France Van Wambeke
email: france.van-wambeke@univmed.fr

Marc Tedetti

Solange Duhamel

Richard Sempéré

Diel variability of heterotrophic bacterial production and underwater UV doses in the eastern South Pacific

Keywords: Eastern South Pacific, Heterotrophic bacterial production, Diel variability, Ultraviolet radiation, UV-B and UV-A doses, Mixed layer, Leucine

Diel variability of heterotrophic bacterial production (BP) was investigated in the eastern South Pacific from October to December 2004. Three sites differing by their trophic status were studied: Marquesas Islands, the center (GYR) and the eastern South Pacific Gyre. By using a Lagrangian approach and high frequency measurements, an important increase (2-to 4-fold) in BP was observed at the 3 sites during the afternoon-sunset period within surface layers. To evaluate the impact of solar UV radiation on this variability, we determined, from in situ optical measurements, the mean UV-B (at 305 nm) and UV-A (at 380 nm) doses received within the mixed layer at a daily scale. At GYR, the doses were as high as 0.3 and 11 kJ m -2 nm -1 for the whole day, respectively due to high surface irradiances and very low light attenuations in the water column. The UV-B/UV-A tri-hourly dose ratios (Q) displayed substantial variations during the daytime, with highest values recorded during the periods 9:00 to 12:00 h or 12:00 to 15:00 h. The negative linear correlation observed between Q and BP in the surface waters of GYR suggests that changes in the balance between DNA damages and photorepairs (reflected by changes in the Q-ratio) could have a significant influence on the diel variability of BP in open oceans. However, assessing the effects of UV radiation on diel variability of BP through an in situ measurement approach, independently from other causes like availability of resources, is not so evident, even in these clearest waters of the world ocean.

INTRODUCTION

The context of climate change and stratospheric ozone depletion has stimulated interest in investigating the effects of solar radiation, and more specifically ultraviolet radiation (UVR: 280 to 400 nm), on aquatic ecosystems [START_REF] Häder | Aquatic ecosystems: effects of solar ultraviolet radiation and interactions with other climatic change factors[END_REF]). In the surface waters, UVR can significantly affect heterotrophic bacteria that are too small to efficiently use UVR-absorbing pigments as sunscreens [START_REF] Garcia-Pichel | A model for internal self-shading in planktonic organisms and its implications for the usefulness of ultraviolet sunscreens[END_REF]. Ultraviolet-B (UV-B: 280 to 315 nm) is strongly absorbed by cellular DNA, altering the DNA structure and producing dam-ages such as cyclobutane pyrimidine dimers (CPDs) [START_REF] Häder | Solar ultraviolet radiation-induced DNA damage in aquatic organisms: potential environmental impact[END_REF]. Many studies have reported the inhibition of heterotrophic bacterial production (BP) after exposure of natural microbial assemblages to UV-B and the subsequent production of CPDs [START_REF] Aas | Inhibition of bacterial activities by solar radiation in nearshore waters and the Gulf of Mexico[END_REF], Jeffrey et al. 1996). On the other hand, DNA damages can be repaired through 2 pathways: (1) photoenzymatic repairs (PERs) via the action of photolyase, induced by both ultraviolet-A (UV-A: 315 to 400 nm) and photosynthetically available radiation (PAR: 400 to 700 nm) and (2) excision repairs that occur in the dark [START_REF] Häder | Solar ultraviolet radiation-induced DNA damage in aquatic organisms: potential environmental impact[END_REF]. PERs have been observed in nat-ural assemblages and bacterial cultures exposed to artificial UV-B after secondary irradiation with UV-A and PAR. It appears that UV-A is more efficient than PAR in inducing photoreactivation [START_REF] Kaiser | Rapid recovery of marine bacterioplankton activity after inhibition by UV radiation in coastal waters[END_REF][START_REF] Joux | Marine bacterial isolates display diverse responses to UV-B radiation[END_REF]. Therefore, the rates of CPDs production and PERs in marine microbial assemblages may be related to the UV-B and UV-A doses (irradiances integrated over time) received by cells [START_REF] Boelen | Accumulation and removal of UVBR-induced DNA damage in marine tropical plankton subjected to mixed and simulated non-mixed conditions[END_REF]). In the surface ocean, the intensity of UV-B and UV-A doses for a given time period depends on several parameters: (1) the level of surface UV-B and UV-A irradiances, (2) the attenuation of these surface irradiances in the water column, which is mainly controlled by chromophoric dissolved organic matter (CDOM) [START_REF] Diaz | UV physics and optics[END_REF] and ( 3) the depth of the mixed layer (Z m ), i.e. the depth at which cells can be transported within the upper water column [START_REF] Boelen | Accumulation and removal of UVBR-induced DNA damage in marine tropical plankton subjected to mixed and simulated non-mixed conditions[END_REF].

The French project BIogeochemistry and Optics SOuth Pacific Experiment (BIOSOPE) was dedicated to a multidisciplinary exploration of the eastern South Pacific including the hyper-oligotrophic South Pacific Gyre (SPG) as well as its western (Marquesas Islands) and eastern (Chilean upwelling) borders in late 2004 (Claustre et al. 2008a). As in other subtropical regions, the eastern South Pacific is subjected to high surface UV-B and UV-A irradiances due to the small solar zenith angle (SZA) and the relatively low ozone amounts (200 to 300 Dobson Units) (TOMS satellite data; http://toms.gsfc.nasa.gov/ozone/ozone_v8.html). Moreover, the very low content in chlorophyll a (chl a) and CDOM in the highly stratified surface waters of the SPG allow for a very deep penetration of UVR [START_REF] Morel | Optical properties of the 'clearest' natural waters[END_REF], Tedetti et al. 2007). In the surface layers of this oceanic area, nitrogen (N) has been shown to be a common factor limiting both phytoplankton and heterotrophic bacteria (Van Wambeke et al. 2008a). Consequently, in the SPG, the diel variability of BP could be particularly intense, influenced by the UV-B and UV-A doses received within Z m (i.e. the balance between DNA damages and repairs), but also by the trophic dependence of heterotrophic bacteria on phytoplankton exudates and/or regeneration processes.

The first goal of the present study was to determine the diel variability of BP as well as underwater UV doses received at fixed depths and within the mixed layer in the eastern South Pacific at 3 selected sites differing by their trophic status: the Marquesas Islands, and the center and the eastern border of the SPG. The second goal was to examine whether, in the most oligotrophic and transparent waters of the world ocean, a causal relationships between UVR and BP can be inferred simply from high frequency in situ measurements of BP and UV doses, without processing any experimental set-up in bottles.

MATERIALS AND METHODS

Sampling strategy

The BIOSOPE campaign was conducted from 24 October to 11 December, 2004 aboard RV 'l'Atalante' in the eastern South Pacific. The cruise consisted of 'short' and 'long' sites. Diel variability of BP was estimated at 3 of these long sites: MAR (vicinity of Marquesas Islands), GYR (center of the SPG) and EGY (eastern border of the SPG). These sites differed in terms of physical and biological characteristics (Claustre et al. 2008a; our Table 1). CTD casts were performed every 3 h during successive days (i.e. at local times 00:00, 03:00, 06:00, 09:00, 12:00, 15:00, 18:00 and 21:00 h). Samples were collected from a CTD rosette fitted with twenty 12 l Niskin bottles equipped with Teflon rings to avoid carbon contaminations. Samples were processed within 0.5 h of collection. For the study of BP diel variability, 9 to 10 levels were sampled along the water column down to 170 m at MAR, 280 m at GYR and 250 m at EGY. The period of high frequency sampling for BP measurements was 36 h at MAR on 27 to 28 October (every 3 h), 48 h at GYR on 12 to 14 November (every 3 h) and 42 h at EGY on 26 to 28 November (every 6 h).

Determination of the mixed layer depth

Z m was determined for each CTD cast and at all sites. It corresponded to the average of the following 4 measures: the depths at which the density difference with that of surface reached (1) 0.05 kg m -3 and (2) 0.1 kg m -3 ; and the depths at which the density gradient reached (3) 0.01 kg m -4 and (4) 0.02 kg m -4 . This average value based on 4 different criteria occasionally exhibited large variability, particularly at GYR where the density profile showed a succession of pycnoclines not very well contrasted (see Fig. 1). In addition, slight increases in temperature in the upper surface layers during the evening led to micro-stratification within the 10 m subsurface layers. Therefore, for these CTD casts, we reconsidered Z m values using only the first criterion and neglecting the first 10 m micro-gradient. Estimates of Z m at GYR must, thus, be considered with caution.

Heterotrophic bacterial production

BP was determined by [ 3 H] leucine incorporation, applying the centrifugation method (Smith & Azam 1992). Duplicate 1.5 ml subsamples and one trichloroacetic acid-killed blank (5% final concentration) were incubated (2 h in the dark at the respective in situ temperatures) with a final concentration of 20 nM added leucine (mixture of 6.6 nM [4, 5-3 H] leucine, Amersham, 160 Ci mmol -1 and 13 nM 'cold' leucine). The leucine-carbon conversion factor was 1.5 kg C mol -1 leucine incorporated. Assumptions of (1) linearity of incorporation of leucine during time of incubation and (2) an isotopic dilution factor set to 1 with the 20 nM final leucine concentration added were checked previously in a companion paper (Van Wambeke et al. 2008b). Bovine serum albumin was added before the first centrifugation (100 mg l -1 final concentration) as it gave results compatible with the filtration technique (see details in Van Wambeke et al. 2008b). Error associated with the variability between replicate measurements (half the difference between the 2 replicates) averaged 13 and 6% for BP values less and more than 10 ng C l -1 h -1 , respectively.

UVR measurements and determination of UV-B and UV-A doses

Two profiles of downward irradiance (E dZ,λ in µW cm -2 nm -1 ) were made at each site close to solar noon using a Satlantic MicroPro free-fall profiler equipped with OCR-504 downward irradiance sensors in the UV-B (305 nm) and UV-A (325, 340 and 380 nm) spectral domains. Surface irradiance (E d0 + ,λ in µW cm -2 nm -1 ) was simultaneously measured at the same channels on the ship deck using other OCR-504 sensors to account for the variations of cloud conditions during the cast as well as to monitor UV-B and UV-A irradiances during the day time. For in-water and in-air sensors, the Full-Width Half-Maximum (FWHM) of the channels was 2 nm for 305, 325 and 340 nm, and 10 nm for 380 nm. A detailed description of UVR measurements is given in Tedetti et al. 2007.

We determined UV-B and UV-A doses at MAR (28 Oct), GYR (13 Nov) and EGY (26 Nov) at fixed depths into the water column (H Z,UV-B and H Z,UV-A in kJ m -2 nm -1 ) as well as mean UV-B and UV-A doses received within the mixed layer (H m,UV-B and H m,UV-A in kJ m -2 nm -1 ). The wavelengths 305 nm (UV-B) and 380 nm (UV-A) were used as biologically effective wavelengths for the induction of CPDs and PERs, respectively [START_REF] Häder | Solar ultraviolet radiation-induced DNA damage in aquatic organisms: potential environmental impact[END_REF]. H Z,UV-B and H Z,UV-A were assessed using the following formula:

H Z,λ = H 0 - ,λ × e (-Kdλ × Z ) (1)
where H 0 -,λ is the dose beneath the sea surface (kJ m -2 nm -1 ), K dλ is the diffuse attenuation coefficient for downward irradiance (m -1 ), Z is the depth considered (m) and λ is the wavelength (305 or 380 nm). Here we calculated H Z,UV-B and H Z,UV-A at 5 and 10 m depth (i.e. H 5m,UV-B , H 5m,UV-A , H 10m,UV-B , and H 10m,UV-A ). Conversely, H m,UV-B and H m,UV-A were evaluated using an equation taking into account the depth of the mixed layer [START_REF] Boelen | UVBR-induced DNA damage in natural marine picoplankton assemblages in the tropical Atlantic Ocean[END_REF]:

H m,λ = H 0 - ,λ × [1 -e (-Kdλ × Zm) ] / (K dλ × Z m ) (2)
where Z m is the mixed layer depth (m) that corresponds to the mean of the 2 Z m observed at the beginning and at the end of the exposure period considered for the dose calculation (3 h, see below). These 2 Z m values were determined from CTD profiles as described above. All the doses were computed over trihourly exposure periods: 06:00-09:00 h, 09:00-12:00 h, 12:00-15:00 h and 15:00-18:00 h (local time). These tri-hourly doses were summed to obtain daily (6:00-18:00 h) doses. The detailed methodology for the appraisal of H 0 -,λ and K dλ , which are both used in Eqs. ( 1) and (2), is as follows.

H 0 -,λ (dose beneath the sea surface) was obtained by integrating downward irradiance beneath the sea surface (E d0 -,λ in µW cm -2 nm -1 ) over the exposure period: (t, time)

H 0 - ,λ = Σ E d0 - ,λ ∆t (3)
E d0 -,λ was theoretically computed from measurement of E d0 + ,λ using the formula:

E d0 - ,λ = E d0 + ,λ / (1 + α) (4)
where α is ocean surface albedo determined using a 'look up table' available online at http://www-cave. larc.nasa.gov/cave/ [START_REF] Jin | A parameterization of ocean surface albedo[END_REF]). This look up table, based on the validated Coupled Ocean-Atmosphere Radiative Transfer model, requires 4 input parameters to retrieve α at any spectral band of the solar spectrum: SZA, wind speed, aerosol/cloud optical depth and chl a concentration [START_REF] Jin | A parameterization of ocean surface albedo[END_REF]). SZA (°) was retrieved every hour from the T. Dibble SZA calculator (www.esf. edu/chemistry/dibble/atmoschemcalc.htm) that uses location (latitude, longitude), date (day, month), and universal time (UT) of measurements. Wind speed (knots converted into m s -1 ) was measured continuously on board. At MAR and GYR (cloudless days), aerosol optical depth at 550 nm was estimated at 0.15 and 0.05, respectively [START_REF] Myhre | Intercomparison of satellite retrieved aerosol optical depth over ocean during the period September 1997 to December 2000[END_REF]. At EGY (cloudy day), cloud optical depth at 500 nm was assessed at 15 (second highest value in the table). Total chl a concentrations (TChla, mg m -3 ) were used from Ras et al. (2008). Therefore, α, computed every hour at 305 and 380 nm, was highly variable at MAR (ranging from 0.050 at 12:00 h to 0.097 at 17:00 h) and GYR (ranging from 0.057 at 12:00 h to 0.108 at 17:00 h), and very stable at EGY (~0.068 from 06:00-18:00 h). K dλ was determined at each station close to solar noon from the slope of the linear regression of the logtransformed downward irradiance versus depth:

K dλ = ln (E d0 - ,λ / E dZ,λ ) / Z (5)
where Z was 15 m (MAR) or 30 m (GYR and EGY). Correlation coefficients (r) of the linear regressions were > 0.99 for the 3 sites. Because of the SZA increasing during the day time, K dλ , measured only close to solar noon, was corrected for the geometric condition of the light field by dividing by [START_REF] Gordon | Can the Lambert -Beer law be applied to the diffuse attenuation coefficient of ocean water?[END_REF] correction factor (D oλ ):

D oλ = (f λ / cos SZA 0 -) + 1.197 × (1 -f λ ) ( 6 )
where f λ is the direct fraction of global irradiance and SZA 0 -is the SZA beneath the sea surface. Since diffuse irradiance was not measured, we used average f λ values available from literature for the UV spectral domain [START_REF] Kuhn | Penetration of ultraviolet radiation in the waters of the estuary and Gulf of St. Lawrence[END_REF]): 0.36 and 0.57 at 305 and 380 nm, respectively, for cloudless days (MAR and GYR). To facilitate the calculation, EGY was considered with an overcast sky, i.e. with no direct radiation (f λ = 0.00). SZA 0 -was obtained by applying Snell's law to SZA above water:

SZA 0 -= sin -1 (sin SZA / n) ( 7 )
where n (~1.34) is the refraction index of water. D oλ , which is wavelength dependent, was computed every hour at 305 and 380 nm and then averaged over the trihourly periods. D oλ ranged from 1.11 (12:00-15:00 h) to 1.30 (06:00-09:00 h and 15:00-18:00 h) at MAR and GYR, whereas it was constant (1.197) at EGY. Assuming a vertical homogeneity of the distribution of attenuating substances and organisms over the depth interval 0 -m to TChla maximum (Tedetti et al. 2007; our Table 1), the 10% irradiance depth (Z 10%λ in m), which is the depth where the downward irradiance is 10% of its surface value, was extrapolated from K dλ :

Z 10%λ = ln(10) / K dλ = 2.30 / K dλ (8)

RESULTS

Physical, biological and optical characteristics at the 3 sites

The main physical and biological characteristics of the 3 sites studied for diel variability of BP are presented in Table 1. There were significant differences between the 3 sites for most of the parameters: temperature, Z m , depth of the euphotic zone (Z e ), depth of TChla maximum and concentration of TChla at this depth (Table 1). Mean surface temperature (5 m) was higher at MAR (27.8°C) compared to GYR and EGY (22.3 and 18.1°C,respectively). The variations in temperature during the diel cycles were very low, as seen by the standard errors (< 0.14°C; Table 1). Z e was the deepest at GYR (155 ± 9 m), and 92 ± 2 and 72 ± 5 at EGY and MAR, respectively. In terms of depth of TChla maximum (176 m), Z e (155 m), Z 10%UV-A (100 to 118 m) and Z 10%UV-B (21 to 25 m), GYR confirmed its hyper-oligotrophic status (Table 1). From data acquired during the diel cycles, BP values at 5 m depth and integrated data down to Z e and Z m were statistically different (higher) only for MAR. BP at 5 m depth was on average 97, 17 and 26 ng C l -1 h -1 at MAR, GYR that even with daily-averaged profiles of BP, subsurface peaks were still visible (Fig. 1).

Diel variability of BP

For the 3 sites investigated, coefficient of variation of BP (CV = SD/mean, in percentage) for the whole set of volumetric data at 5 m depth ranged 24, 40 and 36% at MAR, GYR and EGY, respectively. At MAR, BP at 5 m depth increased up to 2.1-fold between 12:00 and 18:00 h, and decreased from 18:00 h to 12:00 h more gradually (increase and decrease rates: 12 ± 3 and 3.7 ± 0.5 ng C l -1 h -1 , respectively). At GYR, a higher increase (4.7-fold) in activity was recorded later in the day, between 15:00 and 21:00 h (Fig. 2). Again, the decrease was more gradual. Trend at EGY is more difficult to describe because of the longer interval between CTD casts (6 h). However, higher BP values were associated with the late afternoon to midnight period, with up to 1.5-fold variations in 6 h. Considering integrated data, CV of BP integrated down to Z e ranged 13, 16 and 19% at MAR, GYR and EGY, respectively. CV of BP integrated down to Z m was close, with slightly higher values at EGY (25%). At MAR, the correlation between BP at 5 m and integrated data was insignificant for integration down to Z m and slightly significant for integration down to Z e (r = 0.61, p = 0.02; Table 2). On the contrary, correlation between BP at 5 m and BP integrated down to Z m were significant at GYR and EGY (r = 0.68, p < 0.01 and r = 0.73, p = 0.02, respectively). However, the correlations were lower or insignificant when relating BP at 5 m with BP integrated down to Z e at these sites (r = 0.55, p = 0.02 at GYR and p > 0.05 at EGY).

At MAR, the diel variation in volumetric rates of BP at individual depths was not only visible at 5 m depth, but also down to ~50 m depth (Fig. 3), with lower values during morning/noon and increasing values during afternoon/night. Indeed, BP at 5 m depth was significantly correlated with BP at 10 m (r = 0.77, p < 0.01) and at 15 m (r = 0.90, p < 0.001) for the whole cycle, as well as with BP at 20 and 40 m the first day (r = 0.91, p < 0.001 and r = 0.89, p < 0.01, respectively). However, at GYR the diel variation in volumetric rates of BP at depths other than at the surface was less visible. Succession of max./min. spots were less regular when comparing different depths and successive days, i.e. the first night maximum values occurred between 18:00 and 00:00 h for 5, 40 and depths between 90 and 170 m, and the second night at 21:00 h for 5, 40 and 90 m. ), integrated BP down to Z e (euphotic layer) and down to Z m (mixed layer), and surface PAR irradiance with time at the 3 sites, i.e. MAR, GYR and EGY. Sampling period and frequency for BP measurements were -MAR: every 3 h from 27 Oct, 9:00 h to 28 Oct, 21:00 h; GYR: every 3 h from 12 Nov, 18:00 h to 14 Nov, 18:00 h; EGY: every 6 h from 26 Nov, 18:00 h to 28 Nov, 12:00 h (plus 1 isolated data point at 9:00 h on 26 Nov, 2004). Z e was fixed to depth of 1% incident PAR, i.e. 72 m at MAR, 155 m at GYR and 92 m at EGY. Z m value for integration down to Z m is the average of the 2 Z m values obtained for the preceding 3 h period (e.g. to estimate integrated BP at 12:00 h, Z m was averaged from that obtained at 9:00 h and that obtained at 12:00 h). Arrows: days for which UV-B and UV-A doses were calculated (MAR: 28 Oct, GYR: 13 Nov, EGY: 26 Nov, 2004) (see Figs. 4 & 5). Error bars indicate variability within duplicate samples and 18:00 h. Thus, at GYR, diel variations in BP were observed much deeper than at MAR (Fig. 3).

Underwater UV-B and UV-A doses

Daily underwater UV-B and UV-A doses determined at MAR (28 Oct), at GYR (13 Nov) and at EGY (26 Nov) are presented Fig. 4. For the 3 stations, min- imal values were found when considering the mean doses within the mixed layer (Fig. 4). At MAR, H 0 -,UV-B

(2.1 kJ m -2 nm -1 ) and H 0 -,UV-A (21.5 kJ m -2 nm -1 ) were slightly higher than those at GYR (2.0 and 20.5 kJ m -2 nm -1 , respectively) and much higher than those at EGY (1.3 and 13.4 kJ m -2 nm -1 , respectively). This is explained by high levels of surface solar irradiance at MAR and GYR (sunny days), while the cloud cover encountered at EGY led to a significant decrease of irradiance (Fig. 2). Since the attenuation of UVR into the water column was much lower at GYR (K dUV-B = 0.10 m -1 , K dUV-A = 0.02 m -1 ), UV-B and UV-A doses at 5 and 10 m depth observed at this site were higher than those at MAR. In the same way, the notable discrepancy between MAR and EGY in the doses received beneath the sea surface tended to decrease with depth (even though H 5m,λ and H 10m,λ were still higher at MAR), because of a lower UVR attenuation at EGY (K dUV-B = 0.16 m -1 , K dUV-A = 0.04 m -1 ) (Fig. 4). At GYR, H m,UV-B and H m,UV-A were 0.30 and 11.0 kJ m -2 nm -1 , respectively. These values were considerably higher than those observed at MAR (0.11 and 4.2 kJ m -2 nm -1 , respectively), for which relatively elevated K d λ (~0.20 and 0.06 m -1 ) and Z m (~89 m) caused a large diminution of doses received within the mixed layer. Interestingly, at EGY, H m,UV-B and H m,UV-A were 0.28 and 8.4 kJ m -2 nm -1 , respectively, and thus were higher than those recorded at MAR due the relatively small Z m (~32 m). The latter also contributed to drastically reduce the difference between H 10m,λ and H m,λ (Fig. 4).

Daytime variations of UV-B/UV-A tri-hourly dose ratios (Q in %) are reported Fig. 5. Clearly, Q was 104 0 5 10 15 20 25

H 0 - ,λ H 5m,λ H 10m,λ H m,λ
UV dose (kJ m -2 nm -1 ) -A ) and at 10 m depth (H 10m,UV-B , H 10m,UV-A ), as well as mean UV-B and UV-A doses received within the mixed layer (H m,UV-B , H m,UV-A ) are represented. Daily doses were obtained by summing doses calculated over tri-hourly exposure periods, i.e. 06:00-9:00 h, 09:00-12:00 h, 12:00-15:00 h and 15:00-18:00 h, and thus correspond to the period 06:00-18:00 h (local time). Average depth of the mixed layer (Z m in m) and diffuse attenuation coefficient for UV-B and UV-A downward irradiances (K dUV-B, K dUV-A in m -1 ) measured at solar noon (see Table 1 always higher around solar noon (at the periods 09:00-12:00 h and 12:00-15:00 h) and beneath the sea surface, where it could be as high as 10.5% (MAR: H 0 -,λ , 12:00-15:00 h). Q decreased when moving away from solar noon (in the periods 06:00-09:00 h and 15:00-18:00 h) and with depth. Minimal Q values were obtained at 10 m depth (MAR, EGY) or within the mixed layer (GYR) and could be as low as 2% (MAR: H 10m,λ , 6:00-9:00 h) (Fig. 5).

K dUV-B = 0.23 K dUV-A = 0.07 Z m = 89 K dUV-B = 0.11 K dUV-A = 0.02 Z m = 61 K dUV-B = 0.18 K dUV-A = 0.04 Z m = 32 H 0 - ,λ H 5m,λ H 10m,λ H m,λ H 0 - ,λ H 5m,λ H 10m,λ H m,λ MAR GYR EGY UV-A UV-B

DISCUSSION

Diel variability in BP has been reported for various marine environments. For instance, Torréton & Dufour (1996) found CV of 10 to 13% in a Tuamotu atoll lagoon (surface layer), and [START_REF] Gasol | Diel variations in bacterial heterotrophic activity and growth in the northwestern Mediterranean Sea[END_REF] 16 to 32% in western Mediterranean coastal waters (surface layer to TChla maximum depth), both estimates were based on the thymidine technique. The latter authors also measured leucine incorporation, which provided a higher variability (CV = 10 to 70%) than that obtained using thymidine. The main feature we observed in the eastern South Pacific was an abrupt increase (2-to 4-fold) in leucine activity at 5 m depth during the afternoon-sunset period and lowest activity around solar noon (between 10:00 and 14:00 h). Previous studies reach no clear consensus about the period in which BP obtains maximal value: at night (Zohary & Robarts 1992), at noon [START_REF] Gasol | Diel variations in bacterial heterotrophic activity and growth in the northwestern Mediterranean Sea[END_REF] or no significant trend (Torréton & Dufour 1996) with regard to the biogeochemical characteristics of the site and the depth considered. Therefore, the marked diel pattern in BP determined from high-frequency sampling at MAR, GYR and EGY supports the hypothesis that daily variations of some factors controlling BP, either abiotic (solar UVR) or biotic (bottom-up / top-down control) might be particularly pronounced in the eastern South Pacific (Figs. 2 &4).

Because the SPG likely contained the clearest oceanic waters [START_REF] Claustre | The many shades of ocean blue[END_REF][START_REF] Morel | Optical properties of the 'clearest' natural waters[END_REF], Tedetti et al. 2007), we hypothesized that the UV effects could be of great importance and compared surface irradiance and doses received at different depths and within the mixed layer with previous studies. Like [START_REF] Boelen | UVBR-induced DNA damage in natural marine picoplankton assemblages in the tropical Atlantic Ocean[END_REF] and [START_REF] Jeffrey | Ambient solar radiation induced photodamage in marine bacterioplankton[END_REF], we observed mean UV doses in the mixed layer, lower than UV doses beneath the sea surface (H 0 -, see Eq. 3) by a factor ranging between 2 and 18 at the 3 sites investigated. These differences emphasize the importance of taking into account vertical mixing to determine the actual doses received by organisms in the surface oceanic waters. In addition, the high H m,λ found in the hyper-oligotrophic part of the SPG (GYR) were explained by a very low UVR attenuation.

Although H m,λ seems more appropriate than H 0 -,λ to describe the potential effect of UVR on heterotrophic bacteria in the surface ocean, little data are reported for this parameter. In the tropical Atlantic Ocean, [START_REF] Boelen | UVBR-induced DNA damage in natural marine picoplankton assemblages in the tropical Atlantic Ocean[END_REF] measured daily mean UV doses of 0.11 to 0.56 kJ m -2 in the mixed layer. Since the latter values were determined from the DNA action spectrum of Setlow (1974) normalized at 300 nm, they may be comparable to our daily H m,UV-B , which ranged from 0.11 to 0.30 kJ m -2 nm -1 . At GYR, daily doses beneath the sea surface (H 0 -,UV-B = 2.00 kJ m -2 nm -1 , H 0 -,UV-A = 20.5 kJ m -2 nm -1 ) were of the same order of magnitude than those above the sea surface measured at 305 and 380 nm (H 0 + ,UV-B and H 0 + ,UV-A ) in the subtropical Atlantic Ocean (1.7 and 19 kJ m -2 nm -1 ; [START_REF] Obernosterer | Spatial and diurnal dynamics of dissolved organic matter (DOM) fluorescence and H 2 O 2 and the photochemical oxygen demand of surface water DOM across the subtropical Atlantic Ocean[END_REF], in Antarctic waters (1.6 and 25 kJ m -2 nm -1 ; Figueroa 2002) and in the northwestern Mediterranean Sea (1.5 and 21 kJ m -2 nm -1 ; Tedetti & Sempéré unpubl. data). Nevertheless, if the whole UV-B (integration over 280 to 315 nm) and UV-A (integration over 315 to 400 nm) spectra are considered, H 0 + ,UV-B and H 0 + ,UV-A become much higher. As a matter of fact, in the South Atlantic Ocean [START_REF] Buma | Patterns of DNA damage and photoinhibition in temperate South-Atlantic picophytoplankton exposed to solar ultraviolet radiation[END_REF] and in South West New Caledonia [START_REF] Conan | Effect of solar ultraviolet radiation on bacterio-and phytoplankton activity in a large coral reef lagoon (southwest New Caledonia)[END_REF], values measured with the broad band ELDONET radiometer were 40 and 1800 kJ m -2 and up to 30 and 2000 kJ m -2 , respectively. As a result, the UV-B (at 305 nm) and UV-A (at 380 nm) doses received by heterotrophic bacteria beneath the sea surface and within the mixed layer that we report here for the eastern South Pacific, and more particularly for the SPG, are among the highest doses ever recorded for the marine environment.

The UV-B/UV-A tri-hourly dose ratios (Q in %) presented significant variations during the daytime (Fig. 5), and we tentatively used it as an indicator of daily changes of the balance between DNA damages (CPDs) and repairs (PERs), i.e. when Q increases, the importance of CPDs should increase relative to PERs. The higher 'relative' contribution of UV-B-induced CPDs around solar noon (Fig. 5) could be related to the lower values of BP observed between 10:00 and 14:00 h (Fig. 2). [START_REF] Jeffrey | Ambient solar radiation induced photodamage in marine bacterioplankton[END_REF] demonstrated that UV-B was responsible for 85% of the total leucine inhibition (30% relative to dark controls) when heterotrophic bacteria were exposed to full solar radiation in surface waters. From a linear relationship determined experimentally between the CPD formation in naked calf-thymus DNA dosimeters and the UV-B irradiance at 305 nm (Wilhelm et al. 2002), we calculated a mean daily potential production of CPDs within Z m (regardless of photorepair processes): 420, 800 and 760 CPDs (Mb DNA) -1 at MAR, GYR and EGY, respectively. These daily potential productions were in the same range as those measured with DNA dosimeters in the surface waters of other oceanic areas: ~500 CPDs (Mb DNA) -1 in the Gulf of Mexico [START_REF] Jeffrey | Ambient solar radiation induced photodamage in marine bacterioplankton[END_REF]) and ~1000 CPDs (Mb DNA) -1 in the South coast of Curaçao [START_REF] Boelen | Accumulation and removal of UVBR-induced DNA damage in marine tropical plankton subjected to mixed and simulated non-mixed conditions[END_REF], Visser et al. 2002). The period 09:00 h to 15:00 h accounted for 67, 75 and 71% of the daily potential production of CPDs at MAR, GYR and EGY, respectively. Accordingly, these assumed high amounts of UV-B-induced CPDs may lead to the inhibition of BP around solar noon. On the other hand, the lower 'relative' contribution of DNA damages (i.e. lower Q values) at the end of the afternoon (Fig. 5) may explain the abrupt increase or the slower decrease in BP in this period (Fig. 2). In tropical coastal waters, Visser et al. (2002) showed that the inhibition of leucine incorporation in bacterioplankton exposed to full solar radiation strongly increased from 10:00 to 12:30 h or 15:00 h (up to 80% of dark controls) and slightly decreased from 15:00 to 18:00 h. This slower inhibition of leucine incorporation in late afternoon was attributed to PERs. To better appreciate the potential impact of the balance between DNA damages and repairs on the diel variability of BP, at MAR and GYR we examined the relationships between Q and BP for data at 5 m and integrated down to Z m (EGY was excluded because of the lack of BP data). A significant inverse linear relationship was observed only at GYR between Q 5m and BP 5m (r = 0.97, p < 0.05, n = 4, data measured at 09:00, 12:00, 15:00 and 18:00 h). The real limits of the mixed layer at GYR (due to micro-stratification; Fig. 1) as well as the limited number of data could partly explain the absence of systematic correlations. In addition, our results illustrate the fact that using in situ observations to examine causal relationships between UVR and BP on a daily scale is not easy, particularly when considering cells in their natural environment, i.e. moving within a gradient of light in the mixed layer and influenced also by resource supply and top-down controls.

With regard to phytoplankton resources, if heterotrophic bacteria are closely related to DOC release to satisfy their needs for growth (the first nutrient limiting BP at EGY was labile organic carbon; Van Wambeke et al. 2008a), they should be completely coupled to photosynthesis during the daytime [START_REF] Marañón | Continuity in the photosynthetic production of dissolved organic carbon from eutrophic to oligotrophic waters[END_REF]. Marked diel cycles in BP are more expected in oligotrophic areas [START_REF] Gasol | Diel variations in bacterial heterotrophic activity and growth in the northwestern Mediterranean Sea[END_REF][START_REF] Church | Light dependence of [ H]leucine incorporation in the oligotrophic North Pacific ocean[END_REF]) due to this coupling. However, a lag was clearly obtained in the SPG between the process of photosynthesis, PP (as seen from beam attenuation coefficient used as a proxy of biomass changes; Claustre et al. 2008b) and BP (this study). So, it is hypothesized that because of the UV-B-induced production of CPDs around solar noon, there is a delay before BP increases, which is detected when Q starts decreasing, i.e. after 15:00 h (Fig. 5). This hypothesis has also been formulated in other studies dealing with experimental tools, such as the confinement of samples in enclosed quartz tubes subjected to different spectral radiations [START_REF] Aas | Inhibition of bacterial activities by solar radiation in nearshore waters and the Gulf of Mexico[END_REF][START_REF] Conan | Effect of solar ultraviolet radiation on bacterio-and phytoplankton activity in a large coral reef lagoon (southwest New Caledonia)[END_REF]. In the latter study, phytoplankton was more affected by UV-A, whereas it was UV-B for heterotrophic bacteria, suggesting that variations of relative amounts of UV-B and UV-A during the daytime must affect the coupling between phytoplankton/bacteria on a large extent. The lag is also interpreted as a shift in the bacterial metabolism towards expense of energy, because nucleotide excision repair of UV-B-induced DNA damages is energetically expensive [START_REF] Pakulski | Effect of stratospheric ozone depletion and enhanced ultraviolet radiation on marine bacteria at Palmer Station, Antarctica in the early austral spring[END_REF].

Regarding regeneration processes, it must be noted that N was the primary limiting nutrient for bacterial production at GYR just before labile organic carbon (Van Wambeke et al. 2008a) and thus regeneration of N could influence BP. Factors limiting bacteria can vary during the day [START_REF] Kuipers | Diel periodicity of bacterioplankton in the euphotic zone of the subtropical Atlantic Ocean[END_REF]. There was a characteristic and reproducible diel pattern of size spectrum of particles ranging between 0.6 and 4 µm at GYR, with a displacement of its distribution toward higher values around 03:00 h and lower values around 15:00 h (A. Sciandra unpubl. results). This suggests synchronicity in cell division among some cyanobacteria and picoeukaryotes and diel changes of cell physiology, which in turn could lead to diel rhythm in grazing [START_REF] Christaki | Dynamic characteristics of Prochlorococcus and Synechococcus consumption by bacterivorous nanoflagellates[END_REF] and lysis (Winter et al. 2004[START_REF] Motegi | Enhancement of viral production by addition of nitrogen or nitrogen plus carbon in subtropical waters of the South Pacific[END_REF] and, thus, in DOM (including N) regeneration.

Finally, photoheterotrophs could also play a role in diel cycle of BP. First, anoxygenic phototrophic bacteria represented up to 19% of 'heterotrophic bacteria' as counted by flow cytometry just above the depth of TChla at GYR, in conjunction with a peak of bacteriochlorophyll a [START_REF] Lami | High abundances of aerobic anoxygenic photosynthetic bacteria in the South Pacific ocean[END_REF], Claustre et al. 2008b). Second, Prochlorococcus contributed on average to 36% of the sum 'heterotrophic bacteria' + Prochlorococcus between 120 and 140 m, and still represented 15% down to 190 m at GYR [START_REF] Grob | Contribution of picoplankton to the total particulate organic carbon concentration in the eastern South Pacific[END_REF]. It is thus possible that some patterns in leucine incorporation rates at these very deep layers, not influenced by UV, could be due to the circadian cell cycle of some cyanobacteria able to assimilate leucine [START_REF] Cottrell | Carbon cycling by microbes influenced by light in the Northeast Atlantic Ocean[END_REF][START_REF] Mary | Diel rhythmicity in amino acid uptake by Prochlorococcus[END_REF].

The pronounced diel pattern observed in the eastern South Pacific for heterotrophic bacterial production, delayed with that of primary production, suggests a probable interactive control on a daily scale by bottom-up resources (phytoplankton and regenerating micro organisms as producers of labile organic carbon and nitrogen source) and by the quality and intensity of natural solar radiation (UV-B, UV-A and PAR). Further measurements on a daily scale of DNA damages, fluxes of DOM release and regeneration, as well as bacterial re-assimilation of these by-products are necessary in these original, unique, transparent oceanic waters of the South Pacific Gyre to validate these hypotheses.

Fig. 1 .Fig. 2 .

 12 Fig.1. Vertical profiles of mean heterotrophic bacterial production (BP) at the sites MAR (mean of 9 CTD casts, every 3 h from 27 Nov, 09:00 h to 28 Nov, 09:00 h), GYR (mean of 9 CTD casts: every 3 h from 13 Nov, 18:00 h to 14 Nov, 18:00 h) and EGY (mean of 8 CTD casts every 6 h from 26 Nov, 18:00 h to 28 Nov, 12:00 h, 2004). Error bars = SD. All density profiles (sigma theta, grey lines) from these CTD casts are also indicated

Fig. 3 .

 3 Fig.3. Contour plots of heterotrophic bacterial production (BP) with time at the sites MAR (0 to 175 m depth) and GYR (0 to 250 m depth). The time periods are as in Fig.2. Note the different color scales for BP between the 2 sites. Linear color mapping was used for the MAR graph, and non linearity around the value of 15 ng C l -1 h -1 for GYR to show better color contrasts between 10 and 20 ng C l -1 h -1 at this site (see color scales)

Fig. 4 .

 4 Fig. 4. Daily underwater UV-B and UV-A doses (kJ m -2 nm -1 ) determined at MAR on 28 Oct, at GYR on 13 Nov and at EGY on 26 Nov, 2004. UV-B and UV-A doses received beneath the sea surface (H 0 -,UV-B , H 0 -,UV-A ), at 5 m depth (H 5m,UV-B , H 5m,UV-A ) and at 10 m depth (H 10m,UV-B , H 10m,UV-A ), as well as mean UV-B and UV-A doses received within the mixed layer (H m,UV-B , H m,UV-A ) are represented. Daily doses were obtained by summing doses calculated over tri-hourly exposure periods, i.e. 06:00-9:00 h, 09:00-12:00 h, 12:00-15:00 h and 15:00-18:00 h, and thus correspond to the period 06:00-18:00 h (local time). Average depth of the mixed layer (Z m in m) and diffuse attenuation coefficient for UV-B and UV-A downward irradiances (K dUV-B, K dUV-A in m -1 ) measured at solar noon (see Table1) are given for each station

Fig. 5 .

 5 Fig. 5. Daytime variation in UV-B/UV-A tri-hourly dose ratios Q (%) recorded beneath the sea surface (H 0 -,UV-B /H 0 -,UV-A ), at 5 m depth (H 5m,UV-B /H 5m,UV-A ), at 10 m depth (H 10m,UV-B /H 10m,UV-A ) and within the mixed layer (H m,UV-B /H m,UV-A ) at MAR on 28 Oct, at GYR on 13 Nov and at EGY on 26 Nov, 2004

  Claustre H, Uitz J (2008) Spatial variability of phytoplankton pigment distributions in the subtropical South Pacific Ocean: comparison between in situ and predicted data.Biogeosciences 5:353-369 Setlow RB (1974) The wavelengths in sunlight effective in producing skin cancer: a theoretical analysis. Proc Natl Acad Sci USA 71:3363-3366 Smith DC, Azam F (1992) A simple, economical method for measuring bacterial protein synthesis rates in sea water using3 H-Leucine. Mar Microb Food Webs 6:107-114 Tedetti M, Sempéré R, Vasilkov A, Charrière B and others (2007) High penetration of ultraviolet radiation in the south east Pacific waters. Geophys Res Lett 34:L12610, doi:10.1029/2007GL029823 Torréton JP, Dufour P (1996) Temporal and spatial stability of bacterioplankton biomass and productivity in an atoll lagoon. Aquat Microb Ecol 11:251-261 Van Wambeke F, Bonnet S, Moutin T, Raimbault P, Alarçon G, Guieu C (2008a) Factors limiting heterotrophic bacterial production in the southern Pacific Ocean. Biogeosciences 5:833-845 Van Wambeke F, Obernosterer I, Moutin T, Duhamel S, Ulloa O, Claustre H (2008b) Heterotrophic bacterial production in the eastern South Pacific: longitudinal trends and coupling with primary production. Biogeosciences 5: 157-169 Visser PM, Jaap Poos J, Scheper BB, Boelen P, Van Duyl FC (2002) Diurnal variations in depth profiles of UV-induced DNA damage and inhibition of bacterioplankton production in tropical coastal waters. Mar Ecol Prog Ser 228: 25-33 Wilhelm SW, Jeffrey WH, Suttle CA, Mitchell DL (2002) Estimation of biologically damaging UV levels in marine surface waters with DNA and viral dosimeters. Photochem Photobiol 76:268-273 Winter C, Herndl GJ, Weinbauer MG (2004) Diel cycles in viral infection of bacterioplankton in the North Sea. Aquat Microb Ecol 35:207-216 Zohary T, Robarts RD (1992) Bacterial numbers, bacterial production, and heterotrophic nanoplankton abundance in a warm core eddy in the Eastern Mediterranean. Mar Ecol Prog Ser 84:133-137 108 Editorial responsibility: Matthias Seaman, Oldendorf/Luhe, Germany Submitted: December 9, 2008; Accepted: April 21, 2009 Proofs received from author(s): June 30, 2009

Table 2 .

 2 Coefficient of variation (CV = standard deviation to mean percentage) and correlation coefficients (r) between volumetric rates (bacterial production at 5 m depth, BP 5m ) and integrated data at the mixed layer (IBP Zm ) or euphotic zone (IBP Ze ). n: number of data; *p < 0.05, **p < 0.01, ns: correlation not significant

	103

Acknowledgements. We are grateful to the captain and crew of the RV 'l'Atalante' for their help during the cruise. We warmly thank H. Claustre for leadership of the BIOSOPE project and A. Sciandra for his leadership during the second leg. C. Bournot, D. Tailliez and D. Merien are acknowledged for CTD-rosette operations and data processing. We are grateful to 2 anonymous reviewers for improving the quality of the manuscript. This research was founded by CNRS-INSU, France, as a part of the PROOF -BIOSOPE and UVECO projects.


Florian Berton

Anne-H Él Ène Olivier

Julien Bruneau

Ludovic Hoyet

Julien Pettre

Studying Gaze Behaviour During Collision Avoidance With a Virtual Walker: Influence of the Virtual Reality Setup

ABSTRACT

Simulating realistic interactions between virtual characters has been of interest to research communities for years, and is particularly important to automatically populate virtual environments. This problem requires to accurately understand and model how humans interact, which can be difficult to assess. In this context, Virtual Reality (VR) is a powerful tool to study human behaviour, especially as it allows assessing conditions which are both ecological and controlled. While VR was shown to allow realistic collision avoidance adaptations, in the frame of the ecological theory of perception and action, interactions between walkers can not solely be characterized through motion adaptations but also through the perception processes involved in such interactions. The objective of this paper is therefore to evaluate how different VR setups influence gaze behaviour during collision avoidance tasks between walkers. To this end, we designed an experiment involving a collision avoidance task between a participant and another walker (real confederate or virtual character). During this task, we compared both the participant's locomotion and gaze behaviour in a real environment and the same situation in different VR setups (including a CAVE, a screen and a Head-Mounted Display). Our results show that even if some quantitative differences exist, gaze behaviour is qualitatively similar between VR and real conditions. Especially, gaze behaviour in VR setups including a HMD is more in line with the real situation than the other setups. Furthermore, the outcome on motion adaptations confirms previous work, where collision avoidance behaviour is qualitatively similar in VR and real conditions. In conclusion, our results show that VR has potential for qualitative analysis of locomotion and gaze behaviour during collision avoidance. This opens perspectives in the design of new experiments to better understand human behaviour, in order to design more realistic virtual humans.

Index Terms: Human-centered computing-Visualization-Visualization techniques-Treemaps; Human-centered computing-Visualization-Visualization design and evaluation methods

INTRODUCTION

Virtual Reality (VR) is today one important element of the palette of tools to study human behaviour, and more specifically human locomotion, navigation in dynamic environments and interaction with other walkers. Studies have for long focused on kinematics data only, but there is a growing interest to include in addition gaze tracking data because of the fundamental role of the visual perception system in such tasks [START_REF] Patla | Understanding the roles of vision in the control of human locomotion[END_REF][START_REF] Warren | Visually controlled locomotion: 40 years later[END_REF]. In parallel, affordable consumer VR devices now include gaze tracking possibilities, which raises the question of the usability and validity of VR to perform studies combining motion and gaze tracking data.

The objective of this paper is therefore to evaluate and compare how different VR setups influence gaze behaviour during collision avoidance between walkers. To this end, we designed an experiment involving a collision avoidance task between a participant and another walker, during which we recorded participants' gaze using eye-tracking devices. For comparison, this experiment has been performed in both real and virtual environments using different virtual setups (including a cave, a screen and a Head-Mounted Display).

In the context of collision avoidance task between walkers, the contributions of this article are as follows:

• We propose a methodology based on several objective criteria to evaluate both gaze and kinematic behaviours in virtual and real environments.

• We provide recommendations for the design of VR platforms to perform human locomotion studies in the context of collision avoidance between walkers.

The remaining of the article is organized as follows. Section 2 describes related work with respect to the use of VR and eye-tracking devices to study human interactions during locomotion. Section 3 presents the general description of our experimental study. Section 4 describes the different criteria used to compare the trajectories and gaze behaviour. Finally, results are presented and discussed respectively in Section 5 and 6.

RELATED WORK

In this section, we first survey previous works that studied interactions between walkers from the kinematics point of view, followed by a focus on gaze behaviours in such tasks.

Kinematics of Interaction Between Walkers

Human interaction during locomotion and more especially collision avoidance has been extensively studied. Avoidance can be described as the response to a risk of future collision that humans can accurately perceive [START_REF] Olivier | Minimal predicted distance: A common metric for collision avoidance during pairwise interactions between walkers[END_REF]. Note that this paper describes risk of collision by introducing the mpd function that we use as well in this paper. Avoidance strategies -i.e., how speed and/or orientation adjustments are combined [START_REF] Basili | Strategies of locomotor collision avoidance[END_REF][START_REF] Olivier | Collision avoidance between two walkers: Role-dependent strategies[END_REF] -depend more on the characteristics of the situation (e.g. walking speed, angle of crossing) rather than the characteristics of the walkers (e.g., gender, personality) [START_REF] Huber | Adjustments of Speed and Path when Avoiding Collisions with Another Pedestrian[END_REF][START_REF] Knorr | Influence of Person-and Situation-Specific Characteristics on Collision Avoidance Behavior in Human Locomotion[END_REF][START_REF] Olivier | Collision avoidance between two walkers: Role-dependent strategies[END_REF]. Those studies were performed in real conditions (i.e., human participants in a physical environment) and face the difficulty of standardizing situations of interactions, this limited the study of the many factors that can influence such tasks.

VR offers ways to overcome such limitations, e.g., it was used: to study the human behaviour and its neural mechanisms [START_REF] Tarr | Virtual reality in behavioral neuroscience and beyond[END_REF], to study locomotor control by manipulating the perceived optical flow [START_REF] Chou | Effects of optic flow speed and lateral flow asymmetry on locomotion in younger and older adults: a virtual reality study[END_REF][START_REF] Sheik-Nainar | The utility of a virtual reality locomotion interface for studying gait behavior[END_REF][START_REF] Warren | Optic flow is used to control human walking[END_REF], to explore interactions of higher dimension with groups [START_REF] Bruneau | Going through, going around: A study on individual avoidance of groups[END_REF] or to evaluate small-scale interaction details such as the effect of mutual gaze and head movements [START_REF] Lynch | Effect of virtual human gaze behaviour during an orthogonal collision avoidance walking task[END_REF][START_REF] Nummenmaa | I'll walk this way: Eyes reveal the direction of locomotion and make passersby look and go the other way[END_REF][START_REF] Varma | Assessing the relevance of eye gaze patterns during collision avoidance in virtual reality[END_REF]. Furthermore, nonrealistic scenarios can be studied in VR to isolate and understand specific factors difficult to evaluate in real situations. For instance, Lynch et al. [START_REF] Lynch | Collision avoidance behavior between walkers: global and local motion cues[END_REF] investigated motion cues used during collision avoidance by displaying the avoided agent in different ways, showing the full body motion, global cues only (moving cylinder or sphere) or local cues only (moving legs or trunk only). Finally, VR is also a way to investigate factors while reducing potential risks for participants, typically in specific population such as post-stroke patients [START_REF] Aravind | Virtual realitybased navigation task to reveal obstacle avoidance performance in individuals with visuospatial neglect[END_REF][START_REF] Darekar | Dynamic clearance measure to evaluate locomotor and perceptuo-motor strategies used for obstacle circumvention in a virtual environment[END_REF] .

The increasing interest in VR tools to perform human experiments stresses the need for validating them, i.e., to compare human behaviour in VR and reality. Several works already performed this comparison for locomotion studies, either considering goal directed [START_REF] Agethen | Behavior analysis of human locomotion in the real world and virtual reality for the manufacturing industry[END_REF][START_REF] Cirio | Kinematic evaluation of virtual walking trajectories[END_REF] or collision avoidance tasks [START_REF] Agethen | Behavior analysis of human locomotion in the real world and virtual reality for the manufacturing industry[END_REF][START_REF] Bühler | Circumvention of pedestrians while walking in virtual and physical environments[END_REF][START_REF] Fink | Obstacle avoidance during walking in real and virtual environments[END_REF][START_REF] Olivier | Walking with virtual people: Evaluation of locomotion interfaces in dynamic environments[END_REF]. It was shown that virtual and real trajectories have common properties, even if some quantitative differences remain such as slower walking speeds or increased clearance distances. In a similar context, Gerin Lajoie et al. [START_REF] Gérin-Lajoie | Characteristics of personal space during obstacle circumvention in physical and virtual environments[END_REF] found that the personal space during locomotion has a similar elliptic shape but is wider in VR. Moussaid et al. [START_REF] Moussaïd | Crowd behaviour during high-stress evacuations in an immersive virtual environment[END_REF] however found that participants seemed to display similar patterns and social conventions. All these works converge to the same conclusion: VR can be used to qualitatively study the kinematics of human locomotion during goal directed trajectories or collision avoidance tasks, while quantitative differences remain.

Gaze Behaviour

Most of previous studies considered trajectories performed by walkers only, few also explored participants' gaze behaviour in relation with trajectory adjustments. Yet, vision plays an important role in the control of locomotion [START_REF] Patla | Understanding the roles of vision in the control of human locomotion[END_REF][START_REF] Warren | Visually controlled locomotion: 40 years later[END_REF]. In particular, it enables the collection of information about the environment and about the relative movement of surrounding objects.

Using eye-tracking in real environments, it was shown that walkers looked at zones which maximize the level of information that can be used to navigate safely [START_REF] Marigold | Gaze fixation patterns for negotiating complex ground terrain[END_REF]. Cinelli et al. [START_REF] Cinelli | Behaviour and gaze analyses during a goal-directed locomotor task[END_REF] observed participants going through 2 motor-driven sliding doors, and concluded that gaze fixations depended on the complexity of door movements. Few studies considered collision avoidance between walkers. Kitazawa and Fujiyama [START_REF] Kitazawa | Pedestrian vision and collision avoidance behavior: Investigation of the information process space of pedestrians using an eye tracker[END_REF] studied the relationship between gaze and the Personal Space and observed that gaze allocation was equally distributed between ground, objects and pedestrians. Croft et al. [START_REF] Croft | Watch where you're going? interferer velocity and visual behavior predicts avoidance strategy during pedestrian encounters[END_REF] studied avoidance strategies between two participants with different velocities, paths and gaze behaviour conditions and found that they predict crossing order. Finally, Jovancevic-Misic and Hayhoe [START_REF] Jovancevic-Misic | Adaptive gaze control in natural environments[END_REF] demonstrated that gaze strategies depend on the behaviour of surrounding people, where participants typically looked more at near actors displaying risky behaviours than at other actors.

The integration of eye-tracking capabilities in VR devices such as HMDs greatly facilitate coupled studies of gaze and kinematics data. Jovancevic et al. [START_REF] Jovancevic | Control of attention and gaze in complex environments[END_REF] asked participants to walk in VR among few virtual humans (VH) and they studied the relation between the distribution of gaze fixations in the environment depending on the nature of interactions with VHs, i.e. they focus on following rather than on avoiding. More recently, Meerhoff et al. [START_REF] Meerhoff | Guided by gaze: Prioritization strategy when navigating through a virtual crowd can be assessed through gaze activity[END_REF] studied gaze while walking through a crowd in a virtual environment: gaze was more attracted towards the VH with the highest risk of collision, suggesting a close relation between gaze behaviour and trajectory adaptations. In a different context, VR devices equipped with an eye-tracker have also been used to study human locomotion. For instance, redirected walking techniques [START_REF] Sun | Towards virtual reality infinite walking: dynamic saccadic redirection[END_REF] recently exploited eye blinks to imperceptibly reorient users. Furthermore, Gandrud et al. [START_REF] Gandrud | Predicting destination using head orientation and gaze direction during locomotion in vr[END_REF] found that gaze data activity in relation with head rotation could be used to predict the future direction of a person. This could improve redirected walking techniques, but also improve the realism of avatar's animation.

As for locomotor trajectories, it is necessary to investigate the similarities and biases induced by VR on gaze behaviour if we aim at using VR to draw conclusions on human behaviour. Foulsham et al. [START_REF] Foulsham | The where, what and when of gaze allocation in the lab and the natural environment[END_REF] studied participants' gaze behavior while walking in a university campus or watching first person videos of this walk. Differences were noticed regarding eye and head movements, however scene objects were mostly looked at in the same proportions. Similar gaze behaviors were also found during experiments conducted in both virtual and real environments where participants sat on a chair and observed either a realistic avatar [START_REF] Rubo | Virtual reality as a proxy for real-life social attention[END_REF] or a light [START_REF] Pfeil | A comparison of eye-head coordination between virtual and physical realities[END_REF], despite differences in head rotations [START_REF] Pfeil | A comparison of eye-head coordination between virtual and physical realities[END_REF]. To the best of our knowledge, no study compared gaze behaviour during a collision avoidance locomotor task in real and virtual environments. This is however a daily situation of interest to understand how humans interact together. The purpose of our study is therefore to evaluate and compare gaze behaviour during a collision avoidance task between walkers in real and virtual conditions.

METHODS

We designed an experiment to evaluate and compare gaze behaviour during a collision avoidance task. We considered a real baseline condition as well as four virtual conditions. Virtual conditions varied by the type of visual display (Cave, HMD or screen). As it has been demonstrated that participants' gaze can be impacted by action requirements [START_REF] Dicks | Examination of gaze behaviors under in situ and video simulation task constraints reveals differences in information pickup for perception and action[END_REF], we also considered two types of navigation techniques (physically walking or using a game controller). Our hypotheses are:

• H1: the nature of the visual information retrieved from the environment to achieve the collision avoidance task (i.e., gaze allocation) will be similar between the real and the virtual conditions, similarly to what was previously observed for locomotion behaviour between VR and real conditions. Moreover, we hypothesize that gaze allocation will not be affected by the type of VR display.

• H2: the type of display will however affect gaze movements.

Since each type of display does not convey visual information in the same way (e.g., HMD and screen have limited field of view), we expect that gaze movements will adapt accordingly. Especially, we expect that displays with limited field of view will induce larger head (HMD) or eye movements (Screen because we used a chin rest) to explore the environment and compensate for the limited field of view.

• H3: the type of navigation controller will not affect gaze behaviour. Again based on previous studies that demonstrate that locomotion is performed similarly in real and virtual conditions in spite of the use of various locomotion techniques, we expect that the nature of the visual information to control virtual locomotion will remain similar, and therefore will induce similar gaze behaviour compared to the real baseline condition.

Participants

Seventeen unpaid participants, recruited via internal mailing lists among students and staff, volunteered for the experiment (6F, 11M; age: avg.=23.6 ± 3.3, min=19, max=29). They were all naive to the purpose of the experiment, had normal or correct-to-normal vision, and gave written and informed consent. The study conformed to the declaration of Helsinki, and was approved by the local ethical committee. Because of tracking issues that appeared specifically for five participants (motion capture tracking issues, or eye gaze calibration issues), only the data from twelve participants was finally used in this paper.

Task

Participants were asked to navigate in a real or virtual environment towards a target while avoiding any collision with another walker crossing their way (a virtual character or a real confederate). As shown in Figure 2, the other walker had an orthogonal trajectory to the one of the participant, he could come either from the right-or lefthand side, he walked straight at constant speed (with no adjustment of his trajectory). Participant reached a target in front of them, visible from the beginning. Walls hid the walker to participants at the beginning of the trial to let them reach their comfort speed before they react to the virtual walker. We varied the risk of collision with the other walker by defining 13 offset positions for the walker, that resulted into advance or delay on the participants' motion (6 giving advance, 6 delaying him and 1 symmetrical situation with full risk of collision). Participants were asked to navigate towards a target, while avoiding any collision with a real or virtual confederate crossing their path perpendicularly. Walls prevented participants from seeing the real or virtual confederate before reaching their comfort speed.

Environment

Participants performed the task in a real and a virtual environment.The real environment was delimited by a 9mx9m square, with four walls placed along its the diagonal at 2.2m from the center (see Figure 2). They have to reach the opposite corner of the square. Furthermore, the confederate walker did not perform eye contact and did not react to participants. In comparison, virtual conditions took part in a virtual copy of the physical environment (dimensions, start and target positions, walls) which we designed in Unity 2017.4. The target represented as a green cylinder. The virtual walker based animated based on motion captures of the real confederate. Note that animations did not include gaze movement or facial expression to keep similar behaviour of the walker with respect to the real condition.

Experimental Design

Participants were asked to perform the task presented in Section 3.2 in the five following conditions (summarized in Table 1):

Real: this is our baseline condition to compare VR situations to. Participants were asked to walk at comfort speed and to avoid any collision with the confederate walker. A 28-camera Vicon system (120Hz) was used to record their trajectory, where participant and confederate positions were approximated as the centre of their head using bike helmets equipped with reflective markers. Participants also wore Tobii eye-tracking glasses which recorded both their gaze behaviour (50Hz) and what they saw of the environment (scene camera: 25Hz, 90 • field of view).

HMD-W: participants were immersed in the virtual environment using a FOVE HMD (70Hz, 100 • field of view), which comes with an integrated eye-tracker1 (100Hz). They performed the collision avoidance task with the virtual character by walking in the co-localised physical setup (Figure 1.b). As for the Real condition, the 28-camera vicon system was used to track participants' trajectories using markers positioned on the HMD, as well as to update the participants' viewpoint in the virtual environment.

HMD-C: participants were immersed in the virtual environment using a FOVE HMD (see above), which was also used to record their gaze behaviour. However, in this condition we were interested in a different navigation technique, where participants used a game controller (Logitech F710 S) to avoid collisions with the virtual character, while seated at a desk. With no action on the controller, participants moved straight with a default speed of 1.33m.s -1 (comfort walking speed from [START_REF] Bohannon | Comfortable and maximum walking speed of adults aged 20-79 years: reference values and determinants[END_REF]). They were able to linearly adapt their speed from 0.8m.s -1 to 2m.s -1 with the longitudinal axis and their angular speed from -25deg.s -1 to 25deg.s -1 with the lateral axis of the controller. This control scheme was previously validated by Olivier et al. [START_REF] Olivier | Walking with virtual people: Evaluation of locomotion interfaces in dynamic environments[END_REF] for such collision avoidance tasks.

Cave: participants were immersed in the virtual environment using a 4-screen Computer Assisted Virtual Environment (CAVE). the system is equipped with 13 projectors, has a 15MPixels resolution in total, and is 9m large, 3m high and 3m deep. Participants wore volfoni 3D glasses for active stereo vision, as well as the eye-tracking Tobii glasses, assembled with a custom-built rig. Participants were standing in the middle of the CAVE and asked to avoid collisions with the virtual character using a joystick (Logitech Attack 3), which used the same control scheme described above.

Screen: participants sat at a desk in front of a 24-inch screen, and were asked to avoid collisions with the virtual character using a joystick (Logitech Attack 3), which used the same control scheme described above. Gaze behaviour was recorded using The Eyetribe (60Hz) positioned under the screen, and a chin rest was used to increase tracking accuracy.

Participants performed the experimental task for each of these conditions. This was combined with 13 levels of initial risk of collision with the confederate or the virtual walker. Finally, we included two repetitions of each situation, where the walker to avoid can come from the left or from the right. In total, each participant performed 130 trials (5 Conditions × 13 Risk levels × 2 Repetitions). They were also invited to perform up to 20 additional training trials at the beginning of each condition (most participants performed 4 to 5 in each case).

ANALYSIS

Collected Data

During the experiment, the participant and the walker trajectories were recorded, as well as participants' head rotation and gaze behaviour (origin and direction). The image of what the participant saw (Figure 3 a-b) was recorded either with the Tobii glasses for the real environment , or with Unity for the virtual environment. This image was divided into three different items that could be targeted by the gaze (Walker, Target or Environment). This segmentation (Figure 3 c-d) was done either using shaders in Unity for the virtual environment, or manually with the help of a CNN network [START_REF] Caelles | One-shot video object segmentation[END_REF] on the gaze video for the real environment. Furthermore for each trial, we re-sampled the recorded data (trajectories, eye movements) at a frequency of 60Hz.

Time Variables

To describe the interaction period2 , we defined two variables: the moment when the confederate walker is not obstructed by the wall anymore, which corresponds to the first instant when the participant can first see him (T see), and the moment when the two walkers are the closest to each other (T cross). We then normalized the time along this interaction period from 0% (T see) to 100% (T cross).

Collision Avoidance Trials

The goal of this paper is to study gaze behaviour during a collision avoidance task. We therefore needed to distinguish trials where a collision avoidance was performed (i.e., the initial risk of collision was high enough to trigger collision avoidance manoeuvres) from those that did not require trajectory adjustments. To this end, we based our analysis on the minimum predicted distance (mpd) [START_REF] Olivier | Minimal predicted distance: A common metric for collision avoidance during pairwise interactions between walkers[END_REF],

which can be computed at any time t between T see and T cross. At any time t, mpd(t) therefore corresponds to the closest distance the two walkers would be passing by in the future if they were to maintain their current velocity. Any variation of mpd in time necessarily results from motion adaptions performed by walkers. More specifically, these two types of trials (with or without avoidance manoeuvres) will differ in the evolution of mpd(t). Trials with trajectory adjustments to avoid collision typically have a significant increase of mpd in time, i.e., mpd(T see) < mpd(T cross), while the latter type of trials typically has a more constant mpd, with the exception of the effect of trajectory noise (e.g., swaying in locomotion), i.e., mpd(T see) ≈ mpd(T cross). As a result, a threshold mpd CA on the value of mpd(T see) over which Collision Avoidance is not necessary anymore can be identified in order to divide our data into trials with collision avoidance and trials without collision avoidance. This threshold was automatically computed by fitting the following model describing the evolution of mpd(T cross) in relation to md p(T see) on our data (see Figure 4 for illustrative representation):

minimize a,mpd CA N ∑ i ( f (T see i ) -T cross i ) 2 subject to f (x) =    a × x + b if x < mpd CA subj. to b = (1 -a) × mpd CA x otherwise
It is important to note that we computed the best parameters a and mpd CA which minimized the sum of squared residuals (SSR) independently for each condition, in order to compute a threshold adapted to the data from each condition.

Kinematics of the collision avoidance task

In the real condition, we asked the confederate not to react to participants. We assessed the absence of reaction by computing his acceleration during the interaction period.

Then, for all conditions, we analyze the kinematic characteristics of the collision avoidance task by computing for each trial:

• Number of collisions: we defined a collision as occurring when the distance between the two walkers (computed from center to center) is less than 50cm. • Number of inversions:by linearly extrapolating the participant trajectory from current positions and velocities, we can estimate the future crossing order at time T cross. We count the number of trials for each condition where an inversion of this order occurs along the trial.

For trials were there was a collision avoidance, we computed:

• Clearance Distance: the actual distance (in m) between the two walkers at T cross (i.e., md p(T cross ))). • Speed : average participant's speed over the interaction period. • Mpd evolution: to characterize trajectories adaptations, we computed the mpd evolution during the normalized interaction as well as its temporal derivative.

Gaze Behaviour

As our goal in this paper is to understand gaze behaviour leading to avoidance adaptations, we analyzed participants' gaze behaviour only for trials with motion adaptations. We considered two main aspects of participants' gaze behaviour namely fixations, and head and eyes movements. In the case of fixations, we also define the gaze allocation as the ratio of time spent looking at each object during the whole interaction.

Fixation. According to Parasuraman and Rizzo [START_REF] Parasuraman | Neuroergonomics: The brain at work[END_REF], the gaze behaviour can be described as a succession of fixations separated by fast eye movement called saccades. An important task in eyetracking studies is to well define these two movements [START_REF] Hessels | Is the eye-movement field confused about fixations and saccades? a survey among 124 researchers[END_REF]. Depending on the task and situation different definitions can be found in the literature, and we therefore used the definition of fixations given by Kitazawa and Fujiyama [START_REF] Kitazawa | Pedestrian vision and collision avoidance behavior: Investigation of the information process space of pedestrians using an eye tracker[END_REF], whose experimental task shared common properties with ours: a fixation was defined as a continuous gaze on the same object for more than 80ms. Furthermore, for each temporal window of 80ms, all the gaze points were required to be within a range of 3.0 degrees from the initial point. Considering this definition, our dependent variables related to fixations during the interaction were:

• Number of fixations per second • Average duration of fixations in seconds.

• Gaze Allocation: we reported, in %, where participants looked at considering 3 allocations: the confederate walker, the target and the surrounding environment.

Gaze and head angles. To consider gaze and head movements during the interaction period, we computed the following angles in the XY plan, where both the confederate walker and the target were represented as a circle with respectively a radius of 25cm and 50cm:

• Angle HH : angle between the head vector and the heading. It was not computed for the Screen condition as participants' head was immobilized using a chin rest, and therefore always aligned with the heading direction. • Angle GH : angle between the gaze vector and the head vector.

• Angle GW : angle between the gaze vector and the other walker.

• Angle GT : angle between the gaze vector and the target.

Statistics

For all dependent variables, we set the level of significance to α = 0.05. A Shapiro Wilk test was performed to evaluate whether data followed a normal distribution. If the distribution was not normal, a Friedman test was performed to evaluate the effect of the condition on these variables. Post-hoc comparisons were performed using a Wilcoxon signed rank test with a Bonferroni correction. If the distribution was normal, a one-way analysis of variance (ANOVA) with repeated measures was performed. Greenhouse-Geisser adjustments to the degrees of freedom were applied, when appropriate, to avoid any violation of the sphericity assumption. Bonferroni post-hoc tests were used to further analyze significant effects. For all variables which described an evolution during the normalized time of interaction, we evaluated the effect of the condition using Statistical Parametric Mapping (SPM) methods [START_REF] Friston | Statistical Parametric Mapping[END_REF]. This analysis allows comparing time-series data of different trials taking into account their variability at each time-step.

RESULTS

Locomotion

In the real condition, we confirmed that the confederate walker had approximately a constant speed ( 1.4 ± 0.1m.s -1 in average).

Thresholds for adaptation We computed the threshold mpd CA separately for each condition, and trials where mpd(T see) was lower than mpd CA of the condition. Trials were then considered to contain trajectory adjustments to avoid a potential collision. The threshold for each condition are reported in Table 2. Overall, we found a lower threshold for the real condition than for the virtual conditions, which is further discussed in Section 6.

Average speed of participants (Cf.,Table 2) was affected by the condition (F(1.72, 18.88) = 10.12, p < 0.0001, η 2 = 0.48), with lower values observed for the HMD-W condition. 2. These values were low (NB, values computed for each participant on the 26 trials of each condition). Furthermore, there is no significant effect of the condition on the number of collisions (p=0.79) or inversions (p=0.26).

Clearance Distance, presented in Figure 6, was affected by the condition (F(2.35, 25.92) = 16.72, p < 0.0001, η 2 = 0.60). Bonferroni post-hoc tests showed that it was lower in the real condition than in all the conditions performed in a virtual environment.

Mpd evolution and its time derivative during the interaction period are illustrated in Figure 7. Qualitatively, we can notice that all the curves have similar shapes. From a quantitative point of view, SPM analysis showed an effect of the condition on mpd values during all the interaction (p < 0.001). Post hoc tests revealed that mpd in real conditions was lower than HMD-C, Screen and Cave conditions during all the interaction period, and than HMD-W from 25% to the end of the interaction (p < 0.001). No effect of the condition was however shown when considering mpd time derivative.

Gaze Behaviour

Fixations

Number of fixations per second during the interaction period is illustrated in Figure 8. A significant effect of the condition was observed (F(4, 44) = 4.88, p = 0.0024, η 2 = 0.31). Post-hoc analysis showed that there were less fixations in the Cave than in the conditions involving physical walking, namely HMD-W and Real.

Average duration of fixation is illustrated in Figure 9 and was influenced by the condition (χ 2 (4)=28.46, p<0.0001). It was longer for the Screen than for the Cave and the HMD-W, smaller for the Cave than the HMD-C and smaller for HMD-W than HMD-C.

Gaze allocation is summarized in Figure 10. A significant effect of the condition was observed on the ratio of fixation directed towards the confederate walker (F(4, 44) = 4.25, p = 0.0053, η 2 = 0.28), the target (F(4, 44) = 3.67, p = 0.011, η 2 = 0.25), as well as the en- vironment (F(4, 44) = 3.45, p = 0.015, η 2 = 0.24). Post-hoc analysis showed that gaze allocation towards the confederate walker was lower in the Cave than in the HMD-W condition. Gaze allocation towards the target was higher in the HMD-C than in the Screen condition. Finally, gaze allocation towards the environment was lower in the HMD-C than in Cave condition.

Gaze and head angles

Figure 11 illustrates the evolution during the interaction period of all the angles related to gaze and head. SPM analysis showed an effect of the condition for all angles. Significant differences from the post-hoc SPM analysis are depicted on the top of each sub-figure by an horizontal bar on the corresponding period of the interaction.

Angle HH in Real conditions was significantly smaller than for HMD-W during the first half of the interaction, and than for the Cave from 21% to 31% of the interaction period. Also, angle HH was bigger in HMD-W than in HMD-C at the end of the interaction.

Angle GH in Screen conditions was significantly bigger (up to twice) than in all the other conditions at the beginning of the interaction. A significant difference was also noticed between the Real and the HMD-C conditions, angle GH being bigger in the HMD-C conditions from 0% to 9% of the interaction.

Angle GW in Real conditions at the beginning of the interaction was bigger than in HMD-W and Screen conditions and smaller than in the Cave conditions. It was also smaller than in the HMD-C and the Cave conditions at the end of the interaction. Also the angle GW in the Cave conditions was bigger than all the other three VR conditions at the beginning of the interaction.

Angle GT in Real conditions at the beginning of the interaction was smaller than all the VR conditions. It was also smaller at the beginning of the interaction in Cave compared to Screen and HMD-W.

DISCUSSION

The main objective of this study was to evaluate and compare the influence of various VR setups on the gaze behaviour during a collision avoidance task. To this end, we designed an experiment were participants performed the task in real and virtual conditions and we tracked both their trajectory and gaze.

Collision avoidance behaviour

Kinematics analysis showed that there were some quantitative differences in the metrics of interaction between real and virtual environments. Especially, the threshold mpd CA that triggers motion adaptation was lower in real condition than in virtual conditions, which is consistent with the larger clearance distance observed in the VR conditions. However, when considering the derivative of mpd, i.e., mpd variations and not absolute values, the absence of significant difference between the studied conditions suggests that motion adjustments are performed similarly in both real and virtual environments. This corroborates results from previous studies [START_REF] Bühler | Circumvention of pedestrians while walking in virtual and physical environments[END_REF][START_REF] Fink | Obstacle avoidance during walking in real and virtual environments[END_REF][START_REF] Olivier | Walking with virtual people: Evaluation of locomotion interfaces in dynamic environments[END_REF]. Furthermore, considering the VR conditions only, we did not find any significant difference between mpd values along the whole collision avoidance task. This suggests that collision avoidance behaviour is similar regardless of the employed VR setup. We found a difference in the average walking speed that was lower in the HMD-W condition: participants wore a HMD but had to move among physical obstacles, which may have induced a safer locomotion speed. This result is also consistent with previous works [START_REF] Agethen | Behavior analysis of human locomotion in the real world and virtual reality for the manufacturing industry[END_REF][START_REF] Bühler | Circumvention of pedestrians while walking in virtual and physical environments[END_REF].

Fixations and gaze allocation

Apart from the number of fixations in the Cave condition, our results showed similarities in the number and duration of fixations between real and virtual conditions. The smaller number of fixations in the Cave condition may be linked to technical issues where we found that eye gaze recognition was slightly impaired by the lighting conditions (which is specifically discussed in Section 6.4).

Regarding gaze allocation (i.e., ratio of what participants looked at in the scene), results only showed a few statistical differences between the VR conditions (Figure 10), where participants looked longer on average at the walker in one condition than another (HMD-W > Cave), or looked longer on average at the target (HMD-C > Screen). However we did not find any significant difference between the Real condition and any of the VR conditions. Overall, our results validate our Hypothesis H1. Comparing Real and VR conditions, participants looked at a similar visual content. They spend similar proportion of time on the various elements of the scene, with similar visual patterns. However we observed a few differences between the studied VR setups. This indicates that in spite of significant difference with Real conditions, some setups tend to change gaze behaviours in Virtual Reality and could become more important when studying other type of tasks for example. We discuss more extensively those limitations below.

Gaze and head angles

While the evaluation of what participants look at during the interaction is important, another interesting aspect is to evaluate whether the way they looked at this content was similar in the studied conditions.

First, we measured the evolution of the angle between the participants' gaze vector and both the walker and target, respectively by computing Angle GW and Angle GT . We observed that most of the differences between the angle evolution appeared at the start of the interaction, where participants could first see the other walker (namely, the observation phase [START_REF] Olivier | Minimal predicted distance: A common metric for collision avoidance during pairwise interactions between walkers[END_REF]). We can observe in Figure 11 (top and bottom right) an overall decrease of Angle GW with a simultaneous increase of Angle GT , showing that participants tended to look progressively more towards the walker and less towards the target in the first part of the interaction. As the walker is perceived with a possible risk of collision, this pattern is in correlation with previous work [START_REF] Jovancevic-Misic | Adaptive gaze control in natural environments[END_REF][START_REF] Meerhoff | Guided by gaze: Prioritization strategy when navigating through a virtual crowd can be assessed through gaze activity[END_REF]. However, we observed that the amplitude of these two variations was smaller in the real condition compared to the VR conditions, suggesting that peripheral vision might have played an important role in the real condition. These two variations also seem to happen later for the Cave and Real conditions than for the other VR conditions, which again seem to demonstrate the effect of the larger peripheral vision available in these two conditions. Therefore, it is likely that the limited peripheral vision in the HMD conditions forced participants to actively look for the other walker earlier in the interaction than in the Cave and Real conditions.

Then, we evaluated how participants' gaze direction resulted from the Eye-Head-Body relative orientations. In particular, we explored how the angle of the head relative to the heading evolved over time (Angle HH ), as well as the angle of the gaze relative to the head (Angle GH ). We found statistical differences at the beginning of the interaction between the Real condition and any of the VR conditions, for either Angle HH or Angle GH , which is in correlation with previous work [START_REF] Pfeil | A comparison of eye-head coordination between virtual and physical realities[END_REF] for a stationary situation. In the case of the HMD-W and Cave conditions, statistical differences were found only for Angle HH , differences which were longer in time for HMD-W. Conversely, for the Screen and HMD-C conditions statistical differences were found only for Angle GH . This seems to show that participants moved more their head in relation to their body in the HMD-W and Cave conditions, but displayed similar Eye-Head patterns. These results validate H2. Because of the strong differences for HMD-W, we hypothesize that such differences in behaviour might have been caused by the limited field of view of the HMD, which might have forced participants to induce larger head motions to look for the walker at the beginning of the interaction. This hypothesis should be evaluated through further studies. Similarly, such differences in the Screen condition were expected as participants' head was restricted because of the use a chin rest (required to improve the accuracy of the eye-tracker), which would have caused them to perform the scene exploration only using their eyes.

The results on Angle GH between the HMD-C and Real conditions were however more surprising, especially as we expected the limited field of view to lead to similar patterns than for HMD-W. This difference on Eye-Head-Body relative orientations between HMD-W and HMD-C when compared to the Real condition could therefore also be due to the difference in the locomotion interface, namely walking or seating using a game controller. This suggests that not only the type of VR device, but also the locomotion interface, might have an impact on the Eye-Head-Body coordination, which partially invalidates H3 (at least in terms of differences in eye movements). Therefore, further studies should be conducted to thoroughly evaluate these effects.

Limitations

Even with high-end eye-tracking devices, accurately tracking eye gaze can be influenced by the experimental conditions, such as lighting or participant's eye color. For the experiment reported in this paper, data from four participants were discarded because of eye-tracking issues regarding the quality of the calibration (3 participants with the Tobbi glasses, 1 participant with the FOVE HMD). In particular, we found the Tobbi glasses to be very sensitive to the room illumination while tracking the gaze, both in the Real and Cave situations. While this impacted calibration for three discarded participants, we also found that it led to more noise in the data in the Cave condition across participants, including the inability to identify eye gaze in some frames and influencing the identification of fixations in the data. We believe that such a problem might explain at least partially the lower average of fixations per second for the Cave condition (Figure 8), where illumination is typically reduced for 3D projection purposes. Regarding gaze tracking issues for the one participant in the HMD-W condition, we hypothesize that it could come from the head movements, a combination of head movements and a HMD not tightly fixated, or bad calibration because of eye color. These technical limitations therefore influenced our sample size, which should be increased in future experiments to explore more subtle effects and to strengthen our conclusions.

While the presented experiment is to our knowledge the first attempt to study in VR the kinematics of a collision avoidance task in conjunction to gaze behaviour, it also involves a specific controlled interaction in restricted physical and virtual spaces. For instance, interactions were possible in a 9mx9m area, which can also influence participant behaviour. Therefore, further studies would benefit from evaluating kinematics and gaze behaviours in larger physical setups, as well as more complex situations.

CONCLUSION & FUTURE WORK

In this paper, we explored the use of VR to study locomotion and gaze behaviour during collision avoidance between walkers, where we studied a collision avoidance task between two walkers in both real and virtual environments. We then compared the real condition with four VR conditions (including a CAVE, a screen and a Head-Mounted Display). Our results show that fixations and gaze allocations were similar in both real and virtual environments. However the exploration of the environment, to seek visual information, was different in both real and virtual environments. Our results suggest that VR has potential to study qualitatively the gaze behaviour, even though it does not seem to be befitting to study accurately the movement of the eyes with the current technological setups. Furthermore, not only the type of VR device but also the locomotion interface has an impact on eye movement, which should therefore also be taken into consideration. Regarding motion adaptations, our outcome confirms previous work, where collision avoidance behaviour is qualitatively similar in VR and real conditions.

In the future we want to look deeper into these effects, such as evaluating the impact on the crossing order, or the relationship between gaze behaviour and body motions. As VR also offers the possibility to explore complex or even inextricable situations while preserving qualitative human behaviours, we also want to explore further such complex scenarios in the future, e.g., by considering situations such as walking in a crowd, such as [START_REF] Meerhoff | Guided by gaze: Prioritization strategy when navigating through a virtual crowd can be assessed through gaze activity[END_REF], while manipulating different factors. In line with previous study, participants' gaze activity provides precious insights about individual behaviours in crowds, the nature of information acquired from the environment to achieve crowd behaviours, and the understanding of complex interactions in such contexts.

Figure 1 :

 1 Figure 1: Conditions studied in this paper. (a) Real situation, (b-e) Participants avoiding a virtual character while (b-c) wearing a HMD and (b) walking or (c) navigating using a game controller, (d) standing in a CAVE and navigating using a game controller, (e) interacting with a computer Screen.

Figure 2 :

 2 Figure 2:Participants were asked to navigate towards a target, while avoiding any collision with a real or virtual confederate crossing their path perpendicularly. Walls prevented participants from seeing the real or virtual confederate before reaching their comfort speed.

Figure 3 :

 3 Figure 3: Real and virtual environments (a-b) and their respective segmentation (c-d). Walker is in green, Target in white and the rest is considered as Environment

Figure 4 :

 4 Figure 4: Schematic model used to calculate mpd CA

Figure 5 :

 5 Figure 5: Illustration of the 4 angles we computed to relate head and eye movements.

Figure 6 :Figure 7 :

 67 Figure 6: Average clearance distance (±SD) depending on the experimental condition. Significant post-hoc comparisons are highlighted with stars (***p<0.001)

Figure 8 :Figure 9 :

 89 Figure 8: Number of fixations per second during the interaction for each condition

Figure 10 :

 10 Figure 10: Average (± SD) gaze allocation towards the confederate walker (left), the target (center) and the environment (right) during the interaction period.

Figure 11 :

 11 Figure 11: Evolution of the four angles Angle HH (top left), Angle GH (bottom left) , Angle GW (top right) and Angle GT (bottom right) during the normalized interactions for all the conditions. SPM analysis showed an effect of the condition on each angle. Significant pairwise post hoc results are depicted above each figure where horizontal black lines indicate during which period of time the angle differs between 2 conditions.

Table 1 :

 1 Summary of the conditions presented in this experiment

	Condition	Eye-tracker	Navigation
	Real	Tobii	walking
	HMD-W	Fove (built-in)	walking
	HMD-C	Fove (built-in) game controller
	CAVE	Tobii	game controller
	Screen	Eyetribe	game controller

Table 2 :

 2 mpd CA , average (±SD) speed of participants, average (±SD) number of collisions, and average (±SD) number of inversions of crossing order with respect to the experimental conditions.

	Conditions mpd CA (m)	Speed (m.s -1 )	Collisions	Inversions
	Real	0.97	1.34±0.16 0.16±0.38 1.00±0.85
	HMD-W	1.40	1.08±0.16 0.17±0.39 1.58±1.08
	HMD-C	1.29	1.27±0.07 0.25±0.45 0.50±0.67
	CAVE	1.40	1.25±0.05 0.33±0.65 1.16±0.83
	Screen	1.46	1.

23±0.06 0.58±0.90 1.00±0.85

Number of collisions and inversion are illustrated in Table

It is important to note that we used three different models of eye-trackers in this experiment (Tobii glasses, Eyetribe desktop eye-tracker, and FOVE built-in eye-tracker) to fit the constraints of the VR devices. E.g., it was not possible to use eye-tracking glasses in the HMD, and we therefore used the built-in device.

The term 'interaction' has been used in previous work to provide a temporal description of the task. Even though the virtual character is not reactive, and thus, the interaction is not really present in our experiment, we decided to keep calling this period 'interaction' for the sake of clarity

ACKNOWLEDGMENTS

This work was funded by the ANR OPMoPS project (ANR-16-SEBM-0004).