Keywords: foraging strategy, group behaviour, radar, rakes, synchronised flight

) by the means of information sharing between individuals. This process consists in inadvertent (passive) or deliberate (active) transfers of social information. Inadvertent transfers of information may rely on social cues that convey discrete information about the presence/absence of some feature Coordinated movements of seabirds exploiting a prey patch are known to increase prey encounter and capture rates of individuals. These behaviours, based on effective cooperation between seabirds, have only been reported at small scale, i.e. the scale of the prey patch. However, the efficient prey exploitation by seabirds in vast oceans require larger scale processes such as information transfers between individuals. Indeed, information transfers between foraging seabirds (e.g. changes in behaviour) reduce their search cost while increasing their prey encounter rate. Whether or not these information transfer processes imply active cooperation is unknown. Using images from fishing boat radars in the eastern tropical Atlantic, we show the existence of frequent medium-scale patterns of coordinated flights of seabird groups, consisting in seabird fronts ('rake' patterns) of 0.3-4.4 km width, displacing cohesively over 1.2-10.6 km and lasting between 2 and 19 min. For these rakes to be maintained, seabird groups have to adjust their flight speeds and directions, while they are on average distant of 500 m from each other, what cannot occur by chance. These findings suggest the existence of collective and coordinated movements in seabirds during prey searching at several kilometres' scale. This potential cooperation between foraging seabird groups brings new insight in the evolutionary trajectories of seabirds life-style.

Introduction

Raking the ocean surface: new patterns of coordinated motion in seabirds Camille Assali, Nicolas Bez and Yann Tremblay C. Assali (https://orcid.org/0000-0003-2957-2910) ✉ (camille.assali@gmail.com), N. Bez and Y. Tremblay, MARBEC, IRD, Univ. Montpellier, Ifremer, CNRS, Sète, France. [START_REF] Danchin | Public information: from nosy neighbors to cultural evolution[END_REF]) or on public information (i.e. graded information about the quality of the feature; [START_REF] Valone | Group foraging, public information, and patch estimation[END_REF]. Information sharing between foragers has been considered as an energetically beneficial factor because 1) it allows for the transmission of food location information among individuals, 2) when a resource location is known, it provides better or faster patch quality estimates [START_REF] Valone | Group foraging, public information, and patch estimation[END_REF]).

However, it is largely unknown whether group formation has to be interpreted either within an individual-oriented producer-scrounger game [START_REF] Vickery | Producers, scroungers and group foraging[END_REF][START_REF] Ranta | Competition versus cooperation: success of individuals foraging alone and in groups[END_REF] or within a more group-oriented social foraging game [START_REF] Sih | Social network theory: new insights and issues for behavioral ecologists[END_REF]). In the first case, an individual benefit personally from social information ('scrounger') by maintaining an appropriate distance to other individuals ('producers'). Therefore, coordination between foraging individuals is weak. In the second case, groups are actively maintained by individuals by the means of collective decisions and therefore, coordination between individuals is high. These different contexts are not fully exclusive within a species. For example, fresh-water American white pelicans (Pelecanus erythrorhynchos) may forage solitary, within uncoordinated groups, or within coordinated groups allowing for herding prey (Anderson 1991, McMahon and[START_REF] Mcmahon | Foraging strategies of American white pelicans[END_REF].

In seabirds, synchronous diving between individuals have been described in several penguin species (Tremblay andCherel 1999, Takahashi et al. 2004). Besides, high fish-school attack rates between individuals allow Cape gannets (Morus capensis) to increase their prey capture rates (Thiebault et al. 2016a). So far, coordinated movements in seabirds have thus appeared to be restricted to small-scales, i.e. the scales at which preys are herded and/or captured. However, given that 1) cooperation can be beneficial during foraging [START_REF] Torney | Signalling and the evolution of cooperative foraging in dynamic environments[END_REF], and 2) seabirds can react to each other at scales of about 10 km at least [START_REF] Haney | Geometry of visual recruitment by seabirds to ephemeral foraging flocks[END_REF], Thiebault et al. 2014b), cooperation at those larger scales would exist. In this context, the lack of description of this process is likely due to the observational difficulties at these scales and in such remote and wide places as the high seas.

Recently, radar images obtained from seabird-dedicated radars on board tuna purse seiners provided instantaneous observations of the in-flight seabird community [START_REF] Assali | Seabird distribution patterns observed with fishing vessel's radar reveal previously undescribed sub-meso-scale clusters[END_REF], showing that seabird groups might frequently distribute themselves within temporary and large clusters of around 5-6 km of diameter, possibly as a way to maintain visual contact during foraging. The size of these clusters suggests that they are associated with medium-scale prey searching, but it remains unclear whether they emerge from simple attraction-repulsion rules at the individual level or from more cognitively complex strategies. Using the same radar-based methodology, the goal of this work was to look for the existence of medium-scale patterns of coordinated flights as a way to question seabird cooperation at that scale. In relation with previous considerations on information transfers between foragers, we evaluated these patterns' compatibility with a collective motion of foraging seabird groups, allowed by the use of social information. The patterns found were discussed with respect to prey detectability (potentially altered by seastate or sun glare), and their evolutionary significance.

Methods

Data collection

Selection of data

Radar images recorded over a whole year were scanned visually to detect patterns of clear and obvious coordination in the movement of seabird echoes. This process was facilitated by the use of a 15 min 'trail' in the radar settings, allowing for a good visualization of seabirds traces in time. Seabird traces are very well visible under calm weather conditions, and less so when sea state is such that it introduces wave echoes in the images. Data from May, July and from October to December were either not available, or not exploitable because of bad weather conditions. For the other months, each day of exploitable data was visualised and occurrences of coordinated flights were counted. For the detailed description of these flights, only the days of best data quality were chosen in each month. Since all months were not represented by the same number of exploitable days, this selection allowed for avoiding biases associated with spatial and temporal redundancy (migration period, weather, etc.). In the end, the description of coordinated patterns spread over year 2016 (2 January; 8 February; 2 March; 17 April; 2 June; 30 August; 17 September).

Data processing

Data processing is similar to the methodology described in [START_REF] Assali | Seabird distribution patterns observed with fishing vessel's radar reveal previously undescribed sub-meso-scale clusters[END_REF]. Radar images contain both contextual information (navigation parameters, radar settings, display settings) and signal reflection visualisation ('radar disk'). Contextual data, such as location of the boat and radar settings (e.g. radius of the radar disk), were converted in numerical format by an optical character recognition algorithm adapted from [START_REF] Saroch | Optical character recognition (lower case and space included)[END_REF].

Signal saturation in the very first kilometres from the boat (2.6 ± 1.0 km over an observation range of 11.1-14.8 km) precluded seabird observations around it. This central saturated zone was filtered out by an image closing procedure (dilatation/erosion). In the remaining radar disk, echoes were distinctly identified by a watershed transform algorithm [START_REF] Meyer | Topographic distance and watershed lines[END_REF], allowing for partially lumped echoes to be separated. False echoes resulting from background (white) noise were filtered out considering intensity (less than 10 over 30 levels) and area thresholds (10 pixels) empirically determined, as suggested in [START_REF] Stepanian | An introduction to radar image processing in ecology[END_REF]. Apart from background noise that was deleted, echoes could then stand for seabirds, other boats, sea clutter in the vicinity of the boat, rain clutter or dense rainfalls, and were identified and separated by a procedure further described. All remaining echoes' locations in the image were converted into latitudes and longitudes, knowing the location of the boat both in the image (in the centre of the radar disk) and in space (latitude and longitude coordinates), and the observation range (radius of the radar disk in nautical miles). Echoes' intensity, area and location were saved.

The observation of flying seabirds groups by radar can be biased as the signal is attenuated when the distance to the boat increases. This is automatically corrected by signal processing inside the radar box before visualisation on the screen, but some seabirds might be missed when they are close to the radar disk edge. Furthermore, seabird echoes might either stand for one group of several seabirds, or for one large individual, but as echoes were very often seen merging or splitting, there is a great probability than seabird-echoes rather stand for groups of individuals.

In order to select echoes that had temporal and spatial consistency in subsequent images (i.e. to favour iterative detections of a coherent object, like seabirds or boats), we conducted a tracking procedure of echoes, adapted from [START_REF] Tinevez | Simple tracker -file exchange -Matlab central[END_REF], constrained by 1) a maximum linking speed of 80 km h -1 between echoes' centroids of successive images, 2) a maximum gap closing (maximum number of images in which an echo might temporarily disappear) of 3, which corresponds to 30 s, 3) a maximum azimuth difference of 90° between tracks of two successive images, 4) a maximum azimuth difference of 30° between tracks of two images separated by 1-3 images. All echoes that were not members of tracks of at least 5 points (i.e. appearing during 5 images or 1 min), were considered too inconsistent and classified as noisy echoes. Because sea clutter and rain clutter echoes were very densely distributed, some consistency could be randomly obtained. To distinguish tracks of seabird-echoes, boats, sea clutter, rain clutter and rainfalls, we conducted a supervised classification of tracks (see following paragraph 'Classification of tracks'). Track properties were obtained either from echo position over time (averaged speed, difference of azimuth, averaged distance to the central saturated zone edge), or from echoes' properties along tracks (area, intensity).

Classification of tracks

16 926 tracks were constructed from three days of data (representative of echoes encountered in the dataset). All those tracks were superimposed on radar images, visualised, and manually attributed to one of the following five categories : «seabird» (3767 tracks), «boats» (83 tracks), «sea clutter» (6513 tracks), «rain clutter» (6461 tracks), «dense rainfalls» (102 tracks). A classification procedure was trained with 22 track properties, including their duration, the proportion of missing points (gap closing), the ratio of cumulative distance over absolute distance between starting and ending points of the track, and the mean, the standard deviation and the coefficient of variation of: track ground-speed, difference of azimuth between starting and ending points, averaged echo area, averaged distance to the central saturated zone edge, and mean, median and maximal intensity of the tracked echo. Training was operated by random forest procedure [START_REF] Breiman | Random forests[END_REF] over two thirds of 16 926 echoes tracks (training set).

1. The training set was randomly chosen (11 284 tracks). 2. The number of grown trees was set to 500. 3. The classification was conducted with 1-22 predictors. 4. Each classification model obtained was applied on the validation set (the remaining 5642 tracks) and the confusion matrix was computed. The percentage of correct classification was saved.

This procedure was operated 500 times. The model allowing for the best classification on the validation set was selected (97% of good classification). Classification outputs were approved by visualisation of tracks superimposed on radar images. All the 279 842 tracks (from the seven days used in this study) were classified according to this model, and only the 44 940 tracks associated to seabird-echoes were finally kept for analysis.

Selection of coordinated movement patterns

Patterns of interest were visually detected as evident organised movements of several seabird-echoes, which tracks were temporary parallel (Fig. 1d). Those cohesive flights, or 'rakes', were selected visually, as they were 1) clearly observable on more than 10 successive images (≥ 150 s) and 2) distinctly differing from movements of other seabird groups observed in the radar disk. When clearly mobile, seabird group could either show transit behaviours (Fig. 1a), movements of grouping (merging of seabird-echoes, Fig. 1b) or splitting (division of seabird-echoes, Fig. 1c), but were not systematically involved in organised and coordinated displacements of several seabird groups such as what we refer to as 'rakes' (Fig. 1d).

Radar images were visualised and tracks of seabird-echoes forming rakes were selected manually and saved for each rake. Rakes that were not clearly formed or occurring in confusing high densities of seabird-echoes in the image were not selected even if suspected. Consequently, the 60 selected rakes do not stand for the exhaustive census of such patterns, but rather for the less noisy observations. Similarly, we selected and saved apart all seabird tracks (n = 910) that were concomitant with rake patterns but were clearly not part of them. Apart from the parallel and organised aspect of developed rakes, the latter could show different appearances in the radar image during their observation, such as a clear initiation by groups of seabirds starting to fly synchronously and then spreading regularly in space up to forming parallel lines (i.e. in 'rakes'; Fig. 2b 1-2 , c 1-2 , d 1-2 ), or seabird groups forming parallel lines and then converging so as to finally disappear in one or several points (Fig. 2d 4 ). Conversely, the observation of rakes could be limited because of coordinated seabird-echoes flying out of the radar circle or approaching its edge (Fig. 2a 4 , b 4 ), or already deployed when they got detected by the radar (Fig. 2a 1 ). As a consequence, reported rakes might not be observed during their entire 'lifetime'.

Calculation of rake properties

Once a rake and its participating tracks were selected, corresponding images were visualised again to detect the exact time t when the rake seemed fully and steadily developed. For each rake, the number of involved seabird-echoes, the distances between nearest neighbours in the rake (mean and standard deviation), as well as the lateral extent (maximum distance between echoes within the rake) were calculated and saved at time t. As we knew the value of the radar disk radius (two units: number of pixels and nautical miles) those distances were converted from the pixel space (image) to the geographic space, thus finally reported in km. The proportion of seabird-echoes of the image involved in the rake was deduced from the number of involved seabird-echoes at time t, knowing the total number of tracked seabird-echoes in the image at time t.

As durations of seabird tracks were known, durations of rakes could be estimated. Some tracks could continue even if not taking part to the rake any more (e.g. all other seabirds of the rake sat on water). As a consequence, we defined the duration of rakes as being the time sequence during which two tracks or more were participating to the coordinated, parallel flight. The travelled distance by individuals participating to a rake was calculated for this time sequence, and corresponds to the distance between the locations of rakes centroids at the beginning and the end of the sequence.

The duration of tracks within a rake could be shorter than the whole duration of the rake because of 1) the recruitment of birds that were not present since the rake initiation (Fig. 2c 4 ) and 2) the loss of participants along the rake lifetime.

When it is not specified, results are presented as: (mean ± SD).

Wind and sun data

The wind speeds and directions were calculated from wind surface velocities (x, y) available in the section 'Blended Figure 2. Four examples of seabird flights in rakes, observed in radar images. Orange dots correspond to seabird-echoes. The line (a) shows a rake deployed during all its recording time by the radar, from one of its first occurrences (a 1 ) to its last (a 4 ). The panels b 1-3 and c 1-3 represent rake initiation by splitting echoes. In (b 4 ), echoes' detection is lost while they approach the radar disk edge. In (c 4 ), other seabirds get in flight over time and participate to the rake. The line (d) documents the entire lifetime of a rake. In (d 1 ), seabirds form a single large echo (green circle). In (d 2 ), they spread regularly in space, forming a rake. In (d 3 ), some seabird groups leave the rake while others maintain their direction to another flying seabird group. In (d 4 ), the rake disappears because seabird groups converged and merged into one single echo. The scale indicated in (a 1 ) is the same for all panels.

Sea Winds' of the National Centers for Environmental Information (National Oceanic and Atmospheric Administration), on a 0.25° grid, at a time resolution of six hours (NOAA 2017).

Sun elevation and azimuth were calculated from the location of rakes initiation point (latitude, longitude) and time, with the function sun_position.m written and corrected by [START_REF] Roy | Sun azimuth and elevation[END_REF]. The latter adapted the algorithm developed by [START_REF] Reda | Solar position algorithm for solar radiation applications[END_REF].

Results

Exploitable radar images were exhaustively inspected and patterns of coordinated and parallel flights of seabird groups were visually detected, referred to as 'rakes' in following paragraphs (Fig. 1d). In the 52 days of exploitable data, the number of rakes per day (6.3 ± 5.2) ranged from 0 (five days) to 19 (one day). 60 rakes were selected from seven days (from 5 to 17 rakes per day; 8.6 ± 4.2) spread over one year (Methods).

Description of rakes dynamics

While rakes could appear steadily developed during their entire recording time (Fig. 2a 1-4 ), some initiations and endings have been observed too. Initiations were characterized by seabird groups getting simultaneously in flight, spreading regularly over space and adopting the same direction so as to form a pseudo-linear front (Fig. 2b 1-3 , c 1-3 , d 1-3 ). When rakes were neither disappearing in the central saturated zone circling the boat, or in the last kilometres close to the radar disk edge, they collapsed because of seabird groups converging and merging (Fig. 2d 4 ). This convergence could involve all participants or only a part of them. Finally, rakes did not systematically involve the same seabird groups during their entire lifetime, since some seabirds could be recruited in the rake (Fig. 2c 4 ), or leave it independently from other participants (Fig. 2d 3 ).

Number of involved echoes

The number of seabird-echoes within radar images ranged from 0 to 127 (24.6 ± 22.7). When rakes were fully deployed, they involved from 2 to 11 seabird-echoes (4.4 ± 1.8), corresponding to 4-3% of seabird-echoes tracked at the same time (29 ± 19.6%; Table 1).

Duration of rakes and travelled distance

The duration of sequences of partly or fully deployed rakes (i.e. two synchronous echoes at least, see methods) lasted from 2 to 19 min (8 ± 4 min; Table 1).

The distribution of durations of tracks within rakes was significantly different from the distribution of other seabirdechoes tracks' durations (Kolmogorov-Smirnov test, p-value < 0.01). Seabird-echoes tracks involved in those rakes lasted 5 min in average (SD: 3 min), which is longer than other seabird-echoes tracks (3 ± 2 min).

Sequences during which rakes were involving two seabirdechoes or more consisted in displacements of 1.2-10.6 km (4.4 ± 2.0 km; Table 1).

Speed of seabird-echoes

Seabird-echoes within rakes had a mean ground-speed of 33 km h -1 (min: 4 km h -1 , max: 55 km h -1 , SD: 7 km h -1 ; Table 1). 90% of tracks participating to rakes showed speeds between 19 and 42 km h -1 .

Concomitant non-raking seabird-echoes that were either joining static seabird groups (probably feeding) or transiting, showed a mean speed of 25 km h -1 (min: 0.4 km h -1 , max: 58 km h -1 , SD: 12 km h -1 ), with 90% of speed values between 5 and 44 km h -1 .

Spatial dimensions of rakes

The mean distance between nearest neighbours at the time of fully developed rakes varied from 200 m to 1.5 km (500 ± 200 m), with 75% of them lesser or equal to 500 m (Table 1, Fig. 3a). 38% of mean distances between nearest neighbours were in [300 ± 400] m and 22% in [400 ± 500] m. The lateral extent of rakes varied from 300 m to 4.4 km (1.6 ± 0.89 km; Table 1, Fig. 3b).

The coefficient of variation (CV) of distances between nearest neighbours at time of fully developed rakes equalled 0.3 in average (min 0.0, max 0.9).

Influence of wind speeds and directions

The distribution of differences between the mean directions of rakes and wind directions differed from a uniform distribution (Kolmogorov-Smirnov test; p-value < 0.01), as well as the distribution of differences between non-raking seabird tracks directions and wind directions (Kolmogorov-Smirnov test; p-value < 0.01). Differences between wind direction and 1) rakes or 2) non-raking seabird directions averaged to i) 82°( ± 39°), and ii) 80° (± 48°) respectively. 58% of rakes were directed from 60° to 120° with respect to the wind (Fig. 4a). Only 13% of rakes were against the wind (> 120°) whereas 27% differed of less than 60° in direction. Comparatively, 37% of non-raking seabird tracks differed of 60-120° from wind direction, 24% of more than 120°, and 39% of less than 60° (Fig. 4c).

The mean ground-speeds of non-raking seabird tracks had a tendency to increase with decreasing differences between tracks and wind directions (regression slope as significantly different from zero, p-value < 0.01), whereas they were not affected by wind speeds (regression slope was not significantly different from zero, p-value = 0.08).

Wind speeds ranged from 1.2 to 25.5 km h -1 (11.0 ± 7.5 km h -1 ). The mean distance between nearest neighbours within rakes was not influenced by wind speeds (regression slope was not significantly different from zero, p-value = 0.20; Fig. 5a), whereas the lateral extent showed a tendency to decrease with higher wind speeds (regression slope was significantly different from zero, p-value < 0.01; Fig. 5b).

Influence of sun azimuth and elevation

The difference between the mean directions of rakes and the sun azimuth varied from 5 to 171° (mean: 86 ± 57°) and was uniformly distributed within this range (Kolmogorov-Smirnov test; p-value = 0.48; Table 1, Fig. 4b). Moreover, there was no linear tendency for azimuth differences to be related to sun elevation (p-value = 0.80).

The difference between the mean directions of non-raking seabird tracks and the sun azimuth (103° ± 53°) differed from a uniform distribution (Kolmogorov-Smirnov test; p-value < 0.01), with 62% of values in [90°; 180°] and 35% of values in [135°; 180°] (Fig. 4d).

Discussion

To our knowledge, this is the first observation and description of clearly coordinated movements of seabirds at a medium scale (several kilometres), i.e. a scale not associated with prey capture but rather with prey searching.

Such coordinated flights cannot be randomly obtained. Seabird movements' cohesiveness lasted generally about 8 min, involved about four echoes (seabirds or seabird groups) and covered distances of about 4 km, ruling out the possibility of random events. Furthermore, radar observations covered at most 10 000 km 2 each day, and were collected from 150 km to 900 km off the coast of the Gulf of Guinea. Given that the Gulf of Guinea's area is about 2 300 000 km 2 , one day of radar observation documented less than 1% of the area.

Given that between 5 and 17 rakes were analysed per day (but more were observed, see Methods) and that the explored area was negligible compared to the entire zone, rakes are not anecdotal features and are likely frequent within the seabird community in the open tropical seas. Rake patterns require high coordination levels to maintain consistency in speed, bearing and distances between members. Since flight speed is strongly dependent on body size and morphology of seabirds [START_REF] Pennycuick | Actual and 'optimum' flight speeds: field data reassessed[END_REF][START_REF] Alerstam | Flight speeds among bird species: allometric and phylogenetic effects[END_REF], efficient rake formation might be limited to similar seabirds species, if not to the same species. Since rakes only involved a limited proportion of seabird-echoes observed in surrounding areas (29% in average), we suggest that 1) all seabirds would not have the same ability to form or join a rake, 2) maintaining collective and aligned flights may become difficult when the number of participants increases.

Despite no observer was on board to confirm that raking birds were actually foraging, the context of radar data acquisition, as well as the behaviours of recorded seabird-echoes, indicate a great probability that rake patterns are conducted by foraging groups of pelagic seabirds.

Firstly, captains of tuna purse seiners (our observation platforms) adapt their searching strategy to the oceanographic context (e.g. targeting thermal fronts) so as to increase their fishing success [START_REF] Torres-Irineo | Changes in fishing power and fishing strategies driven by new technologies: the case of tropical tuna purse seiners in the eastern Atlantic Ocean[END_REF]. With the birddedicated radar, they especially focus on foraging seabirds so as to deduce the presence of surface-feeding tuna schools. Consequently, we know that seabirds recorded in radar images are experiencing particularly favorable foraging conditions, because 1) tunas are very likely to be surface-feeding in these zones and seabird may join them in multi-specific feeding assemblages (Au andPitman 1988, Veit and[START_REF] Veit | Positive interactions among foraging seabirds, marine mammals and fishes and implications for their conservation[END_REF]; 2) seabirds have a greater chance to encounter prey patches in this oceanographic context.

Furthermore, because of the spatio-temporal distribution of these radar observations, echoes mostly correspond to pelagic seabirds, and might occasionally correspond to migrating birds.

Indeed, Cory's Calonectris diomedea borealis, Scopoli's Calonectris diomedea diomedea, sooty Puffinus griseus and manx Puffinus puffinus shearwaters have been reported in inshore waters of the Gulf of Guinea during their autumnal migration, or are known to winter partly in this region despite its eccentricity from the major migratory paths of these species [START_REF] González-Solís | Transequatorial migration and mixing in the wintering areas of a pelagic seabird[END_REF][START_REF] Passavy | Migration of the Sooty shearwater Puffinus griseus off Gabon[END_REF][START_REF] Hedd | Trans-equatorial migration and habitat use by sooty shearwaters Puffinus griseus from the south Atlantic during the nonbreeding season[END_REF][START_REF] Péron | Tracking through life stages: adult, immature and juvenile autumn migration in a long-lived seabird[END_REF][START_REF] De Boer | Birds recorded off Ghana and Côte d'Ivoire, April-May 2013 and March-May 2014, including species new to each country. -Bull[END_REF]. In addition, migrating long-tailed skuas can stage in the Gulf of Guinea, especially in October and November [START_REF] Gilg | Trans-equatorial migration routes, staging sites and wintering areas of a high-arctic avian predator: the long-tailed skua (Stercorarius longicaudus)[END_REF]. However, no radar data was exploitable in October, November and December, thus excluding the period during which migrating individuals could have been most probably recorded. Still, Arctic terns may have been recorded during their postbreeding migration, as some individuals follow the west African coast to reach their wintering area in the southern Atlantic [START_REF] Egevang | Tracking of Arctic terns Sterna paradisaea reveals longest animal migration[END_REF]. Nonetheless, we confirm the presence of raking birds almost all year long, as rake patterns have been observed every month when data was available and exploitable (Methods). This temporal distribution suggests that rakes are rather conducted by annually resident species, such as brown booby (Sula leucogaster), brown (Anous stolidus) and black (Anous minutus) noddy, bridled (Sterna anaethetus), sooty (Sterna fuscata) and damara tern (Sterna balaenarum), or white-tailed tropicbird (Phaethon lepturus) that all breed in Tinhosas islands, São Tomé and Príncipe [START_REF] Bollen | An updated assessment of the seabird populations breeding at Príncipe and Tinhosas[END_REF].

Among these, pelagic species such as tropicbirds, noddies and terns display different foraging modes. Whitetailed tropicbirds mainly forage solitary [START_REF] Jaquemet | Seabird community structure in a coastal tropical environment: importance of natural factors and fish aggregating devices (FADs)[END_REF][START_REF] Campos | Foraging behavior and at-sea distribution of white-tailed tropic birds in tropical ocean[END_REF]), and occasionally associate with feeding dolphins (Jaquemet et al. 2004, Spear and[START_REF] Spear | At-sea distributions and abundance of tropicbirds in the eastern Pacific[END_REF]. Comparatively, sooty terns, brown and black noddies are highly gregarious and are often found in multi-specific feeding assemblages [START_REF] Evans | Associations between seabirds and cetaceans: a review[END_REF][START_REF] Au | Seabird interactions with dolphins and tuna in the eastern tropical Pacific[END_REF][START_REF] Jaquemet | Seabird community structure in a coastal tropical environment: importance of natural factors and fish aggregating devices (FADs)[END_REF][START_REF] Hebshi | Associations between seabirds and subsurface predators around Oahu, Hawaii[END_REF][START_REF] Goyert | Facilitative interactions among the pelagic community of temperate migratory terns, tunas and dolphins[END_REF]). Among the numerous species associated with tropical Atlantic tuna fisheries [START_REF] Reynaud | Manuel d'observation et d'identification des oiseaux pélagiques de l'Atlantique oriental et de l'océan Indien[END_REF], sooty terns, brown and black noddies may thus be the most probable species involved in raking patterns.

To our knowledge, many bird species form organised group flights [START_REF] Bajec | Organized flight in birds[END_REF], but only few observations document coordination of foraging seabirds in flight. Underwater movements of penguins revealed synchronised foraging trips and dives (Tremblay andCherel 1999, Takahashi et al. 2004), presumably allowed by constant visual contact. Synchronisation was also observed in departure times of foraging Australasian gannets (Morus serrator) from the near-colony raft [START_REF] Machovsky-Capuska | The contribution of private and public information in foraging by Australasian gannets[END_REF], in sooty shearwaters (Puffinus griseus) flights to join feeding heterospecifics [START_REF] Hoffman | The ecology of seabird feeding flocks in Alaska[END_REF], and in Socotra cormorants (Phalacrocorax nigrogularis) foraging trips [START_REF] Cook | Group foraging in Socotra cormorants: a biologging approach to the study of a complex behavior[END_REF]). However, rakes involved both synchronisation and coordination, and such patterns have never been observed at scales reaching several kilometres. Thus, collective movements in foraging seabirds might also include the preysearching phase/scale, and not only the prey-capture phase/ scale as previously found (Thiebault et al. 2016a).

Accordingly, one striking characteristic of rakes was the narrow distribution of the mean distance between echoes, peaking markedly between 300 and 500 m. Seabirds have been observed to react to each other at around 10 km [START_REF] Haney | Geometry of visual recruitment by seabirds to ephemeral foraging flocks[END_REF], Thiebault et al. 2014b). In addition, tern commonly fly from 1 to 20 m above sea surface, and more usually at 5-10 m [START_REF] Cook | A review of flight heights and avoidance rates of birds in relation to offshore wind farms[END_REF][START_REF] Johnston | Modelling flight heights of marine birds to more accurately assess collision risk with offshore wind turbines[END_REF]. According to [START_REF] Haney | Geometry of visual recruitment by seabirds to ephemeral foraging flocks[END_REF], and considering that atmospheric refraction is negligible for these relatively small heights, the theoretical maximum distance at which a seabird could be visibly detectable by a flying individual would thus range from 7.7 km (if both the target and the observer fly at 1 m above sea surface) to 34.3 km (if the target and the observer fly at 20 m above sea level). Consequently, all seabirds within rakes are undoubtedly within sight of one another even in the largest observed rake (4.4 km long). The functional significance of the inter-seabird distance within rakes is therefore not related to a need for maintaining visual contact. Instead, we suggest that this distance might be related to prey detection. Assuming that rakes were formed for searching prey (we see no other explanation), seabirds may distribute themselves in lines to scan the ocean surface optimally, i.e. avoiding to miss some areas in the scanned space and avoiding duplicate scanning of the same area. Hence, for their common flight altitude [START_REF] Cook | A review of flight heights and avoidance rates of birds in relation to offshore wind farms[END_REF][START_REF] Johnston | Modelling flight heights of marine birds to more accurately assess collision risk with offshore wind turbines[END_REF], seabirds may detect a prey patch at half the distance between them: between 150 and 250 m most of the time (possibly depending on the weather conditions, the sea state and prey crypticity). In a foraging context, individuals taking part to rakes could benefit from an extended search effort proportional to the number of participating seabird groups.

As seabird-echoes likely represent small groups of seabirds, this could be seen as a loss of «foraging eyes» since they could spread much more in space and scan a larger surface. However, remaining in small groups is likely to allow aerodynamic advantages of group flight formation [START_REF] Cutts | Energy savings in formation flight of pink-footed geese[END_REF]Speakman 1994, Weimerskirch et al. 2001). On the contrary, even though the distances between echoes were short, they were not short enough to provide aerodynamic advantage in terms of flight cost. Indeed, echoes distribution within rakes occurs at a much larger scale (order of magnitude: 10 2 m) than bird distribution within grouped flight formations (order of magnitude: 10 0 m).

Since seabirds forming clusters are also likely to be in visual contact between each other [START_REF] Assali | Seabird distribution patterns observed with fishing vessel's radar reveal previously undescribed sub-meso-scale clusters[END_REF], they may also benefit from the scanned area of other seabirds in the same way as forming rakes. However, the recruitment of seabirds while rakes looked already fully developed (Fig. 2c 3-4 ) and the spontaneous initiation of a rake (Fig. 2b 1-3 , d 1-3 ) suggest that individuals may get extra benefit from joining or forming rakes rather than independently cue on feeding opportunities, even within a cluster. Furthermore, the relatively shorter distance between echoes in rakes compared to clusters may allow participants to communicate faster any prey encounter to the others, directly (vocalising; [START_REF] Thiebault | Seabird acoustic communication at sea: a new perspective using bio-logging devices[END_REF] or indirectly (changing flight behaviour), so as to initiate a convergent movement to the point of interest (Fig. 2d 4 ). This would quickly lead to a collective flock feeding event where prey intake rate may increase [START_REF] Götmark | Flockfeeding on fish schools increases individual success in gulls[END_REF], Thiebault et al. 2016a).

For flying seabirds, direct prey detectability can be affected by sea state (partly induced by wind speeds) and sun glare. Assuming that rakes are composed of individuals looking for prey, we could expect the sun azimuth and elevation and the wind directions to induce preferences in the orientation of rakes, or the wind speeds to constrain seabirds spacing. Seabirds in rakes showed a clear preference for using crosswinds, whereas non-raking birds equally used cross-and tailwinds. Seabirds typically fly slower in head-winds and faster in tail-winds [START_REF] Spear | Flight speed of seabirds in relation to wind speed and direction[END_REF][START_REF] Weimerskirch | Fast and fuel efficient? Optimal use of wind by flying albatrosses[END_REF][START_REF] Zavalaga | Marine habitat use of Peruvian boobies: a geographic and oceanographic comparison between inshore and islands[END_REF]. However, some procellariforms tend to make extensive use of cross-tail winds [START_REF] Adams | Correlating seabird movements with ocean winds: Linking satellite telemetry with ocean scatterometry[END_REF]. For species relying partly on olfactory cues to find prey [START_REF] Nevitt | Sensory ecology on the high seas: the odor world of the procellariiform seabirds[END_REF]) the use of cross-winds could allow foraging individuals to optimize their exposure to odours. Still, in this context, the occurrence of raking patterns hints at an additional benefit of the raking strategy compared to solitary foraging.

Cross-winds also allow for increasing the optimal speed, i.e. the speed with the less cost of transport, but not necessarily the fastest ground speed [START_REF] Liechti | Effects of sidewinds on optimal flights speed of birds[END_REF]. Seabirds in rakes flew on average at 33 km h -1 , a ground-speed similar to terns and small gulls flying across-winds under an average wind speed of 27.4 km -1 (Spear and Ainley 1997). Except for small 'oceanites' species and a few terns or prions (i.e. small seabirds), seabirds in rakes flew slower than the average recorded in most seabirds [START_REF] Spear | Flight speed of seabirds in relation to wind speed and direction[END_REF], even under variable wind speeds and directions [START_REF] Alerstam | Flight tracks and speeds of Antarctic and Atlantic seabirds: radar and optical measurements[END_REF]. Consequently, the tendency to use cross-winds is probably not aimed at increasing flying speed. The groundspeeds of concomitant non-raking seabird tracks were on average smaller (25 km h -1 ) but more variable than the ground-speeds of raking seabirds, which can be explained by the diversity of behaviours of non-raking birds (e.g. transiting or joining feeding groups). Moreover, seabird ground-speeds had a tendency to increase when differences with wind directions decreased (i.e. with tail-winds oriented flights), while wind speeds had no significant influence on seabird speeds. This indicates that transiting or solitary foraging birds, that probably show the highest ground-speed values that have been recorded in this study, had a tendency to use tail-winds. Since 1) rakes were not exclusively developed under crosswinds conditions, 2) birds were seen to be recruited in or to leave a rake, 3) rakes only involved a fraction of the bird community at sea while other seabird-echoes behave differently (e.g. flying but not in a rake), 4) concomitant non-raking birds substantially used both cross-and tail-winds, we reject the hypothesis that rake formation is solely a consequence of environmental constraints such as wind speeds and directions. Rather, as it is certainly more difficult to accelerate against the wind or to decelerate with the wind, we propose that cross-winds is the best (most neutral) wind orientation facilitating speed adjustments. Then, rakes would be easier to maintain and preferentially conducted in cross-winds, especially if different species are involved.

Besides, the range of possible ground-speeds of seabirds widens with increasing wind speeds, even in cross-winds where speed increment caused by the wind is not null [START_REF] Liechti | Effects of sidewinds on optimal flights speed of birds[END_REF]. Hypothetically, slowest wind speeds may thus allow lesser ground-speeds variability among seabirds, easier ground-speeds adjustment for those forming rakes, then easier alignment over larger distances and easier recruitment of seabirds in rakes. Accordingly, the lateral extent of rakes tended to decrease when the wind speeds increased. On the contrary, the directions of rakes did not seem to be influenced by sun azimuth or elevation, and the distance between nearest neighbours within rakes was not clearly influenced by wind speeds. The sea surface appearance (calm, with ripples, with foam, etc.) and resulting prey detectability may not drive different raking strategies, in terms of spacing between participants or mean direction of rakes. Raking strategy may allow prey detection under a range of sea states.

Interestingly, rakes may imply that all individuals participate with the same effort to the maintenance of the 'searching front', by adjusting speed, direction and distance with others. In this context, individuals may be considered as equally producing information on potential feeding opportunities.

Foraging zones at large scale might be found using olfactory cues [START_REF] Nevitt | Sensory ecology on the high seas: the odor world of the procellariiform seabirds[END_REF] or knowledge (continental shelves [START_REF] Yen | Marine bird and cetacean associations with bathymetric habitats and shallow-water topographies: implications for trophic transfer and conservation[END_REF], seamounts [START_REF] Haney | Sea-bird aggregation at a deep North Pacific seamount[END_REF], etc.), then, clusters [START_REF] Assali | Seabird distribution patterns observed with fishing vessel's radar reveal previously undescribed sub-meso-scale clusters[END_REF] or rakes (this study) might help finding prey patches at medium scales. Once a prey patch is found, the benefits of jointly exploiting it might induce a cooperation between individuals (Thiebault et al. 2016a). Cooperation is more likely to occur when individuals provide inadvertent information when they feed, and have thus been predisposed to signalling and forming cooperative groups [START_REF] Torney | Signalling and the evolution of cooperative foraging in dynamic environments[END_REF]. Contrary to the producer-scrounger game suggesting that scroungers do not engage into producing information while producers suffer the competition with scroungers [START_REF] Barnard | Producers and scroungers: a general model and its application to captive flocks of house sparrows[END_REF], the rake strategy implies that seabirds voluntarily engage themselves into a producer-only game, i.e. based on information sharing [START_REF] Valone | Group foraging, public information, and patch estimation[END_REF], and which implies obligate collective prey exploitation. Indeed, any prey-finding within a rake formation cannot be hidden to the partners. By participating to coordinated flights, seabirds are likely to optimise their individual prey encounter rate, and could also increase their prey capture rate. Testing this hypothesis would require to combine complementary data such as telemetry, bird-borne video and radar observations, which could allow for assessing the foraging success of raking versus non-raking birds and highlight potential costs and benefits these different foraging strategies.

As reported in this study, rake patterns have also been observed as transitory flights between two aggregative patterns, with seabird groups expanding, raking, then converging. We suggest that rakes can collapse once a feeding opportunity is found (seabird groups converge and merge). However, we still have no clue about the rake initiation process. In a foraging context, rafts of seabirds could act as information centres [START_REF] Weimerskirch | Use of social information in seabirds: compass rafts indicate the heading of food patches[END_REF][START_REF] Machovsky-Capuska | The contribution of private and public information in foraging by Australasian gannets[END_REF], Thiebault et al. 2014a), conveying information on the presence of prey, allowing unsuccessful or poorly informed individuals to join informed birds, or ensuring a collective initiation of raking patterns.

The ability to associate echoes with a given species will be a key advance for future studies. Furthermore, extending radar studies to other marine ecosystems might provide new opportunities for understanding these phenomena.

Figure 1 .

 1 Figure 1. Different seabird flights observed in radar images. Orange dots correspond to seabird-echoes. The large orange area in the upper right corner of (a) and (c), and in the centre of (d), corresponds to the saturated area circling the boat. Blue trails reflect echoes' locations over the last 15 min. The panels show (a) two seabird-echoes transiting independently from each other; (b) three seabird-echoes merging in a single group; (c) two seabird-echoes diverging from a single split echo; (d) a whole radar disk with six seabird-echoes transiting in parallel to each other, forming a rake (white rectangle). Rakes are distinguishable from other seabird movements within the same radar image, because they are formed by coordinated flights of seabirds. The green polygon shows a cluster as defined in Assali et al. 2017.

Figure 3 .

 3 Figure 3. Distribution of spatial dimensions of rakes when they are fully deployed. (a) Mean distance to nearest neighbour within each rake (km); (b) lateral extent of rakes (km).

Figure 4 .

 4 Figure 4. Distributions of the difference between the mean directions of rakes and non-raking seabird tracks and two environmental factors. (a) Distribution of the difference between the mean directions of rakes and wind directions; (b) distribution of the difference between the mean directions of rakes and sun azimuth; (c) distribution of the difference between the mean directions of non-raking seabird tracks and wind directions; (d) distribution of the difference between the mean directions of non-raking seabird tracks and sun azimuth, in degrees.

Figure 5 .

 5 Figure 5. Spatial dimensions of rakes relatively to wind speeds. (a) Mean distance between neighbours within rakes (km), and (b) lateral extent of rakes (km), relatively to wind speeds (km h -1 ). Black line results from linear regression (p-value < 0.01).

Table 1 .

 1 Descriptive statistics of rakes.

			Quantile	Quantile		Quantile	Quantile			Standard
	Variable	Minimum	0.025	0.25	Median	0.75	0.975	Maximum Mean	deviation
	Number of echoes	2.0	2.0	3.0	4.0	5.0	10.0	11.0	4.4	1.8
	Proportion of seabird-echoes	0.04	0.04	0.13	0.24	0.44	0.67	0.83	0.29	0.20
	involved
	Duration (min)	4	4	7	11	14	27	30	11	5
	Duration of sub-sequences	2	2	5	8	10	17	19	8	4
	with multiple (≥2) tracks
	(min)
	Duration of tracks involved	1	1	2	4	6	14	21	5	3
	(min)
	Distance travelled when	1.2	1.2	2.7	4.3	6.0	10.1	10.6	4.4	2.0
	multiple (≥2) tracks (km)
	Mean speed of seabird-echoes	4	7	29	33	37	45	55	33	7
	within tracks (km h -1 )
	Distance to the nearest	0.2	0.2	0.3	0.4	0.5	1.1	1.5	0.5	0.2
	neighbour (km)
	Coefficient of variation of the	0.0	0.0	0.1	0.3	0.5	0.9	0.9	0.3	0.3
	distance to the nearest
	neighbour
	Lateral extent (km)	0.3	0.5	0.9	1.4	2.0	3.4	4.4	1.6	0.9
	Difference between the mean	0	0	55	84	104	164	179	82	39
	directions of rakes and wind
	directions (°)
	Difference between mean	5	5	32	84	137	170	171	86	57
	directions of rakes and sun
	azimuth (°)

Acknowledgements -The authors are grateful to Orthongel for allowing the recording of purse-seiner radar images. The authors are thankful to P. Dewals and A. Damiano, who collected the data and supervised their transfer, as well as P. Bach, P. Kouadio, J. Lebranchu, P. Taconet, N. Billet, P. Cauquil and P. Sabarros for their help in logistics for data acquisition and transfer.

Data availability statement

Data available from the Dryad Digital Repository: <https:// doi.org/10.5061/dryad.8sf7m0cj8> [START_REF] Assali | Data from: Raking the ocean surface: new patterns of coordinated motion in seabirds[END_REF]).


Marta Sebastian

J.-C Auguet

Claudia Ximena Restrepo-Ortiz

Maria Montserrat Sala

Celia Marrase

Josep M Gasol

Marta Sebasti An

Jean-Christophe Auguet

Marı ´a

Montserrat Sala

Celia Marras

Deep ocean prokaryotic communities are remarkably malleable when facing long-term starvation

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

Introduction

The oligotrophic bathypelagic ocean is one of the largest ecosystems on Earth and accounts for 50% of the oceans' heterotrophic activity [START_REF] Arı ´stegui | Microbial oceanography of the dark ocean's pelagic realm[END_REF]. This system is characterized by persistently low dissolved organic carbon (DOC) concentrations that sustain reduced prokaryotic abundances [START_REF] Carlson | DOC persistance and its fate after export within the ocean interior[END_REF]. Even though it is a rather stable environment, life in the bathypelagic is tough from a microbial point of view [START_REF] Herndl | Microbial control of the dark end of the biological pump[END_REF]. The DOC pool consists mainly of increasingly recalcitrant compounds that are by-products of microbial metabolism (the so-called microbial pump; [START_REF] Jiao | Microbial production of recalcitrant dissolved organic matter: long-term carbon storage in the global ocean[END_REF] and some short-lived (hours to weeks) labile or semilabile DOC that becomes available through the transformation and solubilisation of surface-derived [START_REF] Arı ´stegui | Microbial oceanography of the dark ocean's pelagic realm[END_REF] or buoyant particles [START_REF] Baltar | Significance of non-sinking particulate organic carbon and dark CO 2 fixation to heterotrophic carbon demand in the mesopelagic northeast Atlantic[END_REF], or through in situ chemolithoautotrophy [START_REF] Herndl | Contribution of Archaea to total prokaryotic production in the deep atlantic ocean[END_REF]. Sinking particles are considered the main carbon source for bathypelagic communities [START_REF] Nagata | Bacterioplankton distribution and production in deep Pacific waters: large-scale geographic variations and possible coupling with sinking particle fluxes[END_REF][START_REF] Hansell | Bacterioplankton distribution and production in the bathypelagic ocean: directly coupled to particulate organic carbon export?[END_REF], yet the flux of these particles is intermittent and variable on temporal and spatial scales [START_REF] Arı ´stegui | Dissolved organic carbon support of respiration in the dark ocean[END_REF][START_REF] Arı ´stegui | Temporal and spatial changes in plankton respiration and biomass in the Canary Islands region: the effect of mesoscale variability[END_REF]. This frequent carbon scarcity seems to have led to the development of versatile metabolisms, as hinted by increasing findings of potential metabolic flexibility in deep ocean prokaryotes [START_REF] Swan | Potential for chemolithoautotrophy[END_REF][START_REF] Tang | Genomic, physiologic, and proteomic insights into metabolic versatility in Roseobacter clade bacteria isolated from deep-sea water[END_REF][START_REF] Landry | SAR202 genomes from the dark ocean predict pathways for the oxidation of recalcitrant dissolved organic matter[END_REF]. However, the responses of deep ocean microbes to organic carbon starvation have not been explored.

Most of the knowledge we have about long-term starvation responses comes from studies with a few bacterial strains belonging to the phylum Proteobacteria [START_REF] Novitsky | Survival of a psychrophilic marine vibrio under long term nutrient starvation[END_REF][START_REF] Amy | Starvation-survival patterns of sixteen freshly isolated open-ocean bacteria[END_REF][START_REF] Finkel | Long-term survival during stationary phase: evolution and the GASP phenotype[END_REF][START_REF] Soora | Light enhances survival of Dinoroseobacter shibae during longterm starvation[END_REF]. These studies have shown that in the absence of fresh energy supplies, stable populations persist for months to years, as the result of continuous cycles of life and death. Yet, no studies have dealt with prolonged starvation of natural assemblages, which are characterized by astounding levels of phylogenetic and metabolic diversity [START_REF] Pedr Os-Ali O | The rare bacterial biosphere[END_REF]. This is particularly true for the bathypelagic, which is already an energy limited system. One complexity associated to natural communities is that prokaryotes compete with their neighbors for resources. Another level of complexity is that microbial communities contain a seed bank that can resuscitate upon shifts in environmental conditions [START_REF] Lennon | Microbial seed banks: the ecological and evolutionary implications of dormancy[END_REF][START_REF] Gibbons | Evidence for a persistent microbial seed bank throughout the global ocean[END_REF] and that could play an important role in successional dynamics upon starvation.

Here, we investigated how a bathypelagic community withstood prolonged starvation in the absence of their main source of carbon, i.e. the surface-derived particle flux. To this aim, a parcel (600 l) of water from 2100 m depth was enclosed for 1 year, and we followed the taxonomic and functional dynamics of the prokaryotic community with weekly or by-weekly sampling frequency during the first 2 months, and monthly after that. We expected to find a rather stable community maintaining basal activities after most labile DOC had been consumed, but instead we observed a dramatic succession of active phylotypes along the year.

Results and discussion

Long-term dynamics of the starved bathypelagic community

Total organic carbon (TOC) and total nitrogen (TN) were quite constant throughout the experiment (Fig. 1A), with TOC values within the range reported for the world's bathypelagic regions (35-45 mM, Hansell and colleagues (2009)). Heterotrophic prokaryotic production values (estimated as leucine incorporation rates) at the very start of the experiment (Fig. 1B) were higher than previously observed values in the bathypelagic (e.g., [START_REF] Baltar | Prokaryotic extracellular enzymatic activity in relation to biomass production and respiration in the meso-and bathypelagic waters of the (sub)tropical Atlantic[END_REF], likely due to some confinement effect (see methodological considerations in the Experimental procedures section). Then, it decreased drastically within the first 2 weeks of the experiment, when most of the labile DOC was likely consumed. During the second month (day 49), heterotrophic production decreased further but remained rather constant for more than a month (day 105, Fig. 1B, inset), after which it decreased again down to a minimum value on day 165. Later on, production remained low but displayed a relative maximum on day 278. The values detected at the end of the experiment are within the low range of heterotrophic production values observed in the Mediterranean bathypelagic [START_REF] Luna | The dark portion of the Mediterranean Sea is a bioreactor of organic matter cycling[END_REF].

Prokaryotic cell abundances were generally low, in agreement with previous observations in the deep Mediterranean (e.g., [START_REF] Luna | The dark portion of the Mediterranean Sea is a bioreactor of organic matter cycling[END_REF]. They presented a relative minimum between days 105 and 165 and afterward increased back to the initial values (Fig. 1C). Coincident with the initial sharp decrease in leucine incorporation, we observed the presence of a population of small cells (with an estimated carbon content of 6.5 fgC cell 21 ,i n s t e a do f the estimated 12.5 fg cell 21 of the normal-size cells, Supporting Information Fig. S1) that peaked between days 21 and 49 of the experiment. Reduction in size is a well-described response of many bacteria upon carbon starvation, being an important survival strategy for reducing energy costs [START_REF] Novitsky | Morphological characterization of small cells resulting from nutrient starvation of a psychrophilic marine vibrio[END_REF]. However, the decline in this population of microcells observed on day 77 of the experiment likely suggests the demise of a large fraction of these cells, which is supported by the strong decline observed in taxonomic diversity (Fig. 1D). This indicates that size reduction might be one strategy that bathypelagic prokaryotes have to cope with carbon scarcity, but some cells in such stage do not seem to withstand starvation for long periods. In fact, prokaryotic species richness decreased to half of the initial values at the end of the experiment (Fig. 1D).

In terms of taxonomic composition, there was a striking succession of phylogenetic groups in the rDNA profiles (Fig. 2). Alpha-and Gammaproteobacteria dominated the initial community, being the genus Alteromonas the major contributor on day 1 ( 35% of total reads), but the relative abundance of this genus decreased drastically shortly after (Supporting Information Fig. S2). Alteromonas is a major player in the drawdown of labile DOC in the ocean [START_REF] Pedler | Single bacterial strain capable of significant contribution to carbon cycling in the surface ocean[END_REF], and its fast disappearance further supports the idea that labile DOC was rapidly consumed. Unlike most other prokaryotic groups, the relative contribution of Alphaproteobacteria to the community structure remained relatively constant along the experiment (Fig. 2), but there was also a marked succession of phylotypes within this group (Supporting Information Fig. S3).

On day 77, coincident with the decline in the population of microcells, the contribution of Marine Crenarchaoetic Group I (MCGI) of Thaumarchaeota remarkably increased, peaking on day 133, where it represented more than 70% of the sequences. A single operational taxonomic unit (OTU) that was rare at the beginning of the experiment ( 0.02% of the total DNA reads) accounted for 96% of the MCGI population. These changes in species contributions caused the dramatic drop in taxonomic diversity (Fig. 1D). The dominance of Thaumarchaeota lasted until month 6 (day 194). Afterward, the contribution of this phylum notably decreased and members of Bacteroidetes, Alphaproteobacteria and Planctomycetes became the major contributors to the prokaryotic community (Fig. 2).

It has been postulated that PCR-based surveys overestimate the relative contribution of abundant taxa to the community structure and display bias against the ones present in low proportion [START_REF] Gonzalez | Amplification by PCR artificially reduces the proportion of the rare biosphere in microbial communities[END_REF]. Thus, in order to test whether the dominance of archaeal sequences was a consequence of the demise of a large fraction of cells of the different bacterioplankton groups or a real 'bloom' of Archaea, we quantified Thaumarchaeota and MCGI 16S gene abundances through quantitative PCR (qPCR). We observed that the abundance of this phylum was actually higher at the beginning of the experiment (Supporting Information Fig. S4), where most Thaumarchaeota OTUs displayed higher relative abundances (Supporting Information Fig. S5). Nevertheless, a relative maximum was observed on day 133, when we found the maximum contribution in terms of sequences (Supporting Information Fig. S4) and the lowest prokaryotic abundances (Fig. 1C). These results suggest that there was not a real bloom and that the high dominance of Thaumarchaeota sequences was the consequence of a drastic decrease in the abundance of different bacterial phylotypes. The success of these Thaumarchaeota MCGI OTUs likely relies on their ability to fix inorganic carbon [START_REF] Herndl | Contribution of Archaea to total prokaryotic production in the deep atlantic ocean[END_REF]. Indirect evidence of this comes from the nitrite peak that we observed coincident with the rise in Thaumarcheota relative abundance (Supporting Information Fig. S6), because autotrophic Thaumarchaeota obtain the energy to fix inorganic carbon through the oxidation of ammonia to nitrite (K€ onneke et al., 2005).

We then assessed changes in the fluorescent DOM along the experiment through excitation/emission matrices, which allow to identify protein-like (peak T) and humic-like (peak C and M) substances [START_REF] Coble | Characterization of marine and terrestrial DOM in seawater using excitation emission matrix spectroscopy[END_REF]. We found a remarkable signature of protein-like compounds when the relative abundance of Thaumarchaeota reached its maximum value (Supporting Information Fig. S7). This constitutes another indirect evidence supporting the introduction of labile DOM through chemolithoautotrophy. Based on the increase in total prokaryotic cell abundance and the change in community composition observed after the peak of Thaumarchaeota, we hypothesize that these archaeal cells played a central role in DOM cycling in the system and their metabolism by-products enabled growth of bacterial taxa which ended up outcompeting them.

To further explore this idea, we monitored the functional capabilities of the community along the experiment by measuring a suite of hydrolytic enzymes. These enzymes enable microorganisms to utilize complex DOC compounds and high activity values have been reported for deep sea microbial communities [START_REF] Hoppe | Profiles of ectoenzymes in the Indian Ocean: phenomena of phosphatase activity in the mesopelagic zone[END_REF][START_REF] Baltar | Bacterial versus archaeal origin of extracellular enzymatic activity in the northeast Atlantic deep waters[END_REF]2009). Esterase activity was high within the first 2 weeks of the experiment, showed its lowest values when Thaumarchaeota dominated the community and increased back to the original values when Thaumarchaeota relative abundance decreased and Bacteroidetes became the major contributors to the community (Figs 2 and3A). This enzymatic activity is an estimator of overall potential activity of the microbial enzymes involved in organic matter transformations [START_REF] Chrost | Fluoresceindiacetate (FDA) assay for determining microbial esterase activity in lake water[END_REF]. The rise in esterase activity implies that after 1 year with no external carbon inputs the microbial community maintained the ability to regain its metabolic potential.

The trends observed in other enzymatic activities likely reflect changes in the quality of the DOM pool and in community traits associated to the shifts in taxonomic composition. a-glucosidase, ß-glucosidase, leu-aminopeptidase and chitobiase activities followed the same pattern as leucine incorporation (Figs 1B and3B-E), with a drastic decrease within the first 2 weeks of the experiment and a relative maximum later in the experiment coincident with the increase in the contribution of Bacteroidetes and Planctomycetes (Fig. 2) and the rise in esterase activity (Fig. 3A). Fucosidase activity, which is involved in the degradation of polysaccharides [START_REF] Sun | Isolation and complete genome sequence of Algibacter alginolytica sp. nov., a novel seaweed-degrading Bacteroidetes bacterium with diverse putative polysaccharide utilization loci[END_REF], was low at the beginning of the experiment but displayed a maximum on day 223, when the peak in esterase activity was observed (Fig. 3F). Alkaline phosphatase followed a similar trend than bacterial abundance, but values remained relatively high throughout the experiment (Fig. 3G), supporting the view that these enzymes play an important role in the acquisition of carbon from dissolved organic phosphorus compounds in the bathypelagic [START_REF] Hoppe | Profiles of ectoenzymes in the Indian Ocean: phenomena of phosphatase activity in the mesopelagic zone[END_REF][START_REF] Baltar | Prokaryotic extracellular enzymatic activity in relation to biomass production and respiration in the meso-and bathypelagic waters of the (sub)tropical Atlantic[END_REF].

L -1 h -1 nmol L -1 h -1 nmol L -1 h -1 nmol L -1 h -1 nmol L -1 h -1 nmol L -1 h -1

Dynamics of the most responsive OTUs

We next explored the identity and dynamics of the most responsive OTUs along the experiment, i.e., the ones that played the largest role in the taxonomic, and therefore, likely in the metabolic, succession. To that end, we focused on those taxa that displayed the largest changes in relative abundances ('responsive' OTUs, see Experimental procedures). Then, we categorized these responsive OTUs as 'pre-bloom', if their mean relative abundances were higher before the peak of Thaumarchaeota (days 1-49, Fig. 2), or 'post-bloom', if their relative abundances were higher after day 165, when the Thaumarchaeota population started to decline. We identified 91 OTUs in the 'pre-bloom' pool (73 bacterial and 18 archaeal OTUs, see Supporting Information Table S1 for further details), which accounted for roughly 95% of all the sequences during the first 49 days (Fig. 4A). After this date, their contribution to the total number of sequences markedly decreased, and from day 77 to the end of the experiment, they only represented between 10% and 20% of the total sequences. Interestingly, despite the drastic decrease in their contribution to the community, half of these 'pre-bloom' OTUs were still present at the end

PRE -BLOOM

Relative abundance (%) Alphaprot.

Gammaprot. Bacteroidetes

Hydrogen.

Gemmatim.

Chloroflexi Nitrospira Verrucomicrobia Planctom.

Planctom.

Gammaprot.

Alphaprot.

Thaumarchaeota Marinimicrobia

Euryarch.

Betaprot. of the experiment (Fig. 4C), suggesting that they managed to survive but were present in very low abundances. Noticeably, when taking into account all the OTUs that were abundant (representing > 1% of the reads), we found that only one Erythrobacter OTU (Alphaproteobacteria) was relatively abundant before and after the bloom (data not shown).

The 'post-bloom' pool was composed by 76 OTUs, 75 bacterial OTUs and one archaeal MCGI OTU, which was the one that dominated the community during days 77-194 of the experiment (Figs 2 and 4B, see Supporting Information Table S1 for further details). A total of 84% of the 'post-bloom' OTUs were rare (< 0.1% relative abundance) during the first month of the experiment, and only 23 out of the 76 OTUs were detected in the original community (Fig. 4D). After the first month, the relative abundance of the 'post-bloom' OTUs remarkably increased and ended up representing over 80% of the reads at the end of the experiment. Hence, the 'post-bloom' OTUs, which accounted only for 7% of the 1117 OTUs detected during the whole experiment, are mostly 'seed' OTUs, i.e., OTUs that transitioned from very rare or dormant to abundant when they found favorable conditions. Microbial seedbanks have been proposed to play a fundamental role in environmental microbial dynamics [START_REF] Lennon | Microbial seed banks: the ecological and evolutionary implications of dormancy[END_REF][START_REF] Caporaso | The Western English Channel contains a persistent microbial seed bank[END_REF][START_REF] Gibbons | Evidence for a persistent microbial seed bank throughout the global ocean[END_REF][START_REF] Shade | Conditionally rare taxa disproportionately contribute to temporal changes in microbial diversity[END_REF]. Yet recruitment of taxa from the seedbank has only been demonstrated in short-term incubation experiments [START_REF] Aanderud | Resuscitation of the rare biosphere contributes to pulses of ecosystem activity[END_REF][START_REF] Baltar | Prokaryotic responses to ammonium and organic carbon reveal alternative CO 2 fixation pathways and importance of alkaline phosphatase in the mesopelagic North Atlantic[END_REF]. Our results constitute the first demonstration of recruitment from the seedbank over a long-time period in a controlled laboratory setting. The shift in conditions for these responsive OTUs was likely triggered by the introduction of fresh organic carbon into the system through inorganic carbon fixation by the chemolithoautotrophic Thaumarchaeota (Supporting Information Figs S6 and S7), which dominated the community for almost 4 months. We hypothesize that this freshly produced carbon probably had some sort of 'priming effect' [START_REF] Kuzyakov | Review of mechanisms and quantification of priming effects[END_REF] that facilitated the degradation of previously unreactive organic matter.

Both the 'pre' and 'post-bloom' OTUs were composed of an assortment of diverse prokaryotes (Fig. 4E andF), yet the composition of these pools was remarkably different. Alphaproteobacteria were major contributors to both pools, but members of the order Rhodobacterales and the SAR11 clade dominated at the beginning of the experiment, whereas Rhodospirillales dominated at the end. Gammaproteobacteria represented 25% of the OTUs in the 'pre-bloom' pool but accounted for a small fraction of the 'post-bloom' OTUs, and the taxonomic affiliation of the OTUs within this class also differed from the 'pre' counterparts (Fig. 4E andF).

The 'post-bloom' pool was also composed by other phyla that had not been detected in the 'pre-bloom' pool, such as Hydrogenedentes, Gemmatimonadetes, Nitrospirae and Chloroflexi. In addition, members of the Sphingobacteriales and Cytophagales dominated the Bacteroidetes population, whereas this phylum represented a minor fraction of the total community at the beginning of the experiment. The success of these groups in such a carbon-limited system points to complementary alternative metabolisms and the ability to use recalcitrant compounds, as supported by the increase in the hydrolytic activities (Fig. 3). Indeed, Bacteroidetes, Gemmatimonadetes, Planctomycetes and Verrucomicrobia have been reported to harbour genes encoding hydrolytic enzymes involved in the degradation of polymers [START_REF] Andez-G Omez | Ecology of marine Bacteroidetes: a comparative genomics approach[END_REF][START_REF] Li | Genomic and transcriptomic evidence for scavenging of diverse organic compounds by widespread deep-sea archaea[END_REF]. Within the Bacteroidetes, members of the Sphingobacteriales have been observed to have special ability to utilize complex carbon compounds and even predate on other bacteria [START_REF] Mcilroy | The family saprospiraceae. In The Prokaryotes: Other Major Lineages of Bacteria and the Archaea[END_REF]. Hydrogenedentes have the potential ability to utilize hydrogen as energy source [START_REF] Rinke | Insights into the phylogeny and coding potential of microbial dark matter[END_REF] and degrade complex compounds [START_REF] Rinke | Insights into the phylogeny and coding potential of microbial dark matter[END_REF][START_REF] Nikolaki | Microbial diversity in the era of omic technologies[END_REF]. Nitrospirae obtain energy from nitrite oxidation and members of this phylum have been shown to have extremely versatile metabolisms, which allow them to use CO 2 , organic compounds, perform aerobic hydrogen oxidation and even ammonia oxidation [START_REF] Koch | Expanded metabolic versatility of ubiquitous nitrite-oxidizing bacteria from the genus Nitrospira[END_REF][START_REF] Daims | Complete nitrification by Nitrospira bacteria[END_REF]. Chloroflexi bacteria are also thought to be metabolically flexible [START_REF] Yilmaz | Expanding the world of marine bacterial and archaeal clades[END_REF] and have the potential to use recalcitrant DOM compounds [START_REF] Landry | SAR202 genomes from the dark ocean predict pathways for the oxidation of recalcitrant dissolved organic matter[END_REF].

To further confirm that the recruited OTUs drove the metabolic succession we analysed the relationships between the 16S rRNA and rDNA sequences of both the 'pre' and 'post' bloom OTUs to infer the activity status of these OTUs (e.g., [START_REF] Campbell | Activity of abundant and rare bacteria in a coastal ocean[END_REF][START_REF] Hugoni | Structure of the rare archaeal biosphere and seasonal dynamics of active ecotypes in surface coastal waters[END_REF]. If the prokaryotes present at the end of the experiment were starving or growing under suboptimal conditions, the relationship between their rRNA and rDNA reads would have a smaller slope than the slope of those dominating at the beginning of the experiment. Using this approach, we found that the recruited taxa at the end of the experiment ('post-bloom' OTUs) were as active as the ones prevalent before the bloom (Fig. 5). This observation supports the view that the recruited taxa ensured the long-term metabolic potential of the community.

Taken together, our findings show that prolonged starvation in a bathypelagic microbial community led to a consistently active community maintained by a continuous succession of prokaryotic groups. We hypothesize that Thaumarchaeota played a crucial role in this succession by introducing fresh DOC through chemoautotrophy into the system, and this DOC had a 'priming effect' that enabled the resuscitation of metabolically diverse taxa that ended up dominating the microbial community. While our experimental approach is not a perfect reproduction of what occurs in situ (see methodological considerations in the Experimental procedures), it demonstrates the profound malleability of deep-sea microbial populations in the absence of external influences. It has been long assumed that the surface-derived particle flux sustains microbial activities in the bathypelagic but our results show that after 1 year deprived of surface-derived particles, prokaryotic abundance and diversity displayed similar values to the beginning of the experiment. This provides conclusive evidence of how resourceful deep ocean prokaryotes are when facing organic carbon-limiting conditions and hints at which phylogenetic groups are likely to thrive in those extreme conditions.

Experimental procedures

Experimental set up

The bathypelagic water (600 l) was collected from 2100 m in the Northwestern Mediterranean Sea (40838 0 31.01 00 N, 2851 0 1.6 00 W) in October 2014 during the MIFASOL-I cruise on board the R/V Garcı ´a del Cid. The water was collected at night the last day of the cruise before steaming back to harbor, by multiple deployments of a rosette equipped with Niskin bottles. Water was filtered through a 200 mm mesh to remove large zooplankton and poured into different thoroughly acid-rinsed 20-l containers. The content of these containers was transferred to a 600 l HDPE container that had been previously acid-cleaned, rinsed once with distilled water and rinsed five times with the sampled water. This container was attached to a plastic pallet, to simplify the handling, and covered with black plastic to prevent exposure of the water to light throughout the handling and the experiment. Once back in the lab, the sampled water was allowed to sit for 24 h before starting the experiment. The container was gently agitated by moving it back and forth using a pallet jack prior to each sampling and samples were syphoned out of the tank by means of an autoclaved and acid-rinsed glass-tube attached to a long silicon tube, with the help of a 60-ml syringe. The lid of the container covered the tube during the sampling, to avoid immigration of foreign taxa. Samples were taken at different time intervals (on days 1, 7, 14, 21, 33, 49, 77, 105, 133, 165, 194, 223, 278 and 364) during 1 year. The container was maintained in the dark and at 178C, which is only 38C higher than the typical bathypelagic Mediterranean Sea temperature [START_REF] Luna | The dark portion of the Mediterranean Sea is a bioreactor of organic matter cycling[END_REF]. The container was airtight except for the moment of sampling, so a certain gas exchange was allowed. However, given that both oxygen and CO 2 are not limiting in bathypelagic waters [START_REF] Egut | Alkalinity and carbon budgets in the Mediterranean Sea[END_REF][START_REF] Tanhua | The Mediterranean Sea system: a review and an introduction to the special issue[END_REF] and the low prokaryotic biomass observed along the experiment, we believe this gas exchange did not have any significant effect in the C and O 2 budget.

Methodological considerations

We are aware that our approach has some shortcomings, being the most obvious one the dramatic change in conditions during water recovery, particularly in pressure. Only a few studies have dealt with the effect of changes in hydrostatic pressure on microbial activity and diversity, but no firm conclusions can be drawn from these studies (Herndl and Reinthaler, 2013 and references therein). It is certainly possible that some pressure-sensitive prokaryotes died during sample collection. However, since we are interested in the long-term response and the whole experiment was carried out under the same pressure, we believe the dynamics observed are independent of this variable.

Confinement of natural assemblages usually leads to changes in community composition and activity [START_REF] Schafer | Successional changes in the genetic diversity of a marine bacterial assemblage during confinement[END_REF][START_REF] Massana | Changes in marine bacterioplankton phylogenetic composition during incubations designed to measure biogeochemically significant parameters[END_REF], with Alteromonas bacteria playing a main role in these shifts (e.g., [START_REF] Eilers | Succession of pelagic marine bacteria during enrichment: a close look at cultivation-induced shifts[END_REF][START_REF] Mccarren | Microbial community transcriptomes reveal microbes and metabolic pathways associated with dissolved organic matter turnover in the sea[END_REF]. We tried to reduce the confinement effect by performing the incubation with a large volume of water (600 l) (see Ferguson et al., 1984 and references therein). However, the dominance of Alteromonas and the high activity observed during the first week of the experiment (Fig. 1B) suggest that there was some enrichment due to confinement. This issue is inherent to any experimentation with natural communities but we believe mesocosms experiments are still invaluable tools for understanding the functioning of marine ecosystems. Although we acknowledge that the dramatic alterations our parcel of water experienced within the first days probably led to changes in the diversity of the bathypelagic community, our main goal was not to exactly reproduce the bathypelagic environment ex situ but to investigate the long-term response of bathypelagic prokaryotes to the absence of external inputs of organic carbon. To further confirm that the OTUs detected in this study are bathypelagic dwelling organisms, we searched these OTUs in a trans-Mediterranean bathypelagic survey (HOTMIX cruise; Ortega-Retuerta et al., in preparation) and identified 77% of them.

Prokaryotic heterotrophic production and abundance

Prokaryotic heterotrophic production was estimated from the incorporation of tritium-labeled leucine [START_REF] Kirchman | Leucine incorporation and its potential as a measure of protein synthesis by bacteria in natural aquatic systems[END_REF] using the centrifugation procedure. Four replicates of 1.2 ml and two trichloroacetic acid (TCA)-killed controls were incubated with 3 H-Leucine at a final concentration of 40 nM. Incubation was performed in the dark at in situ temperature for 4 h and stopped with 5% TCA, final concentration. The samples were then kept frozen at 2208C until processing, following [START_REF] Smith | A simple, economical method for measuring bacterial protein synthesis rates in seawater using 3H-leucine 1[END_REF]. Prokaryotic cell abundance was estimated by flow cytometry as described elsewhere [START_REF] Gasol | Flow cytometric determination of microbial abundances and its use to obtain indices of community structure and relative activity[END_REF]. Prokaryotic cell size was estimated from the side scatter signal using the equation provided by [START_REF] Calvo-Dı ´az | Seasonal dynamics of picoplankton in shelf waters of the southern Bay of Biscay[END_REF]. The carbon content of the prokaryotic cells was obtained following Gundersen and colleagues (2002).

Enzymatic measurements

We monitored the enzymatic activities of Leu-aminopeptidase, a-glucosidase, b-glucosidase, fucosidase, chitobiase, alkaline phosphatase and esterase using L-leucine-7-amino- 4-methylcoumarin, 4-methylumbelliferyl a-D-glucoside, 4methylumbelliferyl b-D-glucoside, 4-methylumbelliferyl b-Dfucoside, 4-methylumbelliferyl N-acetyl-b-D-glucosaminide, 4-methylumbelliferyl phosphate and 4-methylumbelliferyl butyrate as substrates, respectively (all purchased at Sigma-Aldrich, St. Louis, MO, USA) following the method developed by [START_REF] Hoppe | Significance of exoenzymatic activities in the ecology of brackish water: measurements by means of methylumbelliferyl-substrates[END_REF]. Assays were performed as described in Sala and colleagues (2016). Briefly, each sample (350 ml) was pipetted in quadriplicate into 96 black well plates, and substrates were added to obtain a final concentration of 125 lM. The fluorescence of the 96-well plates was measured at the beginning and after 5 h incubation at in situ temperature conditions with a Tecan Infinite 200 microplate reader at 365 nm excitation and 450 nm emission wavelengths. The increase in fluorescence in each well was converted into activity using a standard curve prepared with the fluorophores 4-methylumbelliferone (MUF) or 4-methylcoumarinyl-7-amide 4 (Sigma-Aldrich, St. Louis, MO, USA).

Total organic carbon and total nitrogen

A total of 10 ml water samples were collected in precombusted (4508C, 24 h) glass ampoules. After adding 50 mlof25%H 3 PO 4 to acidify at pH < 2, the ampoules were heat-sealed and stored in the dark at 48C until analysis. TOC and TN concentrations were quantified with a Shimadzu TOC-LCSV organic C/N analyzer. Between three and five injections of 150 ml per replicate were performed. The final organic carbon and nitrogen concentrations in each sample were calculated by subtracting a Milli-Q blank and dividing by the slope of daily-made standard curves made from potassium hydrogen phthalate and glycine respectively. Reference samples of the Material Reference Certificate (MRC Batch-13 Lot//08-13; Hansell Laboratory, RSMAS, University of Miami) were used daily for quality control.

Inorganic nutrients

Samples for inorganic nutrients (10 ml) were kept frozen at 2208C until analysis, which was performed using a CFA Bran 1 Luebbe autoanalyser following the methods described by [START_REF] Hansen | Determination of nutrients[END_REF].

Nucleic acid extraction

Total nucleic acid extractions were performed using the standard phenol-chloroform protocol with slight modifications [START_REF] Logares | Metagenomic 16S rDNA Illumina tags are a powerful alternative to amplicon sequencing to explore diversity and structure of microbial communities[END_REF]. Once extracted, the volume was split in two. One of the aliquots was treated with the Turbo DNA-free kit (Applied Biosystems, Austin, TX) to remove residual DNA, and PCRs were run to check that DNA had been completely removed. DNA and RNA were quantified using a Qubit fluorometer assay (Life Technologies, Paisley, UK). RNA was reverse transcribed using random hexamers and the Super-ScriptIII kit (Invitrogen, Grand Island, NY, USA), according to the manufacturer's instructions.

Samples were sent for sequencing at the Research and Testing Laboratory (Lubbock, TX). We used the primers 515F and 926-R [START_REF] Parada | Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples[END_REF] to amplify the 16S rRNA from the DNA and cDNA pools. Illumina MiSeq 2 3 250 flow cells were used for sequencing following Illumina protocols described elsewhere (C ucio et al., 2016).

qPCR analyses

The absolute abundance of the Thaumarchaeota phylum and the MCGI lineage were followed by qPCR using the primer sets and the PCR program summarized in Hong and colleagues (2015) and Auguet and colleagues (2011), respectively. Plasmids containing 16S rRNA genes of the Thaumarchaeota phylum and the MCGI lineage were used as standards.

Absolute 16S rRNA gene copies ml 21 were quantified in triplicate from DNA extracts and standard DNA dilution curves by qPCR (LightCycler 480; Roche, Branford, CT, USA). In each qPCR run, amplification of control water samples was done in triplicate in order to confirm the lack of exogenous DNA contaminations. DNA distribution and reactive dispensing was done in 384-well plates, using an automatic pipetting system (Echo 525; Labcyte, San Jose, CA), that allowed the miniaturization of the assays in a final volume of 1.5 ml( 0 . 5mlo fD N A at 0.5 ng ml 21 and 1 ml of reactive mix). Reactive mix solutions were prepared mixing each primer pair (10 mM) with ready-touse hot start reaction mix LightCyclerV R 480 probes master (Roche, Branford, CT, USA) and molecular grade water to a final primer concentration of 0.8 mM. After quantifications, absolute abundances (number of copies per ml) were calculated. Specificity of reactions was confirmed by melting curve analyses and after looking for unspecific PCR products such as primer dimers or gene fragments of unexpected length in agarose gel electrophoresis.

Data analyses

Computing analyses were run at the Marine Bioinformatics Service of the Institut de Cie `ncies del Mar (ICM-CSIC) in Barcelona. The Illumina sequences were quality filtered following [START_REF] Logares | Workflow for analysing MiSeq amplicons based on Uparse v1[END_REF]. The sequences of both the DNA and cDNA pools were clustered into OTUs at 97% cutoff using the UPARSE algorithm implemented in USEARCH [START_REF] Edgar | UPARSE: highly accurate OTU sequences from microbial amplicon reads[END_REF]. Singletons (i.e., OTUs occurring once in just one sample) and chimerical OTUs were removed. The remaining OTUs were taxonomically annotated using BLAST against the SILVA database as reference. OTUs assigned to chloroplasts were removed for subsequent analyses. All raw sequences used in this study are publicly available at the European Nucleotide Archive (ERS1993945-ERS1993972).

Data treatment and statistical analyses were performed with the Rstudio software using version 1.0.44. The OTU table was randomly subsampled down to the minimum number of reads per sample in order to avoid statistical artifacts due to an uneven sequencing effort among samples. This subsampling was obtained by rarefying the OTU table to the minimum number of reads (26,090 reads/sample) using the rrarefy function in the vegan package [START_REF] Oksanen | Vegan: community ecology package[END_REF]. For the subsampled OTU table the sample of cDNA obtained on day 1 of the experiment was excluded, due to the low number of reads it contained (3248).

Analyses of the responsive OTUs. To analyze which OTUs showed the largest changes in abundance, we calculated the average Euclidean distance of the relative abundance of each OTU between all sampling dates, using the R-script presented in Appendix S1 of Ruiz-Gonz alez and colleagues (2015). Those OTUs showing a mean distance > 10 were considered 'responsive' OTUs. This value represented an average maximum change of 626 sequences between two sampling dates for any responsive OTU (range 26-17,749).

Fig. 1 .

 1 Fig. 1. Dynamics of an enclosed bathypelagic community during 1-year of starvation. (A) Total organic carbon (TOC) and total nitrogen (TN), (B) heterotrophic prokaryotic production measured as leucine incorporation, (C) prokaryotic abundance (PA), total cells represented as filled circles and population of microcells (see Supporting Information Fig. S1), as open circles (D) diversity and richness estimated with the Shannon index (solid line) and number of OTUs (dashed line), respectively. Error bars represent the standard deviation of the replicates (see Experimental procedures).

Fig. 2 .

 2 Fig. 2. Taxonomic composition (16S-rDNA based) of the prokaryotic community along the experiment, shown at the phylum level except for the Proteobacteria, which are divided into classes.

Fig. 3 .

 3 Fig. 3. Temporal dynamics of different enzymatic activities as an indication of the metabolic potential of the microbial communities. Each data point represents the average of four replicates and error bars represent the standard deviation.

Fig. 4 .

 4 Fig. 4. Characteristics of the OTUs that displayed the largest changes in abundance during the experiment, i.e., 'responsive OTUs' (see text for details). (A, B) Temporal dynamics of the relative contribution of the 'pre' and 'post-bloom' OTUs to the total number of sequences; solid lines represent only bacterial OTUs and dashed line represents all OTUs (archaeal and bacterial). (C, D) Temporal dynamics of the number of OTUs within the 'pre' and 'post-bloom' pools. (E, F) Taxonomic affiliation of the responsive OTUs before and after the bloom. Classification was performed at the phylum level except for Proteobacteria, Bacteroidetes and Planctomycetes that were split into classes or orders depending on their relative contribution.

Fig. 5 .

 5 Fig. 5. Relationships between the proportion of 16S rRNA and 16S rDNA sequences in the bacterial 'pre' and 'post-bloom' OTUs (see main text for details).

Acknowledgements

This study was partially supported by grants DOREMI (CTM2012-34294), HOTMIX (CTM2011-30010/MAR), REMEI (CTM2015-70340-Rf), SUAVE (CTM2014-54926-R), funded by the Spanish Government, and EcoRARE (CTM2014-60467-JIN), funded by the Spanish Government and the European Regional Development Fund (ERDF). We thank Idaira Santos, Raquel Gutierrez and Carolina Antequera for their help during sampling and laboratory analyses. We thank Ramiro Logares for the amplicon data processing and Clara Ruiz-Gonzalez for her help during data analyses and for comments on a previous version of the manuscript. The authors declare no conflict of interest.