L Barbe
email: lou.barbe@univ-rennes1.fr

A Prinzing
email: andreas.prinzing@univ-rennes1.fr

C Mony

B W Abbott

M Santonja
email: mathieu.santonja@univ-rennes1.fr

K Hoeffner
email: kevin.hoeffner@univ-rennes1.fr

S Guillocheau
email: sarah.guillocheau@univ-rennes1.fr

D Cluzeau
email: daniel.cluzeau@univ-rennes1.fr

A.-J Francez
email: andre-jean.francez@univ-rennes1.fr

N Le Bris

V Jung

Opposing effects of plant-community assembly maintain constant litter decomposition over grasslands aged from 1 to 25 years

Keywords: plant-community assembly, decomposers and detritivores, Home-Field Advantage, grasslands, litter decomposition, plant functional traits

Effects of traits and decomposers on decomposition mutually cancel out each other

INTRODUCTION

The decomposition of plant litter is one of the main bottlenecks regulating carbon storage and nutrient cycling in terrestrial ecosystems (Swift and others 1979). Under a given set of abiotic conditions, litter decomposition is controlled by two main biotic parameters and their interaction (Coûteaux and others 1995): litter quality (Cornelissen and others 1999;Makkonen and others 2012), decomposer community [START_REF] Petersen | A Comparative Analysis of Soil Fauna Populations and Their Role in Decomposition Processes[END_REF][START_REF] Bardgett | Belowground biodiversity and ecosystem functioning[END_REF], and response of decomposers to local litter quality (Austin and others 2014;Veen and others 2015). These biotic factors contribute to differences in decomposition among biomes, and among successional stages (e.g., grassland versus forest), however, it remains unclear whether and how they operate during the assembly of a given successional stage. Grasslands are the most widespread successional stage of temperate terrestrial biomes, and the largest terrestrial ecosystems in the globe. Although the successional stage of grasslands is maintained by agricultural practices (grazing or mowing), community assembly processes still operate, from young, species-poor grasslands dominated by the sown species to old grasslands dominated by spontaneous species (i.e. grassland succession, Cramer and others 2008). In grasslands, between 50 and 90% of plant primary production ends up as litter [START_REF] Cebrian | Patterns in the fate of production in plant communities[END_REF]. In contrast to temperate forest ecosystems, the production of litter in temperate grasslands may be sustained through the year, due to continuous growth and senescence of graminoid species. Consequently, litter decomposition is a central ecosystem process in natural and semi-natural grasslands, providing many ecosystem services, including nutrient mineralization for plant regrowth.

Throughout the assembly of a grassland plant community, litter decomposition might decrease, due to decreasing litter quality of plant species. Litter quality is determined by the digestibility and the nutritional quality of litter for decomposers, which depends on functional traits of plants [START_REF] Cornelissen | Functional leaf attributes predict litter decomposition rate in herbaceous plants[END_REF]. For instance, litter quality decreases with A c c e p t e d m a n u s c r i p t high phenol content or high C:N ratio [START_REF] Hättenschwiler | The role of polyphenols in terrestrial ecosystem nutrient cycling[END_REF]Quested and others 2007;Bakker and others 2011). Along a succession sequence, these functional traits are likely to differ among successional stages, because traits are involved in plant-plant interactions and community assembly [START_REF] Hättenschwiler | The role of polyphenols in terrestrial ecosystem nutrient cycling[END_REF]Garnier and others 2004;Violle and others 2009;Barbe and others 2017). Specifically, resource-conservative traits, which cause litter quality to decline (for instance, high C:N or high leaf dry matter content [LDMC]) may only be expressed in late-succession stages, where plant species are already established and competition is high (Wright and others 2004). During the community assembly of a given successional stage, similar mechanisms might occur and plant species present in older communities might display resource-conservative traits rather than resource-acquisitive traits.

These dynamics could result in litter quality, and hence litter decomposition, decreasing with grassland community assembly.

The abundance of grassland detritivores and decomposers and their ability to decompose local litter might increase with time because of processes such as adaptation, plasticity, or ecological sorting, thereby increasing litter decomposition. Both detritivore macrofauna (earthworms) and mesofauna (Collembola, Acari and Enchytraeidae) fragment litter, accelerating the mineralization and respiration of carbohydrates by fungi and bacteria [START_REF] Petersen | A Comparative Analysis of Soil Fauna Populations and Their Role in Decomposition Processes[END_REF]Coûteaux and others 1995;[START_REF] Bardgett | Belowground biodiversity and ecosystem functioning[END_REF].

Many decomposers are largely immobile and have slow population growth rates. At the onset of grassland community assembly, low decomposer abundance might limit litter decomposition, because grasslands might inherit few decomposers from prior land use: the abundance of detritivores and decomposers decreases considerably in row-crop agriculture, which is considered a major anthropogenic disturbance (Ponge andothers 2003, 2013;Chauvat and others 2007). Consequently, time might be required to recover decomposer populations, from the same patch or from adjacent patches (Decaëns and others 2008).

A c c e p t e d m a n u s c r i p t

Moreover, decomposer communities are able to adapt to locally-derived litter: the communities sometimes break down litter more efficiently from "Home" compared to other places, with this phenomenon being called Home-Field Advantage (HFA, Freschet and others 2012;Austin and others 2014;Veen and others 2015). This phenomenon occurs due to species sorting or selecting particular genotypes, which increase the decomposition rate of a particular type of litter through time. However, there is mixed evidence about the consistency and prevalence of HFA (Gießelmann and others 2011;Veen and others 2015). Of note, the response of decomposers to litter quality may have a lag time, depending on particular litter traits or decomposer assemblages, and might more likely to occur in plant communities with low litter quality [START_REF] Milcu | All size classes of soil fauna and litter quality control the acceleration of litter decay in its home environment[END_REF]Veen and others 2018). Overall, the net effect of changes to decomposer abundance and response to litter quality might therefore depend on the duration of the assembly of grassland plant community, and might increase litter decomposition in old grasslands, counteracting the effects of changes to litter quality.

In this study, we investigated how litter traits, decomposer community, and their interactions affect litter decomposition during community assembly using semi-natural grasslands as a model system. We used a 25-years grassland chronosequence to test whether and how litter decomposition changes with grassland age. Specifically, we tested the following hypotheses:

(i) grassland age slows down litter decomposition because litter quality declines and, (ii) grassland age accelerates litter decomposition because decomposer abundance increases or HFA emerges or increases. The chronosequence consisted of 27 grasslands in an agricultural landscape in western France (Brittany), ranging from 1 to 25 years since the last crop. We measured four functional traits aggregated at the plant community level; namely, specific leaf area (SLA), LDMC, C:N ratio, and phenolic concentration. We characterized the entire decomposer community (macrofauna, mesofauna and microbes). We performed a reciprocal

MATERIALS AND METHODS

Study Site and Grassland Selection

We carried out the study at the Long Term Ecological Site (LTER) « Zone Atelier Armorique », which is a 150 km² research area in Brittany, France (48°36′N, 1°32′W). Row crops and pastureland cover approximately 90% of the landscape, which is intersected by a well-developed hedgerow network ranging from 50 to 100 m.ha -1 (Thomas and others 2016).

We used ground-truthed aerial photos, which were taken every year since 1990, to construct a detailed land-use history for all sites, allowing us to determine the time since cultivation for each grassland precisely. Based on this land-use history and verification with grassland owners, we selected 27 grasslands ranging from one to 25 years since last row cropping. The grasslands were similar with respect to the initial species that were sown (Lolium perenne (L.)

and Trifolium repens or pratensis) and management practices (annual mowing and extensive grazing, approximately 60 cows for two days once per month). The grasslands were also similar regarding environmental conditions (mesophilic grasslands) and the type of soil (brown soil that drained freely with well-developped organic and mineral horizons). The total soil C:N ratio of parcels, measured on the top 15 cm of parcels with a 20 cm diameter stainless steel hand corer, was 13.4 ± 1.7. Parcels were 3 ha ± 1.4 ha.

Characterization of Plant Community and Plant Functional Traits

We characterized the percentage coverage of plant species in each grassland during spring 2015 using 10 quadrats of 1m² that were evenly distributed within the grassland. We identified the most abundant species(i.e. species accounting for 80% of the total abundance of the community; [START_REF] Pakeman | Sampling plant functional traits: What proportion of the species need to be measured?[END_REF], which represented from two to five species for A c c e p t e d m a n u s c r i p t each grassland (total richness from 8 to 27). For these species, we measured four functional traits related to litter quality; namely, specific leaf area (SLA), leaf dry matter content (LDMC), leaf C:N ratio and leaf phenolic concentration. These traits mainly reflect the physical and chemical properties of the litter [START_REF] Hättenschwiler | The role of polyphenols in terrestrial ecosystem nutrient cycling[END_REF]Pérez-Harguindeguy and others 2000;[START_REF] Santiago | Extending the leaf economics spectrum to decomposition: evidence from a tropical forest[END_REF]Quested and others 2007). To obtain a representative mean value for each trait from each species, we measured the traits of 10 individuals that were collected from randomly selected grasslands in our experiment. Phenolic concentration was measured colorimetrically, according to the Folin-Ciocalteu colorimetric method [START_REF] Folin | A colorimetric method for the determination of phenols (and phenol derivatives) in urine[END_REF] modified by Santonja and others (2015), using gallic acid as a standard. A 0.25 g litter sample was dissolved in an aqueous methanol solution, that was shaken for 1-h and then filtered. The filtered extract was mixed with Folin-Ciocalteu reagent, a color-reaction stabilizer (Na 2 CO 3 ), and distilled water. After 1-h, phenolics concentration was measured at 765 nm on a spectrophotometer (Thermo Scientific ® , USA). We measured SLA and LDMC following the standard protocols of Pérez-Harguindeguy and others (2013), and we measured C:N ratio using an elemental analyzer (FLASH EA 1112 Thermo Finnigan, Waltham, Massachusetts, USA). For each grassland, we aggregated the values of species traits to obtain an abundance-weighted mean for each trait (see Appendix Table S1 for species trait values). Correlations between traits (Appendix Table S2), with only two significant correlations being detected among the six trait correlations that we tested for, indicates low multicollinearity among traits.

Characterization of the Decomposer Community

We sampled detritivore macrofauna (i.e. earthworms) in each grassland during spring 2016, according to the method of Ponge and others (2013) and Cluzeau and others (2012). In brief, three 1x1m quadrats were watered three times with 10 l formalin at increasing concentration every 15 min (0.075, 0.075 and 0.12% formaldehyde solution), and a soil block (25x25cm and A c c e p t e d m a n u s c r i p t 20cm depth) was extracted at the end of sampling and hand sorted. Earthworms that were expelled to the surface by the irritant solution, or were recovered during the sorting of the soil block, were collected. Identification was done in the laboratory following the identification key of Cluzeau and others (unpublished, available upon request), based on [START_REF] Bouché | Lombriciens de France: écologie et systématique[END_REF].

Earthworms were characterized by abundance and biomass (fresh weight) and were grouped into ecological categories: epigeic or anecic -we excluded endogeic earthworms as they do not directly influence decomposition of surface litter [START_REF] Bouché | Stratégies lombriciennes[END_REF]. Then, we sampled soil detritivore mesofauna using a cylindrical soil corer (5 cm diameter × 8 cm depth; three soil cores per grassland on two sampling dates). Mesofauna was extracted from the soil cores using the Tullgren funnel method [START_REF] Berlese | Apparicchio per raccogliere presto ed in gran numero di piccolo artropodi[END_REF]) over a 10-day period. Collected organisms were stored in 95% ethanol, counted using a binocular scope and separated into three groups: Acari (Oribatids), Collembola and Enchytraeidae. Finally, we measured soil microbial C:N ratio, as a proxy of the composition of the microbial community, illustrating dominance of fungi or dominance of bacteria [START_REF] Paul | Soil Microbiology and Biochemistry[END_REF]Van Elsas and others 2006). We measured the microbial C:N ratio using the fumigation-extraction method from Brookes and others (1985) and Vance and others (1987). We collected and aggregated 15 soil cores from the top 10 cm layer for each grassland to obtain a representative soil sample. From this sample, we used a 30 g soil sample that was passed through a 2-mm sieve, and was then handsorted to remove any visible organic material (plant roots, litter fragments).

A c c e p t e d m a n u s c r i p t

Abiotic parameters such as soil moisture and soil pH can also affect plant traits, decomposer community and litter decomposition (Coûteaux and others 1995). Thus, we quantified soil moisture and pH in each grassland during spring 2016 using soil samples from the top 15 cm layer collected with a 20 cm diameter stainless steel hand corer. After the manual removal of rocks and roots > 2 mm, soil moisture (ω) was determined by mass loss at 105°C for 24 h according to the formula:

, where W is the wet soil mass on the sampling date and D is the dry soil mass. Soil pH was determined with a 1:10 soil, de-ionized water suspension, following standard methods (Robertson and others 1999). We also accounted for the local landscape surrounding the selected grasslands, which might influence plant traits and decomposer community. Using the database of historical land-use data, we quantified the percentage of each grassland perimeter that was in contact with grasslands older than 5 years, which are adjacent grasslands that represent a significant source of propagules and fauna for the selected grasslands.

Litter Decomposition and Transplant Experiment

In each grassland, we collected litter in the fall of 2015 for the 2-5 species accounting for 80% of total plant abundance. We did not collect stems. For all species, we collected green material for litter as in semi-natural grasslands the main portion of plant material contributing to the litter pool is green material, not senesced litter (due to mowing and grazing, Mazzanti home combination (from home combinations 1 to K). The average decomposition in the data set is α (i.e. the intercept of the model) and ε is the error term. β l and γ s were restricted to prevent perfect collinearity. The value of a given parameter that was estimated (η h , β l and γ s ) indicated, as a single percentage per grassland, the difference to the mean decomposition of the dataset due to this parameter. These single parameters per grassland will be further correlated with grassland age, because there is no variation of age within grasslands.

To explore whether and how HFA is driven by litter traits and detritivore and decomposer descriptors, we calculated the HFA for the five "Home" replicates of each grassland, because plant traits vary within grasslands (similar to Veen and others 2018). We used the following equations (Gießelmann and others 2011, Pérez and others 2013):

(1) HFA a1 = HDD a1 -ADD a -H literature (see above), however, we stress that quantification per grassland (i.e. using means) led to very similar results and are hence not presented.

To explore the role of litters traits and decomposer community on litter decomposition per se, we calculated three mass loss values for each grassland, by averaging mass loss within the three treatment types: home litter at home, home litter away, and away litter at home. This approach allowed us to test for the numerous effects of plant traits and decomposer communities, that may vary within a grassland (similar to [START_REF] Veen | Variation in home-field advantage and ability in leaf litter decomposition across successional gradients[END_REF]. Analyses based on all values within grasslands led to the same conclusions (except that explained variance was slightly smaller and P-values were even more significant, due to the larger sample) and are therefore not presented.

Statistical Analyses

Before all the statistical tests, we center-scaled all variables (i. transplantation treatment (home litter at home, home litter away, away litter at home), however, this factor was not significant and we do not present the results. To summarize the results of the individual regression analyses, we conducted a path analysis [START_REF] Wright | The method of path coefficients[END_REF], which allowed us to visualize the strength of the indirect effects of grassland age on litter mass loss and HFA, which were effects mediated by plant functional traits and decomposer community. For each indirect effect, we calculated a compound path by multiplying: (i) the standardized regression coefficient from the model that related grassland age to plant functional traits or decomposer community with (ii) the standardized regression coefficient from the model that related plant functional traits or decomposer community to litter mass loss. Finally, we compared, for litter mass loss and HFA, the sum of the indirect effects of grassland age with the single overall effect of grassland age. We graphically examined residuals using probability plots and predicted versus residual plots, indicating that all residual distributions fulfilled the assumption of normality and homogeneity. All statistical analyses were performed with R 3.0.3 (R Development Core Team 2016).

RESULTS

Plant Functional Traits and Decomposer Community Depend on Grassland Age

We observed large differences in the functional traits of the plant community and in the composition of the decomposer community among entire grasslands. Grassland age strongly explained all functional traits of the plant community (simple regression analyses, Figure 1).

Specifically, age increased leaf C:N ratio (F 18 =11.07, P=7.10 -4 , adj-R²=0.50), leaf phenolic concentration (F 18 =4.19, P=0.03, adj-R²=0.24), and SLA (F 19 =3.71, P=0.04, R²=0.20), while it decreased LDMC (F 25 =31.64, P=7.10 -6 , adj-R²=0.52). Old grasslands hence harbored a plant community with mainly thin, moist leaves; however, these leaves contained lower nitrogen concentration and higher phenolic content. Grassland age also increased soil A c c e p t e d m a n u s c r i p t microbial C:N ratio (F 20 =5.32, P=0.03, R²=0.17, Figure 1), as well as the abundance of Acari (F 23 =9.20, P=6.10 -3 , R²=0.25, Figure 1). Earthworm abundance, as well as the abundance of Collembola and Enchytraeidae, was not associated with grassland age; however, the percentage of adjacent grasslands increased the abundance of anecic earthworms (Figure S1).

Despite multiple relationships between age and either litter traits or decomposer community, we found one significant correlation between litter traits and detritivore and decomposer community (Acari vs LDMC, r=-0.54, P<0.05, see Table S2).

Litter Decomposition and Home-Field Advantage Differ Among Grasslands and Depend on Plant Functional Traits and the Decomposer Community

We observed large among-grassland differences in litter mass loss and HFA (Figure S2).

Overall, across all of the litters in our experiment, we did not observe a positive mean HFA (no significant difference from a mean of 0, t=1.41, df=133, P=0.16). Grassland age did not directly influence litter mass loss (F 25 =0.29, P=0.60, Figure 2), HFA (F 25 =0.41, P=0.53,

Figure 2), soil ability (F 25 =0.71, P=0.41, Figure 2) nor litter quality (F 25 =0.22, P=0.4, Figure 2). However, several plant functional traits and the soil microbial C:N ratio, related to grassland age, strongly explained litter mass loss (F 56 =12.43, P=7.10 -9, adj-R²=0.53, Figure 3): the leaf C:N ratio decreased litter mass loss, as did LDMC and SLA (Figure 3). Decrease in soil microbial C:N was associated with decrease in litter mass loss (Figure 3). Moreover, soil moisture decreased litter mass loss (Figure S3). None of the plant functional traits influenced HFA, but HFA increased when anecic earthworms were more abundant (Figure S1, F 101 =6.37, P=0.044, semi-partial R²=0.06) and when soil moisture was low (Figure S3, F 101 =6.37, P=0.013, semi-partial R²=0.03). We also note that, when explaining litter mass loss by functional traits and decomposer community, a positive, direct effect of grassland age on litter mass loss was detected after removal of all other significant effects (Figure S4).

Finally, as plant functional traits and soil microbial C:N were influenced by grassland age, we

A c c e p t e d m a n u s c r i p t obtained the following indirect effects of grassland age on litter mass loss (Figure S4): -0.42 via leaf C:N ratio, -0.26 via SLA, 0.53 via LDMC, -0.15 via the soil microbial C:N ratio and 0.22 via an unmeasured parameter. The sum of all indirect effects of grassland age on litter mass loss was -0.08, indicating no significant total effect, which was consistent with the lack of an overall effect of grassland age on litter mass loss that we initially observed (Figure 2).

DISCUSSION

Grassland age influenced all measured functional traits of the plant community, as well as the soil microbial C:N ratio. These changes, in turn, impacted litter mass loss, but they cancelled each other out, resulting in constant litter decomposition across the chronosequence. Litter overall mass loss, litter quality (i.e. litter decomposability), decomposer efficiency, and HFA did not change with grassland age.

Plant Functional Traits Change with Age, but not towards Resource-Conservatism

The four functional traits that we measured on plant communities strongly responded to grassland age. Specifically, the plant communities of old grasslands displayed a higher leaf C:N ratio and phenolic concentration, along with higher SLA and lower LDMC. Thus, grassland assembly favored resource-conservative trait values as much as resource-acquisitive trait values. This result contrasted with what we expected from other studies (Wright and others 2004;Quested and others 2007). These changes in functional traits were probably due to plant turnover during grassland assembly, with new species establishing and dominating initial species that had higher C:N and phenolic concentration (for instance, Ranunculus repens or Dactylis glomerata, Figure S5). These characteristics might enhance the resistance of these new species against herbivores (Bernays and others 1989). Moreover, high SLA and low LDMC might facilitate high resource acquisition and hence regrowth to compensate for herbivory [START_REF] Briske | Strategies of plant survival in grazed systems: a functional interpretation[END_REF]. This observed plant succession during grassland assembly might be A c c e p t e d m a n u s c r i p t attributed to the immigration of new plant species from the surrounding landscape or by the germination of seeds already present in the soil seed bank. Overall, these results show that shifts to plant afterlife traits arise during the assembly of a successional stage (i.e. grasslands), not only across successional stages, for example after grassland abandonment [START_REF] Kahmen | Plant functional trait responses to grassland succession over 25 years[END_REF]Quested and others 2007).

Offsetting Effects of Plant Traits on Litter Decomposition

Individually, each of these changes to plant traits (except changes to phenolics) influenced litter decomposition, but they did not reduce litter decomposition, which remained unchanged with grassland age overall: an increase in SLA and C:N ratio decreased litter decomposition, but this was compensated by a decrease in LDMC which increased litter decomposition.

While the effects of the C:N ratio and LDMC are consistent with the literature (Bakker and others 2011; Pakeman and others 2011), the negative effect of SLA on mass loss was inconsistent with the literature [START_REF] Santiago | Extending the leaf economics spectrum to decomposition: evidence from a tropical forest[END_REF]Makkonen and others 2012). However, studies on SLA usually focus on SLA values distinctly smaller than ours. This negative relationship might be explained by the higher compaction of litter with very thin leaves (i.e. high SLA), slowing down the colonization of litters by detritivores and decomposers. Alternatively, compensatory feeding (i.e. enhanced consumption rate on poor-quality litter to ensure sufficient resource assimilation to meet metabolic needs; Gessner and others 2010) on litter with low SLA might also explain the negative effect of SLA on mass loss. Finally, the lack of influence of leaf phenolic concentration on decomposition was possibly due to the lower leaf phenolic concentration of grassland plants compared to other systems [START_REF] Hättenschwiler | The role of polyphenols in terrestrial ecosystem nutrient cycling[END_REF] which might have prevented the effects from being detectable. Taken together, our results suggest, contrary to what we expected, that community assembly processes result in similar litter quality during grassland succession, likely due to compensatory effects on afterlife traits and litter quality.

A c c e p t e d m a n u s c r i p t

Only some components of decomposer communities responded to grassland age, partially supporting our predictions. Anecic and epigeic earthworms (macrofauna) did not respond to grassland age; however, the abundance of anecic earthworms increased in grasslands that shared more edges with other grasslands, suggesting the immigration of anecics from the surrounding landscape, consistently with their high dispersal ability (Caro and others 2013).

Considering mesofauna and microbes, we observed two shifts: the abundance of Oribatids (Acari, detritivore mesofauna) increased with grassland age, as did the soil microbial C:N ratio. The increase in the abundance of Oribatids might be attributed to the recovery of populations of these soil organisms after land-use turnover and the disruptive agricultural practices before the sowing of grasslands [START_REF] Hülsmann | The effects of different tillage practices on soil mites, with particular reference to Oribatida[END_REF]. Recovery might be permitted by the immigration of new species from an adjacent landscape, or by the demographic development of the remaining initial species. Finally, the increase in soil microbial C:N ratio with grassland age might be attributed to a reduction in mineral N input after cropping, ultimately causing the depletion of microbial N [START_REF] Wardle | A comparative assessment of factors which influence microbial biomass carbon and nitrogen levels in soil[END_REF].

Strong Functional Redundancy of Detritivores and Decomposers in Grassland Ecosystems?

Unexpectedly, the abundance of macrofauna and mesofauna did not influence decomposition (Milcu and others 2008), suggesting that microbial and fungal decomposition were not limited by the fragmentation of leaves. The only decomposer-mediated effect we could identify was the increase in soil microbial C:N ratio that was associated with decreasing litter mass loss.

Possibly, high soil microbial C:N also reflected a change in the composition of the microbial community, from a dominance of bacteria to a dominance of fungi [START_REF] Paul | Soil Microbiology and Biochemistry[END_REF]Van Elsas and others 2006), which might be less efficient at decomposing labile litter (Santonja and others 2018). This slight, microbial C:N-mediated, decrease in litter mass loss was probably compensated for by a slight increase of litter mass loss due to a statistically direct effect of grassland age in the full model; this effect was necessarily that of age on one or several parameters that we did not measure (and it may be a biologically indirect effect, like the effects of plant traits and decomposers that we observed), and it explains why soil ability, which represents decomposer efficiency and abiotic conditions, remained globally similar among grasslands. Overall, the lack of relationships between detritivores and decomposers with decomposition might be attributed to the high functional redundancy of these groups for decomposing grassland litter (Gessner and others 2010). Despite variation in the abundance of detritivores and decomposers in our grasslands, there was no effect of any particular group on decomposition. Given the high decomposability of litter in grasslands, the effects of detritivores and decomposers on decomposition might be particularly redundant, with this feature being very stable across time.

No Impact of Grassland Age on Home-Field Advantage, Due to High Litter Decomposability?

We found, overall, a low HFA, with a substantial variation across grasslands. We found only one significant relationship between the numerous plant traits and detritivores and decomposers that we sampled, not indicating ecological sorting or abundance shifts of detritivores and decomposers in response to litter traits. Overall, we found no relationship between grassland age and HFA: grassland age did not influence HFA, nor did plant functional traits and soil microbial C:N ratio. Besides, litter decomposition rates were very high in our experiment, resulting from the very high decomposability of the green litter of graminoids and Trifolium sp. (Sanaullah and others 2010 for similar decomposition rates).

High decomposition also resulted from the humid and relatively warm, frost-free winter in the study region which increases decomposer activity (this rapidity is also illustrated by the absence of a distinct litter layer in the grasslands). Therefore, our results support that strong HFA may only arise when home litter is very recalcitrant or very dissimilar to other litter Alternatively, in their permanent galleries, the mucus of anecic earthworms might have been a

durable, high quality substrate for decomposer microorganisms, giving them time to adapt to local litter, ultimately triggering HFA. Finally, high soil moisture triggered a slight Home-Field Disadvantage. This might be due to waterlogging triggering anoxy in the top soil, decreasing bacterial and fungal activity (thus decreasing mass loss per se) especially in soft, easily hydratable and compactable litters produced within the home environment. Overall, these results show that grassland maturation did not drive HFA, and that HFA might only occur when litter is very recalcitrant or dissimilar, which was not the case in our system.

Hence, HFA did not require experience of local decomposers with local plants, but rather environments favorable for macrofauna and aerobic decomposition. Earthworms might have a role in this process, despite being quite generalist organisms, with microbes not being the only biotic drivers of HFA (Freschet and others 2012;Austin and others 2014). Further studies, including the sampling of detritivore and decomposer traits, and including the exclusion of fauna, are required to elucidate the precise role of fauna and microbes on HFA.

CONCLUSIONS

We found that community assembly maintained similar litter decomposition along a 25-years chronosequence, and that HFA was not associated with plant community assembly or age.

HFA was quite low and was only associated with the macrofauna community and soil moisture, which were not related to grassland age. However, our results showed that several In turn, these changes acted on decomposition, but cancelled out each other, resulting in the same decomposition rate across succession. These results also suggest that in temperate grassland ecosystems, where decomposition is very fast, numerous and important changes in We observe that, while there is substantial intragrassland variation, there is still important inter-grassland variation for mass loss (ANOVA with grassland as explicative factor: P<10 -16 , r²=0.27) and HFA (ANOVA with grassland as explicative factor: P=4,9x10 -4 , r²=0.23). S1 for species traits.

  s c r i p t litter transplant experiment, to quantify the response of decomposer communities to local litter quality (HFA).

  Before and after fumigation-extraction, we measured microbial C concentration using a Wet Oxidation Total Carbon Analyzer 1010 (OI-Analytical, USA) and microbial N concentration using a continuous flux AutoAnalyzer (measuring total dissolved N, AA3 Bran & Luebbe, USA). The microbial C and N concentrations were obtained by substracting the values obtained before fumigation-extraction from the values obtained after. Microbial C:N ratio was then calculated by dividing microbial C concentration with microbial N concentration.

and others 1994 ;

 1994 Sanaullah and others 2010). The duration of leaf decomposition is very short for the main grassland species in our experiment (especially Lolium perenne and legumes from the genera Trifolium, see Results for mass losses) such their leaves decompose before achieving complete senescence. Litter was cleaned, air-dried, and placed into 8x8 cm mesh bags. Litterbags had 2 mm mesh on the upper side to avoid contamination by allochthonous A c c e p t e d m a n u s c r i p t litter, and 5 mm mesh on the lower side to allow numerous detritivores to shuttle between the soil and litter to freely access the litter -during the period of exposure. Litter always remained moist and was never brittle. Litterbags were positioned on bare ground by gently pushing aside grasses (i.e. without removing any plants). Each litterbag contained 1g of litter, that was oven-dried equivalent (air-dry/oven-dry ratio calculated from subsamples that were oven-dried but not exposed to decomposition), and were constructed with the relative proportion of species from the grassland. We constructed 15 litterbags for each grassland, five of which were placed in the original grassland ("Home" decomposition) and 10 of which we randomly placed in 10 other grasslands of the experiment ("Away" decomposition) to measure HFA. We performed decomposition experiments during early spring 2016 and litterbags were collected 10 days later, when they reached 30-60% mass loss. Mass loss was measured on all 15 samples per grassland after cleaning the litter, and were oven-dried at 65 °C for three days. Mass loss (%) was calculated as, where m 0 is the initial oven-dried equivalent dry weight and m 1 is the oven-dry weight at collection.We calculated HFA, litter quality and soil ability (i.e. abiotic conditions and decomposer efficiency) for each grassland by running the Decomposer Ability Regression Test (Keiser and others 2014) using SAS University Edition (SAS Institute, Cary, NC). This analysis disentangles, for a given mass loss, the effects of HFA, litter quality, and soil ability, estimated respectively by the parameters η h , β l and γ s in the following equation: where Y i is the decomposition of obervation i, and Litter l , Soil s , and Home h are dummy variables that equal 1 or 0, respectively, depending on the presence or absence of the litter mixture (from the litter mixture 1 to N), soil community (from soil community 1 to M) and A c c e p t e d m a n u s c r i p t

  the Home-Field Advantage for litter replicate 1 of grassland a. Positive values indicated, as a percentage, faster home decomposition than expected, while negative values indicated slower home decomposition than expected. HDD a1 represents the Home Decomposition Difference for litter replicate 1 from grassland a. This value was the sum of the differences between the decomposition of this litter in its original grassland a (D a1a ) and the decomposition in this grassland of litters from other grasslands i (D ia ). ADD a represents the Away Decomposition Difference for grassland a. This value was the sum of the differences between the decomposition of litter from grassland a in other grasslands i and the home decomposition of grasslands i. H represents the sum of all HDD for all litters divided by amount of litters minus one. Overall, we quantified HFA per litter bag, as established in the A c c e p t e d m a n u s c r i p t

  e. transformed variables by subtracting their mean and dividing by their standard deviation) to permit comparisons of regression coefficients within and among models. We used multiple ordinary least square (OLS) regression models to test the effect of grassland age on plant functional traits (SLA, LDMC, C:N ratio, phenolics) and decomposer community (soil microbial C:N ratio, abundance and biomass of epigeic earthworms and anecic earthworms, and abundance of Acari, Collembola and Enchytraeidae), accounting for abiotic parameters and local landscape (soil moisture, soil pH and percentage of adjacent grasslands). We used simple OLS regression models to test the effect of grassland age on HFA, soil ability, litter quality, and litter mass loss. We used multiple OLS regression models to test the effect of plant functional traits and decomposer community on litter mass loss and HFA (as calculated in Gießelmann and others 2011, Pérez and others 2013) also accounting for abiotic parameters, local landscape and grassland age. Of note, in the model explaining mass loss we also included the A c c e p t e d m a n u s c r i p t

(

  [START_REF] Milcu | All size classes of soil fauna and litter quality control the acceleration of litter decay in its home environment[END_REF] Li and others 2017; Lu and others 2017;[START_REF] Palozzi | Are leaf litter and microbes team players? Interpreting home-field advantage decomposition dynamics[END_REF] Hoyos-Santillan and others 2018). Only increased abundance of anecic earthworms triggered some HFA. This interesting phenomenon might reflect, for instance, the response of digestive enzymes of anecic earthworms to local litter(Eisenhauer and others 2012): the digestive metabolism of earthworms could acclimate to home litter, and then secrete more enzymes that are adapted to home litter, which would increase digestion and hence litter consumption.

  and the soil microbial C:N ratio changed during community assembly.

  plant and decomposer communities lead to the same levels of ecosystem functions, possibly explaining the outstanding resilience of many grassland ecosystems despite being often a transitional stage.

Figure 1 .

 1 Figure captions

Figure 2 .

 2 Figure 2. Grassland age did not influence Home-Field Advantage (HFA), soil ability

Figure 3 .

 3 Figure 3. Three aggregated functional traits of plant community and soil microbial C:N ratio

Figure S1 .

 S1 Figure S1. Percentage of adjacent grasslands increased the abundance of anecic earthworms,

  35

Figure S2 .

 S2 Figure S2. Boxplot of mass loss and Home-Field Advantage (HFA) for all litters from the

  36

Figure S3 .

 S3 Figure S3. Soil moisture decreased litter mass loss and Home-Field Advantage (HFA). SP-R²

  37

Figure S5 .

 S5 Figure S5. Trend lines of mean cover percentage of the nine species used in the

  39

Table S1 .

 S1 Mean values of functional traits measured on plant species representing 80% of the grassland communities in our experimental design (see Material and Methods) and that were accounted for in the decomposition experiment. Phenolics and LDMC (Leaf Dry Matter Content) are in %, SLA (Specific Leaf Area) is in mm².g -1 , and C:N ratio has no unit.

	Agrostis stolonifera Agrostis tenuis Dactylis glomerata Holcus lanatus Lolium italicum Lolium perenne Ranunculus repens Trifolium pratense Trifolium repens Species	Mean 3.32 3.02 3.58 2.92 2.56 2.80 3.58 3.82 3.76 Phenolics SD 0.19 0.07 0.27 0.09 0.12 0.10 0.30 0.15 0.16	Mean 10.78 10.36 10.47 9.79 9.30 9.17 12.22 9.36 8.93 C:N ratio SD 1.35 0.07 0.30 0.16 0.09 0.05 0.51 0.20 0.09 Functional traits Mean 21.67 32.90 16.81 19.92 22.11 25.00 14.79 21.31 20.22 LDMC m a n u s c r i p t SD Mean SD 2.09 36.22 3.38 0.66 22.60 1.23 1.19 37.48 8.03 2.40 43.53 9.50 1.78 36.25 5.65 2.78 22.81 2.71 2.62 31.70 7.61 1.45 18.90 4.59 2.01 29.45 5.64 SLA
	A c c e p t e d

Table S2 .

 S2 Correlations between plant functional traits, between decomposers and between plant functional traits and decomposers (Leaf Dry Matter Content, LDMC; Specific Leaf Area, SLA). For each correlation, the given parameter is the Pearson's or Spearman's coefficient (depending on the normality of variable distribution) with its significance (*: P<0.05; ***: P<0.001).

							Plant functional traits			Decomposer community
						LDMC	SLA	Leaf C:N ratio Phenolics	Microbial C:N ratio	Collembola Acari Enchytraeidae Anecics
	Plant	functional	traits	SLA Leaf C:N ratio Phenolics	-0.13 -0.07 0.01	0.45* 0.20	0.79***
					Microbial C:N ratio	-0.34	0.26	0.26	0.18
	Decomposer	community	Collembola Acari Enchytraeidae Anecis	-0.22 -0.54* -0.13 0.01	-0.24 0.14 -0.16 -0.34	0.07 0.39 -0.03 -0.10	0.23 0.35 0.31 0.22	0.15 0.43* -0.09 -0.04	0.54* -0.09 0.30	0.07 0.17	0.25
					Epigeics	0.08	0.04	0.02	-0.01	-0.17	-0.23	-0.10	0.40	0.14
												34

ACKNOWLEDGEMENTS

We thank Aurélien Pierre for measuring the soil microbial C:N ratio, and we acknowledge Albin Fertil, Daniel Cylly, André Bastin, Valentin Blanchard, Romain Georges, Olivier Jambon, Jérémy Guy, Nathan Vannier and Stéphanie Llopis for help with sampling earthworms. We are very grateful to Mathilde Le Moing for earthworm identification. We thank Jean-Sébastien Pierre for helping with the SAS statistical analyses. The work benefited from the support of the LTER site "ZA Armorique".

Home-Field Advantage

direct effect of grassland age on litter decomposition in the model (i.e. a biological indirect effect of grassland age that was not mediated by any of the parameters that we measured). The relationship between soil pH and phenolics might be the least causal within the analysis.


Quang Huy Nguyen

Hai Nguyen

Hanh-Nguyen Vu

Son Chu-Ky
email: son.chuky@hust.edu.vn

Thu Trang Vu

Ha Hoang

Ngoc Tung Quach

Hoang Thi Lien Bui

Ha Chu

Thi Nhan Khieu

Hai Van Nguyen

Thi Hanh-Nguyen Vu

Hoang Ha Chu

Thi Nhan Khieu

Samira Sarter

Wen-Jun Li

Quyet-Tien Phi

Synergistic Interactions of its Extract and Essential Oil from Host Plant Litsea cubeba

Keywords: Antimicrobial activity, endophytic actinomycete, essential oil, medicinal plants, membrane permeability, Litsea cubeba, synergistic effect and Streptomyces griseorubens

published or not. The documents may come

Introduction

The misuse and overuse of antibiotics for human and animal health management has become a main factor contributing to the rapid emergence and dissemination of antibiotic-resistant bacteria, which poses a serious threat to global public health [START_REF] Ventola | The antibiotic resistance crisis: Part 1: causes and threats[END_REF]. Consequently, antibiotic resistance increases the cost of healthcare, the risk of treatment failure, and the fatality rate. Thus, the findings of novel antimicrobial agents and alternative therapeutic strategies are urgently needed [START_REF] Alekshun | Molecular mechanisms of antibacterial multidrug resistance[END_REF]. Medicinal plants are rich sources of essential oils (EOs) and secondary metabolite compounds that are often used as natural and safe medicines in the treatment of infectious diseases. Additionally, the combination of antimicrobial agents (EO and antibiotics) may lead to synergistic effects against pathogens that can reduce the side effects of antibiotics, enhance the bioavailability, lower the therapeutic dose, and minimize the antimicrobial resistance [START_REF] Yap | Membrane disruption and anti-quorum sensing effects of synergistic interaction between Lavandula angustifolia (lavender oil) in combination with antibiotic against plasmid-conferred multi-drug-resistant Escherichia coli[END_REF].

The Litsea genus is one of the most diverse genera of evergreen trees or shrubs in the Lauraceae family, comprising roughly 400 species over the world [START_REF] Wang | Chemical composition and antibacterial activity of essential oils from different parts of Litsea cubeba[END_REF]. The Litsea cubeba species is frequently found in southern China, Japan, Taiwan, and South-East Asia including Vietnam, and is widely and safely used in cosmetics, and traditional medicine for the treatment of headache, fatigue, muscle pain, depression, sores and furuncles [START_REF] Bhuinya | Litsea cubeba-Medicinal values-Brief summary[END_REF][START_REF] Chen | Biology and chemistry of Litsea cubeba, a promising industrial tree in China[END_REF][START_REF] Nguyen | Antibacterial activity of Litsea cubeba (Lauraceae, May Chang) and its effects on the biological response of common carp Cyprinus carpio challenged with Aeromonas hydrophila[END_REF]. Interestingly, L. cubeba EO exhibits considerable bioactivities such as antibacterial [START_REF] Nguyen | Antimicrobial activities and interaction effects of Vietnamese Litsea Cubeba (lour.) pers essential oils and its endophytic actinobacteria[END_REF], antifungal [START_REF] Saikia | Screening of fruit and leaf essential oils of Litsea cubeba Pers. from north-east India -chemical composition and antimicrobial activity[END_REF], antioxidant [START_REF] Wang | Antioxidant activity, free radical scavenging potential and chemical composition of Litsea cubeba essential oil[END_REF], and anticancer activities [START_REF] Ho | Compositions and in vitro anticancer activities of the leaf and fruit oils of Litsea cubeba from Taiwan[END_REF]. Furthermore, rich-EO-content medicinal plants like L. cubeba and Cinnamomum spp. could also be one of the prevalent sources for antibiotic-producing endophytic actinobacteria conveying novel features [START_REF] Golinska | Endophytic actinobacteria of medicinal plants: diversity and bioactivity[END_REF][START_REF] Vu | Endophytic actinomycetes associated with Cinnamomum cassia Presl in Hoa Binh province, Vietnam: Distribution, antimicrobial activity and, genetic features[END_REF]. In fact, it has been previously demonstrated that the ecology and evolution of endophytic microorganisms might be affected by the chemical compositions of host plant EO and the participation of endophytes in the biotransformation of EO [START_REF] Da Silva | Does the essential oil of Lippia sidoides Cham. (pepper-rosmarin) affect its endophytic microbial community?[END_REF].

Our previous study primarily explored the accelerated antibacterial effects of the cell-free supernatant of an isolated endophytic actinomycete and L. cubeba EO [START_REF] Nguyen | Antimicrobial activities and interaction effects of Vietnamese Litsea Cubeba (lour.) pers essential oils and its endophytic actinobacteria[END_REF]. Nevertheless, further study providing additional insights into the synergistic interactions between endophyte-derived secondary metabolites and EO from host medicinal plants should be addressed, which may generate a potential strategy to combat the emergence and spread of multidrug-resistant pathogens. Therefore, the present study aimed to characterize the broad-spectrum antibacterial producing endophyte Streptomyces griseorubens MPT42 associated with L. cubeba and evaluate the combinatory antimicrobial activities of the crude ethyl acetate extract (CEAE) of the culture broth of S. griseorubens MPT42 and fruit EO of the host plant against various pathogens including multidrug-resistant bacteria. In addition, the effects of the L. cubeba EO on the bacterial cell viability and morphology of a model Escherichia coli were also investigated to reveal the possible mode of action for the combination of CEAE and EO. Although vancomycin seems to have no effect on Gram-negative bacteria, a recent study showed that the combination of vancomycin with other antibiotics displayed a strong synergic effect against Gram-negative bacteria [START_REF] Zhou | Synergistic interactions of vancomycin with different antibiotics against Escherichia coli: trimethoprim and nitrofurantoin display strong synergies with vancomycin against wild-type E. coli[END_REF]. Therefore, the present study examined the potential synergistic interactions of EO with erythromycin (a broad-spectrum antibacterial substance) or vancomycin (a Gram-positive antibacterial substance) against various Gram-positive and negative pathogens.

Results

Identification and Characterization of Antibiotic-Producing Endophytic Actinomycete MPT42

In order to screen the antibacterial activity of isolates, 35 out of 143 endophytic actinomycetes potentially producing antibiotics were isolated from different organs of L. cubeba (Lour.) Pers plants [START_REF] Lam | Distribution and antimicrobial activity of endophytic actinomycetes isolated from Litsea cubeba (Lour.) Pers. in Northern provinces of Vietnam[END_REF]. Among them, the strain MPT42 isolated from the stem of the host plant inhibited the growth of the majority of tested bacteria with inhibitory zones ranging from 11.4 to 44.0 mm (Table 1). Table 1. Antibacterial activity of the cell-free supernatant of endophytic actinomycete MPT42 against selected bacteria including pathogens.

Bacterial Species

Zone of Inhibition (D ± SD, mm) # Gram-positive bacteria Listeria innocua ATCC 33090 27.5 d ± 0. On ISP2 agar medium, aerial mycelium color of the MPT42 colonies was changed from white to gray after five days of cultivation (Figure 1A) and none of the pigments was observed until 30 days. The spore chain consisted of oval-shaped spores with spiral and spiny surfaces (Figure 1B). The physiological characteristics of strain MPT42 are described in the Table 2, which exhibited well-aerobic growth on an ISP2 medium at pH 8.0, temperature of 35 C, and NaCl concentration of 1%. The strain MPT42 can utilize different 11 carbon and 10 nitrogen sources (Table 2). In addition, the PCR results revealed that endophytic actinomycete MPT42 possessed three secondary metabolite biosynthetic genes: pks-I, pks-II, and nrps (Table 2). Analysis of the 16S rRNA gene sequence identified the strain MPT42 as a member of the Streptomyces genus (homology of 99.5-100%). The neighbor-joining phylogenetic tree indicated the close relationship between strain MPT42 and related Streptomyces species and showed the highest homology to Streptomyces griseorubens type strain NBRC 12780 (bootstrap value of 100%, Figure 2), therefore, it was assigned as Streptomyces griseorubens MPT42.

Antimicrobial Activity of the crude ethyl acetate extract (CEAE) and essential oil (EO)

The CEAE of S. griseorubens MPT42 inhibited the growth of 13 tested microbes at different levels (Table 3). Briefly, it was highly active (MICs ≤ 100 μg/mL) against S. aureus, E. coli, and S. Typhimurium, while the MICs were moderate (between 200 and 600 μg/mL) toward B. subtilis, A. hydrophila, V. parahaemolyticus, B. cereus, P. aeruginosa, L. innocua, and P. vulgaris [START_REF] Kuete | Potential of Cameroonian plants and derived products against microbial infections: a review[END_REF]. For methicillin-resistant Staphylococcus epidermidis ATCC 35984 (MRSE) and methicillin-resistant Staphylococcus aureus ATCC 33591 (MRSA), the MICs were 0.4 and 1.5 mg/mL, respectively.

Analysis of the chemical constituents by gas chromatography-mass spectrometry showed that the main compositions of 98%-purity EO were citral (36.0%), carveol (29.6%), and limonene (20.5%). Interestingly, the antimicrobial activity of the individual substances has been reported previously [START_REF] Saikia | Screening of fruit and leaf essential oils of Litsea cubeba Pers. from north-east India -chemical composition and antimicrobial activity[END_REF][START_REF] Liu | Antimicrobial impact of the components of essential oil of Litsea cubeba from Taiwan and antimicrobial activity of the oil in food systems[END_REF]. Here, we examined the antimicrobial activity of the total EO constituents to explore potential synergistic effects. Nevertheless, in concordance with a previous study [START_REF] Saikia | Screening of fruit and leaf essential oils of Litsea cubeba Pers. from north-east India -chemical composition and antimicrobial activity[END_REF][START_REF] Nguyen | Litsea cubeba leaf essential oil from Vietnam: chemical diversity and its impacts on antibacterial activity[END_REF], the L. cubeba EO showed low MICs (≥ 700 μg/mL) against the tested microbes (Table 3) [START_REF] Kuete | Potential of Cameroonian plants and derived products against microbial infections: a review[END_REF].

Effect of the L. cubeba EO on Viability and Cell Morphology of E. coli

Time-killing assays showed that the viability of E. coli ATCC 25922 cells was gradually reduced according to the increase in EO concentration (Figure 3), where the living cells were not detected after 5-7 h of incubation at EO concentrations of 2 and 1 MIC, respectively (MIC = 5.5 mg/mL). In the EO-free wells, the concentration of living cells was dramatically increased (reaching 10 log10 CFU/mL after 7 h). In contrast, over 4.2 log10 CFU/mL (50% reduction) of the cell viability compared with the initial cell concentration was obtained at 2 MIC (3.74 log10 CFU/mL) and 1 MIC (3.77 log10 CFU/mL) of the EO concentrations after 1 and 5 h, respectively. Cell viability evaluated by using the LIVE/DEAD BacLight viability kit revealed that the number of intact cells decreased significantly in the culture treated with the EO (0.5 and 1 MIC) compared to the control (Figures 4A-C). Moreover, proportions of the membrane-damaged cells were over 62% in the treated samples, whereas this proportion was only 2.5% in the control (Figure 4D). The morphology and size changes of the E. coli cells were examined on images of EO-treated and EO-untreated cells (Figure 4E). The length of the bacterial cells exposed to EO dramatically increased 1.3-1.4 fold (corresponding to 2.41 and 2.54 µm, respectively) when compared with the untreated cells (1.86 μm). Notably, the proportion of filament cells (length ≥ 6 μm) was 3% in the 1 MIC EO-treated E. coli populations. None of the elongated or filament cells was observed in the EO-untreated bacterial populations.

Combined Antimicrobial Effects Against Microbial Strains

To enhance antimicrobial potencies, the combinations of CEAE with EO were tested against the 12 microbial species including several pathogens (Table 4). The vast majority of the combinations significantly decreased MIC from 4 to 16 folds when compared with the use of CEAE and EO separately. Specifically, the combinations of CEAE-EO led to synergistic antimicrobial interactions (n = 8) against B. cereus, B. subtilis, S. aureus, MRSE, L. innocua, A. hydrophila, E. coli, and P. aeruginosa. An additive effect (n = 3) was observed toward MRSA, Proteus vulgaris, and Vibrio parahaemolyticus (Table 4). The highest antimicrobial synergy was found for the combination of CEAE-EO against L. innocua and MRSE (FIC = 0.19) (Table 4). In the present study, the combinations of EO with either antibiotics E or VA also increased the antimicrobial effects toward the microbial species tested. Almost all of these antibiotic-EO combinations led to a decrease of 2-16 MIC folds when compared with the single use of E or VA. Specifically, the combinations of E with EO also led to seven synergistic and four additive effects, while the VA-EO combinations resulted to 10 synergistic-additive effects (Table 4). It is worth noting that the VA-EO combinations exhibited three synergistic antimicrobial effects on Gram-negative bacteria A.s hydrophila and P. aeruginosa (FICIs of 0.31 and 0.33, respectively), and additive effects toward E. coli, P. vulgaris and V. parahaemolyticus (FICIs between 0.54 and 0.56).

Discussion

So far, S. griseorubens species has mainly been found in soils [START_REF] Golinska | Endophytic actinobacteria of medicinal plants: diversity and bioactivity[END_REF][START_REF] Christina | Endophytic bacteria as a source of novel antibiotics: An overview[END_REF][START_REF] Qin | Isolation, diversity, and antimicrobial activity of rare actinobacteria from medicinal plants of tropical rain forests in Xishuangbanna, China[END_REF] and was previously demonstrated as a strong antagonistic species conveying broad antifungal activities [START_REF] Al-Askar | Characterization of alkaline protease produced by Streptomyces griseorubens E44G and its possibility for controlling Rhizoctonia root rot disease of corn[END_REF]. The present research revealed for the first time the activity of the endophytic actinomycete S. griseorubens MPT42 isolated from the stem of L. cubeba. Its CEAE extract exhibited a broad-spectrum antimicrobial activity toward various microbial types including drug-sensitive and multidrug-resistant pathogens. It showed moderate-strong inhibitory effects with MICs values between 80-600 µg/mL on 10 different microbes. Since many novel antibiotics exhibiting strong effects against multiple-drug resistant bacteria have been isolated from medicinal plant-associated endophytic Streptomyces spp. in the last decades [START_REF] Christina | Endophytic bacteria as a source of novel antibiotics: An overview[END_REF][START_REF] Takahashi | Actinomycetes, an inexhaustible source of naturally occurring antibiotics[END_REF][START_REF] Bieber | Alnumycin a new naphthoquinone antibiotic produced by an endophytic Streptomyces sp[END_REF][START_REF] Castillo | Munumbicins, wide-spectrum antibiotics produced by Streptomyces NRRL 30562, endophytic on Kennedia nigriscansa[END_REF][START_REF] Inahashi | Spoxazomicins A-C, novel antitrypanosomal alkaloids produced by an endophytic actinomycete, Streptosporangium oxazolinicum K07-0460(T)[END_REF], endophytic S. griseorubens MP42 could be a potential candidate for the production of valuable bioactive substances.

In agreement with previous reports [START_REF] Saikia | Screening of fruit and leaf essential oils of Litsea cubeba Pers. from north-east India -chemical composition and antimicrobial activity[END_REF][START_REF] Liu | Antimicrobial impact of the components of essential oil of Litsea cubeba from Taiwan and antimicrobial activity of the oil in food systems[END_REF][START_REF] Li | Antibacterial activity and kinetics of Litsea cubeba oil on Escherichia coli[END_REF], the present study demonstrated the potential growth inhibiting effect of L. cubeba EO (MICs of 0.70-5.5 mg/mL) toward a broad array of microbial species, compared with EOs derived from other medicinal plants. For instance, Ocimum gratissimum EO inhibited both Gram-positive bacteria (S. aureus and Bacillus spp.) at the concentration of 93.7-150 mg/mL and Gram-negative bacteria (E. coli, P. aeruginosa, S. Typhimurium, Klebsiella pneumoniae, Proteus mirabilis) at the concentration of 107-750 mg/mL [START_REF] Benitez | Eugenol and methyl eugenol chemotypes of essential oil of species Ocimum gratissimum L. and Ocimum campechianum Mill. from Colombia[END_REF]. Another study showed that Cinnamomum EO (Lauraceae family) possessed low MIC values ranging from 1.2 to 12.5 mg/mL against E. coli, C. jejuni, S. aureus, S. enteritidis, S. Typhimurium, L. innocua, and L. monocytogenes [START_REF] Ooi | Antimicrobial activities of cinnamon oil and cinnamaldehyde from the Chinese medicinal herb Cinnamomum cassia Blume[END_REF][START_REF] Trinh | Effect of a Vietnamese Cinnamomum cassia essential oil and its major component trans-cinnamaldehyde on the cell viability, membrane integrity, membrane fluidity, and proton motive force of Listeria innocua[END_REF]. Thus, different chemical constituents in the EO of medicinal plant species affect their potential antimicrobial activity. Using E. coli as a model for exploring the mechanism of action of L. cubeba fruit EO, we demonstrated that EO-treated cells were rapidly killed after 2 h of treatment at the EO concentrations ≥ 2 MIC. It is worth noting that the cells showed extraordinary deformation and elongation. Our study found citral, carveol, and limonene as the main constituents, accounting together for 86.1% of total L. cubeba fruit EO, concordant with previous studies [START_REF] Saikia | Screening of fruit and leaf essential oils of Litsea cubeba Pers. from north-east India -chemical composition and antimicrobial activity[END_REF][START_REF] Liu | Antimicrobial impact of the components of essential oil of Litsea cubeba from Taiwan and antimicrobial activity of the oil in food systems[END_REF]. Since the effect of each substance on microbes was examined separately in these studies, therefore, the present study aimed at investigating the antimicrobial inhibitory effects possessed by the total EO. This combination showed broad-spectrum inhibitory effects against various microbial species tested. In fact, citral, carveol ,and limonene are phenolic substances with polarity characteristics that might act on the bacterial cell membrane, inactivate cell adhesions, and/or interact with the outer and inner membrane proteins [START_REF] Nguyen | Litsea cubeba leaf essential oil from Vietnam: chemical diversity and its impacts on antibacterial activity[END_REF][START_REF] Li | Antibacterial activity and kinetics of Litsea cubeba oil on Escherichia coli[END_REF][START_REF] Bouhdid | Functional and ultrastructural changes in Pseudomonas aeruginosa and Staphylococcus aureus cells induced by Cinnamomum verum essential oil[END_REF][START_REF] De Sousa | Synergies of carvacrol and 1,8-cineole to inhibit bacteria associated with minimally processed vegetables[END_REF]. Consequently, the EO treatment might result in the weakening of cell structure and inhibition of cell growth by creating holes on the cell wall and increasing the cell permeability [START_REF] Li | Antibacterial activity and kinetics of Litsea cubeba oil on Escherichia coli[END_REF].

Since the modes of action of EO and antibiotics on microorganisms are different, the combination of both agents could enhance antimicrobial effects. The present study also found that combinations of EO with the CEAE of S. griseorubens MP42 or with either E or VA significantly reduced the MIC values on almost all microbes tested in comparison with the individual effects (MIC reductions of 4-16 folds and 2-16 folds for CEAE and antibiotics, respectively), therefore showing significant synergistic antimicrobial effects. Thus, the synergistic effects of S. griseorubens MPT42-extract and L. cubeba fruit EO might be due to the fact that EO disintegrates the bacterial cell membrane, influences the cell wall structure, forming holes and gaps in the cell wall [START_REF] Yap | Membrane disruption and anti-quorum sensing effects of synergistic interaction between Lavandula angustifolia (lavender oil) in combination with antibiotic against plasmid-conferred multi-drug-resistant Escherichia coli[END_REF][START_REF] Li | Antibacterial activity and kinetics of Litsea cubeba oil on Escherichia coli[END_REF][START_REF] Mahboubi | Antistaphylococcal activity of Zataria multiflora essential oil and its synergy with vancomycin[END_REF][START_REF] Van Vuuren | Plant-based antimicrobial studies-methods and approaches to study the interaction between natural products[END_REF]. These processes make antibiotic-like substances diffusive inside bacterial cells, where they might then inhibit metabolic processes, protein and/or DNA synthesis, etc., leading to cell death [START_REF] Yap | Membrane disruption and anti-quorum sensing effects of synergistic interaction between Lavandula angustifolia (lavender oil) in combination with antibiotic against plasmid-conferred multi-drug-resistant Escherichia coli[END_REF][START_REF] Li | Antibacterial activity and kinetics of Litsea cubeba oil on Escherichia coli[END_REF][START_REF] Langeveld | Synergy between essential oil components and antibiotics: a review[END_REF]. Although combinations of different types or generations of antibiotics have shown synergistic effects [START_REF] Ozbek-Celik | Comparative In Vitro Activities of First and Second-Generation Ceragenins Alone and in Combination with Antibiotics Against Multidrug-Resistant Klebsiella pneumoniae Strains[END_REF][START_REF] Mgbeahuruike | Antimicrobial and Synergistic Effects of Commercial Piperine and Piperlongumine in Combination with Conventional Antimicrobials[END_REF], the use of an appropriate EO in combination with antibiotics against bacterial pathogens could bring advantageous alternatives to reducing the use of conventional antibiotics as well as the emergence of multidrug-resistant bacteria [START_REF] Adwan | Synergistic effects of plant extracts and antibiotics on Staphylococcus aureus strains isolated from clinical specimens[END_REF]. For instance, streptomycin and kanamycin combined with lemongrass (Cymbopogon citratus) EO exhibited synergistic or additive effect against S. Typhimurium [START_REF] Langeveld | Synergy between essential oil components and antibiotics: a review[END_REF]. The combination of gentamicin or tobramycin with tea tree (Melaleuca alternifolia) EO had a synergistic effect against both E. coli and S. aureus [START_REF] Langeveld | Synergy between essential oil components and antibiotics: a review[END_REF]. Notably, VA combined with Shiraz oregano (Zataria multiflora) EO showed synergistic activity against MRSA [START_REF] Mahboubi | Antistaphylococcal activity of Zataria multiflora essential oil and its synergy with vancomycin[END_REF]. It is well known that Gram-negative bacteria are normally resistant to VA as this antibiotic cannot significantly penetrate the outer membrane of cells. In the present study, the use of VA was not as the control for the inhibitory growth of Gram-negative bacteria, but aimed to discover the potential synergistic antimicrobial effects of VA-EO against Gram-negative bacteria. We found that in the presence of EO, the MICs of VA toward Gram-negative bacteria were significantly reduced and had strong synergistic inhibitory effects for A. hydrophila and P. aeruginosa, and additive effects on E. coli, P. vulgaris, and V. parahaemolyticus. This finding could be explained though two mechanisms: first, the EO could increase the permeability of the cell wall that allows the VA diffusion inside cells; second, interactions between VA and other antimicrobial substances of EO lead to synergistic antimicrobial effects. In fact, the drug interaction network possesses a special property, and in many cases, the antibiotics interact purely synergistically, leading to increased antibacterial activity [START_REF] Yeh | Functional classification of drugs by properties of their pairwise interactions[END_REF]. A study showed that the combination of VA with either nitrofurantoin or trimethoprim had a strong synergic effect against E. coli at concentrations of VA as low as 6.25 μg/mL [START_REF] Zhou | Synergistic interactions of vancomycin with different antibiotics against Escherichia coli: trimethoprim and nitrofurantoin display strong synergies with vancomycin against wild-type E. coli[END_REF]. The result of our study demonstrated that the EO-VA combination increased antimicrobial inhibitory activities against Gram-negative bacteria when compared with the individual effect of VA. Altogether, our study underlines that the combination of antimicrobial substances with the EO of medicinal plants brings greater and broader effects toward various pathogens and that the synergy can lead to multi-targeted effects on the inhibition of microbial growth and viability.

Materials and Methods

Screening for Antibacterial Activity

The antibacterial activity of endophytic actinomycete MPT42 was examined by using the agar diffusion method [START_REF] Holder | Agar well diffusion assay testing of bacterial susceptibility to various antimicrobials in concentrations non-toxic for human cells in culture[END_REF]

Characteristics of the Endophytic Actinomycete MPT42

The strain MPT42 was cultured at 28 °C on International Streptomyces Project 2 (ISP2) agar medium for 14 days to characterize the biophysical-biochemical properties [START_REF] Shirling | Methods for characterization of Streptomyces species 1[END_REF][START_REF] Goodfellow | Reclassification of Streptomyces hygroscopicus strains as Streptomyces aldersoniae sp. nov., Streptomyces angustmyceticus sp. nov., comb. nov., Streptomyces ascomycinicus sp. nov., Streptomyces decoyicus sp. nov., comb. nov., Streptomyces milbemycinicus sp. nov. and Streptomyces wellingtoniae sp[END_REF]. The spore chain morphology and spore ornamentation were analyzed by scanning electron microscope (SEM) JSM-5410 (JEOL, Tokyo, Japan) at a magnification of 7500x. The effects of different conditions (ranges of pH, NaCl concentration and temperature, carbon and nitrogen sources) to the growth were investigated as previously described [START_REF] Vu | Antimicrobial and Cytotoxic Properties of Bioactive Metabolites Produced by Streptomyces cavourensis YBQ59 Isolated from Cinnamomum cassia Prels in Yen Bai Province of Vietnam[END_REF].

Amplification of Secondary Metabolite Biosynthetic Genes and 16S rRNA Encoding Gene

The amplification of the 16S rRNA gene sequence of actinomycete MPT42 was performed by using the universal primer pair 27F (5′-TAACACATGCAAGTCGAACG-3′) and 1429R (5′-GGTGTGACGGGCGGTGTGTA-3′) [START_REF] Phi | Assessment of root-associated paenibacillus polymyxa groups on growth promotion and induced systemic resistance in pepper[END_REF]. The multiple sequence alignment was employed for the MPT42 and type strains (sequences retrieved from GenBank, National Center for Biotechnology Information (NCBI)) by using MEGA6 [START_REF] Tamura | MEGA6: Molecular evolutionary genetics analysis version 6.0[END_REF]. The phylogenetic tree was computed by using the neighbor-joining method with 1000 bootstrap in MEGA6 [START_REF] Tamura | MEGA6: Molecular evolutionary genetics analysis version 6.0[END_REF] and Bacillus thuringiensis strain ATCC 10792 (NR_114581) was used as the outgroup branch. The 16S rRNA gene sequence of strain MPT42 was deposited at GenBank (NCBI) under the accession number MG021134.

The presence of genes non-ribosomal polyketide synthases (nrps), polyketide synthase type I (pks-I), and type II (pks-II) encoding for secondary metabolite biosynthesis in actinomycetes was explored by using three sets of degenerated primer pairs A3F (5'-GCS TAC SYS ATS TAC ACS TCS GG-3') and A7R (5'-SAS GTC VCC SGT SCG GTA S-3'), K1F (5'-TSA AGT CSA ACA TCG GBC A-3') and M6R (5'-CGC AGG TTS CSG TAC CAG TA-3'), KSaF (5'-TSG CST GCT TGG AYG CSA TC-3'), and KSaR (5'-TGG AAN CCG CCG AAB CCG CT-3'), respectively [START_REF] Metsa-Ketela | An efficient approach for screening minimal PKS genes from Streptomyces[END_REF][START_REF] Ayuso-Sacido | New PCR primers for the screening of NRPS and PKS-I systems in actinomycetes: detection and distribution of these biosynthetic gene sequences in major taxonomic groups[END_REF]. PCR components and conditions were performed as previously described [START_REF] Salam | Endophytic Actinobacteria Associated with Dracaena cochinchinensis Lour.: Isolation, Diversity, and Their Cytotoxic Activities[END_REF].

Preparation of L. cubeba Fruit EO and CEAE From MPT42 Culture

The L. cubeba fruit EO were extracted by hydro-distillation using a Clevenger-type apparatus for 4 h, and then dried over anhydrous sodium sulfate. Finally, the EO density obtained was 0.88 g/mL (98% purity) and it was stored at 4 °C in a dark bottle until use. The L. cubeba fruit EO was then completely dissolved in distilled water supplemented with 0.5% Tween 80 (an emulsifier usually used to dissolve EOs in water) to achieve final concentrations ranging from 0.25 mg/mL to 64 mg/mL.

The strain MPT42 was cultivated in YIM38 antibiotic-producing medium at 28 °C with 200 rpm shaking. After five days, the culture broth was centrifuged and the cell-free supernatant (CFS) was extracted with ethyl acetate (1:1, v/v) and evaporated to determine the weight of CEAE. After that, the CEAE solution was prepared in ethanol with the concentration ranging from 0.0625 to 16 mg/mL.

Determination of Minimum Inhibitory Concentration

The minimum inhibitory concentrations (MICs) of the CEAE and EO were determined separately by using serial microdilution assays [START_REF] Nguyen | Antibacterial activity of Litsea cubeba (Lauraceae, May Chang) and its effects on the biological response of common carp Cyprinus carpio challenged with Aeromonas hydrophila[END_REF] For each microorganism, suspensions were prepared in Mueller Hilton broth (MHB, Merck) to obtain the final concentration of 10 7 CFU/mL. Each well contained 20 μL of the CEAE or EO and 180 L of each bacterial suspension. After incubation 24 h at 30 °C for A. hydrophila and V. parahaemolyticus and at 37 °C for the other strains, the optical density (OD) was measured at 600 nm using a microplate reader (Bio-Rad Model 680, Japan). All experiments were performed in triplicate. The MIC value was determined as the lowest concentration showing the inhibition of bacterial growth. The standard antibiotics erythromycin (E) and vancomycin (VA) were used to evaluate the synergetic antimicrobial effects of the EO-antibiotic combinations by a microdilution checkerboard assay (described below). The antibiotic concentrations were prepared in a range from 0.25 to 512 μg/mL, and the test was performed under the same conditions.

Time-killing Assay

In order to investigate the activity of the L. cubeba EO against pathogenic bacteria, the time-kill curve assay was performed using E. coli ATCC 25922 as a model [START_REF] Nguyen | Litsea cubeba leaf essential oil from Vietnam: chemical diversity and its impacts on antibacterial activity[END_REF]. The experiments were designed as follows: tubes including bacterial suspension (10 8 CFU/mL) were exposed to four different EO concentrations (1, 2, 5, and 10 MIC) and control tube (bacterial culture without EO). The tubes were incubated at 37 °C for 24 h with stirring at 120 rpm/min. Total viable bacteria were enumerated by spreading 100 µL of culture on Muller Hinton agar plates and after 0, 1, 3, 5, 7, and 24 h of incubation at 37 °C, bacterial colonies were counted and the bacterial concentration was expressed as log10 CFU/mL. Assays were carried out in triplicate.

Effects of L. cubeba EO on Bacterial Cell Viability and Morphology

The effect of the L. cubeba fruit EO on cell viability was evaluated using the LIVE/DEAD BacLight viability kit (Invitrogen TM , Molecular Probes Inc., OR, USA) [START_REF] Nguyen | Litsea cubeba leaf essential oil from Vietnam: chemical diversity and its impacts on antibacterial activity[END_REF]. Briefly, the E. coli cultures were prepared in MHB as described above, then each tube containing 5 mL of cell suspension was incubated with 1 MIC and 0.5 MIC of the EO at 37 °C. After 2 h of exposure, the cells were harvested by centrifugation at 10,000x g for 10 min, then the supernatant was removed and pellets were re-suspended in NaCl 0.85%. The mixture of SYTO 9 and propidium iodide (PI) was added into the suspension with the ratio of 1:1. The cell suspension with 0.5% Tween 80 (v/v) and without EO was used as the control. After 15 min of staining, cells were examined under the microscope on 1% agarose (in water)-covered slides. Using a LEICA DM6000 photomicroscope equipped with an ORCA-ER C4742-80 camera (Hamamatsu, Japan), at least 20 photomicrographs were taken of different fields of view as previously described [START_REF] Visvalingam | Temperature-dependent effect of sublethal levels of cinnamaldehyde on viability and morphology of Escherichia coli[END_REF]. The percentages of cells stained with PI (permeabilized cells) and SYTO 9 (viable cells) were determined.

The effect of EO on the size of bacterial cells was evaluated by analyzing fluorescence microscopy images with ImageJ [START_REF] Nguyen | Litsea cubeba leaf essential oil from Vietnam: chemical diversity and its impacts on antibacterial activity[END_REF]. E. coli cell length was measured and recorded with and without the EO treatment. Cell length was measured as the distance along the two axes of the cell. The dividing cells that were not separated yet were counted and measured as one single cell. Cells were counted as two separated cells only when the constriction was completed. At least 1000 cell sizes were measured for each experiment. Filament cells were considered when the cell length was ≥6 μm.

Microdilution Checkerboard Assays

The checkerboard method was performed using 96-well microtiter plates to obtain the fractional inhibitory concentration (FIC) index for determining the interaction between the CEAE and EO; and those between EO and antibiotics (E and VA) [START_REF] Gutierrez | Antimicrobial activity of plant essential oils using food model media: efficacy, synergistic potential and interactions with food components[END_REF]. Serial two-fold dilutions were prepared to examine the combinatory antimicrobial activity of the CEAE and EO (25 pair combined concentrations from 0.0625 MIC to 1 MIC). For each well, 20 μL CEAE and 20 L EO were inoculated with 160 μL of each bacterial suspension (10 7 CFU/mL). The plates were then incubated 24 h at 30 °C for A. hydrophila and V. parahaemolyticus and at 37 °C for the other strains. The FIC index was calculated as: ΣFIC = FICCEAE + FICEO, where FICCEAE = MICCEAE combination/MICCEAE alone and FICEO = MICEO combination/MICEO alone. The interaction results were interpreted as synergy (ΣFIC < 0.5), addition (0.5 ≤ ΣFIC ≤ 1), indifference (1 < ΣFIC ≤ 4), or antagonism (ΣFIC > 4) [START_REF] Gutierrez | Antimicrobial activity of plant essential oils using food model media: efficacy, synergistic potential and interactions with food components[END_REF]. All experiments were performed in triplicate. Antibiotics E and VA were also included in the assay with the ratio of concentrations ranging from 0.0625 to 1 μg/mL.

Statistical Analysis

The data were expressed as mean ± standard deviation (SD) of three replicates. The MIC and FIC values of the CEAE and EO were analyzed by one-way analysis of variance (ANOVA), followed by a Fisher's least significant difference (LSD) at the threshold of P < 0.05.

Conclusions

This study underlines that the endophytic actinomycete S. griseorubens MPT42 associated with medicinal plant L. cubeba can be a potential producer of broad-spectrum antimicrobial substances. The cytotoxic effect of L. cubeba EO was explored to understand its mode of action on E. coli cells. Moreover, the combination of S. griseorubens MPT42-extract and EO from the host plant revealed remarkable synergistic antimicrobial effects toward various microbes, and this synergy was equivalent to those between EO and antibiotics (E and VA). In the presence of EO, the MIC of VA was decreased on all Gram-negative bacteria tested and exhibited five additive-synergistic effects. These results underline that the combined antibiotic-EO and CEAE-EO would be an effective strategic choice to reduce the use of antibiotics and fight the emergence and spread of multidrug-resistant microbes. Further studies aim to isolate S. griseorubens MPT42-derived antimicrobial substances for determining their mode of action and molecular targets.

4 #:

 4 Mean of diameter of inhibition zone (D) ± standard deviation (SD) of three replicates. Mean values with different letters (a-h) are significantly different according to the Fisher LCD test (P < 0.05).

Figure 1 .

 1 Figure 1. Colony morphology (A) and scanning electron microscope showing the spore-chain morphology and spore-surface ornamentation of the endophytic actinomycete strain MPT42 grown on ISP2 agar medium for 14 days at 28 C at a magnification of 7500× (B).

Figure 2 .

 2 Figure 2. Neighbor-joining phylogenetic tree based on 16S rRNA gene sequences of Streptomyces griseorubens MPT42 and type strains retrieved from GenBank (accession numbers are shown in parentheses). Bacillus thuringinensis strain ATCC 10792 was used as the outgroup. Only bootstrap values >50% are shown.

Figure 3 .

 3 Figure 3. Time-kill kinetics of the L. cubeba fruit EO on the viability of E. coli ATCC 25922 (n = 3 ± SD). The bacterial cells were treated with different concentrations of EO (1, 2, 5, and 10 MIC) for 24 h. The control was EO-untreated cells.

Figure 4 .

 4 Figure 4. Effects of L. cubeba fruit EO on the viability of E. coli ATCC 25922. Fluorescence microscopic images with the LIVE/DEAD Baclight kit of E. coli cells after 2 h of exposure to the EO. Control: EO-untreated cells (A); EO-treated cells at 0.5 MIC (B) and at 1 MIC (C), and proportion of permeabilized cells (D). Viable cells are indicated by green fluorescence, whereas cells with damaged membranes are indicated by red fluorescence. The white arrows indicate elongated cells (E) and the filament cells were >6 m in length. Scale bar represents 2 μm.

Table 2 .

 2 Physiological and biochemical characteristics of the actinomycete MPT42.

		(A)		(B)
	Characteristic	Result	Characteristic Result
	Morphological Characteristics	Fructose	+
	Aerial mycelium	Grey and white	Rhamnose	+
	Substrate mycelium	Faint-brown	Saccharose	-

Table 3 .

 3 Antimicrobial activity of S. griseorubens MPT42-CEAE and L. cubeba fruit EO against microbial strains including pathogens.

	Bacteria	EO	MIC of antimicrobial agents (mean ± SD, µg/mL) CEAE Erythromycin Vancomycin
	Gram-positive bacteria
	Bacillus cereus ATCC 13061	2800 ± 0		300 ± 0	6.7 ± 2.3	1.0 ± 0
	Bacillus subtilis ATCC 11778	2800 ± 0		200 ± 0	10.7 ± 4.6	2.0 ± 0
	Staphylococcus aureus ATCC 25923	2800 ± 0		80 ± 0	0.7 ± 0.3	1.0 ± 0
	MRSA	3700 ± 1.6		1500 ± 0	4.0 ± 0	2.0 ± 0
	MRSE	3700 ± 1.6		400 ± 0.1	8.0 ± 0	3.3 ± 1.2
	Listeria innocua ATCC 33090	1200 ± 0.4	600 ± 0	16.0 ± 0	16.0 ± 0.6
	Gram-negative bacteria
	Aeromonas hydrophila ATCC 35654	2800 ± 0		600 ± 0	128.0 ± 0	128.0 ± 0
	Escherichia coli ATCC 25922	5500 ± 0		100 ± 0	32.0 ± 0	64.0 ± 0
	Proteus vulgaris ATCC 49132	700 ± 0		600 ± 0	16.0 ± 0	64.0 ± 0
	Pseudomonas aeruginosa ATCC 9027	2300 ± 0.8	400 ± 0.1	256.0 ± 0	32.0 ± 0
	Salmonella Typhimurium ATCC 14028	5500 ± 0		100 ± 0	64.0 ± 0	64.0 ± 0
	Vibrio parahaemolyticus ATCC 17802	5500 ± 0		200 ± 0	42.7 ± 18.5	128.0 ± 0

Table 4 .

 4 Interaction between L. cubeba fruit EO and CEAE, antibiotics expressed as the fractional inhibitory concentration (FIC) index.

			Combined EO and Antimicrobial Agents
	Bacterial Species	MIC (μg/mL)	CEAE FIC Index* (mean ± SD)	MIC (μg/mL)	E FIC Index * (mean ± SD)	MIC (µg/mL)	VA FIC Index* (mean ± SD)
	Gram-positive Bacteria
	B. cereus ATCC 13061	20	0.31 ± 0.0 (S)	0.56	0.25 ± 0.06 (S)	1.0	1.1 ± 0.04 (I)
	B. subtilis ATCC 11778	50	0.31 ± 0.0 (S)	0.89	0.21 ± 0.04 (S)	0.21	0.35 ± 0.04 (S)
	S. aureus ATCC 25923	10	0.27 ± 0.04 (S)	0.04	0.19 ± 0.00 (S)	0.08	0.21 ± 0.04 (S)
	MRSA ATCC 33591	380	0.58 ± 0.14 (A)	0.67	0.23 ± 0.07 (S)	0.05	0.50 ± 0.0 (A)
	MRSE ATCC 35984	50	0.19 ± 0.0 (S)	2.67	0.42 ± 0.13 (S)	1.65	0.58 ± 0.04 (A)
	L. innocua ATCC 35984	40	0.19 ± 0.0 (S)	2.0	0.63 ± 0.11 (A)	3.33	0.38 ± 0.0 (S)
	Gram-negative Bacteria
	A. hydrophila ATCC 35654	60	0.35 ± 0.04 (S)	42.7	0.54 ± 0.07 (A)	8.0	0.31 ± 0.0 (S)
	E. coli ATCC 25922	20	0.27 ± 0.04 (S)	4.0	0.23 ± 0.04 (S)	16.0	0.56 ± 0.0 (A)
	P. vulgaris ATCC 49132	60	0.60 ± 0.04 (A)	1.33	0.33 ± 0.04 (S)	26.7	0.54 ± 0.04 (A)
	P. aeruginosa ATCC 9027	100	0.31 ± 0.0 (S)	21.3	1.08 ± 0.04 (I)	2.67	0.33 ± 0.04 (S)
	S. Typhimurium ATCC 14028	6	1.06 ± 0.0 (I)	32.0	0.56 ± 0.0 (A)	53.3	1.83 ± 0.29 (I)
	V. parahaemolyticus ATCC 17802 100	0.56 ± 0.0 (A)	14.2	0.58 ± 0.14 (A)	26.7	0.54 ± 0.07 (A)
	CEAE: crude ethyl acetate extract; E: Erythromycin; VA: Vancomycin. * Synergism (S): ΣFIC < 0.5;
	Additive (A): 0.5 ≤ ΣFIC ≤ 1; Indifferent (I): 1 < ΣFIC ≤ 4 [20,21]. NA: Not applicable

  against Gram-negative bacteria (Escherichia coli ATCC 25922, Proteus vulgaris ATCC 49132, Pseudomonas aeruginosa ATCC 9027, Salmonella Typhimurium ATCC 14028, and Enterobacter aerogenes ATCC 13048) and Gram-positive bacteria (Listeria innocua ATCC 33090, Bacillus cereus ATCC 13061, Bacillus subtilis ATCC 11778, Staphylococcus aureus ATCC 25923, and Methicillin-Resistant Staphylococcus epidermidis ATCC 35984 (MRSE).

  against 12 bacterial species: E. coli, P. vulgaris, P. aeruginosa, S. Typhimurium, L. innocua, B. cereus, B. subtilis, S. aureus, MRSE, Aeromonas hydrophila ATCC 35654, Vibrio parahaemolyticus ATCC 17802, and methicillin-resistant Staphylococcus aureus (MRSA) ATCC 33591.

Acknowledgments:

The authors are grateful to the Institute of Biotechnology (IBT, VAST); University of Science and Technology of Hanoi (USTH, VAST); Hanoi University of Science and Technology (HUST); and CIRAD for supporting this work.

Funding: This research is funded by Graduate University of Science and Technology (GUST), Vietnam Academy of Science and Technology (VAST), grant number GUST.STS.ĐT2017-SH03.

Author Contributions: Conceptualization, Q.H.N., H.V.N., S.C-K., T.T.V., and Q-T.P.; Methodology, Q.H.N., H.V.N., T.H.N.V., S.S., T.T.V., S.C-K., and Q-T.P.; Software, Q.H.N., H.V.N., T.L.B., and H.H.; Formal analysis, Q.H.N., H.V.N., T.H.N.V., N.T.Q., H.H.C., T.N.K., S.S., and Q-T.P.; Writing-original draft preparation, Q.H.N. and H.V.N.; Writing-review and editing, Q.H.N., H.V.N., N.T.Q., S.C-K., S.S., W.J.L., and Q-T.P.; Supervision, S.C-K. and Q-T.P.; Project administration, T.H.N.V.

Conflicts of Interest:

The authors declare no conflicts of interest.


domain: spi phys

text:
Chloé Sauvage
email: chloe.sauvage@onera.fr

Clélia Robert

Béatrice Sorrente

Didier Erasme

Temporal characterization of an urban horizontal atmospheric telecom channel

Introduction

Many applications are opening to the FSO, therefore this technology is booming because it holds many advantages, like a faster deployment than fiber optics or a lack of spectrum regulation unlike radio frequencies [START_REF] Khalighi | Survey on Free Space Optical Communication: A Communication Theory Perspective[END_REF]. However the atmosperic telecom channel suffers from weather (fog, haze, rain) and physical phenomena like turbulence and scattering effects (Rayleigh and Mie [START_REF] Wallace | Atmospheric Science -An Introductory Survey[END_REF]), which damage the optical link and reduce the communication's performances [START_REF] Esmail | Outdoor FSO Communications Under Fog : Attenuation Modeling and Performance Evaluation[END_REF].

Atmospheric transmittance depending on wavelength

Laser beam transmission through the atmopshere depends on several parameters such as weather, aerosols composition or wavelength. Environmental parameters cannot be controlled but the wavelength can be selected in order to optimize the photometric budget. Thank to a radiative transfer software (MATISSE [START_REF] Onera | MATISSE v2.1[END_REF]), three wavelengths of the infrared atmospherical window are compared (1.55 µm, 3.96 µm and 10.05 µm) with a focus on low visibility situations. Figure 1 shows the transmittance as fonction of visibility for a 10 km-long horizontal path. For low visibilities, transmisttance at 3.96 µm and 10.05 µm is better than 1.55 µm. For instance, transmitance through a 200 m-visibility atmosphre (thick fog) is null for 1.55 µm but at 3.96 µm and 10.05 µm the transmission ratio are -17 dB and -15 dB, respectively. Although transmitance is better at 10.05 µm than at 3.96 µm, the availability, the cost and the user-friendlyness of components are more interesting at 3.96 µm.

Description of the SCINDAR experiment and its available database

The SCINDAR (SCINtillation Detection And Ranging) experiment uses an infrared Shack-Hartmann analyser (λ = 3.4 to 4.2 µm) which aims at two halogen lamps 1 m-apart and that are located at 4.2 km of the receiver. On average the horizontal line of sight is about 40 m above ground [START_REF] Nguyen | Measurement of the spatial distribution of atmospheric turbulence with SCINDAR on a mosaic of urban surfaces[END_REF]. A 5×5 array of sub-apertures, 20 of which are effectively operative, samples the wavefront. Thus 2×20 = 40 sub-channels, collected by the 20 subapertures imaging the 2 sources, are characterizated in form of temporal intensity series. Concretely the SCINDAR experiment gathers intensity, phase and Cn 2 profiles series in a big data base for several weather conditions.

Coherence time and fading duration

Two time slots that had been recorded during a past SCINDAR experiment are analysed here. The first one corresponds to a 4 hour recording done in the October 19, 2015, when the sky was overcast and the visibilities ranging between 6 km and 12 km. During this period the wind was low, 2.5 m/s in average, and perpendicular to the line of sight. The second period was recorded on the October 30, 2015, during 5 hours, there were sunny spells, better visibities, from 16 km to 40 km and the wind speed was 3.1 m/s in average, and parallel to the sight line. Then the temporal intensity series are used for computing the intensity sub-channel's coherence time (∆t), i.e. the full width at half maximum of the normalised autocorelation of intensity. For each source, we compute the mean of ∆t and its standard deviation for the 20 sub-apertures. For the first period, shown on figure 2, the computed ∆t varies between 20 ms and 55 ms, and for the second period it varies between 30 ms to 65 ms. The values obtained reveal a coherence time for an horizontal propagation channel slower than in astronomy, a typical value of which is 5 ms and that is derived from an analytical formula using a "one layer" equivalent turbulence profile [START_REF] Garcia-Lorenzo | Adaptive Optics Parameters connection to wind speed at the Teide Observatory[END_REF]. With the same temporal series, a statistical study of fading duration is performed. By fixing the threshold at 5% of the maximun we compute the cumulative density fonction (CDF) of the fadings. We derive a mean fade time of 24 ms and of 38 ms respectively for the first and the second period. As expected, these values are ranging in the interval of the coherence time. Notice that [START_REF] Canuet | Statistical properties of single-mode fiber coupling of satellite-to-ground laser links partially corrected by adaptive optics[END_REF] simulated median fading durations in an adaptive optics GEO scenario showing comparable values. So our data base could be usefull to predict horizontal FSO performance, by using our experimental CDF of fadings, which is necessary to design a time interleaver coupled with error correcting codes.

Conclusion and perspectives

This study shows the good transmittance of 3.96 µm communication channel for a 10 km range even in the case of low visibility just below 1 km. The richness of our data base comes from its capability of considering various weather conditions such as rain or strong turbulence. It allows us to link the FSO performance to some temporal characteristics of the propagation channel (as coherence time and CDF of fadings). The next step requires to focus on a physical explanation of the difference of order of magnitude between the coherence time for horizontal and slant path despite comparable fading values. Some specificities of such urban horizontal propapation channel could be involved : we need first to revist the analytical formula of the coherence time applied to near ground turbulence profiles (Cn 2 and wind), second, envisage a possible interaction between dust and turbulence.

Fig. 1 .

 1 Fig. 1. Transmittance as a function of visibility for three wavelengths.

Fig. 2 .

 2 Fig. 2. Temporal evolution of coherence time (∆t) for each source with its standard deviation.