Island[END_REF] suggested that in the Caribbean Sea, leatherbacks may perform most of their horizontal movements during the middle of the day.

In the coastal zone

Within the first 12 hours of departure, the shape of dives changes according to the dispersal mode: in addition to the travel-like Type 1-dives, leatherbacks remaining in the costal zone (within 50 km from the coast) also perform dives of similar shape but slightly deeper (15m-deep, Type 2-dives), suggesting that travelling also occurs at deeper depths.

These Type 2-dives have a particularly slow (0.08 m s -1 ) and progressive ascent to the surface compared to other dives, similar to the 'S-shaped' dives described for green and loggerhead turtles by [START_REF] Minamikawa | Buoyancy control in diving behaviour of the loggerhead turtle, Caretta caretta[END_REF], [START_REF] Hochscheid | Reptilian diving: highly variable dive patterns in the green turtle Chelonia mydas[END_REF], [START_REF] Georges | The diving behaviour of green turtles undertaking oceanic migration to and from Ascension Island: dive duration, dive profiles and depth distribution[END_REF] and [START_REF] Houghton | Diving behaviour during the interesting interval for loggerhead turtles Caretta caretta nesting in Cyprus[END_REF]. In green and loggerhead turtles, the slow and progressive ascent has been interpreted as a passive locomotion due to positive buoyancy. Contrary to green and loggerhead turtles, however, the small lung volume of leatherbacks probably does not function for buoyancy control [START_REF] Minamikawa | Buoyancy control in diving behaviour of the loggerhead turtle, Caretta caretta[END_REF]). This progressive ascent may also correspond to prey searching and/or capture as suggested in other diving predators (e.g. [START_REF] Ropert-Coudert | Feeding strategies of free-ranging Adélie penguins Pygoscelis adeliae analysed by multiple data recording[END_REF]). In the case of leatherback turtles in French Guiana, the nesting season coincides with the peak of Amazonian influence, resulting in rings of Amazonian waters crossing the continental shelf [START_REF] Wyneken | Spectral remote sensing reflectances of coastal waters in French Guiana under the Amazon influence[END_REF], enhancing biological productivity. This includes the production of gelatinous plankton whose relatively large individual size and quantity may have a potentially significant contribution to the marine food web on the continental shelf [START_REF] Fromard | Half a century of dynamic coastal change affecting mangrove shorelines of French Guiana. A case study based on remote sensing data analyses and field surveys[END_REF], particularly for leatherbacks which predominantly feed on jellyfish [START_REF] James | Feeding of Dermochelys coriacea on Medusae in the Northwest Atlantic[END_REF]. Massive stranding (up to several hundred individuals on the 2km long beach) of jellyfish of genus Rhizostoma sp. and occasionally Aurelia sp. are regularly observed on Awala-Yalimapo beach during the nesting season (personal observation). Both species have been reported as common prey for leatherbacks in the Atlantic [START_REF] James | Feeding of Dermochelys coriacea on Medusae in the Northwest Atlantic[END_REF] supporting the hypothesis that in French Guiana leatherbacks may encounter profitable food conditions during their inter-nesting intervals, as reported for neighbouring Caribbean sites (Eckert et al. 1989, press, Myers & Hays in press).

In the neritic zone and the edge of the continental shelf

When moving further on the continental shelf, leatherbacks maintained Type 2-dives but also performed 10m-deep Type 4-dives until they reached the edge of the continental shelf, where Type 4-dives were replaced by Type 3-dives. Type 3 and Type 4 dives are 'Wshaped' dives [START_REF] Wilson | Foraging ecology[END_REF] characterised by a relatively long bottom phase during which numerous wiggles of several meters amplitude (> 2m) occur. These wiggles are commonly interpreted as corresponding to prospecting and foraging behaviours (e.g. [START_REF] Schreer | Comparative diving patterns of pinnipeds and seabirds[END_REF], supporting the hypothesis that leatherbacks may attempt to feed throughout the continental shelf. Type 4-dives mostly occurred during the day, suggesting that if they were associated with prospecting and foraging, leatherbacks may feed on gelatinous prey during daytime. If that was the case, the Guyanese situation would differ from the Caribbean, where leatherbacks are supposed to be nocturnal feeders [START_REF] Eckert | Diving and foraging behavior of Leatherback sea turtles (Dermochelys coriacea)[END_REF]). Such a difference may explain why leatherbacks travel predominantly during the night in French Guiana (this study) but during the day in the Caribbean Sea [START_REF] Eckert | Swim speed and movement patterns of gravid Leatherback sea turtles (Dermochelys coriacea) at St Croix, US Virgin Island[END_REF]. Inter-site differences may also result from local bathymetry. In the deep Caribbean Sea, jellyfish may only be accessible to leatherbacks at night, as they migrate to the surface, whereas in French Guiana, the relatively shallow water depths of the continental shelf restrict jellyfish vertical movement both during day-and night-time. Further investigations are required to the behaviour and abundance of jellyfish in Guyanese waters, since nocturnal shallow Type 1-dives may also contribute to nocturnal foraging when jellyfish migrate to the surface. In addition, if gravid leatherbacks of French Guiana feed during the inter-nesting intervals, this could explain why they are on average heavier than their relatives from other nesting sites (Georges & Fossette 2006). [START_REF] Wallace | Bioenergetics and diving activity of interesting leatherback turtles Dermochelys coriacea at Parque Nacional Marino Las Baulas, Costa Rica[END_REF] hypothesized that gravid leatherbacks in Costa Rica do not actively forage during the nesting season as they rarely approach their Aerobic Diving Limits (ADLs). Leatherbacks in French Guiana also dive within their ADL yet our results suggest that they may feed opportunistically rather than optimally [START_REF] Thompson | How long should a dive last? A simple model of foraging decisions by breath-hold divers in a patchy environment[END_REF].

Thus our combined analysis of dispersal and diving data suggests that leatherbacks may compensate for high reproductive expenditure by extensively prospecting for food during the inter-nesting intervals. Direct evidence is however, required to confirm this. It could be tested using underwater video, or by measuring the energy balance or body mass changes during the inter-nesting intervals. Further investigations are also required to clarify the observed inter-individual variability we found in the dispersal and diving patterns, and their implications in terms of individual fitness.

Conservation implications

If leatherback turtles forage or actively prospect for food during the nesting season, they may be exposed to high risks of interactions with fisheries, particularly in the Guiana shield where international fisheries operate [START_REF] Charuau | Le secteur de la pêche en mer[END_REF]. For some sea turtles populations which remain close to their nesting beach during the breeding season exploiting restricted foraging grounds, focused conservation efforts on such areas can prove tremendously successful (e.g. Hays 2004b). However in French Guiana, leatherback turtles move far away their nesting beach during a single inter-nesting interval and cross international borders to move into Surinam waters. This emphasises the need for regional conservation strategies and international control of fishing practices in this area which is heavily exploited by two local fisheries (Charuau 2002, Georges et al. in press). In addition, leatherbacks exploit the entire water column, following the bathymetry of the continental shelf. As a consequence, trawlers operating in French Guiana and Surinam waters should adopt appropriate fishing gear (e.g. [START_REF] Lutcavage | Human impacts on sea turtle survival[END_REF][START_REF] Epperly | Fisheries-related Mortality and Turtle Excluder Devices (TEDs)[END_REF] 3 for details).

Dives chronologically consisted in a descent phase, a bottom time, an ascent phase and a postdive surface interval (shown here for Type 3 as an example). Type 1 11733 56.9 4.9 ± 3.5 2.3 ± 1.6 0.11 ± 0.06 0.12 ± 0.06 30 ± 11 7.9 ± 5.5 0.6 ± 0.4 1.1 ± 5.5

)

  except for #200202, for which time spent diving per hour did not vary significantly(H 16, 191 = 19.5, n = 192, p = 0.24). For each inter-nesting interval, two phases were identified according to the way the time spent diving per hour varied through time (Figure8cand Appendix).

Table 3 .

 3 Statistics (mean ± SD) of parameters for the 4 types of dive in 10 gravid leatherback turtles during their interesting interval in French Guiana during the nesting seasons 2001, 2002, and 2003. For each parameters and each dive type, the mean values were always significantly different to each other (post-hoc Tukey test, P< 0.05 in all cases). A PCA identified two axes associated with dive duration (axe 1) and bottom time (axe 2) shown in bold in the table.

Fig. 1 .

 1 Fig.1. Inter-nesting movements performed by 11 gravid leatherback turtles nesting in French

Fig. 2 .

 2 Fig. 2. Frequency distribution of (a) dive depth and (b) dive duration (n = 6000 dives) in 10

Fig. 3 .

 3 Fig. 3. (a) Dive profile a gravid leatherback turtle (#200102) during a simple inter-nesting

Fig. 5 .

 5 Fig. 5. Schematic representation of the four dive types in 10 gravid leatherback turtles during

Fig. 6 .

 6 Fig. 6. Distribution of dives in relation to time of the day and dive depth in 10 gravid

Fig. 7 .

 7 Fig. 7. Hourly distribution of each dive type (indicated by the number on the right hand side;

Fig.

  Fig. Diving behaviour and diving effort throughout the inter-nesting interval performed by

Table 2 .

 2 to reduce accidental captures of endangered leatherback turtles. 1. Summary of the inter-nesting movements performed by 11 Argos tracked leatherback turtles nesting in French Guiana in 2001, 2002 and 2003. Six turtles were simultaneously equipped with TDR (+ ; see Table 2). Summary of diving behaviour (for dives > 2m in depth) in 10 leatherback turtles nesting in French Guiana in 2001, 2002 and 2003. Values are expressed as mean ± SD (max value). Six turtles were simultaneously tracked with PTTs during one complete inter-nesting interval (+; see Table 1). * Mean trip duration was calculated only from the seven complete diving records.

Acknowledgements. We are grateful to the Ministry of Ecology and Sustainable Development and the Direction Régionale de l'Environnement-Guyane in French Guiana. We thank all participants of sea turtle monitoring programmes developed in Awala-Yalimapo beach (Réserve Naturelle de l'Amana, Kulalasi and WWF) for logistical help in the field. We also thank D. Grémillet for his comments on the first draft. S. Fossette was supported by a studentship from the French Ministry of Research. Funding was provided by grants to Y. Le Maho from European FEDER program, to H. Tanaka from Research Fellowships of the Japan Society for the Promotion of Science for Young Scientists, from the Japan Society for the Promotion of Science (14405027 and 15255003) and from the MEXT Grant-in-Aid for the 21st Century COE Program "Neo-Science of Natural History" Program at Hokkaido University. This study was carried out under CNRS institutional license (B67-482 18).

LITERATURE CITED

Anthony EJ, Dolique F (2004) The influence of Amazon-derived mud banks on the morphology of sandy headland-bound beaches in Cayenne, French Guiana : a short to long term perspective. Mar Geol 208: 249-264.


Morgan Le Chénéchal
email: morgan.lechenechal@b-com.com

Sébastien Chalmé
email: sebastien.chalme@b-com.com

Thierry Duval
email: thierry.duval@telecom-bretagne.eu

Jérôme Royan
email: jerome.royan@b-com.com

Valérie Gouranton
email: valerie.gouranton@irisa.fr

Bruno Arnaldi
email: bruno.arnaldi@irisa.fr

Toward an Enhanced Mutual Awareness in Asymmetric CVE

Keywords: Awareness in Collaboration Systems, Cognitive and Psychological Issues in Collaboration, Shared Virtual Reality and Applications, Coordination, Cooperation and Collaboration

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

I. INTRODUCTION

Virtual reality (VR) provides the user with an interactive and immersive virtual world in order to ease interactions and improve performances. To this end, the immersed user needs to understand accurately the consequences of his actions in the VE. This is the goal of awareness techniques that provide feedbacks from the Virtual Environment (VE) to the user.

The extension of a VR system for multiple users (potentially distant) is handled through an immersive 3D Collaborative Virtual Environment (CVE). Thus, CVE derived from the convergence of VR and Computer-Supporter Cooperative Work (CSCW) [START_REF] Santos | Finding solutions for effective collaboration in a heterogeneous industrial scenario[END_REF]. The literature leverages different sensory channels and suggests metaphors for presenting the awareness of others' activities as well as their perceptive abilities.

Collaborative awareness techniques, from simple avatars to more sophisticated techniques that can take into account the limitations of a specific user caused, for instance, by physical constraints [START_REF] Duval | Improving Awareness for 3d Virtual Collaboration by Embedding the Features of Users' Physical Environments and by Augmenting Interaction Tools with Cognitive Feedback Cues[END_REF], can be used to enhance the awareness of the others activities. These techniques greatly improve interaction performances, but many situations still suffer from a lack of mutual awareness. Indeed, a crucial need when interacting with other distant people consists in understanding accurately their own perception and comprehension of the interactive VE. This awareness of others' activities as well as their perceptive abilities can be achieved through different awareness techniques. All these methods are largely symmetric in that they synchronously trigger the same feedbacks for each user. Thus, they take place in a continuous awareness loop. We postulate that this loop is not enough, and that an additional asymmetric loop could greatly improve the awareness of the others, in particular in a two-users CVE with asymmetric roles and viewpoints. This could be used in a guiding scenario [START_REF] Nguyen | Collaborative exploration in a multi-scale shared virtual environment[END_REF] or any application that involves a user with a global viewpoint collaborating with another immersed user in the CVE.

First, section II presents the state of the art of awareness in CVE and highlights some limitations. Section III introduces our concept of asymmetric loop to improve collaborator's awareness. Then, the experiment is presented in section IV and results in section V. Last, we discuss the results in section VI and propose perspectives in section VII.

II. AWARENESS IN CVE

Different situations require collaborative activities. First, users may symmetrically collaborate in order to perform the manipulation of an object or to achieve a succession of tasks on an industrial machine. Second, they may have different roles and viewpoints, such as in asymmetric guiding for the exploration of a VE or to take advantages of a multi-scale approaches of a VE involving scientific visualization. In any case, collaborators may try to show or explain something to each others. To deal with this challenge, the literature already provides solutions to ease the distant communication D R A F T and understanding between users. These features are part of the workspace awareness as defined by Gutwin et al. [START_REF] Gutwin | A descriptive framework of workspace awareness for real-time groupware[END_REF].

In the real world, social interactions are interpreted through many crossed modalities. For instance, gaze direction is a substantial cue in terms of understanding what others are currently seeing, and can even help to interpret the current cognitive process of people: Are they thinking? Have they analyzed what I have shown them? In CVE, behavior such as gaze direction [START_REF] Steptoe | Eye-tracking for avatar eye-gaze and interactional analysis in immersive collaborative virtual environments[END_REF] and facial expression [START_REF] Niewiadomski | Constraint-based model for synthesis of multimodal sequential expressions of emotions[END_REF] can be used to simulate these helpful features, but they are rarely available. Thus, metaphorical representations have been proposed for handling some of the missing natural features in collaborative interactions.

Sometimes, collaborators can be only represented by a viewing frustum that informs others about their field of view and viewable objects in a coarser way [START_REF] Fraser | Supporting awareness and interaction through collaborative virtual interfaces[END_REF]. Moreover, it is important to be aware of collaborators' interaction abilities in order to not misunderstand a shared situation [START_REF] Duval | Improving Awareness for 3d Virtual Collaboration by Embedding the Features of Users' Physical Environments and by Augmenting Interaction Tools with Cognitive Feedback Cues[END_REF].

Lastly, some additional communication features are necessary to improve collaboration and to allow direct communication between users (who can be remote users). These can be based on verbal, video [START_REF] Pauchet | Mutual awareness in collocated and distant collaborative tasks using shared interfaces[END_REF], haptic [START_REF] Chellali | Haptic communication to enhance collaboration in virtual environments[END_REF] or visual communication [START_REF] Bacim | Collaborative navigation in virtual search and rescue[END_REF].

We note that none of the existing solutions takes into account the awareness of what others currently analyzed regarding a remote informative interaction, such as an object pointing. We think this would be an interesting feature to provide in order to improve collaboration, especially in a nonverbal situation [START_REF] Dourish | Awareness and coordination in shared workspaces[END_REF]. Indeed, in some cases, verbal communication cannot be used, such as when users speak different languages, when environments are too noisy or when a user is deaf-and-mute. Thus, in this study, we focus on this particular context without any available audio channel.

III. AN ASYMMETRIC LOOP TO IMPROVE COLLABORATOR'S AWARENESS

In a collocated (where users share the same physical and virtual environment) or a remote setup, users can only interact in an asynchronous way since they are independent [START_REF] Card | The model human processor: An engineering model of human performance[END_REF]. Even if they co-manipulate a shared object and try to synchronize their motion, they are still two individuals. For example, the main difficulty in extending a bimanual manipulation toward a cooperative manipulation is the lack of the proprioceptive sense of the others [START_REF] Le Chénéchal | From 3d Bimanual Toward Distant Collaborative Interaction Techniques: An Awareness Issue[END_REF]. This limitation can explain the meaning of the asynchronous aspect that we introduce (differently from the classical definitions of asynchronous interaction that takes place at a very different moment as in [START_REF] Gericke | Asynchronous understanding of creative sessions using archived collaboration artifacts[END_REF]). Indeed, in this sample case, the lack of proprioception generates a desynchronization between collaborators, and thus an asynchronous collaborative interaction (with a very high frequency for time-step). We propose to manage this feature with a new asymmetric awareness loop for collaboration. Especially, we can explain more simply its interest in an asymmetric context as illustrated in Figure 1. This asymmetry can be useful in many scenarios, such as in a guiding task, in order to benefit of different capabilities. In this setting, the guide has a global viewpoint of the scene, and the visitor is immersed in the VE. Here, it can be tricky for the guide to be sure that the visitor saw and analyzed an object he pointed to because:

• Their viewpoints are different,

• The duration of their analysis is user-dependent Thus, we extend the classification of collaborative awareness with a new item that we call the awareness of collaborators' analysis. Unlike the other ones, this part takes place in an asymmetric interactive loop that can provide awareness features in an asynchronous way, as explained in Figure 2. Using this new loop, we aim to reduce the lack of mutual knowledge, especially concerning what others currently analyzed.

Due to implicit feedbacks of the classical symmetric loop that implements some workspace awareness features, some users could estimate the distant activity. But, some misunderstanding are still possible. Thus, depending on the users' expertise (novices vs. experts), our new asymmetric loop could bring redundant awareness informations. But some novice users who are not able to interpret implicit feedbacks could use it to improve their estimation.

D R A F T

IV. EXPERIMENTAL CONTEXT

A. Task description

The visitor used an HMD to be immersed in a room containing six boards from among 66 possibilities. These boards presented pictures. Each picture was composed of two figures: The left one was always the same image of reference, whereas the right one could be the rotated image of reference or a different one. The task of the visitor consisted in finding the board pointed to by the guide, and then to answer the question: Are these two figures identical modulo a 2D rotation? This task is called "2D mental rotation" in the psychology literature [START_REF] Cooper | Mental rotation of random two-dimensional shapes[END_REF]. This literature proves that everyone can achieve it with few performance differences between people. Thus, this 2D mental rotation task was a reasonably simple task that nevertheless required an analysis process that simulated the one described in our new asymmetric loop. The guide's task (i.e. the subject's task) consisted in estimating the moment when the visitor had analyzed the board and answered. With this task, we wanted to evaluate the estimation performances of the guide regarding the visitor's activity and the analysis process.

B. Independent variables

To evaluate the impact of the analysis feedback compared with classical awareness techniques, we compared different conditions leveraging different feedback types of the visitor's activity for the guide's awareness.

Estimation features. They are usually used in the symmetric awareness loop. In our experiment, the visitor was always presented as an avatar with a frustum representing his exact field of view (¬⊗). Feature ⊗ added a squared spotlight matching the frustum dimensions and highlighting the objects in his field of view (see Figure 1). Safe-explicit feature. It implements an analysis feedback from the visitor to the guide. In our experiment, an arrow ( ) was presented to the guide as soon as the visitor had completed his task (cf. Figure 3). We assumed that, for the visitor, the time between the decision following the analysis process and the answer recorded by the system was insignificant (less than 100ms, as observed in [START_REF] Card | The model human processor: An engineering model of human performance[END_REF]).

Single and successive tasks. A single task did not cause a perceptible behavior of the distant user when he completed his analysis, while a successive task brought about implicit feedback due to a specific behavior of the distant user when he finished his analysis. In our experimental process, the most obvious implicit indication was the motion of the visitor used to inform the guide about his analysis process. For example, if the visitor looked at the board indicated, then looked elsewhere, it could suggest that he had completed the analysis. To simulate this behavior, we gave instructions to the recorded visitor. In one case, called single task ( †), we asked him to keep looking at the board after he completed the task until the next iteration. In another case, called successive task ( ‡), we asked him to return directly to a phase of VE exploration after solving the task ( ‡).

Combining the three variables of the experiments leaded to eight conditions. Every subject met all the conditions.

C. Experimental protocol

We designed the experiment in order to be able to evaluate the guide's perception of the distant visitor's activity. Thus, if we used a real human visitor for each trial, it could have skewed our experimental results due to heterogeneous behaviors. To solve this issue, we recorded one real human visitor achieving the tasks in a pre-process phase. Therefore we were able to replay human behaviors in a counterbalancing design for each subject of the experiment without any bias regarding the visitor's behavior.

Afterwards, each participant was first informed about the complete study proceedings. Then they completed an identification questionnaire allowing us to collect general information about their experience with VR. Next, a demonstration introduced the experiment and gave instructions about the objective for 10min. The experimental manipulation was composed of 56 iterations (8 conditions * 7 iterations) and took 10min.

Each condition iterated on seven boards from among the 66 available ones (using a counterbalancing order). We ensured each board was pointed to at least once for each condition. One iteration was decomposed as follows:

1) The system simulated a distant pointing of the guide: The symmetric awareness technique effects board flicking; 2) The system started to replay the recorded visitor's behavior sequence; 3) The visitor searched for the board indicated; 4) The visitor found it and watched it; 5) The visitor solved the task by analyzing the mental rotation; 6) Only in conditions, an analysis feedback was send to the guide meaning that the visitor had completed the analysis of the board pointed to.

Moreover, we ensured that the visitor always completed his task before a 10s timeout (actually he usually completed it in under 5s). During an iteration, the subject's task consisted in clicking when he estimated that the visitor had completed the D R A F T board analysis. After each validation, we asked the subject the chosen strategy for estimating when the visitor had completed the analysis of the indicated board. He could choose between five options: No specific strategy, self mental rotation, time count, visitor motion, other: Type any strategy a .

We briefly explained the meaning of each proposal in the trial phase. For each condition change, a black screen displayed the state of the three independent variables for the next conditions. Three icons always remained visible to the subjects to remind them of the current independent variables. At the end, participants were asked to fill in a questionnaire to provide their impressions, comments and subjective judgments about the estimation features, the safe-explicit feature and the tasks with:

• A dichotomous preference test between the estimation features (¬⊗ /⊗), the safe-explicit feature ( /¬ ) and the tasks ( † / ‡); • A Likert-scale (1: Not certain at all, 4: Totally certain) with the following questions about the estimation features, the safe-explicit feature and the tasks:

-"Are you confident that the visitor has seen the board indicated?" -"Are you confident that the visitor has completed the analysis of the board?" D. Measurements and hypotheses 1) Measurements: We measured the delta time between the answer of the visitor and the estimation of the guide. Thus, we compared the estimation accuracy of the analysis process of the visitor by the guide according to each condition. The goal was to improve this estimation and thus the narrower the gap between collaborators' validation, the better the guide's estimation.

It should be noted that, in order to remove possible bias, we analyzed only data that respect the following conditions: The guide validated before the 10s timeout and the response to the task was 'yes'. b 2) Hypotheses: We posit some hypotheses:

• H1: In successive task ( ‡), the additional estimation feature (⊗) improves the estimation accuracy. • H2: In single task ( †), the safe-explicit feedback ( ) improves the estimation accuracy.

E. Participants

Our panel was composed of 20 subjects aged from 23 to 54 (M = 32, SD = 8). There were 12 males and 8 females. Eight of them were considered as experts and 12 as novices. This a With this text field, subjects could specify any strategy they used. We did not explicitly propose the apparition of the arrow as strategy, in order not to influence the subjects' answer.

b Some boards (∼ 10%) still presented different figures in order to keep the recorded visitor focused on his task.

consideration was based on their personal experience in VR and, more broadly, in 3D and/or collaborative video games. We considered a subject as expert if he had spent more than an hour per week during the last weeks, and already spent more than 14 hours per week during their lifetime using these kinds of applications. Our subjects had various backgrounds: PhD students, R&D engineers, communication and human resources staff, managers and assistants.

V. RESULTS

We present the results from our analysis as below:

• Time to validate (V-A) • Time to validate after the visitor (V-B) • Estimation & safe-explicit features when success (V-C) • Single versus successive task (V-D) • Strategies applied (V-E) • Successful strategies (V-F) • ROI of strategies applied (V-G) • Questionnaire results (V-H)
Note that a comparison between answers to a short initial and a final fatigue questionnaire revealed no significant differences between the level of subjects' fatigue before and after the experiment.

A. Time to validate

We analyzed the delta time between both validations (visitor and guide). If this difference is positive, that means that the guide validated after the visitor. If the difference is negative, that means that the guide validated before the visitor.

Figure 4a illustrates the significant difference between novices and experts (F(1, 939) = 19.78, p < .001). Novices (M = 0.48, SD = 0.07) validated slower than experts (M = 0.00, SD = 0.06). Experts validated at the same time than the visitor, and even before the visitor.

This was a main result: Some experts validated before the visitor. Subjects had to validate when they were convinced that the visitor had analyzed the board. When the participants validated before the visitor, we considered that they failed.

In the case of safe-explicit feature ( ), an arrow appeared when the visitor just completed his current task. The mode ( † or ‡, or ¬ , ⊗ or ¬⊗) was presented at the screen. So, the subject knew if an arrow would appear or not. Rationally, in the case of safe-explicit feature ( ), subjects had just to wait the arrow appeared to validate. In fact, some of them validated before. We describe this in subsection V-F.

B. Time to validate after the visitor

We analyzed the cases of subjects' success, i.e. when the guide took the mental decision after the visitor. As action's duration to click was assumed to be less than 100ms, as observed in [START_REF] Card | The model human processor: An engineering model of human performance[END_REF], we assumed the mental decision was the time to click minus 100ms. 0,57% 0,93% 0% 0,1% 0,2% 0,3% 0,4% 0,5% 0,6% 0,7% 0,8% 0,9% 1% Results show that experts are better than novices to evaluate the right time the visitor achieved the mental rotation analysis.

C. Estimation & safe-explicit features when success

The analysis of the additional estimation feature results (⊗ vs ¬⊗) gave no significant differences. The spotlight was not a useful additional estimation feature in this study. As the results were not significant, we do not analyze the effect of this feature (⊗) in the following. Moreover, H1 is not validated and we will discuss this result in section VI.

At the contrary, the safe-explicit feature results ( vs ¬ ) gave significant differences for experts (F(1, 148) = 5.34, p < .05) and for novices (F(1, 411) = 4.304, p < .05). Figure 4c shows that experts and novices validated slower without (expert: M = 0.66, SD = 0.07; novices: M = 1.05, SD = 0.07) than with the safe-explicit feature (experts: M = 0.48, SD = 0.04; novices: M = 0.83, SD = 0.08)

The safe-explicit feature that implemented an analysis feedback from the visitor to the guide improves the estimation accuracy, as stated in H2 for single task ( †).

In our experiment, the safe-explicit feature was represented by an arrow above the board for the guide indicating that the visitor had just completed his task (cf. Figure 3). Even when the participants had to validate as soon as they saw the arrow, the experts were better. This result could be explained by the skills of experts in VE.

D. Single versus successive task

In the single task ( †), the analysis of the time to validate gave not any significant difference between experts and novices, even if experts (M = 0.7, SD = 0.09) were quicker than novices (M = 0.8, SD = 0.07). When the visitor were motionless after the completion of his task ( †), experts and novices met the same difficulties to evaluate the right time to validate. Obviously, in this case of uncertainty, the safe-explicit feature helped the subjects to decide (F(1, 250) = 4.6, p < .05). We could appreciate how the safe-explicit feature guided the decision-making process in two ways:

15%$ 8%$ 31%$ 21%$ 31%$ 24%$ 35%$ 24%$ 50%$ 37%$ 34%$ 22%$ 0%$ 27%$ 0%$ 29%$ 0%$ 10%$ 20%$ 30%$ 40%$ 50%$ 60%$ 70%$ Novice$3$WITHOUT$ explicit$feature$ Novice$3$WITH$explicit$ feature$ Expert$3$WITHOUT$ explicit$feature$ Expert$3$WITH$explicit$ feature$

Frequency)of)strategies)applied)(success)and)unsuccess))) Single)task)

Cme$count$ mental$rotaCon$ visitor$moCon$ safe3explicit$feature$$ 100%$=$144$strategies$ 100%$=$135$strategies$ 100%$=$95$strategies$ 100%$=$87$strategies$

7%# 7%# 16%# 13%# 33%# 21%# 19%# 15%# 57%# 45%# 64%# 38%# 0%# 25%# 0%# 31%# 0%# 10%# 20%# 30%# 40%# 50%# 60%# 70%# Novice#3#WITHOUT# explicit#feature# Novice#3#WITH#explicit# feature# Expert#3#WITHOUT# explicit#feature# Expert#3#WITH#explicit# feature#

Frequency)of)strategies)applied)(success)and)unsuccess))) Successive)task)

Cme • Subjects validated quicker with (0, 67) than without (0, 91) the safe-explicit feature; • The standard deviation was smaller with the safe-explicit feature (SD = 0.07) than without it (SD = 0.09).

The safe-explicit feature was truly useful when the visitor's behavior did not clarify when the analysis process was ended.

In the successive task ( ‡), the analysis of the time to validate gave significant difference between experts and novices (F(1, 327) = 6.6, p < .05). Novices (M = 0.84, SD = 0.06) validated after experts (M = 0.58, SD = 0.04). In this case, the safe-explicit feature significantly helped experts, not novices. Indeed, experts validated earlier with (M = 0.39, SD = 0.01) than without the safe-explicit feature (M = 0.82, SD = 0.03). The difference was significant: F(1, 19) = 152.7, p < .001. Even if novices validated earlier with (M = 0.82, SD = 0.11) than without the safe-explicit feature (M = 0.87, SD = 0.06), the difference was not significant. This result could be explained by the strategies applied (cf. subsection V-E) The visitor's behavior was an implicit feedback; as the experts validated earlier, experts were better than novices to understand this feedback.

E. Strategies applied

The strategies applied depended on the tasks ( † / ‡), the situations ( / ¬ ) and the groups (novices/experts) (cf. Figure 5). In the single task, for each situation ( / ¬ ), novices applied the visitor motion strategy more often than experts. For each situation, experts applied time count and mental rotation strategies more often than novices. In the successive task, the most common strategy applied was the visitor motion strategy. Without safe-explicit feature, experts applied the visitor motion strategy more often than experts. Experts applied time count strategy more often than novices. And novices applied mental rotation strategy more often than experts.

F. Successful strategies

Figure 6 illustrates the success of strategies for validation after the visitor validation. For each strategy, we calculated the ratio of times the strategy was applied with success. With a ratio less than 50%, the guide validated more often before than after the visitor. When the subjects validated before the visitor, we considered that they applied an unsuccessful strategy to estimate the right time to validate.

Figure 6 shows that the less successful strategies were the mental rotation (47%) and time count (53%) strategies and the most successful strategies were visitor motion (62%) and safe-explicit feature (100%) strategies.

In the mental rotation strategy, the guide realized a mental rotation, with the implicit assumption that the visitor took the same time to analyze the board. In the time count strategy, the evaluation was quite more approximated: The guide did not truly analyzed the board but evaluated the complexity of the rotation and adjusted the time that the visitor took to complete it. These two strategies were based on how the subject analyzed himself the board, not on the visitor's behavior or on the safe-explicit feature. These two strategies illustrated an egocentric perspective, which were frequently unsuccessful in this study. One of the reasons to explain this result could be the difference of viewpoint between the visitor and the guide (cf. Figure 1). The guide had a global viewpoint that allowed to watch the 7 boards. The visitor had an immersive viewpoint with a narrow visual angle that could contain only 3 boards at the time. The guide could see the board pointed earlier than the visitor, thus he could validate before the visitor by applying egocentric strategies.

Visitor motion and safe-explicit feature strategies illustrated an allocentric perspective. The guide was waiting for a visitor's information: A punctual successive behavior or a safe-explicit feedback. The visitor motion strategy was often successful in the successive task, less in single task. In the successive task, the visitor looked at the board to analyze it, answered and then moved. Thus, his behavior was useful to detect when the analysis was completed. One would have thought that the success score would be higher. The participants told they met difficulties to detect the fixation when the analysis process duration was too short. In this condition, the visitor explored the VE to look for the pointed board, found the board, analyzed it, answered and returned exploring the VE. When the analysis process was short, the guide could perceive the visitor's behavior as continuous. In the single task, the visitor watched the board to analyze it and stayed on it after answering. So, his behavior was not useful to detect when the analysis was completed. This behavior was just useful to detect when the fixation began. Then the guide had to evaluate the time to validate after this fixation. Often they validated too early. Figure 6 illustrates this difficulty. The safe-explicit feature was a very successful strategy. Obviously, a validation after the arrow indication was always successful.

G. ROI of strategies applied

We calculated a ROI (Return On Investment) by multiplying the frequency F of the strategy i applied (0 Fi 1) by the score of success of this strategy S (0 Si 1) and by adding these results obtained for the four main strategies (0 < i 4). ROI = ∑ i (F i * S i ) with ∑ i Fi 1 and 0 S i 1. Thus, 0 ROI 1. The better was the ROI, the better was the global success of the strategies chosen by each group of participants. Table 1 summarizes the most applied and successful strategies, and table 2 gives the associated ROIs scores.

The analysis of the ROIs proved that experts applied strategies that fit better than novices. This analysis proved that the highest ROIs in successive task were due to the visitor motion and the safe-explicit feature strategies. The visitor's behavior was an implicit feedback that worked. We proved that as the experts validated earlier, they were better than novices to use this feedback. The safe-explicit feature was truly useful when the visitor's behavior did not clarify when the analysis had been achieved. Experts used more often than novices this feature to validate. H. Questionnaire results a) Question: "Are you confident that the visitor has seen the board indicated?": Globally, all the participants were very confident that the visitor had seen the board indicated (> 82%) (a little less for novices in the successive task: 67%). The estimation feature with the spotlight was more appreciated, particularly by novices. The participants preferred the single task to be firmly convinced the visitor had seen the board. To optimize confidence that the visitor has seen the board, the best configuration was: Spotlight (⊗) and single task ( †). b) Question: "Are you confident that the visitor has completed the analysis of the board?": Obviously, with the safeexplicit feature, the participants thought with a higher degree of confidence that the visitor had completed the analysis of the board. The participants preferred successive task to be firmly convinced the visitor had completed his analysis. "If the visitor moves after steadying, that means he completed the task, so I validate" said numerous participants. To optimize confidence that the visitor had achieved his analysis, the best configuration was: Safe-explicit feature ( ) and successive task ( ‡).

VI. DISCUSSION

The proposed additional estimation feature, implemented as a spotlight matching the frustum dimensions, did not prove its usefulness (i.e we did not validate H1). An explanation could be that the boards were distributed on a horizontal halfcircle around the visitor. Thus, the rotation of his head was almost around only one axis. This partially decreases the 3D aspect of the task, and could be resolved using a half-sphere to dispose the boards. The head would rotate around the three axis, and it would be harder to estimate his field of view without the spotlight. This would be a more realistic use case for CVE, in addition to provide more interactivity, and would also be interesting to generalize the new asynchronous loop we propose to enhance awareness in CVE. Thus, we plan to extend this work in more complex scenarios while improving limits of this experiment.

Moreover, experts and novices did not apply the same strategies to interpret awareness features of the distant activity. Thus, future work should take into account the users' expertise D R A F T to enhance awareness and improve performances by adapting the interface.

VII. CONCLUSION AND PERSPECTIVES

In this paper, we focus on the awareness of collaborator's activities. More specifically, we propose to add an asynchronous awareness loop in an asymmetric CVE (in terms of roles and viewpoints) in order to propose a new way to be aware of what the others currently analyzed in the CVE.

We ran a user study in a CVE involving a simple cognitive task called 2D mental rotation [START_REF] Cooper | Mental rotation of random two-dimensional shapes[END_REF]. We analyzed the strategies collected from the participants to estimate a time when a prerecorded collaborator, called the visitor, achieved his analysis of a pointed board. Mental rotation and time count strategies illustrated an egocentric perspective. They were frequently unsuccessful in this study, i.e. by applying these strategies, the participants validated before the visitor. One of the reasons to explain this result could be the asymmetric setup in terms of viewpoint. If the guide (i.e. the subject), with a global viewpoint, applied egocentric strategies to analyze the pointed board, their validations were earlier than the visitor's validation due to his immersive viewpoint.

In the opposite, visitor motion and safe-explicit feature strategies illustrated an allocentric perspective. The guide better took into account the visitor's activity, which led to an enhanced estimation accuracy. In this study, the guide could use implicit feedback such as the visitor motion to estimate the completion of the visitor's analysis process. Moreover, in some conditions, we provided a safe-explicit feature that enabled the guide to know the exact moment the visitor completed his analysis. This last has proved to be very useful for experts and novices that used it successfully in most cases.

We also differentiated two groups of users. Experts and novices applied strategies to estimate the time to validate:

• Taking into account the visitor motion;

• Waiting for a safe-explicit feedback;

• Achieving the same analysis as the visitor had to achieve (2D mental rotation); • Counting mentally a time (few seconds) depending on the analysis complexity perceived by the guide.

But, experts and novices did not succeed in the same manner, because experts chose their strategies in a better way than novices. Experts were able to select their strategies in function of the condition better than novices. For example, in single task and with an available safe-explicit feature, novices applied visitor motion strategy whereas experts applied the most successful strategy: the safe-explicit feature. An interpretation can be found in the following definition: "It is human perception and experience of events that generate awareness" [START_REF] Cai | Event-based awareness promotion for distributed collaborative activities[END_REF]. Thus, even if we provide adequate awareness features, their interpretation stay user-dependent (especially according to the expertise).

This study has focused on the analysis process that is a first step in the human cognitive system. It is followed by the understanding that can be right or wrong. This aspect seems to be an interesting feature to investigate and could pave the way toward the awareness of distant collaborators' understanding in order to further improve remote collaboration in CVE.

Figure 1 :

 1 Figure 1: (top) The guide's global view; (bottom) The visitor's immersive view.

Figure 2 :

 2 Figure 2: The internal black loop shows the classical continuous awareness loop and the external orange loop illustrates the new asymmetric loop that handles the analysis feedback.

Figure 3 :

 3 Figure 3: Illustration of our implementation of the safe-explicit feedback for this experiment from the guide's point of view.

Figure 4 :

 4 Figure 4: Difference of time between the visitor validation and the subject estimation: (a) Globally, (b) when subject validated after the visitor, (c) and regarding the availability of .

Figure 4b illustrates the

  Figure 4b illustrates the significant difference between novices and experts (F(1, 561) = 15.76, p < .001). Novices (M = 0.93, SD = 0.005) validated slower than experts (M = 0.57, SD = 0.04).

Figure 5 :

 5 Figure 5: Strategies applied according to the task and the expertise.

Figure 6 :

 6 Figure 6: Successful applied strategies according to the task and the expertise.

TABLE 1 :

 1 THE MOST APPLIED AND MOST SUCCESSFUL STRATEGIES IN ALL THE CONDITIONS FOR EXPERTS AND NOVICES.

	Mental'rotation'
	Visitor'motion

TABLE 2 :

 2 ROI OF THE STRATEGIES APPLIED ILLUSTRATING

	THEIR GLOBAL SUCCESS