E Martinez
email: elodie.martinez@ird.fr

M Rodier

M Pagano

R Sauzède

Plankton spatial variability within the Marquesas archipelago, South Pacific

Keywords: Island mass effect, Marquesas Islands, South Pacific, oceanographic cruise, plankton distribution

The Marquesas Islands, in the central South Pacific, are a place of an outstanding phytoplankton enrichment visible from space and is a hotspot of endemism and biodiversity; however, it has been poorly studied. In situ physical-chemical-biological concomitant observations are almost non-existent and all located close to the main northern island, while ocean dynamics based on satellite observations and numerical modeling show contrasting north/south patterns within the archipelago. Thus, we took the opportunity of the Pakahi I te Moana cruise conducted in 2012 to collect hydrological and plankton

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Journal Pre-proof samples and investigate biogeochemical spatial patterns, especially north/south, over the archipelago. These data provide the first description of the spatial and vertical distribution of physical, chemical and plankton characteristics over the entire Marquesas archipelago, and gave a first hint to improve our understanding of this planktonic enrichment. The whole archipelago appeared to be a macronutrient-rich environment. Different patterns in the physical, chemical and biological vertical distributions were observed between the northern vs. southern part of the archipelago, and offshore vs. nearshore stations.

Phytoplankton biomasses were higher in the north where stratification was weaker, compared to the south and higher close to the islands than offshore. Phytoplankton all over the archipelago was largely dominated by picophytoplankton; specifically, Prochlorococcus presented a more widespread distribution than previously thought and were present over a large range of nutrient concentrations. Copepods were always the most abundant taxa in the archipelago but showed higher mean relative abundances near the islands. The importance of suspension-feeding zooplankton in the northern islands coincided with the highest biomasses of phytoplankton with a predominance of nano-and micro-phytoplankton. This was consistent with a young community responding to nutrient enrichment by an increase of suspension-feeders animals and bottom-up effect on zooplankton. Opposite to this, small copepods were significantly less abundant in the northern offshore region and the dominance of carnivorous forms coincided with low phytoplankton biomass and strong dominance of picoplankton, suggesting a microbial grazing pathway and more mature communities.

Introduction

The Marquesas Islands are located in the northeast of French Polynesia, central South Pacific (218°-222°E/8°-11°S; Fig. 1). The archipelago and its dozen of 10 to 25 km wide islands extend over about 350 km from north to south and rises steeply from the abyssal plain at 4000 m. Deep channels between the islands (40 to 100 km wide) divert the southwestward South Equatorial Current (SEC) [START_REF] Martinez | Central South Pacific thermocline water circulation from a high-resolution ocean model validated against satellite data: Seasonal variability and El Niño 1997-1998 influence[END_REF][START_REF] Raapoto | Modeling the wake of the Marquesas archipelago[END_REF]2019). The Marquesas Islands are a place of an outstanding phytoplanktonic/biological enhancement, referred to as an island mass effect (IME, Doty and Ogury 1956). This IME is remarkable in many ways. Surface observations of chlorophyll-a concentration (Chla, a proxy of phytoplankton biomass) derived from satellite radiometric observations is higher than 0.2 mg m -3 in the vicinity of the islands throughout the year (Fig. 1) except during El Niño events [START_REF] Martinez | Island mass effect in Marquesas islands: time variations[END_REF]. Downstream of the islands, the wake can extend up to 600 km (see Plate 4 from [START_REF] Signorini | Mixing and phytoplankton bloom in the wake of the Marquesas Islands[END_REF]), a huge length scale compared to the radius of the islands. The archipelago is remote and isolated in the central South Pacific and most of the islands are inhabited by only 10,000 inhabitants living on the five main islands. Thus, observations of plankton pattern are expected to be free from anthropogenic impact. A very high endemism level of oceanic species (close to 20%, as found in the Hawaiian archipelago and in the Red Sea) has recently been reported (Galzin et al. 2016). Finally, this is a hotspot for pelagic fisheries which are the main protein resources for French Polynesia inhabitants and a source of income for the country.

Nonetheless, the Marquesas have been poorly studied so far. Part of the studies focusing on this IME are based on satellite observations which allowed characterizing the Chla time variability as well as some physical mechanisms at the regional scale. Chla seasonal variability was observed and correlated with an intensification of the surface currents from 1997 to 2002 [START_REF] Martinez | Island mass effect in Marquesas islands: time variations[END_REF], while a decrease in productivity occurred during the 1997-1998 El Niño event and inversely during La Niña J o u r n a l P r e -p r o o f 1998-1999 [START_REF] Signorini | Mixing and phytoplankton bloom in the wake of the Marquesas Islands[END_REF]. Advection of upwelled cooler water from the iron-rich Equatorial Under Current toward the Marquesas during La Niña 1998-1999 has been hypothesized [START_REF] Legeckis | The influence of tropical instability waves on phytoplankton blooms in the wake of the Marquesas Islands during 1998 and on the currents observed during the drift of the Kon-Tiki in 1947[END_REF], then revisited and mitigated [START_REF] Martinez | Influence of Tropical Instability Waves on Phytoplankton Biomass near the Marquesas Islands[END_REF].

More recently, north/south contrasting patterns on ocean dynamics have been reported based on satellite observations and numerical modeling. A more intense activity, likely related to mesoscale processes, occur in the southern part of the archipelago (Martinez et al. 2018), while surface currents are weaker than in the north [START_REF] Martinez | Central South Pacific thermocline water circulation from a high-resolution ocean model validated against satellite data: Seasonal variability and El Niño 1997-1998 influence[END_REF][START_REF] Raapoto | Modeling the wake of the Marquesas archipelago[END_REF]2019).

Few in situ biogeochemical data are currently available around the Marquesas islands and most of them come from two oceanographic cruises where efforts were concentrated nearby Nuku Hiva, the main island in the northern region of the archipelago.

First, the Biogeochemistry and Optics South Pacific Experiment (BIOSOPE) cruise conducted at the end of October 2004 (see Special Issue in Biogeosciences 2008) covered a large range of biogeochemical and optical measurements at only three very close stations north-west offshore of Nuku Hiva (hereafter, referred as Station MAR, see Fig. 2).

Significant amounts of nitrate associated with the high-nutrient low-chlorophyll (HNLC) waters of the equatorial upwelling region (Fig. 1; Claustre et al. 2008 and references therein;[START_REF] Raimbault | Distribution of inorganic and organic nutrients in the South Pacific Oceanevidence for long-term accumulation of organic matter in nitrogen-depleted waters[END_REF], and micronutrient-poor (iron-depleted) waters were reported [START_REF] Blain | Dissolved iron distribution in the tropical and subtropical South Eastern Pacific[END_REF]. A nanophytoplankton community predominancewas observed [START_REF] Ras | Spatial variability of phytoplankton pigment distributions in the subtropical South Pacific Ocean: comparison between in situ and predicted data[END_REF]) and zooplankton was barely documented. Second, the adaptation and acclimation to iron in different groups of phytoplankton were observed during the Tara Oceans expedition in July to August 2011, at four stations also located around Nuku Hiva (see stations 122 to 124 in Fig. 2) [START_REF] Caputi | Community-Level Responses to Iron Availability in Open Ocean Planktonic Ecosystems[END_REF].

A biogeochemical north/south contrasting patterns or the overall spatial variability in the Marquesas could not be described with previously available in situ data. In February 2012, the Pakaihi I te Moana ("respect of the ocean" in arquesan) cruise took place in J o u r n a l P r e -p r o o f Journal Pre-proof the archipelago with the aim to explore the flora and fauna biodiversity within the framework of its inscription as a Marine Protected Area. Although this cruise (and consequently the sampling strategy) was not dedicated to physical or biogeochemical oceanography, we took this opportunity to collect hydrological and plankton samples all around the archipelago. The objective here was to investigate if some spatial patterns over the archipelago, especially north/south, could also imprint the biogeochemical properties.

We also aimed to provide the first description of the spatial and vertical distribution of physical, chemical and plankton characteristics over the whole archipelago, and thus the first hint to improve our understanding of the Marquesan IME.

Material and methods

Field survey and sampling processing

The Pakaihi I te Moana cruise (hereafter referred to as PM-12) was conducted onboard a fishing vessel through the archipelago from February 2 to February 22, 2012 (Table 1). Vertical profiles of temperature, salinity, pressure and fluorescence-derived Chla were obtained at 25 stations (Fig. 2) from casts of a SBE 19 Seabird CTD equipped with a WET Labs ECO-AFL fluorometer. The use of a fishing vessel without its own scientific equipment limited the number and depth of samples. Profiles were performed down to100 m (i.e., the maximum depth allowed by the CTD) on a 0.25 m vertical resolution. Water samples were collected with a Niskin bottle at five depths (5, 30, 40, 60, and 100 m) for nutrients and phytoplankton analyses. Vertical net hauls were performed at all stations for zooplankton biomass and taxonomy and, at only three stations for microphytoplankton microscopy.

In this study, distinctions were made between the northern and the southern regions, and between offshore stations and coastal stations. Stations (St.) 1-3 and 23-24 were referred to as the northern and southern offshore stations (N Off and S Off ), respectively. St.

J o u r n a l P r e -p r o o f 4-12 and 14-22, 25, 26 were referred to as the northern and southern island stations (N Isl and S Isl ), respectively. St. 13 was not associated with any specific acronym as it lies offshore right in the middle, between the north and the south of the archipelago.

Nutrients

Ammonium (NH 4 + ) was analyzed immediately after sampling by fluorometry using a Turner trilogy fluorometer (module # 7200-041) as described in [START_REF] Holmes | A simple and precise method for measuring ammonium in marine and freshwater ecosystems[END_REF].

Nitrate (N) and nitrite (Nox), phosphate (PO 4 ) and silicic acid Si(OH) 4 (hereafter referred to as silicates-Si) were analyzed after the cruise on HgCl 2 -poisoned samples with an Autoanalyzer AA 3 (SEAL Analytical) following the standard colorimetric methods [START_REF] Aminot | Dosage automatique des nutriments dans les eaux marines: méthodes en flux continu[END_REF].

Chlorophyll a (total and size fractionated) extraction and analysis

Chla were determined by fluorometry after methanol extraction (Le [START_REF] Bouteiller | Size distribution patterns of phytoplankton in the western Pacific: towards a generalization for the tropical open ocean[END_REF]. Analyses were performed with a Trilogy fluorometer equipped with the Chla extracted-acidification module (# 7200-040) and calibrated with pure Chla standard (Sigma). Total Chla was determined from 0.5 L water samples filtered onto GF/F Whatman filters. Size-fractioned Chla >3µm and >10 µm were determined from 1 L water samples collected onto 3 µm and 10 µm nucleopore membranes, respectively. Sizefractioned Chla <3 µm, between 3 µm and 10 µm, and >10 µm were considered here as proxies of pico-, nano-and micro-phytoplankton biomass respectively. All analyses were performed post-cruise on frozen samples (-80°C).

Pico-and nanophytoplankton analyses by flow cytometry (FCM)

Water samples of 1.2 mL were fixed by adding paraformaldehyde solution (2% final concentration), immediately frozen and stored in liquid nitrogen. Cell counts for pico-and

Microphytoplankton taxonomy

At three stations (St. 3, 5 and 16), samples were collected with a 20 µm mesh size net and vertical hauls from 100 m to the surface, and fixed with a formalin solution (4% final concentration). Counting and identification were carried out using a Nikon Eclipse TE2000-E inverted microscope. Sedimented volume was 50 mL or less, depending on the densities of organisms as well as the presence of detritus. Phytoplankton were identified to the lowest possible taxon.

Mesozooplankton biomass and taxonomy

Zooplankton were collected using a 200 µm mesh-size WP2 net, mostly during the day; sampling during both day and night was performed only at two sites: Nuku Hiva (St. 5 vs. 6) and Ua Huka (St. 7 vs. 8). At each station, two successive vertical hauls were made down to 150 m and 300 m for vertical distribution. Samples were immediately preserved in a 5 % buffered formalin solution. The relative biomass in each sampled layer 0-150 m and 0-300 m was first estimated by the "sedimented volume" technique. For the 0-300 m tow, each sample was then divided into two equal sub-samples. One half-split was used for a dry weight (DW) measurements after desiccation 48h at 60 °C [START_REF] Lovegrove | The determination of the dry weight of plankton and the effect of various factors on the values obtained[END_REF]) and the second half was used for abundance determination and natural isotopes [START_REF] Hunt | Isotope ratios of plankton size spectra as a tool for investigating pelagic trophic dynamics[END_REF].

For the 0-150 m tow, taxa identification and enumeration were made on subsamples of the total sample, and the biomass (DW) was estimated from the ratio 0-150 m/0-300 m of sedimented volumes. Zooplankton taxa were identified, using appropriate keys mainly from [START_REF] Tregouboff | Manuel de planctonologie méditerranéenne[END_REF], Razouls et al. (2005Razouls et al. ( -2020) ) and [START_REF] Conway | Guide to the coastal and surface zooplankton of the South-Western Indian Ocean[END_REF].

J o u r n a l P r e -p r o o f The CTD data were processed following the standard Argo quality control [START_REF] Wong | Argo Quality Control Manual for CTD and Trajectory Data[END_REF]. The mixed layer depth (MLD) was defined as the depth at which the potential density differs from that at 10 m depth by 0.03 kg m -3 (de Boyer [START_REF] Boyer Montegut | Mixed layer depth over the global ocean: An examination of profile data and a profile-based climatology[END_REF].

Each profile of fluorescence was converted into Chla following the standard BGC-Argo protocol [START_REF] Schmechtig | Bio-Argo quality control manual for the Chlorophyll-a concentration[END_REF]). The daytime non-photochemical quenching issue occurring at high irradiance was here corrected using the method developed by [START_REF] Xing | Quenching correction for in vivo chlorophyll fluorescence acquired by autonomous platforms: a case study with instrumented[END_REF]. Additionally, the global bias correction recommended by Roesler et al. (2016) was applied to each profile.

Satellite observations

To provide a synoptic view of the physical and biological environment within the to each of the original data source [START_REF] Maritorena | Consistent merging of satellite ocean color data sets using a bio-optical model[END_REF]. The 4-km horizontal grid product well illustrates the Chla activity around the small Marquesan islands.

Statistical analyses

Links between plankton and their physical and biogeochemical environment were investigated through one-way ANOVA and associated post-hoc tests. The spatial variability of the zooplankton community compositions in relation to their potential habitats, was investigated through a factorial correspondence analysis (FCA) performed on the percentages of the abundance of zooplankton taxonomic groups, and a principal component analysis (PCA) performed on the environmental variables (temperature, salinity, nutrient concentrations, Chla, and phytoplankton assemblages) using the ADE4 software [START_REF] Thioulouse | ADE-4, a multivariate analysis and graphical display software[END_REF]). These two data set were transformed (log x+1) to tend towards a normal distribution prior to performing the analyses. Then, FCA and PCA results were associated through a co-inertia analysis (Doledec and Chessel 1994).

To better understand changes in the zooplankton community structure, rank frequency diagrams (RFDs) were constructed by plotting the ranks of all identified species

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Journal Pre-proof on the x-axis (in decreasing order of frequency) against their logarithmic frequency value on the y-axis [START_REF] Frontier | Utilisation des diagrammes rang-fréquence dans l'analyse des écosystèmes[END_REF].

Results

Environmental characteristics of the Marquesan archipelago

Satellite observations provided a synoptic view of SST, SSS and surface Chla during the PM-12 cruise in February 2012 (austral summer). A time evolution of these three parameters was pointed out between the first week of the cruise when the sampling was dedicated to the northern islands (February 4 to 10, 2012), and the last 10 days (February 12 to 22, 2012) when it was dedicated to the southern islands (Fig. 3).

The archipelago was characterized by SST and SSS latitudinal gradients with colder and fresher waters in the north as the imprint of the equatorial upwelling [START_REF] Wyrtki | An Estimate of Equatorial Upwelling in the Pacific[END_REF]). In the northern region (Fig. 3e). By the time the vessel reached the southern region, Chla activity had weakened over the entire sampling zone (Fig. 3f).

The archipelago latitudinal gradient of temperature and salinity observed by remotesensing also appeared on the vertical sections of the hydrographic parameters (Fig. 4).

Indeed, environmental conditions in the northern region of the archipelago, from Nuku Hiva to Ua Pou islands, were significantly (ANOVA, p <0.001) cooler and fresher with a relatively homogeneous water column down to 100 m than in the south from Hiva Oa to Fatu Iva islands. The tongue of high salinity (>36) deeper than 80 m was the imprint of the highly salted surface core of the SPTW subducted from the south-east. This core became thinner and deeper as it flowed northward through the archipelago. Superimposed onto this general pattern, local particularities were observed, such as a surface warming in the upper 15 m behind Nuku Hiva and Ua Pou islands (km 200 and 380, respectively, in Fig. 4).

Nitrate and phosphate concentrations were quite high all over the Marquesas (>1 and 0.3 mol L -1 , respectively, Fig. 4). Significantly higher concentrations and a deeper and homogenous distribution occurred in the north than in the south (ANOVA, p <0.001). This latitudinal contrast, although noticeable, was less pronounced for silicates with surface maxima near the two southernmost stations. Ammonium concentrations reached 0.2 µmol L -1 on average in the 100 m upper layers with subsurface maxima between 60 and 100 m (data not shown). In a broader context, the north-south nutrient decrease reflected the latitudinal gradient from the equatorial mesotrophic area to the subtropical oligotrophic gyre. However, this pattern was disrupted near the islands with superficial enrichments, mainly silicic acid and phosphates, near Hiva Oa and Ua Pou Islands, and conversely, N and PO 4 consumption leeward of the Nuku Hiva and Ua Huka Islands. The proximity of the islands may also change the nutrient limitation. Indeed, the N/Si ratio >1 was reported north of 9° S likely implying a potential Si-limitation for diatom growth [START_REF] Brzezinski | The Si: C: N ratio of marine diatoms: Interspecific variability and the effect of some environmental variables[END_REF], except close to the Nuku Hiva Island. Such a limitation was not expected in the south,

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Journal Pre-proof where the N/Si ratio was <1 in the 0-60 m layer. Anyhow, the N/ PO 4 ratio (6.9 ± 2.43) was largely below the Redfield ratio across the archipelago, disclosing a PO 4 excess or N deficit in the archipelago, which is also a general feature in the South Pacific Ocean down to 250 m excepted in N 2 fixation areas [START_REF] Raimbault | Distribution of inorganic and organic nutrients in the South Pacific Oceanevidence for long-term accumulation of organic matter in nitrogen-depleted waters[END_REF].

3.2 Phytoplankton biomass, abundance and composition

Chla distribution

In the northern region, the in vivo Chla distribution appeared to be highly impacted by proximity of islands with surface enrichments leeward of the Nuku Hiva and Ua Huka regions, the observed trends remained consistent throughout the months. Chla was on average higher in the north (45.3 ± 17.0 mg m -² Chla 0100m ) and more homogeneously distributed within the mixed layer than in the south (Chla 0100m = 35.7 ± 5.9 mg m -²) where a more pronounced DCM-like pattern appeared below the MLD. These observations agreed with the PM-12 north/south observations.

Phytoplankton assemblages

During PM-12, picophytoplankton (size-fractioned Chla <3 µm) dominated the phytoplankton biomass all around the archipelago with 79 ± 6.0% of total Chla, except at the two N Isl , off Nuku Hiva (St. 4) and Ua Huka (St. 7-8) Islands, while nanophytoplankton only represented 7 ± 8% of abundance only (FCM observations; Figs.

and 8

). There was no clear north/south pattern, but rather an offshore vs. islands pattern in the picophytoplankton assemblages. Indeed, abundances of Synechococcus increased near the islands (>75% of cell counts), with maxima around Nuku Hiva Island. Although

Prochlorococcus predominated at the more oceanic stations (i.e., N Off and St. 13, the two S Off stations were not sampled) accounting for more than 65% of picoplanktonic cell counts, their highest abundances were recorded in surface nearshore Ua Pou (N Isl ) and Fatu Iva (S Isl ) Islands where they co-dominated with Synechococcus (40% and 53%, respectively). Throughout the cruise, picophytoeucaryotes were less abundant than the other picoplanktonic groups, with maxima abundance between 60-100 m where they codominated with Prochlorococcus.

The nano-and microphytoplanktonic fraction became dominant only at two N Isl (off Nuku Hiva and Ua Huka Islands) in the upper 30 m (Chla >3 µm up to 63% of total Chla) and remained relatively abundant at depth (> 30%). The maximum abundance of nanophytoplankton abundance was recorded in the surface layer near Nuku Hiva and in the DCM near Ua Pou (Fig. 7), with a high proportion of nanocyanobacteria, (data not shown).

J o u r n a l P r e -p r o o f

The abundance and composition of microphytoplankton assemblages obtained by microscopic counting at three contrasting stations (St. 3 N Off , St. 5 Nuku Hiva -N Isl and St.

16 Hiva Oa -S Isl ) showed a maximum of 15,000 cells L -1 over 0-100 m leeward of the Nuku Hiva Island, consistently with the highest percentage of size-fractioned Chla > 10 µm observed there (20-38%). At these three stations, diatoms clearly dominated the microphytoplankton assemblages, accounting to more than 80% of total cell counts (up to 96% at St. 5) but the species composition at Nuku Hiva Island differed from the two other stations (Table 2). Indeed, around Nuku Hiva Island T. nitzschioides/frauendfeldii outnumbered the other diatoms, accounting for 82% of diatom counts, followed by two Island. The other taxa were scarce or undetectable at least for the forms >20 µm caught with the plankton net. Overall, it appears that the Nuku Hiva Island stood out from the other islands by its composition of micro-and nano-phytoplanktonic assemblages.

Zooplankton biomass, abundance and composition

Zooplankton biomass (as dry weight, DW) was mainly distributed in the upper 0-150 m layer (72% ± 12%) while a smaller proportion of the total abundance was present in this J o u r n a l P r e -p r o o f

Journal Pre-proof layer (43% ± 15%) (Fig. 9). Biomass and abundance were on average five times higher in N Isl than in N Off and S Isl except for the high biomass at St. 21, close to Hiva Oa Island (Fig. 9), in relation to the high abundance of large copepods of the genera Paraeuchaeta and Euchaeta (body length around 5 mm). These contrasting north-south and offshorenearshore patterns within the archipelago reflected those of the 0-100 m integrated Chla (Fig. 5).

A total of 99 zooplankton taxa were recorded during the PM-12 cruise, including 48 copepod species, 8 other crustaceans, 24 gelatinous taxa and 13 meroplanktonic forms (Table 3). The number of taxa per station ranged from 44 to 61. Copepods were always the most abundant (64 to 92% of total abundance), but their relative abundance was on average higher near the islands (80% at N Isl and 78% at S Isl ) than offshore (68% at N Off ). Small copepods were significantly less abundant in the N Off region than in the two other regions (ANOVA, p <0.001), and the other crustaceans (mostly euphausiids) were rare or almost absent in this region.

The first axis of the Coinertia analysis (50% of the total variance) clearly showed an opposition between (i) the northern (i.e., N Off and N Isl ) stations characterized by high Nox and N/Si ratio as well as high Chla values (with fraction >10 µm) associated to noncopepod crustaceans and (ii) the more oligotrophic southern stations (S Isl ) characterized by pico and nano-phytoplankton and bacteria associated to appendicularians (Fig. 10a-c). On the second axis (36% of the variance) the N Isl stations were opposite to the N Off stations (Fig. 10d), clearly suggesting a coastal (island)-offshore gradient. N Isl stations were associated with higher Chla with a high percentage of the >10 µm fraction and small to medium size copepods. On the other hand, pico-phytoplankton at N Off stations were associated with detritivorous (polychaetes) and carnivorous (chaetognaths) zooplankton.

Good correlations between the first and second axes of both environmental and zooplankton systems, as well as the proximity of the stations to the 1:1 regression lines J o u r n a l P r e -p r o o f highlighted a strong link between zooplankton assemblages and environmental conditions either along a north-south (Fig. 10C) or island-offshore gradients (Fig. 10d).

The rank-frequency diagrams (RFDs) also confirmed the regional difference in terms of zooplankton (or copepods) community structure (Fig. 11). The average RFDs for the N Isl region were clearly different from those of N Off , with a more concave shape, whereas the S Isl area had an intermediate situation. In N Isl , small herbivorous-omnivorous copepods were the most abundant (Paracalanus/Clausocalanus at rank 1 in Table 4), as also shown in the Coinertia analysis (Fig. 10). In N Off , carnivorous forms (chaetognaths at rank 1) were prominent, but large herbivorous-omnivorous copepods (Cosmocalanus darwini, at rank 2)

and, to a lesser extent, small herbivorous-omnivorous copepods (Oithona spp., Acartia negligens, and the appendicularians Oipleura spp.; ranks 3, 4 and 5 respectively) were also abundant.

Finally, microzooplankton, samples collected with the 20 µm net at St. 3, 5 and 16 revealed the presence of numerous Tintinnids. The lowest abundance was observed at the N Off station and the highest one around Hiva Oa Island. Naked ciliates, not considered here, were probably undersampled since they can be broken during the filtration process.

Discussion

Observations from the PM-12 cruise here provide a complementary view at the archipelago scale to the sparse physical, chemical and plankton observations that were collected during the BIOSOPE cruise and Tara Oceans expedition and which were limited to the neighborhood of Nuku Hiva Island. We unveiled north-south differences in the phytoplankton and zooplankton spatial distributions within the archipelago. For instance, surface and integrated Chla values were stronger in the north than in the south, and DCM were stronger and wider around the N Isl than S Isl . While high nitrate and phosphate concentrations were previously reported at few stations in the northern region from J o u r n a l P r e -p r o o f BIOSOPE and Tara Oceans [START_REF] Raimbault | Distribution of inorganic and organic nutrients in the South Pacific Oceanevidence for long-term accumulation of organic matter in nitrogen-depleted waters[END_REF][START_REF] Caputi | Community-Level Responses to Iron Availability in Open Ocean Planktonic Ecosystems[END_REF]), here we observed such high concentrations at the archipelago scale, with, however, higher values in the north than in the south. This macro-nutrient latitudinal gradient with decreasing values from the equatorial mesotrophic area toward the subtropical oligotrophic gyre prevents phytoplankton from developing with the same efficiency in the southern region and also likely drive phytoplankton vertical distribution toward a more pronounced DCM-like structure in the south.

Although clear physical and biogeochemical mechanisms at the origin of the Marquesas IME remains undetermined, some clues do exist. For instance, the surface warm wake behind Nuku Hiva (and Ua Pou) Islands refutes the hypothesis of a winddriven coastal upwelling to uplift nutrients toward the upper lit layer. It rather suggests a wind-sheltered area thanks to the abrupt relief of high islands, which induce calm waters leeward these islands as previously reported from both remote sensing and high-resolution numerical modeling [START_REF] Raapoto | Modeling the wake of the Marquesas archipelago[END_REF]. As the weak wind-driven turbulence is unable to erode the near-surface stratification, the diurnal warming could intensify giving rise to warm wakes [START_REF] Basterretxea | Eddy and deep chlorophyll maximum response to wind-shear in the lee of Gran Canaria[END_REF]. The interruption of the trade wind systems by the islands also induced wind shear perturbation in the is and's wake, which in turn could lead to Ekman pumping and eddy generation leeward from the islands [START_REF] Jimenez | A numerical study of the relative importance of wind and topographic forcing on oceanic eddy-shedding by tall, deep water islands[END_REF], possibly uplifting nutrients and allowing phytoplankton growth [START_REF] Basterretxea | Eddy and deep chlorophyll maximum response to wind-shear in the lee of Gran Canaria[END_REF][START_REF] Hasegawa | How islands cause phytoplankton to bloom in their wakes[END_REF][START_REF] Andrade | Island mass effect in the Juan Fernandez Archipelago (33°S)[END_REF]. Consistently, the generation of such eddies has been reported in the wake of the largest Marquesas Islands (such as Nuku Hiva Island) through numerical modeling [START_REF] Raapoto | Modeling the wake of the Marquesas archipelago[END_REF]). However, they were not efficient enough in the model to uplift nutrients and allow phytoplankton blooms [START_REF] Raapoto | Role of iron in the Marquesas island mass effect[END_REF], likely due to shallow MLD (Raapoto, pers comm).

On the other hand, these latest simulations showed that iron fertilization by the island sediments was a prerequisite to explain the Marquesan blooms. Indeed, weak dissolved

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Journal Pre-proof iron concentrations were reported offshore during BIOSOPE suggesting a possible iron limitation of phytoplankton growth [START_REF] Blain | Dissolved iron distribution in the tropical and subtropical South Eastern Pacific[END_REF], as generally accepted in HNLC regions [START_REF] Boyd | A mesoscale phytoplankton bloom in the polar Southern Ocean stimulated by iron fertilization[END_REF][START_REF] De Baar | Synthesis of iron fertilization experiments: From the iron age in the age of enlightenment[END_REF][START_REF] Boyd | Mesoscale iron enrichment experiments 1993-2005: Synthesis and future directions[END_REF]). Thus, land drainage of these volcanic islands could provide iron to the upper lift layer. That being said, the combination of a warm and calm area behind Nuku Hiva Island, associated with an island-driven fertilization would provide favorable conditions for larger cells micro algae to thrive [START_REF] Glibert | Margalef revisited: a new phytoplankton mandala incorporating twelve dimensions, including nutritional physiology[END_REF] in the island wake, as recently suggested leeward of the island of Tahiti by [START_REF] Sauzède | Enhancement of phytoplankton biomass leeward of Tahiti as observed by Biogeochemical-Argo floats[END_REF]. This agrees with the 0-30 m surface predominance of micro-and nanophytoplankton observed leeward Nuku Hiva island during PM-12. This is also consistent with the highest surface and integrated Chla reported at the N Isl and S Isl stations rather than offshore.

At three contrasting stations (N Off , N Isl and S Isl ) during PM-12, the abundance and composition of microphytoplankton assemblages collected in the 0-100 m layer with a phytoplankton net revealed a clear dominance of diatoms while the proportion of dinoflagellates never exceeded 20 %. However, differences in the species composition of diatoms appeared between the N Isl (St 5, Nuku Hiva Island again) vs. N off and S Isl . Diatom communities were dominated by T. nitzschioides/frauendfeldii behind Nuku Hiva. In 2017, a study (as part of the RETROMAR project) conducted in the Taipivai bay, 3 km away from St. 5, also showed a marked predominance of T. nitzchioides (more than 70 000 cells L -1 , > 70% of total diatoms; unpublished data). These observations confirm the neritic character of these algae which seems to be favored under eutrophic conditions. An open question is whether the T. nitzchioides observed at St. 5 were produced in the bay before being advected offshore. In contrast, at the N Off and S Isl stations, Pseudo-nitzschia delicatissima sp. were predominant. It has been shown from the expression of ironresponsive genes that this genus was genetically more adapted to a low iron environment [START_REF] Caputi | Community-Level Responses to Iron Availability in Open Ocean Planktonic Ecosystems[END_REF], likely explaining its higher abundance in offshore regions, as observed J o u r n a l P r e -p r o o f

Journal Pre-proof here and in the previous studies. The N/Si ratio >1 north of 9°S indicates a possible Silimitation for diatoms in this region which could reflect the influence of waters advected from the equatorial region [START_REF] Leynaert | Silicon limitation of biogenic silica production in the equatorial Pacific[END_REF] to the northern Marquesas region; This would also explain the weakly silicified diatoms assemblages observed in the north of the archipelago (Gomez et al. 2008), except close to some islands where superficial enrichments from land could temporary disrupt this limitation and lead to changes in the microphytoplankton assemblage. Consistently with this hypothesis, extremely high concentrations of Si were measured at the mouth of two rivers in Nuku Hiva and Hiva Oa Islands during PM-12 (403 µmol L -1 and 120 µmol L -1 respectively; pers. comm.). In contrast, environmental conditions around the southern islands were closer to those of the oligotrophic gyre where other trophic conditions and limitations prevail [START_REF] Raimbault | Distribution of inorganic and organic nutrients in the South Pacific Oceanevidence for long-term accumulation of organic matter in nitrogen-depleted waters[END_REF]. Obviously, those observations can not be generalized and definitely deserve to be confirmed, especially additional studies are required to determine factors controlling the composition of diatom (and more generally microphytoplankton) assemblages.

Interestingly, while nano-to microphytoplankton communities were expected in such an IME context, we rather observed a large dominance of picophytoplankton all over the archipelago. Although they predominated at the more oceanic stations, Prochlorococcus appeared to have a more widespread distribution along PM-12 than previously thought and were present over a wide range of nutrient concentrations [START_REF] Flombaum | Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus[END_REF]. They also exhibited a vertical distribution not restricted to the surface layer likely due to the coexistence of two light adapted-ecotypes, high-light (HL) and low-light (LL) ecotypes [START_REF] Moore | Utilization of different nitrogen sources by the marine cyanobacteria Prochlorococcus and Synechococcus[END_REF]. In contrast, Synechococcus were restricted to the surface layer and dominant near the islands, which was consistent with observations during Tara Oceans around Nuku Hiva Island (Caputi et al. 2019, Supp-Inf). These authors reported an increase of Synechococcus abundance towards the coast (their St. 123 vs. 122 and 124), linked to a drastic shift in clade composition related to iron bioavailability. Indeed, under mesotrophic was reported at St. MAR [START_REF] West | A novel clade of Prochlorococcus found in high nutrient low chlorophyll waters in the South and Equatorial Pacific Ocean[END_REF]. This clade adapted to high temperature (>27°C), and light conditions were atypical compared to the other HL clades because it showed no negative correlation with nitrate, nitrite and phosphate but significant positive correlations with total Chla, Synechococcus abundance, ammonium uptake and regeneration rates. According to the authors, this HNLC clade is presumably more adapted to nutrient-rich environments than other ecotypes and is more dependent on ammonium in low densities, and few rare species [START_REF] Frontier | Utilisation des diagrammes rang-fréquence dans l'analyse des écosystèmes[END_REF]. At the opposite, the importance of carnivorous forms (chaetognaths) in the N Off region suggested a microbial grazing pathway and more mature communities also evidenced by the convex shape of the rank-frequency diagram typical of a more advanced stage in succession with several species having similar high abundance [START_REF] Pinca | Zooplankton community structure in the Western Mediterranean Sea related to mesoscle hydrodynamics[END_REF]. This evolution of the food chain away from the islands suggests a plankton enrichment originating from the coast with communities becoming more mature as long as they are advected offshore [START_REF] Caputi | Community-Level Responses to Iron Availability in Open Ocean Planktonic Ecosystems[END_REF][START_REF] Raapoto | Role of iron in the Marquesas island mass effect[END_REF].

The μm in size during PM-12. These differences among others can be induced by differences of the filter and net sizes, the seasonal variability (austral winter during BIOSOPE and Tara Oceans vs. summer here), or shorter-term variability as explained above. On its side, Tara

Oceans in the Marquesas attempted to assess the impact of iron availability on plankton communities, by exploring bio-oceanographic and bio-omics data sets. They identified subcommunities covarying with iron. However, some limitations arise from the part of their analysis based on iron distribution which was derived from two advanced biogeochemical models rather than discrete measurements, which are absolutely necessary to understand the specific functioning of this ecosystem.

Conclusion

During the PM-12 cruise in February 2012, we had the opportunity to collect hydrological and plankton samples all around the archipelago. Although this cruise (and therefore the sampling strategy) was not dedicated to physical or biogeochemical oceanography, it provided the first view of physical-biogeochemical observations all over the archipelago and emphasize a north/south as well as an island vs. offshore gradient. In this HNLC environment, phytoplankton and zooplankton biomass were higher in the north where stratification was weaker than in the south and also higher close to the islands than offshore. Picophytoplankton and copepods dominated throughout the archipelago, except in the wake of the N Isl where the importance of suspension-feeding coincided with the highest biomass of phyto-and zooplankton and with the predominance of nano-and microphytoplankton. This corresponds to a young community's response to nutrient enrichment through an increase of herbivorous animals highlighting a bottom-up effect on zooplankton. Contrastingly, small copepods were significantly less abundant in the N Off J o u r n a l P r e -p r o o f J o u r n a l P r e -p r o o f J o u r n a l P r e -p r o o f

 The Marquesas islands are the place of an outstanding island mass effect  Phytoplankton biomass was higher in the north than in the south of the archipelago  Phytoplankton biomass was also higher close to the islands than offshore  A north/south gradient shapes the physical and biogeochemical distributions  Zooplankton, largely dominated by copepods, echo phytoplankton spatial patterns J o u r n a l P r e -p r o o f

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  the south, warmer SST was the south-eastward imprint of the western Pacific warm pool(Yan et al. 1992), while saltier SSS was the surface imprint of the South Pacific Tropical Water (SPTW) advected from the south-east(O' onnor et a . 2005). This high salinity water mass finds its origin at the sea surface, where evaporation exceeds precipitation from the eastern Polynesian Islands to South America. Consistently, SST was warmer and SSS saltier in the south than in the north during PM-12. This latitudinal gradient of temperature may even have been sharpened by the summer warming of the region during the second part of the cruise (Fig.3 a, b). Inversely, the freshening as seen on SSS during the second part of the cruise has weakened the north-south salinity gradient. Although some clouds can be seen in the southern region of the archipelago (white pixels in Fig.3f), almost no rain occurred along the cruise.Ocean color observations showed a contrasting biological signature between the two periods. A strong surface Chla activity occurred around the islands during the sampling of J o u r n a l P r e -p r o o f

  Islands and strong deep maxima (DCM) between 30-80m with a deepening and intensification of DCM (up to 1.5 mg m -3 ) around Ua Pou. In the southern region, the Chla distribution around the islands was smoother with shallower DCM (20-40m). At the N Off and S Off stations the vertical Chla distribution was rather homogeneous following the thermohaline structure, with no clear DCM. However, two offshore stations were slightly richer, St. 3 in the north and St. 23 in the south, likely due to the proximity of seamounts culminating at 14 m deep below the sea level (Banc Lawson) and 54 m (Guyot Meihano), respectively. Consistently, the 0-100 m integrated Chla (Chla 0-100m ) were significantly higher (ANOVA, p-value <0.001) at the N Isl (St. 4-12) with a maximum near the Nuku Hiva Island compared to the S Isl (St. 14-22, 25-26) (Fig.5). The more superficial Chla distribution in the south was confirmed with close values of Chla 0-60m and Chla 0-100m . Chla 0-100m were also significantly contrasted (ANOVA, p-value <0.001) between N Isl and N Off where the lowest values were observed. The BGC-Argo float deployed during the Tara Oceans expedition provided a synoptic view of the Chla vertical distribution (Fig. 6). The BGC-Argo float spent approximately 7 months (August 2011 to February 2012) in the northern part of the archipelago and 10 months (March to December 2012) in the southern one. In both J o u r n a l P r e -p r o o f

  centric forms: Chaetoceros spp. and Bacteriastum spp. In comparison, diatom populations at the N Off and S Isl stations were dominated by three pennate diatoms Pseudo-nitzschia delicatissima spp. and, to a lesser extent, Thalassionema spp. and Cylindrotheca closterium, representing more than 80% of the total abundance. The other abundant genera were Navicula spp. and Thalassiosira spp. At the three stations dinoflagellates were the second group in abundance with 76-179 cells L -1 . Assemblages were rich in autotrophic species of the genus Tripos, mainly T. furca and T. pentagonum, and heterotrophic species such as Protoperidium spp., Phalochromas argus and Podolampas palmipes were also in the top dominant species at the three sampled stations but Gonyaulax spp were abundant only around Hiva Oa Island. Finally, the filamentous cyanobacterium Trichodesmium was present in very low concentrations (< 15 trichomes L -1 ) and even absent around Nuku Hiva

J

  o u r n a l P r e -p r o o f Journal Pre-proof and even eutrophic conditions as observed around the Marquesas, the macro-nutrient availability is likely not important determinant and other factors might therefore control the distribution or trade-offs between Prochlorococcus and Synechococcus. In accordance with this assumption, the presence of a new clade of Prochlorococcus (named HNLC-HL clade)

  observed during PM-12 (457 to 4820 mgDW m -2 based on the 0-300 m sampling and 313 to 4465 mgDW m -2 based on the 0-150 m sampling) were globally higher than previous values recorded in the upper 200 m of both oligotrophic (~ 400-600 mgDW m -2 ) and HNLC (~ 1200-1500 mgDW m -2 ) regions of the equatorial Pacific (Le[START_REF] Borgne | Net zooplankton and the biological pump: a comparison between the oligotrophic and mesotrophic equatorial Pacific[END_REF][START_REF] Borgne | Pacific warm pool and divergence: temporal and zonal variations on the equator and their effects on the Biological Pump[END_REF]. On the other hand, the N Isl zooplankton biomass (2700-4800 mgDW m -2 over 0-300 m and 200-4500 mgDW m -2 over 0-150 m) was comparable to the values reported by[START_REF] Caputi | Community-Level Responses to Iron Availability in Open Ocean Planktonic Ecosystems[END_REF] at the three Tara Oceans stations around Nuku Hiva island over 0 -100 m (1000 to 6000 mgDW m -2 from St. 122 to 124, see their Fig.8). High zooplankton biomass near the N Isl coincided with high phytoplankton biomass dominated by nano-and microphytoplankton, while low copepod abundance in the N Off region was associated with lower phytoplankton biomass dominated by picoplankton. The link between zoo-and phytoplankton was also suggested by a high correlation between zooplankton biomass and integrated Chla (r = 0.80, p = 0.002). The importance of suspension-feeders (small and medium-size copepods) in the N Isl corresponded to a young community's response to nutrient enrichment, typical of the J o u r n a l P r e -p r o o f Journal Pre-proof initial phase of zooplankton succession, as also shown by the concave shape of the rankfrequency diagram (RFD), with few predominant taxa, a large component of taxa present

  three cruises (BIOSOPE, Tara Oceans and PM-12) have improved our knowledge of the physical and biogeochemical characteristics of the Marquesas waters and their spatial structure. The homogeneous vertical distribution in Chla and thermohaline structure over the 100 m upper layer at the northern offshore stations (St. 1 and 2, ~30 mg m -2 ) was consistent with previous observations during Tara Oceans (from 27.6 to 33.6 mg m -² over 0-100 m at St. 122-124; Caputi et al. 2019) and BIOSOPE (30.2 ± 2.8 mg m -² mean over 0-65 m,[START_REF] Grob | Contribution of picoplankton to the total particulate organic carbon concentration in the eastern South Pacific[END_REF][START_REF] Claustre | Introduction to the special section bio-optical and biogeochemical conditions in the South East Pacific in late 2004: the BIOSOPE program[END_REF][START_REF] Ras | Spatial variability of phytoplankton pigment distributions in the subtropical South Pacific Ocean: comparison between in situ and predicted data[END_REF]) revealing a relative temporal ecosystem stability at the N Off stations. However, the north-south Chla variability as shown on satellite and BGC-Argo observations was indicative of quick changes in the environmental variables, especially in the south. Such changes might be related to the stronger ocean dynamics in the south highlighted on Finite Size Lyapunov Exponent satellite observations[START_REF] Martinez | Influence of Tropical Instability Waves on Phytoplankton Biomass near the Marquesas Islands[END_REF], and likely associated with mesoscale activity flowing from the south-east(Cassianides et al. in rev). Consequently, different datasets with different environmental conditions may lead to tricky comparisons.Comparisons between the three cruises was also weakened by several shortcoming linked to heterogeneous sampling strategies and methodologies. Some differences have emerged J o u r n a l P r e -p r o o f Journal Pre-proof and are not explained such as the nanophytoplankton (i.e. 2-20 μm in size) community predominance reported during BIOSOPE[START_REF] Ras | Spatial variability of phytoplankton pigment distributions in the subtropical South Pacific Ocean: comparison between in situ and predicted data[END_REF] vs. a dominance of organism <3

JFig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5

 12345 Fig. 1 GlobColour-GSM satellite derived chlorophyll-a concentration (Chla in mg m -3 ) averaged over 1998 to 2012 for the South Pacific Ocean (white line delineates the French Polynesian Exclusive Economic Zone, EEZ). The oligotrophic region (i.e., Chla <0.1 mg m -3 ) is highlighted with the blue color

Fig. 6 a

 6 Fig. 6 a) Trajectory along time of the BGC-Argo float deployed in the wake of Nuku Hiva during Tara Oceans expedition in August 2011. b) Vertical distribution of BGC-Argo derived Chla along time. The black line delimits the MLD as defined in the BGC-Argo float section. The bold black line illustrates in both panels the approximate transition from the northern to the southern part of the archipelago

Fig. 7

 7 Fig. 7 Picophytoplankton (Prochlorococcus, Synechococcus, and picophytoeukaryotes) and nanophytoplankton abundances (×10 3 cells mL -1 ), obtained by flow cytometry along the north-south transect as pictured on Fig. 4

Fig. 8 Fig. 9

 89 Fig. 8 Prochlorococcus, Synechococcus, and picophytoeukaryotes (Picophytoeuk.) and nanophytoplankton (Nanophyto.) abundances integrated over 0-100 m.

Table 3 :

 3 Mean indexes of the abundance of the zooplankton taxa in the three zones: N Off (St. 1 to 3), N Isl (St. 4 to 12), and S Isl (13 to 26). Given the similarity of taxonomic composition, St.13 was included in S Isl to lighten the table.

Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10

 12345678910 Figure1

Figure 11

 11 Figure11

Table 1

 1 Station number and corresponding CTD and Niskin measurements as well as zooplankton net (*additional phytoplankton net).

	Station #	Longitude (°W) Latitude (°S)	CTD	Niskin	Zooplankton net
	1	140°05.1	8°20.0	x	x	x
	2	140°55.6	8°22.1		x	x
	3	140°47.4	8°42.7	x		x*
	4-6	140°15.1	8°54.6	x	x	x*
	7-8	139°38.7	8°56.0	x	x	x
	9	140°04.9	9°15.3	x	x	x
	10	140°04.9	9°15.3	x
	11	140°02.9	9°19.8	x
	12	140°16.4	9°26.9	x
	13	139°29.0	9°30.2		x	x
	14	139°02.2	9°38.8	x
	15	139°09.5	9°52.6	x		x
	16	139°08.5	9°57.4	x	x	x*
	17	139°08.6	9°58.1	x
	18-20	139°55.3	9°50.2	x
	21	138°55.6	9°51.1	x	x	x
	22	138°45.5	9°39.1	x
	23	138°9.8	10°0.5	x
	24	137°55.6	10°14.1	x
	25	138°44.1	10°27.7	x	x	x
	26	138°43.8	10°32.2	x	x	x

Table 4

 4 Table of the zooplankton taxa having the first 10 ranks in the RFDs diagrams shown in Fig. 11.

Acknowledgements

We thank captain and crew of the fishing vessel Braveheart, as well as the head of mission M. Taquet, and the AAMP founding. The authors wish to thank Philippe Gérard (US IMAGO, New Caledonia) for providing nutrient data and Véronique Cornet-Barthaux for phytoplankton microscopic determinations. We also thank Antoine Nowaczyk for the zooplankton counting and microscopic determinations. We thank ACRI-ST France for the development, validation, and distribution of the GlobColour product (http://globcolour.info). We would like to warmly thank the two anonymous reviewers for their valuable comments and suggestions which helped to deeply improved this manuscript.

J o u r n a l P r e -p r o o f

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Nathan Jeger-Madiot
email: nathan.jeger-madiot@espci.fr

Mauricio Hoyos
email: mauricio.hoyos@espci.fr

Jean-Luc Aider
email: jean-luc.aider@espci.fr

Cluster structuration of particle mixture in multi-node acoustic levitation with optical exclusion

Introduction

Acoustofluidic field is used for many applications as for the focusing [START_REF] Goddard | Ultrasonic particle-concentration for sheathless focusing of particles for analysis in a flow cytometer[END_REF], the separation [START_REF] Wu | Acoustofluidic separation of cells and particles[END_REF] or the trapping [START_REF] Evander | Acoustofluidics 20: Applications in acoustic trapping[END_REF] of particles or cells. These approaches are based on the control of the acoustic radiation forces (ARF) [START_REF] Friend | Microscale acoustofluidics: Microfluidics driven via acoustics and ultrasonics[END_REF]. For a classical standing wave resonator, the acoustic force is generated by matching the height of the cavity ℎ with the acoustic frequency 𝑓 !" . If we want 𝑛 acoustic node trapping, we have to made a cavity with a height equal to 𝑛 𝜆 2 ⁄ . Recently, Dumy and al. [START_REF] Dumy | Observation of selective optical manipulation of particles in acoustic levitation[END_REF] has observed a new phenomenon resulting from an acoustic and optical coupling. They show that after the particles are trapped and aggregated at the center of the acoustic node, the particles can be ejected by applying to them a specific optical wavelength. Hereby we propose a new approach to structure multi-clusters in multi-nodes acoustic levitation combining microfluidic channels, the acoustic radiation forces (ARF) and the specific optical illumination.

2D ejection with the optoacoustic phenomenon

In a multi-nodes standing wave cavity, the particles can be trapped in a central cluster. After a short transient period and using the proper parameters, the clusters become circular monolayers levitating in the surrounding fluid. The control of the flow, the ARF and the optical illumination allows various manipulations. For example, on the figure 1, it is possible to eject the particles from the monolayer successively from the first to the last aggregate of the acoustic levitation column. By turning off the illumination, the effect is reversible and the particles come back to their initial positions.

Multi-particle aggregation : toward 3D construction

This approach can be extended to shape clusters simultaneously with several particles. Indeed, the optical ejection only appears if the optical wavelength corresponds to the absorbance of the particle. To proof this concept we have used a binary mixture of two type of particles. The first one is red and have absorption at a wavelength equal to 𝜆 = 465𝑛𝑚 and the second one with white color, which is not sensitive to the previous wavelength. By injecting a suspension of the mixture with an active opto-acoustic effect, we can trap simultaneously on all the aggregates, the white particles at the center with all around the red particles. See Figure 2.

Conclusion

With our set of experiments, we show different ways to manipulate and structure mixture of particles. One of the advantages of such approaches are the use of both optical absorption and acoustic properties. It offers a large panel of setting for the manipulation of particles.

Figure 1 :

 1 Figure 1: Examples of the optical ejection of particles coupled with multi-node acoustic levitation. At the beginning the particles are trapped in acoustic levitation, then the optical illumination ejects the particles from the center of aggregate. By turning off the illumination, the effect is reversible and the particles come back to their initial positions.

Figure 2 :

 2 Figure 2: Example of multi-nodes structuration with a binary mixture. The mixture of particles is injected with a flowrate, then the optical illumination and the acoustic forces are triggered. Afterwards, the flowrate is stopped. After ten seconds, the illumination is turned off. These results show a large cluster of white particles enclosed by a circular ring of red particles.


Alexis Dusart
email: alexis.dusart@irit.fr

Karen Pinel-Sauvagnat
email: karen.sauvagnat@irit.fr

Gilles Hubert
email: gilles.hubert@irit.fr

Capitalizing on a TREC Track to Build a Tweet Summarization Dataset

Today there is a lack of standard collection for automatic tweet summarization evaluation. The construction of such a large dataset is very tedious. In this paper, we check whether the dataset proposed for the TREC Incident Streams track, which was not created for automatic summary generation, could be used in this way. Indeed, when filtering the TREC Incident Streams (IS) dataset with the assessors' annotations, it appears to respect the citeria identified in the literature related to automatic summarization. For this, we studied the TREC IS dataset and then proposed a subset summarizing each event, based on the assessors' annotations. This subset is evaluated according to the criteria previously mentioned. Several widely used state-of-the-art models for automatic text summarization, adapted to tweet summarization, were finally tested on the proposed dataset. The code, the annotations and the results are provided on our Github.

INTRODUCTION

Information Retrieval (IR) is tightly linked with evaluation. Evaluation campaigns were proposed by the community to promote evaluation and allow the researchers to confront their approaches. Since the beginning of TREC1 in 1992, many evaluation collections were built and widely used to support experiments.

Information sources are simultaneously evolving and the wellformed text from the beginning is now often replaced by less conventional texts such as user-generated contents (UGC). Those contents raise new open issues and require the construction of new evaluation frameworks. Twitter is an example of such UGC that generate a huge and heterogeneous volume of data. Among linked evaluation tracks one can cite the TREC Microblog one between 2001 and 2015 [START_REF] Soboroff | Overview of the TREC-2012 microblog track[END_REF] [23], the TREC RTS (Real-Time Summarization) one built between 2016 and 2018 [START_REF] Lin | Overview of the TREC 2017 real-time summarization track[END_REF], the TREC IS (Incident Streams) one built since 2018 [START_REF] Mccreadie | Trec incident streams: Finding actionable information on social media[END_REF], and the INEX Tweet Contextualization task [START_REF] Bellot | Inex tweet contextualization task: Evaluation, results and lesson learned[END_REF].

Those campaigns mainly evaluated adhoc retrieval [START_REF] Soboroff | Overview of the TREC-2012 microblog track[END_REF] [23] or filtering [START_REF] Lin | Overview of the TREC 2017 real-time summarization track[END_REF]. One of the non-evaluated task refers to tweet summarization, although it is a key issue for Twitter users that are often overwhelmed by the continuous stream of information.

Many tweet summarization approaches were proposed by IR researchers [START_REF] Rudra | Summarizing situational tweets in crisis scenarios: An extractive-abstractive approach[END_REF] [2], [START_REF] He | Twitter summarization with social-temporal context[END_REF], but it is difficult to compare them due to the lack of publicly available evaluation collections. Most of the approaches evaluate their performance on self-built collections that are often very small (only 3 or 4 events to summarize) and scarcely accessible. To solve the issue of inadequate evaluation collections, two solutions can be considered: building a new collection, which is very costly, or transform an existing collection of tweets for the purpose of summarization evaluation.

Our research question in this paper is to see if the existing TREC IS collection [START_REF] Mccreadie | Trec incident streams: Finding actionable information on social media[END_REF] could potentially be adapted for event summarization evaluation in the context of Twitter.

The rest of the paper is organized as follows. Section 2 presents related works about tweet summarization and evaluation. Section 3 describes the original collection proposed by the organizers of the TREC IS tracks. Section 4 introduces the extension we made to adapt it to summarization and validates our propositions, particularly regarding the proposed Gold Standard. We finally conclude the paper in Section 5.

RELATED WORK

The aim of this section is not to present tweet summarization approaches but rather to focus on (tweet) summarization evaluation. Given the Twitter context, we restrict our analysis to multidocument summarization which aims to summarize a set of documents.

To be or not to be a good summary

Building an evaluation collection for summarization requires on the one hand documents to summarize and on the other hand an "ideal" summary that will be used as gold standard. This summary should be as "ideal" as possible, since it is considered as the summary toward which summarization methods should tend.

What is an "ideal" summary? A first and very simple definition of a summary can be "a text that is produced from one or more texts, that conveys important information in the original text(s), and that is no longer than half of the original text(s) and usually, significantly less than that" [START_REF] Radev | Introduction to the special issue on summarization[END_REF]. To be "ideal" or "perfect", a (multidocument) summary should also meet the following requirements [START_REF] Goldstein | Multidocument summarization by sentence extraction[END_REF][START_REF] Huang | Modeling document summarization as multi-objective optimization[END_REF]:

• coverage: the main points across documents of the collection are present in the summary; • anti-redundancy: no redundant information can be found in the summary;

• cohesion, i.e., the ability to combine text passages in a useful manner for the reader. The authors of [START_REF] Goldstein | Multidocument summarization by sentence extraction[END_REF] mention some ways to guarantee cohesion, such as document ordering, topic cohesion, and time line ordering; • coherence, i.e., relevance and readability;

• contextualization: the summary should include sufficient context to be understandable to a human being; • no inconsistencies in the information reported from different sources (e.g., billion vs million, etc.).

These requirements are valid regardless of the type of considered documents (long or short texts, very specific texts such as medical or legal ones, etc.). Tweets are of course no exception, even if some additional requirements may be considered such as source validity [START_REF] Vosoughi | Rumor gauge: Predicting the veracity of rumors on twitter[END_REF].

Existing datasets for tweet summarization

Datasets for the evaluation of automatic summarization systems were mainly built as part of specific evaluation challenges organized during dedicated conferences, such as DUC (Document Understanding Conferences) between 2001 and 20072 , TAC (Text Analysis Conference) between 2008 and 20153 , and TREC with the Temporal Summarization track from 2013 to 2015 4 . During all those conferences, many summarization tasks were organized, such as multi-document, query-based, biomedical, temporal, or opinion summarization. In most cases, a Gold Standard summary was built by human assessors, either using participants runs and pooling, or directly on the source documents. Whereas these evaluation collections are known and recognized collections of the domain, similar collections with tweets as source documents are surprisingly missing.

Evaluation collections with tweets however exist: one can cite the TREC Microblog collection built between 2011 and 2015 [START_REF] Soboroff | Overview of the TREC-2012 microblog track[END_REF] [23], the TREC RTS (Real-Time Summarization) one built between 2016 and 2018, and the TREC IS one (Incident Streams) built since 2018. The associated research tasks however do not deal with summarization, even for the RTS one, which is in fact rather a filtering task. Indeed, the aim of the RTS task is to filter the Twitter stream according to a given topic in order to push to the user phone only those which are relevant and new. The task focuses on topics that are not limited in time such as 'Women's rights in Saudi Arabia' or 'Sudoku tournaments'. Furthermore, for some topics all the incoming tweets about the topic have to be pushed since there are very few (i.e., sometimes less than one relevant tweet per day). Summarizing these tweets thus would not have any sense.

Among the rare tweet summarization collections that can be found in the literature, one can cite those listed in Table 1, line 1 to 3. Those collections either rely on very few tweets to summarize [START_REF] Sharifi | Summarization of twitter microblogs[END_REF] or very few events [START_REF] Rudra | Identifying sub-events and summarizing disaster-related information from microblogs[END_REF] [START_REF] Nguyen | Tsix: A humaninvolved-creation dataset for tweet summarization[END_REF]. They are moreover scarcely accessible to the research community.

Evaluation metrics

There are two ways to evaluate the performance of text summarization [START_REF] Allahyari | Text summarization techniques: a brief survey[END_REF][START_REF] Gambhir | Recent automatic text summarization techniques: a survey[END_REF]:

• The aim of extrinsic evaluation is to determine to which extent the provided summary help to other tasks such as text classification or question answering. • The objective of intrinsic evaluation is to measure the quality and informativeness of the provided summary against a human-made summary. The collections dedicated to text summarization aim at doing such type of evaluation [START_REF] Ermakova | A survey on evaluation of summarization methods[END_REF].

On the one hand, quality refers to properties such as grammatical correctness, structure, or coherence. On the other hand, informativeness is evaluated with metrics such as precision, recall, or ROUGE [START_REF] Lin | ROUGE: A package for automatic evaluation of summaries[END_REF].

ROUGE was widely used for the evaluation of text summarization. It identifies the common units between the summary to evaluate and the gold standard summary. ROUGE includes five measures: ROUGE-N, ROUGE-L, ROUGE-W, ROUGE-S, and ROUGE-SU. ROUGE-1 for instance compares the unigrams of both summaries. BLEU (BiLingual Evaluation Understudy) [START_REF] Papineni | Bleu: a method for automatic evaluation of machine translation[END_REF] initially intended for machine translation evaluation was also applied to evaluation of summaries. Other metrics exist in the literature such as METEOR, (Metric for Evaluation of Translation with Explicit ORdering) [START_REF] Denkowski | Meteor 1.3: Automatic metric for reliable optimization and evaluation of machine translation systems[END_REF], Pyramid [START_REF] Nenkova | The pyramid method: Incorporating human content selection variation in summarization evaluation[END_REF], and a ROUGE-based metric integrating word embeddings [START_REF] Ng | Better summarization evaluation with word embeddings for ROUGE[END_REF].

Discussion

As previously introduced, existing tweet summarization collections are rare and small in terms of tweets or topics/events considered, even for the evaluation of approaches published in one of the top conferences of the domain [START_REF] Rudra | Identifying sub-events and summarizing disaster-related information from microblogs[END_REF].

While participating to the TREC Incident Streams track [START_REF] Dusart | Irit at trec 2019: Incident streams and complex answer retrieval tracks[END_REF], which was not created for automatic tweet summarization, we noticed that some characteristics of the ground truth could be used to generate a gold standard for tweet summarization. We present this collection in the following section.

TREC IS ORIGINAL COLLECTION 3.1 Objectives of the TREC IS track

The TREC Incident Streams track exists since 2018. The purpose of this track is to use social information, in this case information from Twitter, to help emergency services to respond to an appearing incident, as shown in Figure 1 [START_REF] Mccreadie | Trec incident streams: Finding actionable information on social media[END_REF]. On the one hand, a set of tweets has to be classified according to predefined categories called Information types. "Information wanted" or "Request for Search and Rescue" are examples of Information types. On the other hand, a priority score has to be associated with each tweet in order to return alerts if necessary. An alert is raised when the priority score exceeds 0.7. Particular attention is paid to information types named "Requests for Goods/ Services", "Requests for Search and Rescue", "Calls to Action for Moving People", "Reports of Emerging Threats", "Reports of Significant Event Changes" and "Reports of Services" that are considered actionable, which means that they should lead to actions from emergency services. Two editions of the track took place, the first in 2018 and the second in 2019 that has run in two parts (one for prototyping and the other for validation).

Dataset

# of tweets # of events Gold Standard length trending-topics-2010 [START_REF] Sharifi | Summarization of twitter microblogs[END_REF] 2,500 25 4 tweets disaster-related-2018 [START_REF] Rudra | Identifying sub-events and summarizing disaster-related information from microblogs[END_REF] 130,000 3 200 words events-oct-nov-2016 [START_REF] Nguyen | Tsix: A humaninvolved-creation dataset for tweet summarization[END_REF] 60,000 6 N/A trec-is-news-priority 35,000 26 45 tweets on average

Features of the TREC IS collection

This section presents the main features of the original TREC IS collection built since 2018.

Collected tweets.

In fact, the collection was incrementally built through three steps. On the one hand, in 2018 the tweets were collected from the CrisisLexT26 source [START_REF] Olteanu | What to expect when the unexpected happens: Social media communications across crises[END_REF]. On the other hand, several sources were used to collect the tweets for the part A and B of the 2019 edition: CrisisLexT26, CrisisNLP Resource #2 [START_REF] Imran | Extracting information nuggets from disaster-related messages in social media[END_REF], CrisisNLP Resource #1 [START_REF] Imran | Twitter as a lifeline: Human-annotated twitter corpora for NLP of crisis-related messages[END_REF], additional tweets crawled by the organizers of the track themselves via Twitter API, other tweets collected via the GNIP service, and finally tweets donated by participant groups. The event related to these collected tweets refers to the following incident types: wildfire, earthquake, flood, typhoon/hurricane, shooting and bombing.

Regarding the CrisisLex and CrisisNLP sources, the organizers used the tweets labeled as relevant to the events. For the remaining events and the corresponding tweets collected by the organizers, a clustering was performed to select one tweet per cluster. Then, an additional keyword filtering led to a reduced, diverse, and likely relevant set. Finally, for all events, the tweets identified by the Twitter's language classifier as written in a language other than English ('en') were removed.

Tweet annotation.

For the evaluation process, the organizers created a ground truth according to a set of information type labels for both the training and test sets. To create this ground truth set, a labelling interface was developed by the organizers, see Figure 2. This interface was intended to help the assessors annotating the collected tweets for each event (see section 3.2.1) with:

• information types : "Request-GoodsServices", "Request-SearchAndRescue", "Request-InformationWanted", "CallToAction-Volunteer", "CallToAction-Donations", "CallToAction-MovePeople", "Report-FirstPartyObservation", "Report-ThirdPartyObservation", "Report-Weather", "Report-EmergingThreats", "Report-MultimediaShare", "Report-ServiceAvailable", "Report-Factoid", "Report-Official", 'Report-CleanUp", "Report-Hashtags", 'Report-News", "Report-NewSubEvent", "Report-Location", "Other-Advice", "Other-Sentiment", "Other-Discussion", "Other-ContextualInformation", "Other-Irrelevant", "Other-OriginalEvent". Tweets can be multi-labeled. In particular, the information type "Report-News" is the label corresponding to the posts providing/linking to continuous coverage of the event.

• priority levels : "low", "medium", "high", "critical" which correspond respectively to the scores 0.25, 0.5, 0.75, and 1. The tweets were labeled by several TREC assessors across three assessment periods. Due to the volume of the tweets collected for each event, the assessments were divided among multiple assessors.

Collection statistics.

In the end, the merged TREC IS collection totalizes 35,266 assessed tweets according to 33 distinct events. Initially, the organizers mentioned 34 events but only 33 were really assessed. Table 2 reports the considered events and some statistics about the dataset.

Between 96 and 5,863 tweets were labeled for an event with an average of 1068 tweets per event. Table 2: Some statistics about the TREC IS dataset. For each event, the table reports the number of tweets labeled as "Reportnews" as information type (column 3), and the number of tweets per priority level (columns 4 to 7). The last column indicates the number of tweets in the Candidate Gold Standard summaries. The percentage in parenthesis corresponds to the number of tweets kept from the event (column 2).

groups can be observed, the tweets before 2018 and those after 2018.

From 2018 tweets have a maximum size around 280 characters while it is around 140 before, which corresponds to the increase of the maximum tweet size decided by Twitter on 7 November 2017. However, the events flschoolshooting2018 and floodchoco2019 have surprisingly a maximum size up to 140 characters although the limit has been already extended to 280 by Twitter for this period.

PROPOSED DATASET FOR TWEET SUMMARIZATION EVALUATION 4.1 First intuitions

By analyzing the way the collection was annotated, we had the following intuitions that we discuss in more detail in the following sections:

• tweets labeled as "Report-News" bring novel information and are thus a priori by definition non-redundant between them, • these tweets, when associated to a "High" or "Critical" priority level are also essential to cover the event.

These two properties meet two of the aforementioned requirements of what is an "ideal" summary, i.e., anti-redundancy and coverage. For each event, the summary composed of tweets both labeled "Report-News" as information type and "High" or "Critical" as priority can thus be considered as a candidate gold standard, named CGS summary in the remaining of the paper. It is important to notice that all CGSs we consider are chronologically ordered according to the tweet creation dates. Some statistics about the CGSs are reported in the last column of Table 2. Notice that the events named earthquakeCali-fornia2014, fireColorado2012, floodChoco2019, floodColorado2013, laAirportShooting2013, typhoonPablo2012, westTexasExplosion2013 have empty CGSs. They are thus skipped in the remaining of the paper.

On this basis, we decided to further investigate the possibilities of the candidate gold standard to fulfill the requirements of an ideal summary.

Analysis of the candidate gold standard summaries

4.2.1 Anti-redundancy. As reported in [START_REF] Mccreadie | Trec incident streams: Finding actionable information on social media[END_REF], an event could have been annotated by several assessors on disjointed sets of tweets. Some redundancy could thus remain in some events. To deal with those cases we automatically remove redundant tweets from the candidate gold standard (CGS) as follows. We took all the tweets per event and according to their creation timestamp. For each considered tweet, we evaluated the ROUGE-2 metric between this tweet and all the preceding ones. All tweets getting a ROUGE-2 score greater or equal to 0.3 were removed from the CGS summaries. We set the threshold to 0.3 according to our experiments. In total 349 tweets were removed (see We then manually checked the filtered CGSs using an assessment tool we specifically developed. A screen of this tool interface is depicted on Figure 3. For a given event, tweets are presented in chronological order in the area numbered 1. For each tweet, the contained information is annotated as New (area 2) or Already known (area 3). Tweets labeled as New are added in the area numbered 4 to be used as annotator memory and help him/her to label next tweets. Each labeling action can be undone thanks to the button in area 5. After this phase, 174 tweets were skipped from the summaries (see Table 3, third column).

At the end of the redundancy-check phase, 1,170 tweets were kept to form the "new" CGS summaries, simply referred as CGS in the remainder of the paper. The smallest CGS is now composed of one tweet, while the biggest contains 161 tweets.

Coverage.

Regarding coverage, we hypothesized that it is intrinsic to the original TREC IS collection construction. Indeed, important news (i.e., high or critical ones) from the original dataset are kept to build our CGS summaries.

To support this hypothesis we analysed the CGS with respect to the sub-events associated to our events reported in the Wikipedia portal Current_events 6 . This portal daily reports world-wide current events.

For each event, we extracted from the portal the sub-events corresponding to the period covered by the original TREC IS collection. Each event of the TREC IS collection corresponds at least to one sub-event in the portal (see Table 4 column 2). The total number of sub-events is 154 with 6 sub-events per event on average.

Here is an example of the extracted sub-events for the event albertaFloods2013.

• In an additional step, we compared the sub-events represented in the CGS with the sub-events extracted from the Wikipedia portal. To do so, we made another assessment tool (see Figure 3). With this tool, for each pair (tweet of the CGS, sub-event) we determined if the tweet is related to either the whole sub-event, a part of it, or not at all. Note that a tweet can thus be associated to multiple sub-events. In total, we annotated 6,560 pairs as 'not at all' (92 %), 543 pairs as 'partly' (about 7 %), and 29 pairs as 'whole' (less than 1 %). 68 % of the CGSs tweets do not refer to any sub-events of the Wiki portal.

Among the 154 sub-events appearing in the Wikipedia portal, are represented in the CGS. The 51 sub-events for which there are no tweet references are spread over 10 of the 26 events, which represents one third of the sub-events and affects 38 % of the events. The distribution of the number of sub-events per event in the Wiki portal and in the CGS is presented in Table 4, third column.

A limitation of our proposed dataset is highlighted here: it does not cover all sub-events reported in the Wikipedia portal for each event. One of the reasons can be that the Wiki events portal covers collateral consequences of the main events, such as cancellation of sporting fixtures. These consequences, although probably present in the original IS collection, are not labelled as High or Critical by assessors and are thus not appearing in our CGSs.

Cohesion.

The cohesion criterion is defined by [START_REF] Goldstein | Multidocument summarization by sentence extraction[END_REF] as the way to organize the different passages of text (here the different tweets) in a way that is efficient for the reader. Cohesion can be expressed, among others, in terms of topic-cohesion or time line ordering.

First, we evaluated the time line cohesion of our CGSs by comparing their chronological ordering with the chronological order of the sub-events in the Wikipedia portal. The comparison results showed that the chronological order of the tweets does not strictly follow the chronological appearance of the sub-events. Indeed, some tweets referring to a previous sub-event can continue to be written after other tweets referring to a new sub-event.

Second, the manual checking for coverage presented in section 4.2.2 allowed to shed some light on topic-cohesion. As previously indicated, 68 % of the CGS tweets do not refer to a sub-event reported in the Wikipedia portal. Consequently, topic-cohesion is guaranteed only for the subset comprising the 32 % of the CGS tweets that refer to Wikipedia portal sub-events.

To sum up we provide a subset of tweets of the CGS that respect the cohesion criteria.

Coherence.

The coherence criterion is defined by [START_REF] Goldstein | Multidocument summarization by sentence extraction[END_REF] as the ability for a summary to be relevant and readable to the reader. On the one hand, the relevance is induced by the annotations of the TREC IS task. On the other hand, we evaluated readability with state-of-the-art metrics. These metrics are Flesch-Kincaid [START_REF] Kincaid | ) for Navy Enlisted Personnel. Research Branch report[END_REF], Gunning-Fog [START_REF] Gunning | The technique of clear writing[END_REF], Coleman-Liau [START_REF] Coleman | A computer readability formula designed for machine scoring[END_REF], Dale-Chall [START_REF] Dale | A formula for predicting readability[END_REF], Automatic readability index (Ari) [START_REF] Smith | Automated Readability Index[END_REF], Linsear write [START_REF] Klare | Assessing readability[END_REF] and Spache [START_REF] Spache | A new readability formula for primary-grade reading materials[END_REF]. The features used by these metrics focus on number of words, number of characters, number of syllables, number of difficult words. We used the U.S. grade score obtained by these metrics with the library py-readability-metrics 7 in our readability evaluation. We performed the evaluation of the entire dataset and the CGS for each event whose CGS contains more than 100 words, which affect the 7 https://pypi.org/project/py-readability-metrics/ events earthquakeBohol2013, costaRicaEarthquake2012, philipin-nesFloods2012, manilaFloods2013 that do not contain 100 words. Table 4 presents results for the Flesch-Kincaid, Coleman-Liau, and Dale-Chall metrics. The results for the Gunning-Fog, Ari, Linsear write, and Spache metrics were highly correlated to Flesch-Kincaid (Pearson correlations, 𝑟 > 0.9, 𝑝 -𝑣𝑎𝑙𝑢𝑒<0.01).

We can observe that the readability scores are, except for the event italyEarthquake2012, between the 7th grade and the college graduate grade (college grade is greater than 12 and college graduate grade is greater than college grade). We can also notice that readability scores are weaker (i.e., higher values) for CGS than for all tweets per event in most cases (80 %). However, except for the Coleman-Liau metric, the difference is not significant (Student ttest, without the outlier italyEarthquake, 𝑝 -𝑣𝑎𝑙𝑢𝑒 < 0.05). The Coleman-Liau measure takes into account the number of words in sentences and the number of characters in words. Indeed, the average length of words in terms of characters is 4.3 for the entire TREC IS dataset and 4.8 for the CGS, and the average length of sentences in terms of words is 11.6 for the entire TREC IS dataset and 13.8 for the CGS. We think that these reserved results are due to the technical aspect of the tweets labeled News and High Priority.

Context and inconsistencies.

We assume that the title of an event is sufficient to understand the context. We also assume that the inconsistencies were verified by the TREC IS assessors.

Experiments on state-of-the-art approaches

We tested several state-of-the-art methods for summarizing in order to verify the following hypothesis. If the CGS represents good gold standard summaries then the summaries generated by the state-ofthe-art methods should get better evaluation scores than random summaries. To do so, we generated 50 random summaries per event from the entire TREC IS dataset. All the random summaries comprised the same number of tweets as the CGS for each event. Then, we used the average number of terms of these random summaries and of the CGS to generate summaries by applying the state-of-theart methods from the entire TREC IS dataset. The tested methods are Centroid-based [START_REF] Radev | Centroid-based summarization of multiple documents[END_REF], ClusterCMRW [START_REF] Wan | Multi-document summarization using cluster-based link analysis[END_REF], Coverage, LEAD, Lex-PageRank [START_REF] Erkan | Lexpagerank: Prestige in multi-document text summarization[END_REF], Submodular 1 [START_REF] Li | Multi-document summarization via submodularity[END_REF] and 2 [START_REF] Lin | Multi-document summarization via budgeted maximization of submodular functions[END_REF], as well as Textrank [START_REF] Mihalcea | Textrank: Bringing order into text[END_REF]. We used the available PKUSUMSUM implementation 8 .

We evaluated the summaries (i.e., the random summaries and the summaries generated by the state-of-the-art methods) according to ROUGE metrics. The ROUGE metrics compute the overlap between the gold standard and the evaluated summary in terms of n-grams. We used the ROUGE-1 score based on the unigram overlap, the ROUGE-2 score based on the bigram overlap, and the ROUGE-SU score relying on the overlap of both unigrams and skip-bigrams. With regard to the results presented in Table 5 there is no significant ROUGE score differences between the state-of-the-art methods and the random summaries. This can be due to the fact that the state-of-the-art methods are not particularly dedicated to tweet summarization. Further experiments with tweet summarization methods are needed, but the preceding experiments can be seen as a good preliminary step. Table 4: On the left side, the coverage statistics for each event with the number of sub-events in the Wiki portal and the number of these sub-events present in the CGS. On the right side, the readability scores for the different measures regarding the entire dataset (noted A) and CGS (noted GS) for each event.

Summary

CONCLUSION

In this paper, we presented the benefits of the TREC IS dataset for the task of tweet summarization. We considered a particular subset of this dataset for which we produced additional annotations. These annotations, as well as the code and the results, are available in our Github 9 . We showed that a subset of this dataset can be used as gold standard for this task. We studied this subset with respect to the properties a good summary has to reach. We showed that all these properties are satisfied for tweet summarization, although some limits of this dataset can been found, specially regarding cohesion or readability. The well-known document summarization methods showed limits to outperform random summaries. These limits might be explained by the fact that the TREC IS dataset is oriented towards event reporting. Future work includes a study of more specific summarization methods.

Table 1 :Figure 1 :

 11 Figure 1: Incident Streams task, as shown on the task's website (http://dcs.gla.ac.uk/~richardm/TREC_IS/)

Figure 2 :

 2 Figure2: TREC-IS Task Assessment Interface[START_REF] Mccreadie | Trec incident streams: Finding actionable information on social media[END_REF]

  June 20, 2013 (Thursday): Extensive flooding begins throughout southern Alberta, Canada, leading to the evacuation of more than 100,000 people, notably in the City of Calgary and Town of High River. It would become the costliest natural disaster in Canadian history. • June 21, 2013 (Friday): 75,000 people are evacuated from their homes during flooding in Calgary, Alberta, Canada. (CNN) • June 22, 2013 (Saturday): 100,000 residents are displaced on the third day of flooding in Alberta. (CBC)

Figure 3 :

 3 Figure 3: Redundancy Tool Interface

Table 3

 3

	, second column). One can find
	below some examples of redundant tweets automatically detected:
	• cases of correct redundancy removals
	-exactly the same tweet because of retweet
	Tweet 1: 'RT : Italy earthquake: modern buildings, not
	ancient ones, pose biggest threat'
	Tweet 2: 'Italy earthquake: modern buildings, not ancient
	ones, pose biggest threat ( video) -'
	-same information but differently expressed

Tweet 1: 'RT : Just released by the FBI: Suspect 1 and Suspect 2 in the Boston Marathon bombing' Tweet 2: 'Photo of Suspects 1 and 2 in the Boston Marathon Bombings. #boston #FBI #help'

Table 3 :

 3 Some statistics about the CGS. The percentage in parentheses in the second (resp. third) column corresponds to the number of tweets kept from the CGS (see Table2), (resp. from the summary of the second column). The columns 4 to 7 show some additional statistics about the tweets in the final CGS (third column). These statistics are evaluated after URLs, useless spaces, HTML characters removal.

	Event	# of tweets after automatic redundancy removal redundancy removal # of tweets after manually	min. # of characters*	max. # of characters*	min. # of words**	max. # of words**
	joplinTornado2011	17(100 %)	17(100 %)	46	136	8	30
	costaRicaEarthquake2012	1(100 %)	1(100 %)	111	111	19	19
	guatemalaEarthquake2012	19(76%)	15(78.9%)	44	130	7	32
	philipinnesFloods2012	1(100 %)	1(100 %)	88	88	17	17
	italyEarthquakes2012	16(94.1 %)	13(81.3 %)	43	115	8	22
	bostonBombings2013	54(93.1 %)	51(94.4 %)	36	136	9	30
	typhoonYolanda2013	47(94 %)	43(91.5 %)	32		6	25
	australiaBushfire2013	20(100 %)	20(100 %)	65	133	11	28
	queenslandFloods2013	127(93.4 %)	117(92.1 %)	22	139	5	29
	earthquakeBohol2013	2(100 %)	2(100 %)	111	115	20	22
	albertaFloods2013	18(100 %)	18(100 %)	51	132	11	28
	manilaFloods2013	6(100 %)	6(100 %)	57	127	12	23
	chileEarthquake2014	12(100 %)	7(58.3 %)	37	114	5	21
	typhoonHagupit2014	168(87 %)	159(94.6 %)	15	139	4	31
	parisAttacks2015	75(93.8 %)	52(69.3 %)	42	129	10	27
	nepalEarthquake2015	169(97.7 %)	161(95.3 %)	5	140	1	33
	fireYMM2016	129(96.3 %)	111(86.0 %)	36	140	8	33
	shootingDallas2017	17(100 %)	17(100 %)	47	140	8	30
	flSchoolShooting2018	29(90.7 %)	22(75.9 %)	63	135	7	26
	hurricaneFlorence2018	36(100 %)	36(100 %)	53	274	9	56
	philippinesEarthquake2019	87(92.6 %)	72(58.6 %)	43	277	6	51
	southAfricaFloods2019	60(75 %)	50(83.3 %)	43	241	7	47
	cycloneKenneth2019	81(94.2 %)	62(74.1 %)	48	277	7	60
	albertaWildfires2019	104(88.9 %)	90(86.5 %)	36	280	6	59
	coloradoStemShooting2019	29(11.8 %)	15(51.7 %)	40	252	7	51
	sandiegoSynagogueShooting2019	20(51.3 %)	12(60.0 %)	49	157	8	31
	Mean	51.69	45.0	48.58	161.15	9.69	33.12
	Std	49.95	46.11	23.84	59.67	4.27	12.35

Table 5 :

 5 ROUGE scores for each summary considering CGSs as gold standards, using the t-test there is no significant difference between methods and random summaries.

		ROUGE-2 ROUGE-1 ROUGE-SU
	centroid	0.20926	0.4626	0.22939
	clustercmrw	0.20901	0.45821	0.23681
	coverage	0.18985	0.4639	0.22641
	lead	0.19352	0.46848	0.22988
	lexpagerank	0.21217	0.46685	0.22988
	submodular1	0.20046	0.46585	0.24169
	submodular2	0.17876	0.43346	0.21173
	textrank	0.22771	0.46512	0.23522
	Mean random summaries	0.1937	0.4632	0.2283
	Std random summaries	0.009	0.009	0.007

Text REtrieval Conference, http://trec.nist.gov

https://www-nlpir.nist.gov/projects/duc/

https://tac.nist.gov/

http://www.trec-ts.org/

For this event, one tweet was labeled as "Unknown"

https://en.wikipedia.org/wiki/Portal:Current_events

https://github.com/PKULCWM/PKUSUMSUM

https://github.com/AlexisDusart/SetSummTweet