Roberta Sciascia
email: roberta.sciascia@sp.ismar.cnr.it

Katell Guizien
email: guizien@obs-banyuls.fr

Mariana Padrón
email: mariana.padron@gmail.com

Lorenzo Bramanti
email: lorenzo.bramanti@obs-banyuls.fr

Marcello Magaldi
email: marcello.magaldi@sp.ismar.cnr.it

Larval buoyancy matters: sinking

Keywords: changes larval transport predictions among Paramuricea clavata populations sinking changes larval transport predictions among Paramuricea clavata populations sinking changes larval transport predictions among Paramuricea clavata populations Larval dispersal modelling, connectivity, Paramuricea clavata, NW Mediterranean, larval behaviour, graph theory and conservation

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Introduction

Anticipating the impact of the on-going climate change on the already altered ocean biodiversity is a major challenge for the next decade (ONU SGD 14, IPBES 2030 work program, https://ipbes.net/o4-supporting-policy). To do so, models incorporating knowledge and understanding of mechanisms regulating species spatial distribution such as dispersal will be crucial to produce biodiversity maps for the future [START_REF] Urban | Improving the forecast for biodiversity under climate change[END_REF]. The Mediterranean sea has been highlighted as a climate change hotspot (IPPC 2015). Over the last decades, mass mortalities of conspicuous species have been increasingly reported in the Mediterranean coastal ecosystems and related to climate anomalies (Paramuricea clavata: [START_REF] Garrabou | Mass mortality in northwestern mediterranean rocky benthic communities: effects of the 2003 heat wave[END_REF], Pinna nobilis: Vázquez-Luis et al. 2017). Severe population depletion or local extirpation occurrences poses the question of species resilience [START_REF] Holling | Resilience and stability of ecological systems[END_REF]. For sessile species, propagules dispersal which establishes connections among populations is a key mechanism for species resilience [START_REF] Hanski | Metapopulation dynamics[END_REF]. As a metter of fact the recovery of impacted populations have been observed, [START_REF] Cupido | Response of a gorgonian (paramuricea clavata) population to mortality events: recovery or loss?[END_REF][START_REF] Santangelo | Effects of increased mortality on gorgonian corals (cnidaria, octocorallia): different demographic features may lead affected populations to unexpected recovery and new equilibrium points[END_REF]. Recovery was attributed to larval dispersal from less impacted populations located in neighbouring Marine Protected Areas (MPA) (Padrón et al., 2018a). Thus, anticipating the recovery potential of impacted populations thanks to dispersal might help optimize biodiversity management. Numerical simulations of larval dispersal between species-suitable habitats have the potential to forecast such population recovery pathways.

Larval dispersal simulations require hydrodynamical models to provide a reliable and validated description of the flow and its variability at the spatio-temporal scales relevant for both dispersal processes and regional population connectivity estimates. Connectivity studies have used a variety of ocean circulation models, from coarse-resolution global models (10 kms, [START_REF] Treml | Modeling population connectivity by ocean currents, a graph-theoretic approach for marine conservation[END_REF][START_REF] Wood | Modelling dispersal and connectivity of broadcast spawning corals at the global scale[END_REF] to high-resolution regional models (100 m to 1 km, [START_REF] Guizien | Dispersal of owenia fusiformis larvae by wind-driven currents: turbulence, swimming behaviour and mortality in a three-dimensional stochastic model[END_REF]. Ocean circulation models are validated against diagnostic variables such as sea surface temperature and salinity, heat content, mass, sea level, meridional transport, turbulent kinetic energy or vorticity [START_REF] Fox-Kemper | Challenges and prospects in ocean circulation models[END_REF]. However, such validation can be insufficient to ensure that simulated velocities used for the larval dispersal simulations are representative of the advection and diffusion experienced by larvae in the flow [START_REF] Pineda | Larval transport and dispersal in the coastal ocean and consequences for population connectivity[END_REF]. Even when flow field simulations are accurate, the specificity of larval dispersal simulations ultimately depends on the knowledge of biological traits such as location and timing of larval release in parental populations and larval traits important for dispersal [START_REF] Scheltema | On dispersal and planktonic larvae of benthic invertebrates: An eclectic overview and summary of problems[END_REF]. Among these, pelagic larval duration has been extensively tested in dispersal simulations [START_REF] Siegel | Lagrangian descriptions of marine larval dispersion[END_REF][START_REF] Edwards | Using 2-dimensional dispersal kernels to identify the dominant influences on larval dispersal on continental shelves[END_REF][START_REF] Guizien | Using larval dispersal simulations for marine protected area design: Application to the gulf of lions (northwest mediterranean)[END_REF]. In contrast, larval motility behaviour is less often accounted for, despite being recognized as essential in regulating larval position in the water column [START_REF] Leis | Behaviour as input for modelling dispersal of fish larvae: behaviour, biogeography, hydrodynamics, ontogeny, physiology and phylogeny meet hydrography[END_REF]. Assuming neutrally buoyant larvae in dispersal studies can be a reasonable hypothesis but it should be grounded on larval motility behaviour experimental studies [START_REF] Guizien | Dispersal of owenia fusiformis larvae by wind-driven currents: turbulence, swimming behaviour and mortality in a three-dimensional stochastic model[END_REF][START_REF] Martínez-Quintana | Quantification of larval traits driving connectivity: the case of corallium rubrum (l. 1758)[END_REF]. In fact, in most larval dispersal modelling studies, the neutrally buoyant assumption is not based on data but justified by the lack of knowledge on larval motility. The few species for which larval motility behaviour has been documented are commercial species such as oysters [START_REF] North | Vertical swimming behavior influences the dispersal of simulated oyster larvae in a coupled particle-tracking and hydrodynamic model of chesapeake bay[END_REF] or scallop [START_REF] Tremblay | Drift of sea scallop larvae placopecten magellanicus on georges bank: a model study of the roles of mean advection, larval behavior and larval origin[END_REF] and species of tropical reef fishes (review in [START_REF] Leis | Are larvae of demersal fishes plankton or nekton?[END_REF]. However, regardless of the larval behaviour implemented in dispersal simulations larval transport predictions should be tested against independent observations. For most benthic invertebrates, the larval stage is difficult to identify at the species level for two reasons: the larval stage is too small to be tagged and the loss during the dispersive phase is so large that recollection is almost impossible. As a consequence, larval transport observations of benthic invertebrates are very rare, and population genetics is the main measurable effect of larval transport that keeps track of larvae population origin [START_REF] Lowe | What can genetics tell us about population connectivity?[END_REF]. Despite several works assuming the importance of connectivity to favor the persistence of sessile benthic species, empirical validation of larval dispersal simulations are still not that frequent [START_REF] Padrón | Passive larval transport explains recent gene flow in a mediterranean gorgonian[END_REF][START_REF] Bode | Successful validation of a larval dispersal model using genetic parentage data[END_REF]. Moreover, the most appropriate population genetics metrics to test larval dispersal simulations are debated [START_REF] Bode | Successful validation of a larval dispersal model using genetic parentage data[END_REF].

In the present study, we chose Paramuricea clavata (Risso 1826) in the Ligurian-North Tyrrhenian shelf as a case study for larval dispersal simulations validation. The Paramuricea clavata is a conspicuous octocoral species of the Mediterranean coralligeneous assemblage [START_REF] Carpine | Les gorgonaires de la méditerranée[END_REF]. Due to its role of ecosystem engineer (sensu [START_REF] Jones | Organisms as ecosystem engineers[END_REF], and its patrimonial value linked to the beauty it gives to seascapes, Paramuricea clavata has been used as a flag species for the detrimental effects of climate change in the Mediterranean Sea. P. clavata is one of the tallest sessile Mediterranean species with erected colonies as high as 1.3 m [START_REF] Harmelin | Population structure and partial mortality of the gorgonian paramuricea clavata (risso) in the north-western mediterranean (france, port-cros island)[END_REF] and is found on hard substrate from 10 to 200 m water depth [START_REF] Gori | Animal Forests in Deep Coastal Bottoms and Continental Shelf of the Mediterranean Sea[END_REF]. In the Ligurian and northern Tyrrhenian seas, populations are patchily distributed within the hard bottom shallow habitats, posing the question if larval dispersal could ensure their resilience.

The Ligurian and North Tyrrhenian seas are the two basins forming the portion of the Northwestern Mediterranean Sea between Italy and France and are connected through the Corsica channel [START_REF] Astraldi | The seasonal characteristics of the circulation in the north mediterranean basin and their relationship with the atmospheric-climatic conditions[END_REF]. Both basins are characterized by a mean cyclonic circulation [START_REF] Millot | Circulation in the western mediterranean sea[END_REF][START_REF] Millot | Circulation in the Mediterranean Sea[END_REF]. In the Ligurian sea, this cyclonic circulation is known as the Ligurian (or Northern) Current and flows along the Ligurian coast. We estimated recent gene flow over a few generations among coastal populations of P. clavata in the Ligurian-north Tyrrhenian sea (Padrón et al., 2018a) as independent observations to test larval dispersal simulations. After selecting the most likely larval motility behaviour to be incorporated in larval dispersal simulations, larval transport patterns were simulated within a wider set of known or potential populations extending to the southern part of the Tyrrhenean sea. The network of populations was analyzed with graph theory metrics to identify the role of different populations in species regional resilience.

Material and Methods

Hydrodynamical simulations in the Northwestern

Mediterranean

The circulation of the Ligurian and Tyrrhenian Seas was simulated using the Ligurian Integrated Modelling Effort (Sciascia et al. 2019, LIME-ROMS hereinafter). LIME-ROMS was based on the Rutgers University kernel of the Regional Ocean Modelling System (ROMS, [START_REF] Shchepetkin | The regional oceanic modeling system (roms): a split-explicit, free-surface, topography-following-coordinate oceanic model[END_REF][START_REF] Haidvogel | Model evaluation experiments in the North Atlantic Basin: simulations in nonlinear terrain-following coordinates[END_REF]. The numerical domain was discretized with an horizontal grid of 590 x 314 points. The mesh was unevenly spaced and the most resolved area, which includes the area of the Ligurian-Northern Tyrrhenian shelf, had a horizontal resolution of 1/64 • x 1/64 • (∼ 1.5 km). The vertical dimension was discretized with 50 sigma layers. Realistic surface forcings came from the 3-hourly, 0.25 • horizontal resolution ERA-Interim reanalysis fields (Dee et al., 2011) available at the http://apps.ecmwf.int/datasets/data/interim-full-daily link, while the daily open boundary values were derived from the Mediterranean Forecasting System (MFS) SYS4a3 product [START_REF] Delrosso | Towards the mediterranean forecasting system myocean v5: numerical experiments results and validation[END_REF]. Model outputs were saved every six hours. The reader is referred to [START_REF] Sciascia | Current reversal and associated variability within the corsica channel: The 2004 case study[END_REF] for a detailed description and validation of the model.

The genetic data used in the present study were collected in 2013 Padrón et al. (2018a).

Most of the sampled populations suffered two massive mortality events in 1999 and 2003 [START_REF] Cerrano | A catastrophic mass-mortality episode of gorgonians and other organisms in the ligurian sea (northwestern mediterranean), summer 1999[END_REF][START_REF] Cupido | Response of a gorgonian (paramuricea clavata) population to mortality events: recovery or loss?[END_REF]. Given the sexual maturation of P. clavata of at least 4 years [START_REF] Coma | Reproduction and cycle of gonadal development in the mediterranean gorgonian paramuricea clavata[END_REF], the reproductive events that more likely contributed to the population genetics used in the present study took place between 2004 and 2009.

These years were also characterized by an abrupt shift in the Western Mediterranean circulation [START_REF] Schroeder | Abrupt climate shift in the western mediterranean sea[END_REF] starting from 2005. The mean circulation in 2004 was similar to that of 2005, but differed between 2004 and 2006 with faster currents across both basins in 2006 and in particular a faster Ligurian Current (LC). Moreover, the atmospheric forcing (i.e. wind speed, air temperature and rain) were also different between the two years with more rain and colder air temperature in 2004 compared to 2006 (not shown). For the aforementioned reasons, in the following larval transport simulations were performed for the years 2004 and 2006.

Figure 1 showed the mean summer (i.e. 15 June-15 August) horizontal velocity vertically-averaged over the first 30 m of the water column, for 2004 (Fig. 1a) and 2006 (Fig. 1b). The model reproduced well the mean cyclonic circulation of the Ligurian and Tyrrhenian Seas [START_REF] Millot | Circulation in the western mediterranean sea[END_REF] and showed that the area was characterized by a strong interannual variability with differences in the mean circulation between the two years.

In 2004 (Fig. 1a), on top of the mean cyclonic circulation of the LC, large eddies and recirculations were found. In 2006 (Fig. 1b), the LC was faster and flowed following the narrow continental shelf of the Ligurian coast and the mean circulation was considered typical of the basin.

Larval dispersal simulations

Dispersal simulations of P. clavata larvae were performed with the offline Lagrangian Transport model (LTRANS, [START_REF] Schlag | Lagrangian TRANSport model (LTRANS v.2) User's Guide[END_REF]. Particles displacement due to advection was calculated after integrating the LIME-ROMS hydrodynamical fields with a 4th order Runge-Kutta scheme with timestep of 120s. LTRANS was selected over other Lagrangian transport models for its capability to cope with sigma layers and to compute 3D trajectories for both neutrally buoyant and active (e.g. sinking, swimming) particles, eventually including turbulent diffusion. Particles were released in the locations of seven P. clavata populations sampled by Padrón et al. (2018a) (Bergeggi, Portofino, Sestri Levante, Punta Mesco, Porto Venere, Calafuria and Cerboli Island, red circles in Figure 1, Table 1), together with 11 additional populations located along the Ligurian and North Tyrrhenian shelf where P. clavata has been recorded (L. Bramanti, Personal Communication, blue squares, Figure 1, Table 1). Each population was described by ten release points, separated by 100 m. Particles were released two grid points away from Overlaid on top of the currents are the populations of P. clavata simulated in the larval dispersal sets (Table 1 the coast line (i.e. ∼ 3 km) in order to avoid any retention bias due to the numerical boundary layer.

Off the Catalan coast P. clavata larvae are released in summer, late June/early July, during short events of a few days separated by 15 days [START_REF] Coma | Reproduction and cycle of gonadal development in the mediterranean gorgonian paramuricea clavata[END_REF][START_REF] Gori | Spatial variability in reproductive cycle of the gorgonians paramuricea clavata and eunicella singularis (anthozoa, octocorallia) in the western mediterranean sea[END_REF]. No information on the exact timing and occurrence of P. clavata larval release in the Ligurian Sea are available in the scientific literature but several observations by recreational divers report this phenomenon in the month of July. For this reason, in each of the ten points forming a population, particles were released every hour in the 30 days between June 15th and July 15th for both 2004 and 2006. Depending on whether turbulence was (was not) accounted for, thirty (one) particles were released in each larval release episodes summing up to a total number of particles of 21600 (7200) for each population. The number of particles was increased by a factor thirty to ensure the convergence of statistical properties when dispersion due to subgrid turbulence was accounted for [START_REF] Guizien | Dispersal of owenia fusiformis larvae by wind-driven currents: turbulence, swimming behaviour and mortality in a three-dimensional stochastic model[END_REF].

Particles were released 1 m above the bottom, at a depth between 25 and 45 m, depending on the local bathymetry (Table 1). P. clavata larvae are less than 1 mm long, lecithotrophic and do not undergo any ontogenetic development. The main competency period was less than 20 days [START_REF] Guizien | Sink or swim ? larval size and migration potential in three sympatric mediterranean octocorals[END_REF]. Thus, particles were tracked up to 21 days after their release. During the pelagic stage, larvae display very limited motility activity, eventually crawling and settling on rigid surface [START_REF] Coma | Reproduction and cycle of gonadal development in the mediterranean gorgonian paramuricea clavata[END_REF][START_REF] Linares | Early life history of the mediterranean gorgonian paramuricea clavata: implications for population dynamics[END_REF]. P. clavata larval motility behaviour was limited to free fall as a recent experimental study reported less than 5% of swimming activity frequency. Larval free fall speed at 20 o C was 0.056 ± 0.021 cm s -1 , as a result of larval body density ranging from 1.025 to 1.034 during the first 30 days of larval life [START_REF] Guizien | Sink or swim ? larval size and migration potential in three sympatric mediterranean octocorals[END_REF]. Given that in summer surface waters are mostly warmer than 20 o C across the whole basin, assuming a sinking behaviour for P. clavata larvae seems reasonable. However, water temperature in some part of the basin can still be lower than 20 o C especially at the beginning of the reproductive season (mid June) or offshore, and the free fall speed of P.

clavata larvae is expected to decrease in colder waters. For these reasons, two ensemble of simulations were performed, one assuming P. clavata larvae drift passively as neutrally buoyant particles and another applying a constant sinking speed of 0.5 mm s -1 . In the latter case, vertical turbulent diffusion was included to depict settling hindering, while turbulence was neglected in the neutrally buoyant dispersal case.

Correlation between larval transport and gene flow matrices

Larval transport values between P. clavata populations in the Ligurian-North Tyrrhenian shelf were calculated as the proportion of particles released during a larval-release event from an origin population that reached a destination population after a given dispersal duration (Pelagic Larval Duration, PLD). Larval transport values were gathered to build connectivity matrices, varying according to PLD, larval-release event and larval buoyancy.

Six values of PLD from 3.5 up to 21 days with 3.5 days increments were considered. The duration of each larval-release event was set to three days, yielding a total of ten 3-day events during the 30 days reproductive season of 2004 and 2006.

In total, twenty connectivity matrices (ten for each year), each one corresponding to a 3-day larval-release event, were built for the two larval buoyancies (neutrally buoyant and sinking) and for the six PLD values, summing up to 240 connectivity matrices. Variances of larval transport values among the seven populations arising from the timing of each larval-release event, larval behaviour or inter-annual variability were computed for each PLD [START_REF] Briton | High-resolution modelling of ocean circulation can reveal retention spots important for biodiversity conservation[END_REF].

Connectivity matrices depicting larval transport for different larval buoyancy, larvalrelease event and PLD were tested against recent gene flow estimated among the seven populations studied by Padrón et al. (2018a). Recent gene flow over the last few generations was calculated on the multilocus genotypes assessed in the seven populations in the form of matrices containing migration rates including a proxy of self-recruitment using the BayesAss 3.0 software [START_REF] Wilson | Bayesian inference of recent migration rates using multilocus genotypes[END_REF]. As gene flow measures focus on destination population and larval transport measures focus on source populations, comparing gene flow and larval transport requires data transformation [START_REF] Padrón | Passive larval transport explains recent gene flow in a mediterranean gorgonian[END_REF].

Hence, gene flow was transformed into the proportion of migrants that left a source population and arrived at each destination population and larval transport matrices were normalized by removing the proportion of larvae dispersed outside the seven populations sampled for genetics as this proportion was not included in the gene flow assessment.

The significance of the Pearson product-moment correlations between the contemporary gene flow and any of the 240 variants of recent larval transport matrices, was tested.

A Mantel test [START_REF] Mantel | The detection of disease clustering and a generalized regression approach[END_REF] was performed based on the 5040 possible permutations, after modifying the random QAP.m Matlab routine by [START_REF] Rombach | Qap test matlab routine[END_REF]. Whenever the Mantel test indicated a significant correlation between the recent gene flow and the larval transport, the determination coefficient was computed. The statistical distribution of the determination coefficients for a same PLD, behaviour and year was described by its quantiles. Highest determination coefficient were used to select the PLDs and buoyancy condition yielding the best agreement between larval transport simulations and gene flow estimates from population genetics observations. All statistical analyses were conducted in Matlab (R2018).

2.4 Analysis of the network formed by Tyrrhenian-Ligurian P.

clavata populations

The weighted directed network formed by the 18 P. clavata populations (nodes) connected by larval transport probabilities (edges) for the PLDs and buoyancy condition yielding the best agreement between larval transport simulations and gene flow estimates was analyzed. Five node metrics taken from graph theory were computed to rank the 18 populations according to different networking features: (1) degree centrality, (2) total strength, (3) clustering coefficient, (4) betweenness centrality and (5) bridging centrality.

Degree centrality is the number of connections of a population in the network. Populations displaying more outgoing connections than in-going ones are identified as sources.

Total strength is the total larval transport departing or arriving in a population of the network. Populations displaying more outgoing flow than in-going one are identified as sources. Clustering coefficient of a population is the average larval transport within all possible groups of 3 populations involving that population [START_REF] Fagiolo | Clustering in complex directed networks[END_REF]. Large values of clustering coefficient depicted those populations with most tightly connected neighbourhood, often called small worlds. Betweenness centrality of a population is the proportion of the network shortest paths that passes through that population. In the case of a larval transport network, shortest path are detected after transforming larval transport probabilities into distances [START_REF] Costa | On the calculation of betweenness centrality in marine connectivity studies using transfer probabilities[END_REF]. Thus, large values of betweenness centrality depicted those populations through which passes the largest larval transport. Bridging centrality is the product of betweenness centrality and bridging coefficient, the latter accounting for the average probability of leaving the direct neighborhood [START_REF] Woochang | Bridging centrality: Graph mining from element level to group level[END_REF]. Thus, large values of bridging centrality depicted those populations that are on the border of population cluster and within which larval transport is large. The five metrics were computed for all populations and for the twenty larval transport connectivity matrices variants using the TATOO Matlab toolbox (connecTivity grAph Theory TOOlbox, Andrea Costa vs 2017.12.18 https://www.researchgate.net/profile/Andrea Costa10, andrea.costa@pusan.ac.kr). Finally, each population of the network was described by two statistics (median and 75 percentile) for each of the five metrics.

Results

Larval transport for sinking larvae with short PLD explains gene flow

Larval behaviour affected the particles spatial distribution, both horizontally and vertically, in the dispersal simulations. Neither the neutrally buoyant nor the constant sinking behaviour led to a constant depth distribution of the particles as a function of time. In particular, the sinking particles can be found at different depths along their tracks, depending on the larval-release event or year (Supplementary Figure 1). Particles could either spread across the first 100 meters of the water column or aggregate at some specific depths (Supplementary Figure 1). As a result, larval transport values varied largely among simulations with different behaviours, but also between years and larval-release event. The standard deviation of larval transport values among release events reached 3 times the mean value in some origin/destination populations pair. The inter-annual variability and the variability due to the different behaviour had the same order of magnitude. The ratio between the variance of larval transport values between the two years and between the two behaviours ranged from 0.35 to 1.76 depending on the origin/destination populations pair. The median value of this ratio in all populations pairs varied between 0.9 and 1 across all PLDs. The variability of larval transport values due to larval-release timing was lower than the one arising from the two aforementioned factors. The ratio between the variance of larval transport values between the two years and among the release events ranged from 1.4 to 4.2, with a median value of about 2 across all PLDs.

Larval transport simulated for each behaviour and for each release event were compared with the recent gene flow estimated among the seven populations sampled by Padrón et al. (2018a). Larval transport for the release event that best correlated (i.e.

highest determination coefficient) with gene flow showed high retention (i.e. diagonal elements) for both larval behaviours in agreement with estimated gene flow (Fig. 2). However, the two behaviours differed with respect to larval transport among POR, SES, PTM and PRV (i.e. off-diagonal elements). For the neutrally buoyant case, in PRV larval trans-port to PTM in higher than local retention. For the sinking case in contrast, PRV to PTM larval transport was lower than PRV local retention whatever the PLD, as observed in gene flow estimates. Yet, for PLD larger than 7 days other connections appeared in the simulations that are not present in the gene flow estimates mainly between SES and POR. These connections varied in strength with PLD and behaviour.

Albeit 2004 and 2006 differed in their hydrodynamical features (Fig. 1), the sinking behaviour better explained the estimated gene flow than the neutrally buoyant behaviour in both years (Fig. 3). The median determination coefficients between simulations for 3.5day PLD and gene flow estimates were > 0.9 for the sinking behaviour and < 0.7 for the neutrally buoyant behaviour. The effect of behaviour on the determination coefficient between simulations and observations decreased more rapidly in 2004 than in 2006 as the PLD increased (Fig. 3). However, in each year the simulated larval transport after 3.5 days with sinking behaviour correlated with gene flow estimates with R 2 ∼ 1 in at least one larval-release event (whiskers box plot, Fig. 3).

Hence, the sinking behavior for a 3.5-day PLD was selected to simulate larval transport within the extended network.

Regional connectivity patterns exhibit both closed and open populations

The 18 populations connected by larval transport after 3.5 days with sinking behaviour were divided into two groups: four populations (POR, SES, PTM and PRV) were open, with more than two connections with other populations of the network (Fig. 4a). The other 14 populations were more closed with maximum one connection with another population (Fig. 4a). Among the four open populations the number of connections was limited to a maximum of five (PTM, Fig. 4a). Connection intensity further differentiated those four populations from the other 14 as large values of larval transport probabilities occurred frequently among them (Fig. 4b). Interestingly, when only the number of connections was considered, PRV was the only source among these four populations while when larval transport probabilities were taken into account, SES appeared as a more important source than PRV. POR and SES total strength varied largely among larval-release events as displayed by the difference between the 50% and 75% percentiles. These four Ligurian populations built up a tightly connected neighbourhood, with POR as the topographical center. The neighborhood was connected to the rest of the network by equally important populations (SES, PTM, PRV, Fig. 4c). Among these four populations, SES displayed the largest betweenness centrality ( i.e. largest larval transport of the network) but only in < 25% of the larval-release events (Fig. 4d). It is worth noting that the large larval transport passing through SES in some larval-release events was not found in gene flow estimates. Surprisingly, comparable larval transport to the one passing in SES was found in one Tyrrhenian population with low number of connections (CPE, Fig. 4d).

Discussion

In the present study, we tested the correlation between larval transport simulations and gene flow patterns among P. Clavata populations in the Ligurian and Northern Tyrrhenian seas accounting for two different buoyancy (neutral and negative). The sinking behaviour established experimentally [START_REF] Guizien | Sink or swim ? larval size and migration potential in three sympatric mediterranean octocorals[END_REF] together with short dispersal duration was singled out based on consistently higher correlations throughout the entire reproductive period and the two years. Difference in correlations was driven by the prevalence of self-recruitment over transport in P. Clavata populations along the Ligurian coast, particularly between Portovenere and Punta Mesco. Larval transport simulations using the sinking behaviour with 3.5-day dispersal duration among a set of known or potential P. Clavata populations extending into the Tyrrhenian sea further suggested that population connectivity was limited to the portion of coast comprised between Portovenere and Portofino.

Larval transport predictions are now frequently used to explore the functioning of marine populations and help understanding their spatial distribution, thanks to progresses in ocean modelling tools availability and performance (reviewed in [START_REF] Swearer | H7 A Review of Biophysical Models of Marine Larval Dispersal[END_REF]). Yet, larval behaviour is still not systematically discussed, with several recent works assuming 2D or 3D dispersal without incorporating behaviour [START_REF] Aguilar | Modelling the differences between el nio and la nia years and planktonic larval duration on dispersal across the southeast australian biogeographic barrier[END_REF][START_REF] Gamoyo | Estimating connectivity through larval dispersal in the western indian ocean[END_REF][START_REF] Hidalgo | Accounting for ocean connectivity and hydroclimate in fish recruitment fluctuations within transboundary metapopulations[END_REF]. Even when larval behaviour was included, its added value was very rarely quantitatively assessed. Behaviour effect on larval transport estimates was either explored in sensitivity studies confronting simulations with different hypothetical behaviours [START_REF] Donahue | Location isnt everything: Timing of spawning aggregations optimizes larval replenishment[END_REF][START_REF] Blanco | Influence of larval traits on dispersal and connectivity patterns of two exploited marine invertebrates in central chile[END_REF] or in projections studies to inform about populations functioning or management, as a replacement of observations [START_REF] Munroe | Modeling larval dispersal and connectivity for atlantic sea scallop (placopecten magellanicus) in the middle atlantic bight[END_REF][START_REF] Goodwin | Estimating connectivity of hard clam (mercenaria mercenaria) and eastern oyster (crassostrea virginica) larvae in barnegat bay[END_REF][START_REF] Mcmanus | Northern quahog (mercenaria mercenaria) larval transport and settlement modeled for a temperate estuary[END_REF]. While some sensitivity studies focused on finding most influential factors between flow and behaviour in larval transport estimates [START_REF] Faillettaz | Larval fish swimming behavior alters dispersal patterns from marine protected areas in the north-western mediterranean sea[END_REF][START_REF] Kvile | Sensitivity of modelled North Sea cod larvae transport to vertical behaviour, ocean model resolution and interannual variation in ocean dynamics[END_REF], assessing the accuracy of larval transport predictions is more than often qualitative only [START_REF] Barbut | How larval traits of six flatfish species impact connectivity[END_REF][START_REF] Crosbie | Effects of step salinity gradients on salmon lice larvae behaviour and dispersal[END_REF]. Yet, quantifying the improvement of larval transport simulations thanks to the incorporation of empirically obtained larval behaviour against independent sets of genetic observations is key to asses the accuracy of larval transport predictions [START_REF] Bode | Successful validation of a larval dispersal model using genetic parentage data[END_REF]. However, quantitative assessment of larval transport predictions versus population genetics is subject to a number of limitations linked to population demography [START_REF] Bohonak | Dispersal, gene flow, and population structure[END_REF]. Gene flow inferences at the population level, like the one used in the present study cumulates multiple reproductive events occurring in different demographic context, while larval transport was simulated at the scale of a single-reproductive event.

Parentage analysis is potentially the most appropriate genetic metrics to compare with larval transport, provided that larval transport is simulated for the sampled cohort of offspring [START_REF] Segura-Garcia | Reconstruction of larval origins based on genetic relatedness and biophysical modeling[END_REF]. Indeed, comparing parentage analysis of individuals of different ages hence corresponding to different recruitment events with larval transport corresponding to a single-reproductive event could potentially fail when recruitment exhibits large temporal variability [START_REF] Cuif | Monthly variability of self-recruitment for a coral reef damselfish[END_REF].

The present study also pinpoints that inferring larval behaviour from field observations (i.e. genetic analysis, larval vertical distribution) can be misleading. Indeed larval transport depends on its interaction with the ocean flow in the area of observations. Sampling a subset of populations only can jeopardize conclusions [START_REF] Lowe | What can genetics tell us about population connectivity?[END_REF]. For P. clavata populations in the Ligurian sea the comparison between gene flow assessment and larval transport simulations could have resulted in very different outcome if the population sampled for the genetic analysis had been Bergeggi, Sestri Levante and Calafuria only. Based on these locations, the simulated connectivity matrices with neutral or sinking behavior would have been similar, rendering the test against gene flow inconclusive regarding behaviour importance. Similarly, using larval vertical distribution observed at sea as a behaviour descriptor in simulations should be avoided [START_REF] Criales | Blue crab larval dispersal highlights population connectivity and implications for fishery management[END_REF]. Although sinking behavior was simplified in the present simulations to a constant free fall speed neglecting any inter-individual buoyancy variability, the vertical distribution of larvae in the simulations was not constant in space and time. For these reasons, we suggest to document species behaviour independently of ocean flow in a specific setting and test its relevancy in different ocean flow conditions (different years or regions, [START_REF] Herbert | Invasion in tidal zones on complex coastlines: modelling larvae of the non-native manila clam, ruditapes philippinarum, in the uk[END_REF][START_REF] Blanco | Influence of larval traits on dispersal and connectivity patterns of two exploited marine invertebrates in central chile[END_REF][START_REF] Fobert | Dispersal and population connectivity are phenotype dependent in a marine metapopulation[END_REF].

Another reason for confronting larval transport to gene flow with caution is because they do not integrate the same phases along a species life cycle. Indeed, settlement and post-settlement processes can strongly alter the gene flow resulting from larval transport.

Among these processes, density-dependent recruitment limitations are frequent in benthic populations due to lack of space [START_REF] Roughgarden | Demographic theory for an open marine population with space-limited recruitment[END_REF][START_REF] Mcgrorty | Population dynamics of the mussel mytilus edulis along environmental gradients: Spatial variations in density-dependent mortalities[END_REF]. This process likely limited gene flow between healthy and densely populated populations of long-living species such as P. clavata and lead to the conclusion that P. clavata dispersal potential is in the order of tens of meters [START_REF] Mokhtar-Jamaï | From global to local genetic structuring in the red gorgonian paramuricea clavata: the interplay between oceanographic conditions and limited larval dispersal[END_REF]. The present study indicates that larval transport of sinking larvae can result in efficient gene flow over a distance of tens of kilometers for a dispersal duration of 3.5 days. The good agreement between larval transport and gene flow found in the present study further suggested that the mass mortality events that affected some Ligurian P.

clavata populations enabled to observe the potential for sinking larvae.

Environmental mortality processes occurring during dispersal, such as predation, are embedded in the PLD used in larval dispersal simulations. Thus, it is expected that effective PLD incorporated in larval transport simulations should be lower than larval survival expectancy observed in laboratory experiments, in which environmental mortal-ity is limited. The effective PLD leading to best agreement between larval transport and gene flow (3.5 days) was indeed considerably lower than the survival expectancy of 70 days reported for P. clavata and E. singularis larvae (Guizien et al submitted). A similar result was found for the white gorgonian Eunicella singularis in the Gulf of Lion [START_REF] Padrón | Passive larval transport explains recent gene flow in a mediterranean gorgonian[END_REF]. We suggest that comparing larval transport incorporating experimentally documented larval behaviour and gene flow could be a methodology to infer mortality risk at sea, particularly due to predation.

In the present study, a same species-specific larval behaviour resulted in different population status in terms of openness or closeness of a population, with different importance with respect to species resilience, reflecting the geographical variations of the flow.

When interested in the capacity of a meta-population to recover from disturbance, poorly connected populations are not ideal. Low input from other populations not only limit demographical recovery but also lead to genetic loss because gene sorting process at each reproduction causes a drift to dominant allele [START_REF] Padrón | Modelling the effect of demographic traits and connectivity on the genetic structuration of marine metapopulations of sedentary benthic invertebrates[END_REF]. In contrast, highly connected networks which are robust to local perturbation, may also spread more rapidly local evolution at a regional scale if a trade-off between local and regional scale resilience exists [START_REF] Nosil | Ecological explanations for (incomplete) speciation[END_REF]. Migration (gene flow) is generally considered a homogenizing force that limits adaptation [START_REF] Slatkin | Gene flow and the geographic structure of natural populations[END_REF]. On the contrary, populations of species with limited dispersal ability among fragmented habitats may evolve local adaptation, because gene flow is low. Local adaptation might have positive demographic effects, increasing population fitness and thus reducing the risk of local extinction in stable environments. However, this fragmentation can also have negative effects by limiting the spread of adaptive mutations, and thus constraining adaptation [START_REF] Cowen | Larval dispersal and marine population connectivity[END_REF]. Non-homogeneous gene flow among P. Clavata populations observed in this study may reinforce the stability of the metapopulation in the region, and help explaining the resilience after several local mass mortality events.

Non-homogeneous connectivity also poses the question of how to optimize geographically protection measures (e.g. Marine Protected Areas). Results of the present study suggest that current protection measures in the Ligurian sea should be efficient for the conservation of P. clavata at a regional scale as they are implemented in locations with high betweenness and bridging centrality. Such properties are expected to be associated in metapopulation to prevailing dispersal pathways and stepping stones, thus playing a key role in populations recovery [START_REF] Rozenfeld | Network analysis identifies weak and strong links in a metapopulation system[END_REF][START_REF] Treml | Modeling population connectivity by ocean currents, a graph-theoretic approach for marine conservation[END_REF]. The study pinpoints that Capraia Island could similarly contribute to the network, and thus deserve protection. Finally, larval dispersal models, after validation against gene flow, could be used to explore new connections between known populations and potential habitats, both natural and artificial, regardless of the presence of the species. In particular, connections present in larval transport simulations should not be disregarded even if not visible in gene flow and incorporated in meta-population models. Such procedure would enable to forecast dynamical species distribution resulting from the coupling between local demography and regional exchange among populations in marine spatial planning scenarios.

Figure 1 :

 1 Figure 1: LIME-ROMS depth averaged (0-30 meters) horizontal velocity for the period June 15th-August15th of (a) 2004 and (b) 2006.

Figure 2 :

 2 Figure 2: Panels (a,c,e): connectivity matrices for different PLDs for the larval-release event among the neutrally buoyant simulations set with the highest R 2 . Panels (b,d,f): connectivity matrices for different PLDs for the larval-release event among the sinking simulations set with the highest R 2 . Panel (g): observed gene flow among the seven Ligurian-Tyrrhenian populations.

Figure 3 :

 3 Figure 3: Box plot of the significant determination coefficient (R 2 ) between larval transport simulations and observed gene flow for different PLDs. Black bars represent neutrally buoyant simulations, grey bars the sinking simulations. Circles inside the bars represent the median value. The determination coefficient is obtained averaging over both 2004 and 2006.

Figure 4 :

 4 Figure 4: Graph analysis metrics for the complete network of P. clavata populations in the Ligurian-Tyrrhenian basin for a PLD of 3.5 days. In all the panels filled circles represent the median value and the contour around each circle represent the 75% percentile. For the panels where two colors are presents, blue (red) circles represent sinks (sources). Panels: (a) Degree centrality; (b) Total strength; (c) clustering coefficient, overlaid bridging centrality (black asterisk); (d) Betweenness centrality.

Table 1 :

 1 ). Red squares represent locations where samples were collected, blue circles locations where P. clavata populations are present but not sampled in this study.

	Abbreviation Name	Lat	Lon	Depth
			[deg N]	[deg E]	[m]
	BER	Bergeggi	44.24	8.45	39.6
	POR	Portofino	44.30	9.22	40.4
	SES	Sestri Levante	44.25	9.40	40.5
	PTM	Punta Mesco	44.12	9.63	37.9
	PRV	Porto Venere	44.02	9.85	27.7
	GOR	Gorgona Island	43.43	9.91	41.8
	CAL	Calafuria	43.47	10.33	38.6
	CPW	Capraia Island -West 43.04	9.80	29.4
	CPE	Capraia Island -East 43.04	9.85	29.7
	ELW	Elba Island -West	42.80	10.12	35.3
	ELN	Elba Island -North	42.82	10.28	2 8.9
	ELS	Elba Island -South	42.73	10.17	31.7
	ELE	Elba Island -East	42.80	10.44	31.1
	CER	Cerboli Island	42.85	10.55	39.9
	BAS	Bastia	42.71	9.46	29.0
	PIA	Pianosa Island	42.59	10.09	28.4
	MCR	Montecristo Island	42.35	10.32	34.0
	GIL	Giglio Island	42.34	10.92	28.3

Latitude, Longitude and average depth of the simulated populations across the Ligurian-Tyrrhenian basin.

Supplementary material

Supplementary material is available at the ICESJMS online version of the manuscript.

Acknowledgments

This work was supported by by the IMPACT EU funded project (PC Interreg VA IFM 2014-2020, Prot. ISMAR n. 0002269)


Soukaina Zeroual

Patrice Estellé
email: patrice.estelle@univ-rennes1.fr

David Cabaleiro

Brigitte Vigolo

Melanie Emo

Said Wafae Halim

Mélanie Emo

Said Ouaskit

Ethylene glycol based silver nanoparticles

Keywords: Silver EG nanofluids, Latex copolymer, Nanoparticle characterizations, Dynamic viscosity, Thermal conductivity, Temperature effect

published or not. The documents may come

Introduction

The concept of nanofluids, based on the dispersion of nanoparticles with high intrinsic thermal properties within usual heat transfer fluids, was introduced in the early 1990s. Since that time, nanofluids received growing attention due to their potential to be used particularly in heat transfer devices and energy systems because of their enhanced thermal conductivity.

Among the common heat transfer fluids, ethylene glycol (EG) has been widely used to produce nanofluids [START_REF] Sohel Murshed | Conduction and convection heat transfer characteristics of ethylene glycol based nanofluids -A review[END_REF][START_REF] Sekrani | Ethylene and propylene glycol based nanofluids: A literature review on their thermophysical properties and thermal performances[END_REF]. A large variety of nanoparticle natures have been considered for ethylene-glycol nanofluid production such as for example MgO [START_REF] Żyła | Viscosity and thermal conductivity of MgO-EG nanofluids[END_REF], TiN [START_REF] Żyła | Thermophysical and dielectric profiles of ethylene glycol based titanium nitride (TiN-EG) nanofluids with various size of particles[END_REF], BN [START_REF] Żyła | Huge thermal conductivity enhancement in boron nitride-ethylene glycol nanofluids[END_REF], TiO 2 [START_REF] Cabaleiro | Rheological and volumetric properties of TiO 2 -ethylene glycol nanofluids[END_REF], ZnO [START_REF] Cabaleiro | Transport properties and heat transfer coefficients of ZnO/(ethylene glycol+ water) nanofluids[END_REF] and carbon-based materials [START_REF] Shamaeil | The effects of temperature and volume fraction on the thermal conductivity of functionalized DWCNTs/ethylene glycol nanofluid: An experimental study[END_REF][START_REF] Żyła | The influence of ash content on thermophysical properties of ethylene glycol based graphite/diamonds mixture nanofluids[END_REF]. In all these works, an enhancement in thermal conductivity of the corresponding nanofluids was generally observed, this enhancement being dependent on nanoparticle content and temperature, as well as nanoparticle nature, shape, size, ... However, such an enhancement can be often accompanied by a rise in viscosity that can penalize the use of nanofluids in real heat transfer and cooling applications. It appears that silver nanoparticles are still weakly utilized to produce nanofluids, with EG as base fluid in particular, while silver possesses a high intrinsic thermal conductivity of about 420 W/mK [START_REF] Pordanjani | An updated review on application of nanofluids in heat exchangers for saving energy[END_REF], with also excellent optical and chemical properties, which make this material an excellent candidate as thermal conductivity enhancer. Microelectronics, energy supply, transportation, manufacturing or microchannels are some of the examples which have been identified as potential applications for advanced heat transfer media such as silver nanofluids [START_REF] Nikkam | Fabrication and thermo-physical characterization of silver nanofluids: An experimental investigation on the effect of base liquid[END_REF][START_REF] Şimşek | Heat transfer enhancement by silver nanowire suspensions in microchannel heat sinks[END_REF][START_REF] Sharma | Enhancement of thermal conductivity of ethylene glycol based silver nanofluids[END_REF][START_REF] Fang | Thermal conductivity enhancement of ethylene glycol-based suspensions in the presence of silver nanoparticles of various shapes[END_REF].

The main previous studies about ethylene glycol silver (EGS) based-nanofluids are presented in the following. It can be noticed that EGS nanofluids are generally produced by one-step methods; silver nanoparticles being obtained from the reduction of silver nitrate (AgNO 3 ) with the help of reducing agents, stabilizers and capping agents. Nikkama and Toprak [START_REF] Nikkam | Fabrication and thermo-physical characterization of silver nanofluids: An experimental investigation on the effect of base liquid[END_REF] prepared and investigated the transport properties of silver nanofluids based on water, ethylene glycol or an ethylene glycol-water mixture and containing nanoparticle mass loadings up to 2%. Authors observed that, in all three nanofluid sets, the enhancements in thermal conductivity were higher in value than the increases in dynamic viscosity, which may indict the potential of such nanostructured thermal fluids for several heat transfer applications.

Polyacrylamide-co-acrylicacid (PAA-co-AA) was used in [START_REF] Sharma | Enhancement of thermal conductivity of ethylene glycol based silver nanofluids[END_REF] for preparing EGS nanofluids and investigating the ratio of PAA-co-AA and AgNO 3 . A thermal conductivity enhancement at ambient condition up to 14% for Ag weight fractions of only 10000 ppm was reported and good dispersion stability was noticed during two weeks despite the presence of aggregates.

The effect of microwave heating and irradiation times on the properties of water and EG silver nanofluids was investigated in [START_REF] Noroozi | Fabrication, characterization and thermal property evaluation of silver nanofluids[END_REF]. Here, the synthesis method was also assisted by polyvinylpyrrolidone (PVP) and the silver nanoparticles were formed during an irradiation process. This process mainly affects the distribution in silver nanoparticles rather than their size. EG appears as a good solvent and leads to better thermal diffusivity of nanofluids than water. The thermal properties of EGS nanofluids were also studied by Khamliche et al. [START_REF] Khamliche | Thermal conductivity enhancement of nano-silver particles dispersed in ethylene glycol based nanofluids[END_REF] focusing on the shape effect of silver nanoparticles. Nanoparticles with high aspect ratio were obtained with EG and PVP as reducing agent that contributed to a larger enhancement in nanofluid thermal conductivity. The influence of the aspect ratio of the nanoparticles was also considered by Zhu et al. [START_REF] Zhu | Silver nanowires contained nanofluids with enhanced optical absorption and thermal transportation properties[END_REF] who characterized EGS nanofluids where silver nanowires of different lengths were produced with PVP and FeCl 3 .6H 2 O. An enhancement in viscosity and thermal conductivity of the prepared nanofluids was revealed by these authors with a stronger effect of silver nanowires with larger length, 100 µm. In addition, Salehi et al. [START_REF] Salehi | Enhancement of thermal conductivity of silver nanofluid synthesized by a one-step method with the effect of polyvinylpyrrolidone on thermal behaviour[END_REF] produced water-based silver nanofluids using sodium borohydride and hydrazine as reducing agents and PVP as surfactant. These stable and non-agglomerated nanofluids were shown to possess higher thermal conductivity than water with an important role of PVP content in thermal behavior. The heat transfer and flow properties of EGS-based nanofluids were experimentally studied by Madhesh and Kalaiselvam [START_REF] Madhesh | Experimental study on the heat transfer and flow properties of Ag-ethylene glycol nanofluid as coolant[END_REF]. In that paper, nanofluids were produced by the reduction of AgNO 3 with PVP and sodium borohydride as stabilizing and reducing agents.

Then, the silver powder was dispersed in EG in the nanoparticle volume concentration 0.1-2 %. The thermal conductivity of nanofluids was shown to linearly increase with nanoparticle loading and temperature in the range 303-363 K. Recently, silver nanoparticles with small size were synthesized in ethanol by using latex copolymer as both reducing and stabilizing agent [START_REF] Ider | Silver metallic nanoparticles with surface plasmon resonance: Synthesis and applications[END_REF]. This study also reported the effect of precursor content and microwave irradiation power on the formation of silver nanoparticles that were stable for a long time in the solvent.

Lastly, PVP-capped Ag nanoparticles, from 0.1 to 1.1 % in weight content and with an average size of 22 nm, were dispersed in poly(ethylene glycol) by Marcos et al. [START_REF] Marcos | NePCM based on silver dispersions in poly(ethylene glycol) as a stable solution for thermal storage[END_REF] for the synthesis of stable nanofluids. The presence of Ag nanoparticles was reported to improve the thermal conductivity and the sub-cooling property of poly(ethylene glycol) in particular for the higher content in Ag nanoparticles. The thermal conductivity of EG-nanofluids containing silver nanoparticles of different shapes (spherical, rod-shaped and plate-shaped nanoparticles) was also experimentally investigated by Fang et al. [START_REF] Fang | Thermal conductivity enhancement of ethylene glycol-based suspensions in the presence of silver nanoparticles of various shapes[END_REF]. Larger improvements in thermal conductivity were reported in this last article for suspensions containing rod-shaped silver (in comparison to nanofluids prepared using similar concentrations of spherical or plate-shaped Ag nanoparticles), which authors attributed to the formation of a nanoparticle network that facilitates heat conduction. However, considerably higher increases in dynamic viscosity were observed for rod-shaped silver (when compared with spherical nanoparticles), which may become a penalty in thermal systems.

The heat transfer and flow performance of Ag/EG nanofluids, flowing through a tubular cooling heat exchanger, were experimentally studied by Madhesh and Kalaiselvam et al. [START_REF] Madhesh | Kalaiselvam Experimental study on the heat transfer and flow properties of Ag-ethylene glycol nanofluid as a coolant[END_REF].

Results revealed that, for the maximum nanoparticle concentration (2% in volume), convective heat transfer coefficient and pressure drop increased by 49% and 23.7%, respectively. On the other hand, the surface plasmon resonance of silver nanoparticles (with a high absorption peak near sunlight's peak irradiance) also makes this nanomaterial well-suited to improve the solar thermal conversion efficiency of conventional working fluids [START_REF] Chen | An experimental investigation on sunlight absorption characteristics of silver nanofluids[END_REF].

Several studies [START_REF] Chen | An experimental investigation on sunlight absorption characteristics of silver nanofluids[END_REF][START_REF] Sani | Optical properties of mixed nanofluids containing carbon nanohorns and silver nanoparticles for solar energy applications[END_REF][START_REF] Gorji | A review on optical properties and application of nanofluids in direct absorption solar collectors (DASCs)[END_REF] investigated the potential of silver aqueous dispersions as solar absorber and heat transfer media in direct absorption solar thermal collectors.

In this study, the synthesis and the characterization of ethylene glycol silver-based nanofluids are reported. As done previously with ethanol [START_REF] Ider | Silver metallic nanoparticles with surface plasmon resonance: Synthesis and applications[END_REF], the synthesis process is based on the reduction of AgNO 3 (99%) with the presence of an aqueous emulsion of latex copolymer namely vinyl-acetate/vinyl-versatate. This copolymer latex is here used for the first time with EG. The presence and characterization of silver nanoparticles is evaluated from UV-Vis spectra, transmission electron microscopy (TEM) and energy dispersive X-ray spectroscopy (EDS) analysis showing the efficiency of the synthesis process in EGS-based nanofluids.

Finally, the dynamic viscosity and thermal conductivity of these nanofluids are experimentally obtained. Such a study is performed considering the influence of the latex copolymer content on the formation of silver nanoparticles and the effect on the thermophysical properties of the developed nanofluids as potential efficient thermal fluids.

Materials and methods

Materials

For the synthesis and the production of EGS-based nanofluids, the following materials were used as received. The silver nitrate (AgNO 3 , 99.9 % purity without any further purification) was used as a precursor of Ag [START_REF] Ider | Silver metallic nanoparticles with surface plasmon resonance: Synthesis and applications[END_REF]. Ethylene glycol (Acros Organics, CAS 107-21-1, New Jersey, USA; density of 1.1130 g/cm 3 and 98 % purity) was used as a solvent. An aqueous emulsion of latex copolymer, prepared by the Maghrebian Polymers Society [START_REF] Ider | Silver metallic nanoparticles with surface plasmon resonance: Synthesis and applications[END_REF], was used as reducing and capping agent. The influence of latex copolymer (acetate versatate) content for production, dispersion and stability of silver nanoparticles was considered in this study.

Nanofluid preparation

The silver nanoparticles were synthesized by the polyol method known as a one-step chemical method involving the simultaneous production and dispersion of the nanoparticles into the base fluid. This method is simple, fast and inexpensive and it can be used for the synthesis of different metallic nanoparticles. In the present study, the EGS-based nanofluids have been prepared by the reduction of AgNO 3 in the presence of latex copolymer within EG. For preparing the nanofluids the following procedure has been established. First, 20 mL of EG is mixed with the latex copolymer in a clean glass container using an ultrasonic bath (Transonic, TI-H-5, Fisher Bioblock Scientific, Illkirch-Graffenstaden, France) for 10 min at a frequency of 130 kHz. Four different contents of copolymer were considered, viz. 99, 126, 305 and 340 mg. Thereafter, a fixed amount of AgNO 3 (21 mg) was added to each solution then placed under magnetic and thermal stirring. The heating setup used for the preparation of silver nanofluids consisted in a hotplate magnetic stirrer (RCT Basic IKA Werke). Each sample was heated up to 120 °C and reacted for 35 min until the initial colorless solution changed from faint yellow to golden yellow color.

Characterization techniques

UV-Vis spectrophotometry analysis was used to characterize the optical properties of the prepared nanofluids and to evidence the formation of silver nanoparticles. The standard UV-Visible analysis was performed with an UV 3100 Selecta Spectrophotometer (J.P. Selecta S.A., Abrera, Spain) in the spectra range from 300 to 600 nm using a quartz cuvette. Before each measurement of nanofluids, the corresponding base fluid composed of ethylene glycol and latex was used as reference to take the blank solution.

Also, scanning electron microscopy (SEM) characterization of EG and both base fluids and nanofluids was performed with a JEOL-JSM-7100F (JEOL USA Inc., Peabody, USA) equipment coupled with energy dissipative X-ray spectrometer (EDS) to evaluate, at first sight the reliability of the synthesis process. The samples were prepared by depositing a drop on a carbon substrate and then dried at ambient conditions before SEM characterization.

Nanofluid samples were diluted in ethanol before being deposited on a copper grid covered by an amorphous carbon film for TEM / Scanning TEM (STEM) experiments. TEM / STEM investigations were performed using a JEOL ARM 200 F (JEOL Ltd., Tokyo, Japan) equipped with a Cold Field Emission Gun (FEG) operating at 200 kV and a spherical aberration (Cs) probe corrector. Both bright field (BF) and high-angle annular dark field (HAADF) modes were used for the observations. Chemical compositions were determined using EDS. The EDS spectra were recorded on a JEOL spectrometer (SDD). Around 30 images were collected for each sample to ensure a good representative analysis of the samples. The Image J software was used for the particle size analysis.

The rheological properties and the dynamic viscosity of both base fluids and nanofluids were evaluated by a stress-controlled rheometer Malvern Kinexus Pro (Malvern Instruments, Ltd, Worcestershire, UK) under controlled temperature and with a cone and plate geometry of 60 mm in diameter and 1° in angle. The temperature of the system was controlled by a unit placed below the lower plate that allowed regulating the temperature by Peltier effect. The volume of the sample was taken with a syringe-type automatic pipette then set up to the lower plate, stand at attention that no air bubbles were formed in the sample. The excess of sample was removed after the displacement of the cone to reach the required sample gap of 0.03 mm. Then, each sample was left to rest for 5 min at the required temperature. Each measurement was reiterated at least twice to check the stability and the repeatability of the results. The measurements were performed as a function of temperature from 20 to 50°C, with an interval of 10 °C, and data were collected under steady-state conditions applying a shear stress ramp to cover a shear rate range between 10-1000 s -1 . The cone and plate were carefully cleaned with acetone after each measurement. EG was also measured to evaluate the uncertainty in viscosity determinations and the possible viscosity enhancement of nanofluids, as described further in Results and Discussion section.

The thermal conductivity of the base fluids and nanofluids was measured in the temperature range 20-50°C with a THW-L2 device (Thermtest Inc., Canada) based on the transient short hot-wire technique following the ASTM D7896 standard. Such a device is appropriated to measure liquid thermal conductivity from 0.01 to 2 W/mK and in the temperature range between -10 and +100 °C with a small quantity of sample of about 20 mL. Testing probe consists of a wire (made of Alumel, with a length of 60 mm and a diameter of 1 mm) and incorporates a temperature sensor (± 0.1 °C). For performing measurements, a sample holder was first filled with the tested sample in which the probe was vertically immersed. Such holder was then placed within a dry bath. After the achievement and stabilization at the required temperature, the measurement started by applying a power supply of 140 mW to the wire with a test time of 1.5 s avoiding convection. The thermal conductivity was evaluated from the slope of the temperature rise versus the time logarithm in the linear region using the THW-L2 software. Previously, both the probe temperature sensor and hot wire sensor have been carefully calibrated with distilled water (DIUF, CAS 1132-18-5, Fisher Chemical, 0.599 W/mK) at 20 °C. Acetone and distilled water were also used to clean the probe and the container between each sample measurement. Each reported value consisted in an average of at least five measurements. The thermal conductivity of EG was measured and compared to the literature to assess the reliability and uncertainty of the measurements, as described later in Section 3.2.

Results and Discussion

Characterizations of nanofluids

First, a sign for the formation of silver nanoparticles within the base fluids could be observed by a visual color change of the solutions from colorless before the reaction to faint yellow and finally golden yellow after the synthesis. Figure 1 shows that the obtained nanofluids, their color depends, as expected, on the latex copolymer content: higher content in copolymer leads in higher silver nanoparticle concentration within the suspensions. This visual observation is also confirmed by UV-Vis measurements as shown in Figure 2. A surface plasmon surface (SPR) band at 350-500 nm with a broad peak entered at about 400-450 nm can be noticed for all the EGS-based nanofluids. This SPR was created by surface plasmon vibrations in the silver nanoparticles, revealing that way their presence within the samples. Moreover, the absorption spectra exhibit a dependence on latex concentration. The absorbance of the localized SPR band is more pronounced as the concentration of copolymer latex increases from 99 to 340 mg. Such enhancement could be due to an increase in yield of the synthesized silver nanoparticles as the copolymer latex content is increased. The position of SPR may also be correlated to the mean size and the size distribution of the metal nanoparticles. When the SPR bands become sharper, this corresponds to the existence of small size and narrow distribution of the Ag nanoparticles. For the studied samples, sharper SPR is observed for the lowest copolymer concentration meaning that the highest copolymer concentrations induce larger Ag nanoparticles with a broader distribution size meaning that the copolymer favors the nanoparticle formation.

Wavelength (nm)

Nanofluid produced with 99mg of copolymer latex Nanofluid produced with 126mg of copolymer latex Nanofluid produced with 304mg of copolymer latex Nanofluid produced with 340mg of copolymer latex flower-like structure appearing is attributed to EG, as confirmed by SEM observations of EG alone (not reported here). As expected, the number of the copolymer latex particles increases (with respect to the EG particles) with the initial content in copolymer. In addition, after the synthesis of the silver nanoparticles (Figs. 3b, 3d, 3f and 3h), a significant part of the copolymer latex is no more present on the SEM images. It has probably been consumed by the synthesis reaction. Particles, mainly in form of aggregates, are visible on the SEM images of nanofluids, and consist in capped Ag nanoparticles, as identified by EDS. The silver-based particle size was investigated by TEM analysis. Compared to SEM, TEM allowed directly visualizing the particles since it was possible to differentiate between the most electronically dense silver particles and the polymer. In BF images, the polymer appears as light grey zone and the silver particles are the dark more or less spherical particles, as shown in figure 4 and figures 5a. The difference in light grey zones in the images of the figures 4a (well visible grey zones) and 4c (grey zone not visible) can be related to the difference in copolymer content for these two samples. Moreover, for the sample prepared with 99 mg of copolymer, the Ag nanoparticles are well dispersed on the TEM grid contrary to the sample prepared with 340 mg of copolymer for which the Ag particles are agglomerated together with the polymer. The HAADF observations allows to well reveal the presence of the polymer around the silver particles but also out of the silver particle zones (shown by the arrows in Figure 5b). EDS spectra of same samples are shown in Figure 6 confirming the presence of silver nanoparticles without the presence of visible impurities.

Due to the presence of large particles that represent a significant part of the images, it was difficult to estimate with eyes only their mean size or their distribution in size, this is the reason why the Image J software was used for the size analysis of the silver nanoparticles.

The results reveal that most of particles have a diameter lower than 8 nm with mean diameter around 2-3 nm (Figure 7). The shape of these low-size particles is rather spherical while the larger particles (whose diameter can reach several tens of nm) can have spherical, elongated or distorted shapes, as shown in Figures 4 and5.

Thermophysical properties of nanofluids

Rheological properties and dynamic viscosity

First, the shear flow curves of EG in the temperature range 20-50°C are reported in Figure 8a.

As expected for pure EG, a Newtonian behavior is observed for each temperature, as the dynamic viscosity is constant within the shear rate range studied. Also, the viscosity exhibits the general inherent downward trend with increasing temperature, as shown in Figure 8b. An absolute average deviation (AAD) of 3 % was obtained in the temperature range 20-50°C in comparison to the results of Yang et al. [START_REF] Yang | Excess Molar Volumes, Viscosities, and Heat capacities for the mixtures of ethylene glycol + water from 273[END_REF]. The shear flow curves of base fluids (EG and copolymer latex) and the EGS-based nanofluids at 40 °C are reported in Figure 9. Contrary to what was observed for EG alone (Figure 8a), the mixtures of EG and copolymer latex used as base fluids behave as shear-thinning materials as the dynamic viscosity decreases with shear rate. This can be explained by the interactions between the molecules of copolymer latex and EG. Thus, higher the content in copolymer latex, stronger the copolymer-EG interactions. The shear-thinning effect is thus much more pronounced over a larger shear-thinning region as the concentration of copolymer latex is increased in the samples. Remarkably, it is observed in the same figure that the nanofluids behave in Newtonian manner with no noticeable difference between each nanofluid within the experimental uncertainty and without any significant modification in viscosity regarding pure EG. The copolymer latex which has been largely consumed by the synthesis of silver nanoparticles (in agreement with SEM/TEM characterizations) and so it cannot induce any intrinsic effect in rheology behavior of the EGS-based nanofluids. Then, the presence of silver nanoparticles does not significantly modify the viscosity of the EGS-based nanofluids compared to that of the EG alone. This is of practical interest in view of possible applications as pressure drop and pumping power of energy systems involving nanofluid flow strongly depend on viscosity.

Thermal conductivity

The thermal conductivity values of pure EG are reported in Figure 11(a) and compared to literature data. It is shown that these values are in quite good agreement with the data of [START_REF] Pastoriza-Gallego | Thermal conductivity and viscosity measurements of ethylene glycol-based Al 2 O 3 nanofluids[END_REF] and the data of [START_REF] Żyła | Huge thermal conductivity enhancement in boron nitride-ethylene glycol nanofluids[END_REF], with AADs of 0.04 % and 0.48 %, respectively, in the temperature range Nanofluid with 99mg of copolymer latex -20°C Nanofluid with 126mg of copolymer latex -20°C Nanofluid with 304mg of copolymer latex -20°C

Nanofluid with 340mg of copolymer latex -20°C Nanofluid with 99mg of copolymer latex -30°C

Nanofluid with 126mg of copolymer latex -30°C Nanofluid with 304mg of copolymer latex -30°C

Nanofluid with 340mg of copolymer latex -40°C Nanofluid with 99mg of copolymer latex -40°C

Nanofluid with 126mg of copolymer latex -40°C Nanofluid with 304mg of copolymer latex -40°C

Nanofluid with 340mg of copolymer latex -40°C Nanofluid with 99mg of copolymer latex -50°C

Nanofluid with 126mg of copolymer latex -50°C Nanofluid with 304mg of copolymer latex -50°C

Nanofluid with 340mg of copolymer latex -50°C The thermal conductivity of the EGS-based nanofluids with different initial contents of copolymer latex and temperature is reported in figures 12 (a) and (b). For the lower contents in copolymer latex (99 and 126 mg), figure 12(a) evidences that the thermal conductivity of EGS-based nanofluids produced is similar to that of pure EG. So, when silver nanoparticles were produced with these two low quantities of copolymer latex, as previously shown in section 3.1, the silver nanoparticle content was not high enough to induce an enhancement in thermal conductivity of EG. On the contrary, when the nanofluids were produced with higher initial contents in copolymer latex (304 and 340 mg,), the thermal conductivity of the nanofluids is significantly higher than that of pure EG (Figure 12(b)). This enhancement is attributed to the higher number of silver nanoparticles produced with a higher amount copolymer latex (in agreement with visual observations and UV-Vis spectra, figures 1 and 2).

For an amount in copolymer latex higher than ~ 300 mg, the content of silver nanoparticles is high enough to induce a thermal conductivity enhancement. This enhancement seems to reach an optimum with the content of precursor initially considered since it is not further modify as higher copolymer latex was used. The temperature dependence of thermal conductivity is similar for both EGS-based nanofluids, and it is also similar to the temperature tendency of pure EG. An average enhancement between 2.5 and 3% is noticed for all temperatures in comparison to EG. Such a result, combined with the unmodified viscosity value, is of interest with regards of the possible use of these nanofluids in real energy systems. This order of magnitude in thermal conductivity enhancement of SEG nanofluids was also reported earlier in [START_REF] Nikkam | Fabrication and thermo-physical characterization of silver nanofluids: An experimental investigation on the effect of base liquid[END_REF][START_REF] Zhang | Enhanced thermal conductivity for nanofluids containing silver nanowires with different shapes[END_REF] for example, for known and higher content in silver nanoparticles. the silver nanoparticles after their preparation probably thank to the presence of copolymer latex. Interestingly, the synthesis approach allowed an efficient synthesis of silver nanoparticles which re-aggregation was limited, producing good stability nanofluids.

In addition, these EGS-based nanofluids behaved as Newtonian fluids without any significant enhancement in dynamic viscosity in comparison to pure EG in the 20-50 °C range. Finally, it was observed that the thermal conductivity of EGS-based nanofluids was enhanced by 3 % in comparison with EG for the two highest used contents in copolymer latex in the same temperature range. Such a result is of interest and promising in terms of application as the dynamic viscosity was still remained unchanged. Future works could consist in adjusting the content in copolymer latex and Ag precursor to increase further the thermal conductivity without modifying the dynamic viscosity.

Figure 1 :

 1 Figure 1: Photographs of the EGS-based nanofluids after Ag/EG synthesis prepared with different content of copolymer latex: (1) 99 mg, (2) 126 mg, (3) 305 mg, (4) 340 mg of copolymer latex, respectively.

Figure 2 .

 2 Figure 2. UV-Vis spectra of EGS-based nanofluids for different initial copolymer latex concentration: 99 mg (open black triangle), 126 mg (open red circle), 304 mg (open dark blue square), 340 mg (open pink diamond) of copolymer latex, respectively.

Figure 3 :

 3 Figure 3: SEM images of base fluids (EG and copolymer latex) (a, c, d, g) and the EGS

Figure 4 .

 4 Figure 4. TEM micrographs of EGS-based nanofluids synthesized with 340 (a and b) and 99 mg of copolymer (c and d), respectively at different magnifications.

Figure 5 .

 5 Figure 5. STEM micrographs in BF (a) and HAADF modes (b) of the sample at initial concentration of 340 mg of copolymer, the same zone is observed for the two imaging modes.

Figure 6 .

 6 Figure 6. EDS spectra focused on the nanoparticles from the samples prepared at two different initial copolymer concentrations, 340 mg (a) and 90 mg (b), respectively.

Figure 7 .

 7 Figure 7. Size distribution of the silver particles analyzed from TEM images of a sample with an initial copolymer content of 340 mg by means of Image J.

Figure 8 .

 8 Figure 8. Dynamic viscosity of EG in function of shear rate (a) and temperature (b)

1 Figure 9 .

 19 Figure 9. Dynamic viscosity versus shear rate for base fluids and nanofluids at a temperature of 40 °C.

1 )Figure 10 .

 110 Figure 10. Dynamic viscosity versus shear rate for all nanofluids in the temperature range 20-50°C (a) -Dynamic viscosity of nanofluids in comparison to pure EG (b).

  . This allows the experimental procedure to be validated and used with base fluids and nanofluids. Note that the error bars represent the standard deviation between measurements.

Figure 11 (

 11 Figure 11(b) shows the thermal conductivity of EG and copolymer latex base fluids with the lowest and highest content of copolymer latex used. The presence of copolymer latex does not significantly modify the thermal conductivity of pure EG. Consequently, the observed thermal conductivity increase of the EGS-based nanofluids is only attributed to the presence of silver nanoparticles.

Figure 11 .Figure 12 .

 1112 Figure 11. Thermal conductivity of pure EG with (a) comparison of literature data and (b) EG mixed with copolymer latex.

fellowship from Xunta de Galicia (Spain). Authors acknowledge Francis Gouttefangeas from CMEBA (Université Rennes 1) for performing SEM studies. B. Vigolo and M. Emo thanks the Competence Center 3M at the Institute Jean Lamour and they are grateful to Dr. J. Ghanbaja for his valuable help for TEM investigation.

Conflicts of Interest:

The authors declare no conflict of interest.