Lucas Bonnin
email: lucasbonnin@hotmail.fr

Christophe Lett

| Laurent Dagorn

| John

David Filmalter

| Fabien Forget

Philippe Verley

Manuela Capello

Lucas Orcid

Bonnin

Can drifting objects drive the movements of a vulnerable pelagic shark?

Keywords: bycatch, fish aggregating devices, Lagrangian drift model, pop-up satellite archival telemetry, silky shark, vulnerability

1. Juvenile silky sharks (Carcharhinus falciformis) regularly associate with floating objects yet the reasons driving this behaviour remain uncertain. Understanding the proportion of time that silky sharks spend associated with floating objects is essential for assessing the impacts of the extensive use of fish aggregating devices (FADs) in the tropical tuna purse-seine fisheries, including increased probability of incidental capture and the potential of an ecological trap.

2. Previous studies provided insight into the amount of time that silky sharks spent at an individual FAD but were unable to assess neither the time spent between two associations nor the proportion of time spent associated/unassociated.

3. The percentage of time that juvenile silky sharks spend unassociated with floating objects was estimated through the analysis of horizontal movements of 26 silky sharks monitored with pop-up archival tags. Under the assumption that a high association rate with drifting FADs would align the trajectories of tracked sharks with ocean surface currents, a novel methodology is proposed, based on the comparison of shark trajectories with simulated trajectories of passively drifting particles derived using a Lagrangian model. 4. Results revealed that silky shark trajectories were divergent from surface currents, and thus unassociated with FADs, for at least 30% of their time. The potential of the methodology and the results are discussed in the context of increasing FAD densities in the Indian Ocean.

with the currents for several months and their positions are remotely tracked in real-time using GPS. During their drifts, large aggregations of tropical tuna form around the FADs, along with several other pelagic species, including the silky shark. Tropical tuna purse-seine fisheries have two direct impacts on silky sharks: incidental bycatch, mainly when vessels set nets around floating objects [START_REF] Dagorn | Is it good or bad to fish with FADs? What are the real impacts of the use of drifting FADs on pelagic marine ecosystems?[END_REF] and shark entanglement in FAD nets [START_REF] Filmalter | Looking behind the curtain: Quantifying massive shark mortality in fish aggregating devices[END_REF]. A third possible impact involves the possibility of an ecological trap, i.e. the risk that sharks become trapped within arrays of drifting FADs (an anthropogenic alteration to the natural habitat) for long periods of time, potentially modifying their movements and negatively impacting their biology [START_REF] Marsac | Drifting FADs used in tuna fisheries: An ecological trap? Pêche thonière et dispositifs de concentration de poissons[END_REF].

Silky sharks constitute 90% of the elasmobranch bycatch caught within tropical tuna purse-seine fisheries [START_REF] Dagorn | Is it good or bad to fish with FADs? What are the real impacts of the use of drifting FADs on pelagic marine ecosystems?[END_REF][START_REF] Gilman | Bycatch governance and best practice mitigation technology in global tuna fisheries[END_REF]. The similarity between the diel associative behaviour of silky sharks and tunas (association with FADs during daytime with excursions at night) explains why they are frequently caught when purse seiners set on floating objects [START_REF] Forget | Behaviour and vulnerability of target and non-target species at drifting fish aggregating devices (FADs) in the tropical tuna purse seine fishery determined by acoustic telemetry[END_REF]. To reduce the fishing mortality of silky sharks, different mitigation methods have been proposed, e.g. targeting bigger schools of tunas to reduce bycatch proportions [START_REF] Dagorn | Targeting bigger schools can reduce ecosystem impacts of fisheries[END_REF] or implementing better release practices [START_REF] Eddy | Rates of at-vessel mortality and post-release survival of pelagic sharks captured with tuna purse seines around drifting fish aggregating devices (FADs) in the equatorial eastern Pacific Ocean[END_REF][START_REF] Hutchinson | Post-release survival of juvenile silky sharks captured in a tropical tuna purse seine fishery[END_REF][START_REF] Poisson | Mortality rate of silky sharks (Carcharhinus falciformis) caught in the tropical tuna purse seine fishery in the Indian Ocean[END_REF][START_REF] Poisson | Collaborative research: Development of a manual on elasmobranch handling and release best practices in tropical tuna purse-seine fisheries[END_REF]. Despite these measures, the increased number of sets on FADs [START_REF] Marsac | Data-derived fishery and stocks status indicators for skipjack tuna in the Indian Ocean[END_REF] results in an increased number of silky sharks being incidentally caught by the purse-seine fisheries. Conversely, the evaluation of the extent of shark entanglement in the underwater nets of the FADs [START_REF] Filmalter | Looking behind the curtain: Quantifying massive shark mortality in fish aggregating devices[END_REF] has resulted in three of the four tuna Regional Fisheries Management Organizations responsible for the conservation and management of tunas, adopting resolutions on the use of nonentangling FADs [START_REF] Murua | Advances in the use of entanglement-reducing drifting fish aggregating devices (DFADs) in tuna purse seine fleets[END_REF]. Finally, the consequences of anthropogenic habitat changes induced by increasing numbers of artificial floating objects [START_REF] Dagorn | How much do fish aggregating devices (FADs) modify the floating object environment in the ocean[END_REF][START_REF] Maufroy | Massive increase in the use of drifting fish aggregating devices (DFADs) by tropical tuna purse seine fisheries in the Atlantic and Indian Oceans[END_REF][START_REF] Moreno | Fish behaviour from fishers' knowledge: The case study of tropical tuna around drifting fish aggregating devices (DFADs)[END_REF] remain largely unknown.

The total number of FADs deployed by purse-seine fishing vessels represents the key variable determining the extent of the impacts of FAD fisheries. Some Regional Fisheries Management Organizations have set maximum limits controlling the number of FADs per vessel (e.g. 350 for the Indian Ocean) (IOTC, 2017). However, assessing the efficacy of such limits remains a point of debate due to limited knowledge on the relationships between the number of FADs and pervessel catch (tunas and bycatch, including the silky shark) and the risk of an ecological trap. A critical variable in assessing such impacts is the proportion of time that these species spend in association with floating objects and how this could change as a function of local FAD density and environmental conditions. Species spending a small proportion of time associated with floating objects are assumed to be less vulnerable to fishing, and would have a lower probability of becoming trapped in an array of drifting objects. Recent behavioural studies using acoustic telemetry methods highlighted the strong associative behaviour of juvenile silky sharks with floating objects, and estimated the average continuous residence times of 15 days and maximum observed continuous associations with the same FAD of 30 days (Filmalter, Cowley, Forget, & Dagorn, 2015). However, these field studies could not measure the time that tagged sharks spent between two floating object associations (absence times). Estimating this variable is critical for assessing the vulnerability of this species to FAD fisheries.

To date, two experimental designs have been utilized to estimate absence times. The first consisted of instrumenting all FADs within an array with acoustic receivers, and detecting fish equipped with coded acoustic tags within the array. Several field studies conducted on tunas at anchored FADs adopted this approach to measure the time spent at FADs and the time between two FAD associations. [START_REF] Dagorn | Behavior of yellowfin (Thunnus albacares) and bigeye (T. obesus) tuna in a network of fish aggregating devices (FADs)[END_REF][START_REF] Robert | Intra-individual behavioral variability displayed by tuna at fish aggregating devices (FADs)[END_REF][START_REF] Rodriguez-Tress | Associative behavior of yellowfin, Thunnus albacares, skipjack, Katsuwonus pelamis, and bigeye tuna, T. obesus at anchored fish aggregating devices (FADs) off the Coast of Mauritius[END_REF]. Using this method [START_REF] Capello | Population assessment of tropical tuna based on their associative behavior around floating objects[END_REF] estimated that yellowfin tuna (Thunnus albacares) spent 72% of their time associated with FADs in the array of anchored FADs around Oahu (Hawaii, USA), which is considered to be a high association rate. However, this approach is near impossible to replicate with drifting FADs due to the difficulty in locating all drifting objects within an area and the fact that arrays of drifting objects do not maintain their integrity for extended periods of time (e.g. floating objects do not always drift in parallel).

The second method consists of using the vertical behaviour of certain species to identify their association status. Some species, such as bigeye tuna (T. obesus) and in certain areas yellowfin tuna, exhibit different vertical behaviours during periods of association with a floating object and when they are free-swimming. The analysis of time series of vertical behaviour data of fish equipped with archival tags allowed the associative dynamics of these animals to be reconstructed [START_REF] Phillips | Revisiting the vulnerability of juvenile bigeye (Thunnus obesus) and yellowfin (T. albacares) tuna caught by purse-seine fisheries while associating with surface waters and floating objects[END_REF][START_REF] Schaefer | Vertical movements, behavior, and habitat of bigeye tuna (Thunnus obesus) in the equatorial Eastern Pacific Ocean, ascertained from archival tag data[END_REF].

These studies estimated that bigeye and yellowfin tuna spend an average of 83 and 77% of their time respectively, in an unassociated state. Although the simple comparison of yellowfin tuna behaviour reported by [START_REF] Capello | Population assessment of tropical tuna based on their associative behavior around floating objects[END_REF] and [START_REF] Phillips | Revisiting the vulnerability of juvenile bigeye (Thunnus obesus) and yellowfin (T. albacares) tuna caught by purse-seine fisheries while associating with surface waters and floating objects[END_REF] highlights a need to conduct paired tests to distinguish between the two methods, this divergence could reveal that the same species can exhibit highly variable association rates. This emphasizes the need for further investigation into the effects of different variables (e.g. density of floating objects, biotic and abiotic environmental conditions) on association rates.

As silky sharks have not been found to display different vertical behaviour during associated and unassociated phases,, and are less commonly encountered within arrays of anchored FADs, neither of the methods described above have been successfully applied to this species for estimating association rates.

In this study, a novel approach for estimating the proportion of time silky sharks spend associated with floating objects is proposed.

Recently, [START_REF] Imzilen | Fish aggregating devices drift like oceanographic drifters in the near-surface currents of the Atlantic and Indian Oceans[END_REF] showed that FADs drifted identically to oceanographic drifters, i.e. followed the ocean near-surface currents.

This result was used to hypothesize that a strong association rate in silky sharks would result in shark trajectories following these currents.

Conversely, a weak correlation between shark trajectories and currents would suggest a low association rate with FADs. As such, a methodology was developed here to estimate this association rate, using tracks of silky sharks obtained with pop-up archival tags compared with outputs of a Lagrangian passive drift model from the western Indian Ocean. S1 for additional information). All sharks were caught and released in association with drifting FADs. Sharks were either captured during purse-seine operations or during dedicated tagging cruises when baited hand lines were used. The majority of the silky sharks were juveniles, with total length ranging from 87 to 235 cm (median: 111.5 cm). Tags were either attached using a tethered anchor inserted into the dorsal musculature near the base of the dorsal fin, or directly to the dorsal fin using a threaded nylon rod to which the tag was tethered. Tags were programmed to automatically release from the animal after either 100 or 150 days. Following release, the tags floated to the surface (popped up) and transmitted archived data, including light levels recorded during the deployment, via the ARGOS satellite system. The deployment period of the 26 tagged sharks ranged from 27 to 141 days with a mean of 55.2 days (SD 33.9 days).

| MATERIALS AND METHODS

| Electronic tagging

| Geolocation estimates

Geographical positions were reconstructed from light intensity data, with some positions determined accurately through acoustic tagging.

This information, along with sea surface temperature and depth data, was processed using the GPE3 model available on the Wildlife Computers Data Portal (http://my.wildlifecomputers.com/data/) to provide the most likely positions on a daily basis. In a few cases, double tagging with acoustic tags allowed the position of the shark to be determine accurately due to their time-stamped detection by acoustic receivers on FADs and its associated GPS data. Apart from the accurate locations obtained through acoustic tagging, the light-based geolocation estimates are subject to considerable errors (Teo et al. 2004).

To account for this error, the overall uncertainty of the geolocation (herein referred to as the radius of uncertainty) was estimated as the average distance to the 99% likelihood contour estimated over all the trajectories (Figure 1).

| Relationship between shark trajectories and ocean surface currents

| Brief overview of the method

Shark trajectories were compared with trajectories of simulated drifting particles released in the proximity of the shark geolocation estimate, within the radius of uncertainty. Divergence of shark subtrajectories from surface currents was assessed through an evaluation of the distance between a shark's position and simulated particles. To assess the validity of the method, the same approach was applied to trajectories of oceanographic drifters that are known to follow ocean currents. Geographical areas where the method was invalidated were FIGURE 1 Accuracy of shark positioning. (a) The black line corresponds to the most likely daily positions of an individual shark, shaded grey presents the daily 99% likelihood contours of the individual's daily positions. (b) Average distance from most likely positions to the 99% likelihood contour was calculated for each daily shark position, the distribution of these daily average distances is plotted here thereby removed from the analyses. For retained areas, the increasing tendency of shark trajectories to separate from simulated particles compared to drifter trajectories was used to draw conclusions about sharks' divergence from surface currents.

| Details of the method

From each shark trajectory, sliding window portions of x days were sampled. The final part of the trajectory corresponding to x-1 days was excluded from the analysis (for example a 20 days long trajectory with a 6-day sliding window resulted in 15 portions each six days long and each one day later than the preceding portion). For each x days long track portion, the hypothesis H 0 : the animal follows sea surface circulation; was tested. For this purpose, a cloud of 1,000 virtual particles was released within the radius of uncertainty R of 100 km around the initial position of the track portion. The choice of R was based on a rounded value of the estimated average 99% likelihood contour (Figure 1). The trajectories of the virtual particles were simulated as passively drifting for x days with the Ichthyop software [START_REF] Lett | A Lagrangian tool for modelling ichthyoplankton dynamics[END_REF], forced with modelled ocean near-surface current data from the OSCAR product (1/3 spatial resolution, five days temporal resolution) [START_REF] Esr | OSCAR third degree resolution ocean surface currents[END_REF]. To assess if the final position of the shark track portion was consistent with passive drift, the distance to the closest simulated particles was measured (CD animal : for 'Closest Distance' = average distance to the N CD =10 closest simulated particles), and compared with distances obtained from strictly drifting objects (CD strict drift ), i.e. measured from simulated trajectories. The latter was obtained by randomly sampling a number N sample of simulated particles. If CD animal was observed to be greater than 95% of CD strict drift , i.e. the animal position separated significantly from the cloud of simulated particles, H 0 was rejected and the animal track portion was considered as incompatible with passive drift.

The analysis was conducted considering track portions of x = 6 days. This duration of six days was selected according to the average ocean surface current speed (v = 0.2 m/s = 17 km/d) and the subsequent required time for a virtual particle strictly moving straight forward to leave the initial 100-km release radius. Accordingly, for durations less than six days, H 0 would not even be rejected for a stationary animal as its position would remain within the cloud of simulated drifting particles. In summary, both the spatial scale of 100 km and the temporal scale of six days are larger than the OSCAR resolution, which limits the bias induced by mesoscale features not represented by the OSCAR product. Figure 2 illustrates the classification approach, H 0 -accepted or -rejected, for two animal tracks.

To assess the accuracy of our classification method and to identify potential biases, the same method was applied to trajectories from a set of oceanographic drifters from the Global Drifter Program (GDP) [START_REF] Centurioni | A global ocean observing system for measuring sea level atmospheric pressure: Effects and impacts on numerical weather prediction[END_REF] as these were known to follow the movements of water masses. The GDP dataset was subsampled for trajectories within the study period (2010)(2011)(2012) and the geographical extent of shark trajectories (central western Indian Ocean), resulting in a dataset of 190 trajectories, encompassing a total of 14,030 daily positions. This comparison allowed for the identification of regions where our method systematically misclassified track portions as having not followed surface currents. To facilitate the comparison and identify areas of systematic error, a 2 resolution grid was applied to the central western Indian Ocean. Grid cells for which more than a given percentage threshold (named below T%) of drifter track portions were classified as H 0 -rejected were removed from the shark trajectory analysis. The baseline analysis was conducted considering a percent threshold T = 50% and sensitivity analysis was conducted for the following values of T%: 20, 30, 40, 60, 70 and 80%.

Even within the selected cells, drifter track portions were not always classified as H 0 -accepted for all of the GDP samples. The proportion P 0 of misclassified GDP track portions was used to establish a lower bound for the proportion of time that a shark actively diverged from the ocean surface currents. As such, if every animal track portion was actually following surface currents as drifters do, there would be a proportion P 0 of them that would still be classified as H 0 -rejected due to the inherent uncertainty within the method. If P is defined as the fraction of animal track portions classified as H 0 -rejected, then a FIGURE 2 Example of classification for compliance with the passive drift hypothesis for two individual shark tracks. Track portions are considered over six days and their starting point are coloured in green if compatible with passive drift hypothesis (H 0 -accepted) and in red if not (H 0 -rejected). Blue tracks represent Ichthyop virtual particles released around specific examples of track positions (circled black dots) and tracked for passive drift for six days. The respective animal positions after six days are represented by black triangles. The animal trajectory portion in grey corresponds to the last five days of the track and was therefore not considered in the analysis minimum proportion Π =( P -P 0 ) of animal track portion would be classified as H 0 -rejected due to actual divergence from the sea surface currents and not because of the method error. The remaining proportion P 0 that is H 0 -rejected could either follow the current but be misclassified (like the drifters), or truly diverge from circulation. As such, the value of Π provided a lower bound for the proportion of time that a shark actively diverged from the sea surface currents.

Standard deviations of P, P, 0 and Π were calculated through a bootstrap procedure over the selected grid cells (100 random samples with replacement of threshold-selected grid cells). All analyses were conducted using R 3.3.2 (R Core Team, 2018).

| RESULTS

The comparison between the Lagrangian model and the oceanic drifter data outlined a spatially-heterogeneous performance of the model (Figure 3a). Regions where the Lagrangian model was not in agreement with the drifter track portions were mainly located in the Mozambique Channel, the equatorial ocean, and the coastal regions close to eastern Africa. Namely, in these regions, the hypothesis H 0 that the drifter followed the simulated surface currents was mainly rejected (see red drifter track portions in Figure 3a). Using a threshold T = 50% over the 234 grid cells considered in the analysis, 104 grid cells where the percentage of incorrectly classified drifter track portions was less than T% were found (Figure 3a). These cells contained 3,646 six-day long track portions, corresponding to 29% of the overall drifter track portions.

The overall shark trajectory dataset encompassed 1,434 daily positions and the average distance to the 99% likelihood contour estimated over all the trajectories corresponded to 79.3 km (Figure 1).

Within the above-identified 104 cells, 842 shark track portions were selected, corresponding to 66% of the overall shark track portions.

Within the selected cells, proportions P = 52% (SD 5%) of shark track portions and P 0 = 20% (SD 1.5%) of drifter track portions were found to be incompatible with H 0 , i.e. did not follow the simulated surface currents tracks (Figure 3). As such, the lower bound for the proportion of shark track portions that diverged from the sea surface currents was estimated as Π = 32% (SD 5%; see also Figure S2).

Interestingly, the sensitivity analysis demonstrated that these estimated values of Π mean (30%) and SD (5%) remained stable for a large range of the T% threshold values (30-80%, Figure 4a) in the baseline case (i.e. using a track portion duration of x = 6 days and R = 100 km). By contrast, Π showed a higher sensitivity relative to the track portion length x and the radius of uncertainty R and increased with x (Figure 4b). When R = 50 km was compared with R = 100 km in the baseline case (T = 50%), Π increased faster with x for R = 50 km, before levelling off at an approximately equivalent level. However, as explained in the methods section, with R = 50 km, the baseline value of x would be three days. This duration of three days can be obtained considering the average ocean surface current speed (v = 0.2 m/s = 17 km/d) and the subsequent required time for a virtual particle strictly moving straight forward to leave the initial 50-km release radius. For these values of R = 50 km and x = 3 days, values of Π 30% and SD 5% were again found (Figure 4b). Finally, the sensitivity of Π relative to x was tested for different thresholds T% and was higher for lower values of the threshold T% (Figure S3). The other parameters that were tested (N CD : the number of particles across which CD was calculated, and N sample : the number of CD strict drift calculated to assess the control distribution and its 95% threshold) had little effect on Π (Figures S4 andS5).

| DISCUSSION

This study provides the first quantitative estimate of the amount of time that silky sharks spend between associations with floating objects. Furthermore, the comparison of in situ animal trajectories derived from pop-up archival tags with outputs of a Lagrangian passive drift model provides a novel approach for estimating the FIGURE 3 Classification for compliance with the passive drift hypothesis of oceanographic drifter (a) and shark (b) track datasets. Track portions are coloured as detailed in Figure 1. A 2 resolution grid applied to the region (central western Indian Ocean) indicates the cells for which >50% of drifter track portions were incorrectly classified as H 0 -rejected and were removed from the analysis (in grey) prevalence of this behaviour. While recent acoustic tagging experiments conducted around drifting FADs successfully estimated the average time that silky sharks spend associated with the same FAD [START_REF] Filmalter | Fine-scale 3-dimensional movement behaviour of silky sharks, Carcharhinus falciformis, associated with fish aggregating devices (FADs)[END_REF], nothing was known about the amount of time that sharks spent in an unassociated state. This information is key for assessing the impacts of FADs on silky shark populations, since the time that they spend between associations can both affect the rate of entanglement (if FADs in the region are built with nets, not following non-entangling FAD design criteria, [START_REF] Murua | Advances in the use of entanglement-reducing drifting fish aggregating devices (DFADs) in tuna purse seine fleets[END_REF], bycatch (direct impacts), and their vulnerability related to the potential influence of artificial floating objects on their movements and ecology (indirect impacts).

Given the large uncertainty related to the position estimates derived from light-based geolocation applied to pop-up satellite tag data (average radius of 99% likelihood contours of nearly 80 km) and the fact that the position of surrounding FADs were unknown, this study required the development of a novel methodological framework that could overcome the low accuracy and uncertainties in the available data. Our method relies on passive drift simulations of virtual particles released around the estimated position of the sharks, using a Lagrangian model. A direct comparison of shark trajectories with those of near-by drifters or FADs would have been a valid approach; however, the number of tracked drifting objects within a meaningful radius of the shark was too small to allow such an approach. Furthermore, even if this were the case, if shark and drifter diverged, it would remain impossible to exclude the possibility that they were following divergent currents. As such, the method developed in this study is able to account for such possible divergences in a statistical manner through the consideration of a large set of particles rather than on comparing two trajectories. In regions where divergence is prominent, all particles will spread over a large distance. In this case, should the shark be located within the cloud of particles but does not follow any of them, the rejection of H 0 remains impossible. A similar approach was used by [START_REF] Briscoe | Active dispersal in loggerhead sea turtles (Caretta caretta) during the 'lost years[END_REF], who interpreted the discrepancies between observed and simulated paths of young sea turtles as an indication of active swimming. However, [START_REF] Putman | Passive drift or active swimming in marine organisms[END_REF] showed that these differences could also partly be explained by uncertainties in the model predictions. To account for such uncertainties, the approach was first applied to oceanographic drifters, and only the areas where the model predictions were satisfactory were selected. Also, the model predictions obtained for the drifter datasets were used to derive a robust lower bound for the time that sharks spent between associations. Another way of understanding the construction of this lower bound (Π) is to deconstruct the model's error into type I and type II errors (Table S6). Let P real be the actual proportion of times the animals separated from the sea surface currents, and the proportion P be an estimate of P real .

The type I error can be approximated by P 0 , the proportion of portion tracks that do follow surface currents but are H 0 -rejected.

Nonetheless, the input of the type II error is of unknown magnitude so the only certainty concerns the lower bound of the P real value (Π << P-P 0 < P real ).

It is also possible that the direct effect of wind (i.e. windage; [START_REF] Johns | The establishment of a pelagic sargassum population in the tropical Atlantic: Biological consequences of a basin-scale long distance dispersal event[END_REF][START_REF] Trinanes | Analysis of Flight MH370 potential debris trajectories using ocean observations and numerical model results[END_REF] and waves (i.e. Stokes drift; [START_REF] Dobler | Large impact of stokes drift on the fate of surface floating debris in the South Indian Basin[END_REF][START_REF] Fraser | Antarctica's ecological isolation will be broken by storm-driven dispersal and warming[END_REF] influence the drift of FADs, and as such the separation of sharks from surface current correctly indicating non-associative behaviour may require further validation.

Finally, an oceanographic ocean near-surface current product (OSCAR) was used, which relies on remote-sensing data, to force a Lagrangian model. Despite their coarse spatial resolution, such products were shown to successfully simulate surface drifter trajectories with equal (or even higher) accuracies than higher-resolution oceanographic model outputs [START_REF] Amemou | Validation of Lagrangian model using trajectories of oceanographic drifters[END_REF][START_REF] Scott | Estimates of surface drifter trajectories in the equatorial Atlantic: A multi-model ensemble approach[END_REF].

Nonetheless, a limitation of our model in evaluating oceanographic drifter trajectories comes from the tendency for OSCAR to underestimate current speed [START_REF] Imzilen | Fish aggregating devices drift like oceanographic drifters in the near-surface currents of the Atlantic and Indian Oceans[END_REF]. This limitation could be of particular importance for areas of high surface current velocity, such as coastal waters off the Somalia horn and off Cape Amber (Northern Madagascar), as illustrated by [START_REF] Peng | Characteristics of the near-surface currents in the Indian Ocean as deduced from satellite-tracked surface drifters. Part I: Pseudo-Eulerian statistics[END_REF], where our model resulted in high levels of rejection in the drifter control tests. As a result, it was not possible to draw any conclusions about the relationship between shark movements and surface currents for several shark tracks located in these areas. Another potential source of rejection of the drifter control test could be the low resolution of ocean current data (1/3 , 37 km). In certain areas such as the east coast of Africa and the Mozambique Channel, such resolution may not be sufficient to account for mesoscale features and coastal currents, resulting in rejections in our approach. Furthermore, the method could be improved by conducting the drifter control tests on a local spatial and temporal scale around each individual animal track portion, rather than globally. However, the limited number of drifter tracks available in our study was insufficient to facilitate such an approach.

The sensitivity analysis allowed us to assess the robustness of our approach. The 30% lower bound that was found for shark association with FADs was insensitive to our model parameters except to track portion duration. For longer durations, higher proportions of track portions were uncorrelated with surface currents, indicating that for an animal initially located in a given water mass, the longer it is subsequently monitored, the higher the likelihood of observing it actively swimming out of that water mass.

When focusing on long track portion duration (>6 days), increasing the level of demand regarding control by the oceanographic drifters track portions (= decreasing the threshold T%) provided better predictions in subsequently selected areas, but naturally reduced the size of the analysed animal track dataset (smaller selected area). This trade-off could be resolved by favouring the use of a threshold of 30%, given the drop of in the dataset size from 30 to 20% (579 for T = 30% to 137 for T = 20% selected shark track portions, Table S7).

Our results can also provide insights on residence times, i.e. the time spent at the same FAD. As the mean duration of our shark complete tracks was 55 days, the lower bound of 30% amounts to an average of 16.5 days spent between two consecutive associations during the monitoring period. Considering that multiple departure events could occur, this result indicates that association to the same FAD for a time period longer than 40 days is very unlikely. This is consistent with the maximum residence time of 30.6 days at the same floating object observed by [START_REF] Filmalter | Fine-scale 3-dimensional movement behaviour of silky sharks, Carcharhinus falciformis, associated with fish aggregating devices (FADs)[END_REF]. Additionally, in light of the average FAD-association duration of 15 days estimated in [START_REF] Filmalter | Fine-scale 3-dimensional movement behaviour of silky sharks, Carcharhinus falciformis, associated with fish aggregating devices (FADs)[END_REF], the 30%-lower bound for the total time spent unassociated provides a lower bound for the mean absence time (i.e. a mean time between two FAD associations) of approximately six days. Indeed, given the average residence time (CRT mean ) and the average absence time (CAT mean ) and the fact that the number of association events equals the number of unassociation events, the proportion of time spent unassociated can also be expressed as the ratio CAT mean / (CRT mean + CAT mean ). From this equality and the measured value CRT mean = 15 days, it is straightforward to obtain CAT mean > 6 days.

For the first time, it was possible to demonstrate that juvenile silky sharks can move independently of surface currents, and thus FADs, for at least 30% of their time. As this value is a lower bound threshold, it is difficult to compare with estimates of association rates of yellowfin and bigeye tuna [START_REF] Capello | Population assessment of tropical tuna based on their associative behavior around floating objects[END_REF][START_REF] Phillips | Revisiting the vulnerability of juvenile bigeye (Thunnus obesus) and yellowfin (T. albacares) tuna caught by purse-seine fisheries while associating with surface waters and floating objects[END_REF][START_REF] Schaefer | Vertical movements, behavior, and habitat of bigeye tuna (Thunnus obesus) in the equatorial Eastern Pacific Ocean, ascertained from archival tag data[END_REF]. Nonetheless, a lower value was expected, considering their relative long residence times at FADs (average of 2 weeks) and their high rate of occurrence as a bycatch species within the FAD fisheries which contrasts with the declining population trends of silky sharks [START_REF] Iucn | The IUCN red list of threatened species 2017: E.T39370A117721799[END_REF]. This striking result provides some optimistic vision, as their vulnerability to purse seiners is strongly correlated to the time they spend associated with FADs. Long residence times at FADs, such as those observed by [START_REF] Filmalter | Fine-scale 3-dimensional movement behaviour of silky sharks, Carcharhinus falciformis, associated with fish aggregating devices (FADs)[END_REF], reveal that floating objects play an important role in the ecology of juveniles of the species. Consequently, one could assume that when an individual leaves a floating object, the motivation to associate with another floating object is strong. In areas with high densities of FADs, this should lead to individuals rapidly re-associating with a new floating object, resulting in short inter-association periods. Such behaviour should also lead to some individuals re-associating with the same FAD after a long excursion (>24 hours). This has never been observed [START_REF] Filmalter | Fine-scale 3-dimensional movement behaviour of silky sharks, Carcharhinus falciformis, associated with fish aggregating devices (FADs)[END_REF], except for sharks returning after recovering from the stress of capture and tagging, which tends to validate that silky sharks are able to home back to an FAD. Given the potential impacts of FADs on shark populations, the precautionary approach implies reducing the numbers of FADs. Indeed, increasing FAD densities would be likely to decrease the total time they spend in a free-swimming state, increasing their vulnerability to FAD-based purse-seine fisheries, and the potential risk of ecological trap.

Using these results to evaluate the impacts of increasing numbers of FADs on silky sharks is not straightforward. Extrapolating these results obtained from a geographical subset of the Indian Ocean to the entire silky shark population can be deceptive. Typically, the areas retained in these analyses are heterogeneous in terms of FAD densities, with high values of FADs densities in the north of Madagascar but lower values to the east [START_REF] Imzilen | Fish aggregating devices drift like oceanographic drifters in the near-surface currents of the Atlantic and Indian Oceans[END_REF][START_REF] Maufroy | Massive increase in the use of drifting fish aggregating devices (DFADs) by tropical tuna purse seine fisheries in the Atlantic and Indian Oceans[END_REF]. One could expect for sharks located in areas with lower floating object densities to be less prone to associative behaviour and thus for their movements to be less correlated with surface currents.

However, due to the reduced sample size of our study, a comparative analysis that accounted for the role of the density of FADs could not be conducted.

As such, future work in this area should aim to quantify the time that silky sharks spend moving independently of surface currents in different time area windows, with contrasting environmental data (e.g. density of floating objects), in order to investigate their effects.

Besides a need to get spatially consistent model outputs, this would clearly require that more silky sharks be tagged with pop-up archival tags in more areas during different periods, as well as the collection of data on the density of natural and artificial floating objects released by all fishing fleets. In parallel, further studies investigating changes in the vertical behaviour of silky sharks associated with FADs could directly facilitate the assessment of association rates, as has been achieved for bigeye tuna [START_REF] Phillips | Revisiting the vulnerability of juvenile bigeye (Thunnus obesus) and yellowfin (T. albacares) tuna caught by purse-seine fisheries while associating with surface waters and floating objects[END_REF][START_REF] Schaefer | Vertical movements, behavior, and habitat of bigeye tuna (Thunnus obesus) in the equatorial Eastern Pacific Ocean, ascertained from archival tag data[END_REF]. Finally, instrumenting an array of drifting floating objects with acoustic receivers and tagging silky sharks with acoustic tags within this array would allow the duration of inter-association periods as a function of inter-FAD distance to be accurately measured, thus providing a complete picture of the association dynamics of this species.

Framework Program 7, "Theme 2-Food, Agriculture, Fisheries and Biotechnology", through the research project MADE (Mitigating adverse ecological impacts of open ocean fisheries). This work does not necessarily reflect the Commission's views and in no way anticipates its future policy in this area. This work was funded by the International Seafood Sustainability Foundation (ISSF). The research was conducted independently by the authors and its results, professional opinions, and conclusions are solely the work of the authors. There are no contractual obligations between ISSF and the authors that might influence the results, professional opinions, and conclusions.

Our gratitude is also extended to the skippers and crews of the various vessels from which this work was carried out, and to the anonymous reviewers for their helpful comments.

  Twenty-six silky sharks were caught at FADs in central western Indian Ocean in water surrounding the Seychelles and in the Mozambique Channel. Sharks were tagged with Pop-up Satellite Archival Tags (miniPAT; Wildlife Computers, Redmond, WA, USA) between 2010 and 2012 (see Table

FIGURE 4

 4 FIGURE 4 Effect of T threshold and R radius on the lower bound Π of the proportion of shark track portions that diverged from the sea surface currents. (a) The threshold value (T%) controls the removal of 2 grid cells, according to the maximum tolerated proportion of incorrectly predicted drifter track portions inside the cell. Error bars represent standard deviation values obtained through bootstrapping of subsequently sampled 2 grid cells. (b) The value of R controls the radius in which virtual particles were released to estimate track correlation with sea surface circulation. The resulting lower bound of the proportion of shark track portions that diverged from the sea surface circulation is displayed according to track portion length (in days)

ACKNOWLEDGEMENTS

This study was carried out with financial support from the Commission of the European Communities, specifically the RTD program of


N Salhi

H Zmerli Triki

J C Molinero

Mohamed Laabir

E Sehli

A Bellaaj-Zouari

N Daly

O Kefi-Daly
email: onskefidaly@gmail.com

Yahia ) N Salhi

N Daly Yahia

O Kefi-Daly Yahia

Seasonal variability of picophytoplankton under contrasting environments in northern Tunisian coasts, southwestern Mediterranean Sea

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

Picophytoplankton cells are autotrophic microorganisms, < 2 μmin size, that substantially contribute to the phytoplankton biomass and carbon export in all aquatic environments [START_REF] Richardson | Small phytoplankton and carbon export from the surface ocean[END_REF][START_REF] Stockner | Phototrophic picoplankton: freshwater ecosystems: an overview from marine and[END_REF]. In particular, these microorganisms dominate the total phytoplankton biomass and production in oligotrophic ecosystems [START_REF] Li | Ultraphytoplankton in the eastern Mediterranean Sea -towards deriving phytoplankton biomass from flow cytometric measurements of abundance, fluorescence and light scatter[END_REF][START_REF] Šilović | Picoplankton composition related to thermohaline circulation: the Albanian boundary zone (southern Adriatic) in late spring[END_REF]. Such dominance is based on the efficient uptake of dissolved nutrients at low bulk concentrations, coupled with reduced sedimentation loss and subsequent grazing [START_REF] Caroppo | The contribution of picophytoplankton to community structure in a Mediterranean brackish environment[END_REF][START_REF] Raven | Physiological consequences of extremely small size for autotrophic organisms in the sea[END_REF]. In environments characterised by a marked seasonality, the abundance and structure of picophytoplankton change throughout the year along with the decrease of nutrient concentration and increasing temperatures, which characterize the shift from new to regenerated production [START_REF] Kuosa | Picoplanktonic algae in the northern Baltic Sea -seasonal dynamics and flagellate grazing[END_REF].

Picophytoplankton is represented by two genera of picocyanobacteria Synechococcus, Prochlorococcus and picoeukaryotes [START_REF] Sieburth | Pelagic ecosystem structure: heterotrophic the plankton and their relationship to plankton size fractions1 compartments of organisms[END_REF]. The genus Synechococcus is ubiquitous in all marine environments, although it shows larger abundances in nutrient-rich environments than in oligotrophic ones (Partensky et al., 1999a(Partensky et al., , 1999b) ) and appears also favoured by low salinity and/or low temperature [START_REF] Dvořák | Synechococcus: 3 billion years of global dominance[END_REF]. Conversely, Prochlorococcus is well adapted to high light levels of stratified oligotrophic waters, although the genus has been also recorded in shallow, meso-and eutrophic systems (Partensky et al., 1999a(Partensky et al., , 1999b)). In turn, Picoeukaryotes (cells < 3 μm) form a diverse group widely distributed in oceanic, coastal, and freshwater ecosystems; their biomass and high productivity suggest that they play a much larger role in planktonic ecosystems [START_REF] Dìez | Study of genetic diversity of eukaryotic picoplankton in different oceanic regions by small-subunit rRNA gene cloning and sequencing study of genetic diversity of eukaryotic picoplankton in different oceanic regions by small-subunit rRNA gene cloning and[END_REF][START_REF] Worden | Assessing the dynamics and ecology of marine picophytoplankton: the importance of the eukaryotic component[END_REF].

The Mediterranean Sea is characterised by substantial proliferations of ultraphytoplankton (< 10 μm) and a dominance of picophytoplankton over large phytoplankton cells in offshore areas [START_REF] Casotti | Composition and dynamics of the phytoplankton of the Ionian Sea (eastern Mediterranean)[END_REF][START_REF] Conan | Variabilite hydrologique et biologique du golfe du Lion. II. Productivitk[END_REF][START_REF] Denis | Ultraphytoplankton basin-scale distribution in the eastern Mediterranean Sea in winter: link to hydrodynamism and nutrients[END_REF][START_REF] Minas | Production de base et de recyclage; une revue de la problématique en Méditerranée nord-occidentale[END_REF][START_REF] Siokou-Frangou | Plankton in the open Mediterranean sea: a review[END_REF]. Research efforts to understand picoplankton dynamics however, have mainly covered northern Mediterranean coasts, whereas in the southern Mediterranean Sea similar studies are scarce. Former reports have shown that Prochlorococcus displays a conspicuous distribution in a variety of Mediterranean habitats, such as offshore stratified areas and mixed coastal waters, whereas Synechococcus has been reported mainly in stratified waters [START_REF] Marty | Seasonal and interannual variations in phytoplankton production at DYFAMED time-series station[END_REF]. This pattern of distribution probably results from the presence of ecotypes favoring different strains of Prochlorococcus and Synechococcus. Here we investigate picophytoplankton communities in the Bay and Lagoon of Bizerte, located in the northern Tunisian coast, southwestern Mediterranean Sea.

Bizerte lagoon is a highly productive and eutrophicated lagoon, and an important economic area holding aquaculture and fishing activities. This ecosystem however is heavily affected by anthropogenic stress as it receives constant discharges of domestic and industrial runoffs that alter the nutrient dynamic balance and therefore autotroph communities at the basis of the food web. Conversely, the Bay of Bizerte is exposed to the influence of oligotrophic Mediterranean waters and is connected to the Lagoon of Bizerte.

In the present study, we focused on seasonal changes of picophytoplankton assessed at the single cell level in these ecosystems characterised by contrasted trophic states. We aim to depict the seasonal distribution and abundance of picophytoplankton groups, their contribution to the total chlorophyll biomass and to quantify potential relationships linking their temporal changes to environmental factors (nutrients, temperature and salinity) prevailing from June 2012 to September 2013.

The study was carried out in the Bay and the Lagoon of Bizerte, northern coast of Tunisia (Fig. 1). The Bay of Bizerte is an open oligotrophic/mesotrophic ecosystem characterised by a salinity ranging between 36 and 38 [START_REF] Addad | Analyse taxinomique, écologique et caractérisation biochimique des scyphoméduses de la baie de Bizerte et de Tunis (Juillet-Décembre 2004)[END_REF], water depth ranging from 16 to 20 m, chlorophyll a ranging between 0.5 and 2.5 mg•m -3 and moderate nutrient concentrations: total nitrogen (1-4 μM), orthophosphate (0.01-0.71 μM) and silicate (0.4-2.2 μM) [START_REF] Sahraoui | Response of potentially toxic Pseudo-nitzschia (Bacillariophyceae) populations and domoic acid to environmental conditions in a eutrophied, SW Mediterranean coastal lagoon (Tunisia)[END_REF]. The Lagoon of Bizerte is a semi-enclosed area covering 128 km 2 with a mean depth of 8 m.

This Lagoon communicates with the Mediterranean Sea via the Bizerte channel (7 km long, 300 m width and 12 m depth) and receives important freshwater inputs from several tributaries [START_REF] Béjaoui | Analysis of hydrobiological pattern in the Bizerte lagoon (Tunisia)[END_REF]. This ecosystem is exposed to heavy anthropogenic stress, i.e. aquaculture activities, industrial discharges, and intensive agriculture in the southeast watershed. It shows eutrophic conditions with (1-24 μM) total nitrogen; (0.14-0.48 μM) orthophosphate, (1.87-13 μM) silicate, and is characterised by high chlorophyll a concentrations (3-8 mg•m -3 ) [START_REF] Grami | The plankton food web of the Bizerte Lagoon (South-western Mediterranean): II. Carbon steady-state modelling using inverse analysis[END_REF][START_REF] Sakka Hlaili | The planktonic food web of the Bizerte lagoon (south-western Mediterranean) during summer: I. Spatial distribution under different anthropogenic pressures[END_REF], 2007).

The seawater samples were collected using a pipe line on a fortnightly basis from June 2012 to September 2013 at the Bay (20 m deep; 37°17′09.2″N, 09°53′91.1″E) and the Lagoon (37°12′46.0″N, 09°50′48.0″E) 8 m deep stations. At each sampling site, 500 cm 3 of seawater were filtered for determining chlorophyll a concentration and the filtrate was used to assess nutrient concentrations (NO 2

- ,N O 3 - , PO 4
3 -and SiO 4

-

). For NH 4 + analysis, 250 cm 3 were collected and stored at -20 °C until their analysis in the laboratory. For flow cytometry analysis, subsamples (2 cm 3 ) of collected seawater were placed into cryogenic tubes, fixed with paraformaldehyde (2% final concentration) and frozen in liquid nitrogen on the boat to be further stored at -80 °C in the laboratory until analysis [START_REF] Vaulot | A simple method to preserve oceanic phytoplankton for flow cytometric analyses[END_REF]. Temperature and salinity were measured using the multiprobe WTW, Cond 3110/SET model. For chlorophyll a analysis, 500 cm 3 of seawater were filtered using low vacuum pressure firstly on 20 μm pore-size polycarbonate membranes then on Whatman GF/D filters (2.7 μm) and finally through Whatman GF/F polycarbonate filters (0.7 μm). Microphytoplankton cells were retained by the 20 μm filters, whereas nanophytoplankton and picophytoplankton were retained by the 2.7 μm and 0.7 μm poresize filters respectively. These successive filtrations allowed the estimation of contribution percentages of the different fractions to total chlorophyll a concentrations that were computed as the sum of the concentrations related to the three fractions. Each single filter was recovered with 90% acetone and grounded manually into glass centrifuge tube filled with 90% acetone. The mixture was kept in the dark at -4 °C for 24 h to allow total extraction of chlorophyll a whose fluorescence was measured using the standard spectrometric Lorenzen method [START_REF] Aminot | Manuel des analyses chimiques en milieu marin[END_REF]. Nutrient concentrations were determined by using conventional colorimetric methods on automated channel Technicon auto analyzer, according to the protocols of [START_REF] Tréguer | Manuel d'analyses des sels nutritifs dans l'eau de mer[END_REF]. Seven independent analyses were performed for each sample. Ammonium concentration analyses were carried out using a spectrometric method as described in [START_REF] Aminot | Manuel des analyses chimiques en milieu marin[END_REF]. The detection limits for ammonium, nitrate, silicates and orthophosphates were 0.04 μM, 0.02 μM, 0.05 μM and 0.002 μM, respectively.

In order to identify picophytoplankton groups, the fixed seawater samples (2 cm 3 ) were analysed according to [START_REF] Marie | Flow cytometry analysis of marine picoplankton[END_REF] protocol using a FacsCalibur flow cytometer (BD Biosciences) and a Cyflow space (Partec) equipped with vertically polarised blue laser (488 nm) and a 638 nm red laser. Cell Quest Pro and Flow Max softwares were used for data treatments. In flow cytometry, allometric (size versus pigment fluorescence) and taxonomic (two-natural pigment fluorescences: Chlorophyll a and phycoerythrin autofluorescences) data were used to resolve phytoplankton groups. Orange autofluorescence corresponds to the presence of phycoerythrin and red autofluorescence corresponds to the presence of chlorophyll a. Before analysis, samples were prefiltred on 3 μm mesh size and fluorescent beads of 0.2 μm and 2 μm were added in order to normalise the flow cytometer settings and enumeration of picophytoplankton cells [START_REF] Olson | Phytoplankton analysis using flow cytometry[END_REF]. Bead counts provided a reference to determine the analysed volumes and derive cell concentrations for each cell group. For Cyflow space (Partec) machine, the software directly calculated cell concentrations (cells•cm -3 ) by using the sample volume injected through the microsyringe.

For statistical analysis, data were normalised, while the abundance of each picophytoplanktonic group was arcsine square-root transformed [START_REF] Legendre | Numerical Ecology[END_REF]. To assess the environmental influence on picophytoplankton we used Structural Equation Models (SEM). The structure of the path model is based on the potential influence of hydrological factors and nutrient loading on picoplankton communities, their structure and functioning; the strength of such links however may vary under contrasting trophic states (mesotrophy vs eutrophy) and anthropogenic stress. For each location, specificeffects of hydrography and nutrient loading and their co-variations on picoplankton biomass were assessed using variance partitioning and explored through SEM [START_REF] Neto | Variation partitioning of species data matrices: estimation and comparison of fractions[END_REF]. The strength of the links and the quantification of the overall model were determined by simple and partial multivariate regression and Monte Carlo permutation tests (1000 replicates), while Chi-square values were used to assess the robustness of models [START_REF] Alsterberg | Consumers mediate the effects of experimental ocean acidification and warming on primary producers[END_REF]. Path coefficients indicate the strength of the connection between causal and response variables. Path analysis was performed using AMOS version 22.

The measured physico-chemical water-sample parameters were temperature, salinity and nutrients. Seasonal changes of temperature showed a close covariation between the two sites (Fig. 2A). In the Lagoon, temperature reached the highest value (28.3 °C) in July 2013 whereas the lowest value (11.4 °C) was registered in February 2013. In the Bay, temperature ranged from 12.7 °C in February 2013 to 25.9 °C in August 2013. Marked differences however, were shown in the pattern of salinity. In the Lagoon, salinity displayed a marked seasonality, ranging from 32.7 to 37.8 with a maximum value observed in winter (December 2012), while the minimum was registered in spring (May 2013). In clear contrast, seasonal changes in salinity were less pronounced in the Bay, which also showed higher values ranging from 35.7 to 37.9 (Fig. 2B). Similar changes were noticed in the nutrient concentration. It was higher in the Lagoon than in the Bay over the entire sampling period (Fig. 3). Nitrate concentrations ranged from 0.02 to 4.94 μM in the Bay, and from 0.02 to 5.03 μM in the Lagoon (Fig. 3A). In the Bay, nitrite was characterised by relatively lower values with concentrations below 0.7 μM, in contrast with the Lagoon where nitrite concentrations ranged between 0.02 and 2.84 μM(Fig. 3B). Ammonium concentrations showed strong temporal variations during the survey in both ecosystems with the highest values being registered in the Lagoon of Bizerte (Fig. 3C). The observed concentration ranged from 0.04 to 8.7 μM in the Bay, while in the Lagoon it ranged 0.04 to 11.53 μM (Fig. 3C). Orthophosphate reached its highest concentrations in summer (August 2012) with 4.76 μM in the Bay, and 8.8 μM in the Lagoon (Fig. 3D). Silicate concentrations were high in both ecosystems, ranging from 0.05 to 6.82 μM in the Bay and from 0.05 to 6.7 μM in the Lagoon (Fig. 3E). Moreover, it is worth noting that water transparency was significantly higher in the Bay than in the Lagoon (Mann-Whitney U test, p < 0.05), with average values of 11.7 m and 3.5 m, respectively.

Total chlorophyll a displayed marked seasonal changes ranging between 0. June and October 2012, 3.47 and 8.54 mg•m -3 respectively (Fig. 4A). Total chlorophyll a concentration resulted from summing the chlorophyll a concentrations in the three fractions (picophytoplankton, nanophytoplankton and microphytoplankton). The Bay of Bizerte, characterised by low nutrient availability and relatively low chlorophyll concentration, was mainly represented by the phytoplankton group with 39.56% of total chlorophyll a, followed by the nanophytoplankton and microphytoplankton fractions with 35.7% and 24.7% of total chlorophyll a, respectively. Regarding the biomass of autotrophs, picophytoplankton was also dominant, reaching up to 60 to 80% of the total phytoplankton biomass. These field observations are in line with previous studies in the Mediterranean Sea, an overall oligotrophic ecosystem [START_REF] Berman | Optical transparency, chlorophyll and primary productivity in the eastern Mediterranean near the Israeli coast[END_REF][START_REF] Siokou-Frangou | Plankton in the open Mediterranean sea: a review[END_REF]. Under low nutrient supply, picophytoplankton develops better than microphytoplankton, which explains their dominance in oligotrophic waters where the primary production is based on regenerated nutrients and rapid recycling of organic matter [START_REF] Dugdale | Uptake of new and regenerated forms of nitrogen in primary productivity 1[END_REF][START_REF] Eppley | Particulate organic matter flux and planktonic new production in the deep ocean[END_REF]. In the Lagoon of Bizerte, the nutrient concentration was substantially higher as a consequence of the continuous nutrient supply by water discharge from urban area and river outflow [START_REF] Harzallah | Transport de polluants dans la lagune de Bizerte simulé par un modèle de circulation del'eau[END_REF]. In this ecosystem, the phytoplankton community was dominated by microphytoplankton in summer with a relative abundance of 41.69%, of total chlorophyll a, whereas in autumn picophytoplankton was the main group representing 41.9% of total chlorophyll a (Fig. 4B,C). Similarly, previous studies pointed out a dominance of microphytoplankton in nutrient-rich waters [START_REF] Casotti | Composition and dynamics of the phytoplankton of the Ionian Sea (eastern Mediterranean)[END_REF][START_REF] Denis | Ultraphytoplankton basin-scale distribution in the eastern Mediterranean Sea in winter: link to hydrodynamism and nutrients[END_REF][START_REF] Li | Ultraphytoplankton in the eastern Mediterranean Sea -towards deriving phytoplankton biomass from flow cytometric measurements of abundance, fluorescence and light scatter[END_REF][START_REF] Siokou-Frangou | Plankton in the open Mediterranean sea: a review[END_REF], including previous studies in the Lagoon of Bizerte [START_REF] Grami | The plankton food web of the Bizerte Lagoon (South-western Mediterranean): II. Carbon steady-state modelling using inverse analysis[END_REF][START_REF] Sahraoui | Response of potentially toxic Pseudo-nitzschia (Bacillariophyceae) populations and domoic acid to environmental conditions in a eutrophied, SW Mediterranean coastal lagoon (Tunisia)[END_REF][START_REF] Sakka Hlaili | The planktonic food web of the Bizerte lagoon (south-western Mediterranean) during summer: I. Spatial distribution under different anthropogenic pressures[END_REF], 2007;[START_REF] Salhi | Réponse des communautés phytoplanctoniques à l'eutrophisation du complexe Lagune-Baie de Bizerte[END_REF].

The analysis of the abundance and structure of picophytoplankton communities highlights the presence of five distinct subgroups in the study area: Prochlorococcus, exhibiting only red fluorescence (divinyl chlorophyll a), two types of Synechococcus (< 1.5 μm) labelled Synechococcus type (I) and Synechococcus type (II), which displayed red (chlorophyll a) and orange (phycoerythrin) fluorescences. In addition, two types of picoeukaryotes (< 3 μm) were identified; these were labelled picoeukaryotes type (I) and picoeukaryotes type (II) mainly emitting red (chlorophyll a) fluorescence. The picophytoplankton structure was thus based on 3 major groups, two of them split into two subgroups (Fig. 5).

The total cell abundance of picophytoplankton was significantly different in the two sites (Mann-Whitney U test, p < 0.05) and exhibited higher values in the Bay from August 2012 to February 2013 and June-July 2013. In the Bay, picophytoplankton abundance ranged between 1365 and 236 * 10 3 cells•cm -3 , whereas in the Lagoon it ranged between 2939 and 291 * 10 3 cells•cm -3 (Fig. 6A). Synechococcus was numerically dominant in both ecosystems with its abundance ranged from 628 to 225 * 10 3 cells•cm -3 in the Bay and from 1575 to 278 * 10 3 cells•cm -3 in the Lagoon (Fig. 6B). The Synechococcus abundance maximum was observed in July 2012 in the Bay and in June 2012 in the Lagoon. These abundances were similar to previously reported field records in the Gulf of Gabès, southern Tunisia, where Synechococcus abundance ranged from 0.30 to 210.51 * 10 3 cells•cm -3 in June 2008 [START_REF] Hamdi | The characterisation and summer distribution of ultraphytoplankton in the Gulf of Gabès (Eastern Mediterranean Sea, Tunisia) by using flow cytometry[END_REF]. In the Bay of Marseille (France), northwestern Mediterranean Sea, maximum abundance registered for Synechococcus was 7 * 10 4 cells•cm -3 [START_REF] Grégori | Phytoplankton group dynamics in the bay of Marseilles during a 2-year survey based on analytical flow cytometry[END_REF]. In the Ionian Sea, eastern Mediterranean Sea, the highest Synechococcus concentration was relatively high with 3 * 10 4 cells•cm -3 [START_REF] Casotti | Composition and dynamics of the phytoplankton of the Ionian Sea (eastern Mediterranean)[END_REF] For Prochlorococcus group, it was more abundant in the Bay showing abundance values ranging from 0 to 123 * 10 3 cells•cm -3 (maximum in December 2012), while in the Lagoon the abundances ranged from 0 to 17 * 10 3 cells•cm -3 (maximum in September 2012) (Fig. 6C). These values were relatively higher than those recorded in other Mediterranean coastal and open waters where Prochlorococcus reached a maximum of 10 4 cells•cm -3 [START_REF] Garczarek | High vertical and low horizontal diversity of Prochlorococcus ecotypes in the Mediterranean Sea in summer[END_REF][START_REF] Šantić | Distribution of Synechococcus and Prochlorococcus in the central Adriatic Sea[END_REF][START_REF] Sommaruga | Differential sunlight sensitivity of picophytoplankton from surface Mediterranean coastal waters differential sunlight sensitivity of picophytoplankton from surface Mediterranean coastal waters[END_REF]. Prochlorococcus abundance reached 26.25 * 10 3 cells•cm -3 in the Gulf of Gabès [START_REF] Hamdi | The characterisation and summer distribution of ultraphytoplankton in the Gulf of Gabès (Eastern Mediterranean Sea, Tunisia) by using flow cytometry[END_REF] and 7 * 10 4 cells•cm -3 in the Bay of Marseille [START_REF] Grégori | Phytoplankton group dynamics in the bay of Marseilles during a 2-year survey based on analytical flow cytometry[END_REF].

In contrast, picoeukaryote was less abundant in the Bay where values ranged from 20 to 161 10 3 cells•cm -3 , and in the Lagoon abundances ranged from 603 to 82 * 10 3 cells•cm -3 (Fig. 6D). The highest picoeukaryotes abundances were recorded in February 2013 in the Bay and in July 2012 in the Lagoon. These values were similar to those observed in other ecosystems (Table 1). For instance, in the Gulf of Gabès, picoeukaryote cells were less abundant than other groups with maximal values of 48.19 * 10 3 cells•cm -3 [START_REF] Hamdi | The characterisation and summer distribution of ultraphytoplankton in the Gulf of Gabès (Eastern Mediterranean Sea, Tunisia) by using flow cytometry[END_REF]. In the Bay of Marseilles, the abundance maximum reported for this group was 3 * 10 4 cells•cm -3 [START_REF] Grégori | Phytoplankton group dynamics in the bay of Marseilles during a 2-year survey based on analytical flow cytometry[END_REF].

Studies conducted on Mediterranean picophytoplankton communities focused mainly on the Mediterranean Sea northern coast [START_REF] Caroppo | The contribution of picophytoplankton to community structure in a Mediterranean brackish environment[END_REF][START_REF] Casotti | Composition and dynamics of the phytoplankton of the Ionian Sea (eastern Mediterranean)[END_REF][START_REF] Denis | Ultraphytoplankton basin-scale distribution in the eastern Mediterranean Sea in winter: link to hydrodynamism and nutrients[END_REF][START_REF] Grégori | Phytoplankton group dynamics in the bay of Marseilles during a 2-year survey based on analytical flow cytometry[END_REF][START_REF] Sommaruga | Differential sunlight sensitivity of picophytoplankton from surface Mediterranean coastal waters differential sunlight sensitivity of picophytoplankton from surface Mediterranean coastal waters[END_REF] and little is known on seasonal changes of autotroph communities in southern Mediterranean coasts, although these ecosystems may be more vulnerable to global anthropogenic changes due to water scarcity, concentration of anthropogenic activities and population density in coastal areas [START_REF] Pachauri | Climate change 2014: synthesis report[END_REF]. The present study provides the first data on the seasonal distribution and contribution of picophytoplankton communities to the total autotrophic biomass in the study area.

Multivariate drivers of picophytoplankton were depicted by SEM (Fig. 7). In the Bay of Bizerte, model results showed that picophytoplankton was modulated by the combination of temperature, salinity, and nutrient loading relative to nitrates and ammonium. A large amount of the variability of picoeukaryotes and Synechococcus was positively linked to nitrates + nitrites, while salinity and ammonium showed a negative effect. In turn, Prochlorococcus abundance variations were mainly linked to temperature and ammonium. SEM results showed noticeable differences regarding the main drivers of picophytoplankton in the eutrophic site, the Lagoon. Results showed an enhanced effect of orthophosphate on the picoeukaryote abundance, Synechococcus appeared mainly driven by temperature, whereas Prochlorococcus was driven by nitrates + nitrites, temperature and salinity.

SEM results pointed out a different configuration regarding the picophytoplankton environmental drivers. The complexity of the configuration, i.e. number of significant links within the system, decreases along with the increase of eutrophication. Indeed, under less exposure to anthropogenic stress, i.e. in the Bay of Bizerte, a larger role of nutrient dynamics and hydrological conditions on the picophytoplankton distribution was outlined, whereas in the eutrophic system, the Lagoon of Bizerte, the picophytoplankton seasonal abundance variation and contribution to the phytoplankton bulk biomass is curved by the excess of nutrient loading yielded by the anthropogenic stress. Indeed, the model highlighted a significant negative effect of eutrophication on picophytoplankton communities. Moreover, linked to the excess of nutrient loading we noticed that water transparency was significantly lower in the Lagoon, which might also impair picophytoplankton communities. These results are in line with previous reports on the relationship between picophytoplankton and nutrients. For instance, Prochlorococcus optimally grows at low nutrient concentration as it cannot use nitrate, and thus depends on the high surface area to volume ratio to uptake ammonium more efficiently or to take up organic nitrogen compounds at high rates [START_REF] Worden | Assessing the dynamics and ecology of marine picophytoplankton: the importance of the eukaryotic component[END_REF]. However, [START_REF] Berube | Physiology and evolution of nitrate acquisition in Prochlorococcus[END_REF] showed that some Prochlorococcus strains are indeed able to grow by using nitrate as the sole nitrogen source. In the case of Synechococcus, a positive correlation with nutrients in both mesotrophic and eutrophic ecosystems has been observed in the North Western Mediterranean Sea [START_REF] Uysal | The annual cycle of Synechococcus (cyanobacteria) in the northern Levantine Basin shelf waters (eastern Mediterranean)[END_REF][START_REF] Vaulot | Winter presence of prochlorophytes in surface waters of the northwestern Mediterranean Sea[END_REF]. In oligotrophic Mediterranean waters, Synechococcus appeared better adapted to nutrient depleted waters because of the small cell size [START_REF] Martin | Etude par cytométrie en flux de la distribution des populations phytoplanctoniques en Méditerranée[END_REF]. These results may explain higher Synechococcus abundances in the bay of Bizerte. Other factors however are not discarded, such a potential larger top-down control in the Lagoon than in the Bay, i.e. microzooplankton grazing on bacteria and picoeucaryotes. Indeed, microzooplankton communities, which are mainly composed of dinoflagellates (Torodinium, Protoperidinium and Dinophysis) and aloricate (Lohmaniellea and Strombidium) and tintinnid (Tintinnopsis, Tintinnus, Favella and Eutintinnus) ciliates have been shown to seasonally control ultraplankton abundances in the Lagoon of Bizerte [START_REF] Dhib | Ultra-and microplankton assemblages as indicators of trophic status in a Mediterranean lagoon[END_REF]. Moreover, microzooplankton biomass can reach up to 359 mg C m -3 , A B whereas their grazing controlled 67-90% of pico-and nanophytoplankton [START_REF] Sakka Hlaili | The planktonic food web of the Bizerte lagoon (south-western Mediterranean) during summer: I. Spatial distribution under different anthropogenic pressures[END_REF].

In the Bizerte Bay, Prochlorococcus showed maximum abundances in both summer and winter seasons. Although this group has been reported dominant in warm (> 20 °C) oligotrophic oceanic waters (Partensky et al., 1999a(Partensky et al., , 1999b)), the South East Atlantic [START_REF] Jameson | Detailed analysis of the microdiversity of Prochlorococcus populations along a North-South Atlantic Ocean transect[END_REF][START_REF] Zwirglmaier | Global phylogeography of marine Synechococcus and Prochlorococcus reveals a distinct partitioning of lineages among oceanic biomes[END_REF] and the North West Atlantic [START_REF] Zinser | Influence of light and temperature on Prochlorococcus ecotype distributions in the Atlantic Ocean[END_REF], high abundances (higher than 10 4 cells•cm -3 ) have been also reported in relatively cold environments (< 15 °C) (Šantić et al., 2011). Moreover, in the north-western Mediterranean Sea [START_REF] Vaulot | Winter presence of prochlorophytes in surface waters of the northwestern Mediterranean Sea[END_REF], as well as in the central Adriatic Sea (Šantić et al., 2011) field studies have shown that Prochlorococcus high abundances are not restricted to the warm seasons [START_REF] Zwirglmaier | Global phylogeography of marine Synechococcus and Prochlorococcus reveals a distinct partitioning of lineages among oceanic biomes[END_REF]. According to the literature, Prochlorococcus is able to develop in a wide range of environmental conditions across marine ecosystems [START_REF] Buck | Basin-wide distributions of living carbon components and the inverted trophic pyramid of the central gyre of the North Atlantic Ocean, summer 1993[END_REF]Partensky et al., 1999aPartensky et al., , 1999b;;[START_REF] Worden | Assessing the dynamics and ecology of marine picophytoplankton: the importance of the eukaryotic component[END_REF], except in polar waters [START_REF] Zwirglmaier | Global phylogeography of marine Synechococcus and Prochlorococcus reveals a distinct partitioning of lineages among oceanic biomes[END_REF]. Synechococcus was recorded throughout the study period in both ecosystems, with high densities under both low and high temperature. Previous works highlighted the eurytherm character of this group, which allows it inhabiting within a wide range of temperatures [START_REF] Goericke | A novel niche for Prochlorococcus sp. in low-light suboxic environments in the Arabian Sea and the Eastern Tropical North Pacific[END_REF][START_REF] Li | Annual average abundance of heterotrophic bacteria and Synechococcus in surface ocean waters[END_REF][START_REF] Ning | Spatial and Temporal Variability of Picocyanobacteria Synechococcus sp[END_REF][START_REF] Tsai | Seasonal diel variations of picoplankton and nanoplankton in a subtropical western Pacific coastal ecosystem[END_REF][START_REF] Tsai | Seasonal variations in trophic dynamics of nanoflagellates and picoplankton in coastal waters of the western subtropical Pacific Ocean[END_REF][START_REF] Zwirglmaier | Global phylogeography of marine Synechococcus and Prochlorococcus reveals a distinct partitioning of lineages among oceanic biomes[END_REF]. In the coastal ecosystems of the Adriatic Sea, highest cell abundances of Synechococcus have been reported in summer in Sibenik, Kastela Bay, Split and Ploce, although in the open sea, higher abundances have been observed in winter (Šantić et al., 2011). In our study, the relationship of Synechococcus abundance with environmental factors (temperature and nutrients) suggests different populations having distinct environmental windows. Indeed, a number of studies demonstrated that Synechococcus is genetically complex with several clades widely distributed in the Mediterranean Sea [START_REF] Ahlgren | Diversity and distribution of marine Synechococcus: multiple gene phylogenies for consensus classification and development of qPCR assays for sensitive measurement of clades in the ocean[END_REF][START_REF] Mella-Flores | Is the distribution of Prochlorococcus and Synechococcus ecotypes in the Mediterranean Sea affected by global warming?[END_REF][START_REF] Scanlan | Ecological genomics of marine picocyanobacteria[END_REF]. Clade II was observed in stable, low-nutrient conditions typical of summer season [START_REF] Alonso-Sáez | Factors controlling the year-round variability in carbon flux through bacteria in a coastal marine system[END_REF][START_REF] Dufresne | Unravelling the genomic mosaic of a ubiquitous genus of marine cyanobacteria[END_REF][START_REF] Scanlan | Ecological genomics of marine picocyanobacteria[END_REF][START_REF] Sohm | Co-occurring Synechococcus ecotypes occupy four major oceanic regimes defined by temperature, macronutrients and iron[END_REF][START_REF] Zwirglmaier | Global phylogeography of marine Synechococcus and Prochlorococcus reveals a distinct partitioning of lineages among oceanic biomes[END_REF] whereas clades I and IV were adapted to high nutrient/low temperature waters of the Mediterranean Sea [START_REF] Mella-Flores | Is the distribution of Prochlorococcus and Synechococcus ecotypes in the Mediterranean Sea affected by global warming?[END_REF]. The seasonal variation of physico-chemical conditions together with the trophic status of each marine ecosystem control the development of the specific clades adapted to the prevailing conditions, as has been described for some coastal sites of the Pacific Ocean [START_REF] Tai | Temporal variation of Synechococcus clades at a coastal Pacific Ocean monitoring site[END_REF].

In this study, we have shown seasonal abundance changes of picophytoplankton communities and their relationship with environmental conditions. This group predominated in the Bay of Bizerte, coping with the local ecological niche of primary producers and substantially contributing to the total chlorophyll a biomass. Moreover, the close relationship of the picophytoplankton community with hydrographic conditions and anthropogenic stress, portrayed by nutrient concentration, warns on the negative effects anthropogenic related eutrophication may have at the base of planktonic food webs. This is important as such influence may be exacerbated under expected scenarios of Mediterranean warming conditions and nutrient release in coastal ecosystems. The seasonal patterns pointed out by our investigation might serve as a baseline for future studies addressing picophytoplankton dynamics that should also consider potential top-down controls in order to gain a thorough understanding of the role these groups play in the functioning of the southern coastal Mediterranean ecosystems.

Fig. 1 .

 1 Fig. 1. Location of the study area and sampling stations in the Bay (A) and Lagoon (B) of Bizerte.

Fig. 2 .

 2 Fig. 2. Seasonal variation of temperature (°C) (A) and salinity (B) in the Bay and Lagoon of Bizerte.

Fig. 3 .

 3 Fig. 3. Temporal variation of nutrient concentrations in the Bay and Lagoon of Bizerte.

Fig. 4 .

 4 Fig. 4. Seasonal variation of total chlorophyll a concentration (mg•m -3 ) and evolution of chlorophyll a concentration (mg•m -3 ) in size fractions of phytoplankton sampled from the Bay and Lagoon of Bizerte.

Fig. 5 .

 5 Fig. 5. Resolution of picophytoplankton cell groups by flow cytometry. Typical red vs orange fluorescence cytogrammes exhibiting the resolution of Synechococcus and picoeukaryotes in [A, 19 February 2013], and Prochlorococcus and Synechococcus in [B, 25 May 2013]. Samples were collected in the Bay of Bizerte and were analysed with a Cyflow space (Partec) by using the same instrument settings which also illustrates seasonal changes in the picophytoplankton composition. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

  628-225 * 10 3 cells cm -3 0-123 * 10 3 cells cm -3 20-161 * 10 3 cells cm -.65 * 10 4 cells cm -3 Max: 40.41 * 10 4 cells cm -3 Max: 64.19 * 10 4 cells cm -3[START_REF] Rekik | Spring plankton community structure and distribution in the north and south coasts of Sfax (Tunisia) after north coast restoration[END_REF]

Table 1

 1 Abundance of different picophytoplankton groups recorded within the Mediterranean Sea. * 10 10 cells m -2 Denis et al., 2010 Levantine basin Prochlorococcus Max: 60.8 * 10 10 cells m -2 Rhodes gyre Prochlorococcus Max: 30 * 10 10 cells m -2 Ionian basin Prochlorococcus Synechococcus 14.1-24.7 * 10 10 cells m -2 Max: 120 * 10 10 cells m -2 South of Crete Synechococcus Max: 275 * 10 10 cells m -2 Southern Adriatic basin Prochlorococcus Synechococcus Max: 23.3 * 10 10 cells m -2 Max: 33.8 * 10 10 cells m -2

	Location	Ecosystem	Period investigated	Taxa	Cells densities	Reference
	A
	North Western Mediterranean	Alboran Sea	2008-2012	Prochlorococcus	Max: 12.7 * 10 4 cells cm -3	Amorim et al., 2016
	Sea		(11 cruises)	Synechococcus	Max: 13.9 * 10 4 cells cm -3
				Picoeukaryotes	Max: 8.6 * 10 4 cells cm -3
		Bay of Marseille	1997-1999	Picoeukaryotes	Max: 3 * 10 4 cells cm -3	Grégori et al., 2001
		Bay of Villefranche	July 1996	Synechococcus	Mean: 43 * l0 3 cells cm -3	Jacquet et al., 1998
				Picoeukaryotes	Mean: 2.5 * 10 3 cells cm -3
		Bay of Blanes (Spain)	May-November 2003	Synechococcus	Max: 1.06 * 10 5 cm -3	Sommaruga et al.,
				Prochlorococcus	Max: 3.64 * 10 4 cm -3	2005
	North Eastern Mediterranean	Varano lagoon	March 1997-February 1998	Picophytoplankton 0.7-448.6 * 10 9 cells cm -3	Caroppo, 2000
	Sea
	North Eastern Mediterranean	Italian Side	Spring 1999	Synechococcus	Mean: 1.62 * 10 4 cells cm -3	Casotti et al., 2003
	Sea			Prochlorococcus	Mean: 7.37 * 10 3 cells cm -3
	(Ionian Sea)			Picoeukaryote	Mean: 3.10 * 10 3 cells cm -3
		Greek Side	Spring 1999	Synechococcus	Mean: 1.29 * 10 4 cells cm -3
				Prochlorococcus	Mean: 2.92 * 10 3 cells cm -3
				Picoeukaryotes	Mean: 2.16 * 10 3 cells cm -3
	North Eastern Mediterranean Sea Max: 117.6 B Greek coast Dec. 1994-Feb. 1995 Prochlorococcus
	South of Mediterranean Sea	Bay of Bizerte	Jun. 2012-Sep. 2013	Synechococcus
	(Tunisia)			Prochlorococcus
				Picoeukaryotes

Acknowledgements

This work benefited from financial support by the JEAI ECOBIZ (Jeune Equipe Associée à l'IRD, Ecologie de la lagune de Bizerte) programme funded by IRD (Institut Français pour la Recherche et le Développement). JCM gratefully acknowledges the European Commission (OCEAN-CERTAIN, FP7-ENV-2013-6.1-1; No: 603773). We are grateful to Christophe Leboulanger (IRD), for providing us the access to flow cytometers and the laboratory of nanoparticles in Bizerte Faculty of Science and also to the reviewer for all suggestions and constructive comments to improve the manuscript.