Amélie A M Cantarel
email: amelie.cantarel@laposte.net

Juliette Bloor

Thomas Pommier

Nadine Guillaumaud

Caroline Moirot

Jean-François Soussana

Franck Poly

R O T

¸O I S S O U S S A N A † J E A N -F R A N C

F R A N C K P O L Y

Four years of experimental climate change modifies the microbial drivers of N2O fluxes in an upland grassland ecosystem

Keywords: AOB, climate change, denitrification, diversity, grasslands, N 2 O, nirK, nitrification, nosZ

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Introduction

In recent decades, changes in land use and human activities have had significant impacts on gaseous nitrogen (N) losses and the global cycle of N [START_REF] Galloway | Nitrogen cycles: past, present, and future[END_REF], contributing to regional and global changes in the atmosphere (IPCC, 2007). Emissions of nitrous oxide (N 2 O) are of particular interest because this trace gas has a strong global warming potential (ca. 310 times greater than that of carbon dioxide) and is the single most important ozone-depleting emission [START_REF] Ravishankara | Nitrous oxide (N2O): the dominant ozone-depleting substance emitted in the 21st century[END_REF]. The magnitude of N 2 O emissions depends on both microbial activities (nitrifiers and/or denitrifiers, [START_REF] Bremner | Sources of nitrous oxide in soils[END_REF][START_REF] Wrage | Distinguishing sources of N 2 O in European grasslands by stable isotope analysis[END_REF] and abiotic factors, including soil temperature, oxygenation, mineral nitrogen, pH, carbon availability and water content [START_REF] Simek | What is the so-called optimum pH for denitrification in soil?[END_REF][START_REF] Smith | Exchange of greenhouse gases between soil and atmosphere: interactions of soil physical factors and biological processes[END_REF][START_REF] Jones | Greenhouse gas emissions from a managed grassland[END_REF]. Consequently, understanding the interplay between microbial and environmental variables is critical for the estimation of potential N 2 O fluxes from soils under climate change.

Despite a large number of studies documenting gaseous N 2 O emissions from grassland ecosystems, few have focused on impacts of climate change drivers on N 2 O fluxes and associated microbial processes [START_REF] Clayton | Nitrous oxide emissions from fertlised grassland: a 2-year of the effects of N fertiliser form and environmental conditions[END_REF][START_REF] Flechard | Effects of climate and management intensity on nitrous oxide emissions in grassland systems across Europe[END_REF]; but see [START_REF] Avrahami | N2O emission rates in a California meadow soil are influenced by fertilizer level, soil moisture and the community structure of ammonia-oxidizing bacteria[END_REF]. In theory, warming is expected to have positive effects on nitrification and denitrification rates (Go ¨dde [START_REF] Conrad | Immediate and adaptational temperature effects on nitric oxide production and nitrous oxide release from nitrification and denitrification in two soils[END_REF], with cascading effects on N 2 O emissions. However, warming responses of both nitrification and denitrification appear to be highly variable across sites [START_REF] Emmett | The response of soil processes to climate change: results from manipulation studies across an environmental gradient[END_REF][START_REF] Horz | Ammoniaoxidizing bacteria respond to multifactorial global change[END_REF][START_REF] Malchair | Do climate warming and plant species richness affect potential nitrification, basal respiration and ammonia-oxidizing bacteria in experimental grasslands[END_REF][START_REF] Szukics | Nitrifiers and denitrifiers respond rapidly to changed moisture and increasing temperature in a pristine forest soil[END_REF], which may partly reflect variable soil water content status during experiments [START_REF] Barnard | Global change, nitrification, denitrification: a review[END_REF]. Impacts of reduced soil moisture status on microbial processes are well established [START_REF] Barnard | Atmospheric CO 2 elevation has little effect on nitrifying and denitrifying enzyme activity in four European grasslands[END_REF][START_REF] Barnard | Global change, nitrification, denitrification: a review[END_REF][START_REF] Bateman | Contributions of nitrification and denitrification to N 2 O emissions from soils at different water-filled pore space[END_REF], typically promoting denitrification at the expense of nitrification via changes in soil aeration and O 2 content [START_REF] Smith | Exchange of greenhouse gases between soil and atmosphere: interactions of soil physical factors and biological processes[END_REF]. In addition, elevated CO 2 may indirectly alter microbial processes by both increasing soil moisture [START_REF] Smith | Effect of roots on soil denitrification[END_REF] and carbon substrate availability (Luo & Mooney, 1999). Previous study suggests that elevated CO 2 may have greater positive effects on denitrification than nitrification [START_REF] Baggs | Nitrous oxide emissions from grass swards during the eighth year of elevated atmospheric pCO 2 (Swiss FACE)[END_REF][START_REF] Barnard | Atmospheric CO 2 elevation has little effect on nitrifying and denitrifying enzyme activity in four European grasslands[END_REF], but considerable variation is observed across studies.

Although information on N 2 O emissions and microbial activities subjected to individual climate change drivers is becoming increasingly available [START_REF] Bateman | Contributions of nitrification and denitrification to N 2 O emissions from soils at different water-filled pore space[END_REF][START_REF] Kammann | Elevated CO2 stimulates N2O emissions in permanent grassland[END_REF][START_REF] Malchair | Do climate warming and plant species richness affect potential nitrification, basal respiration and ammonia-oxidizing bacteria in experimental grasslands[END_REF], data on N 2 O flux responses to multiple and simultaneous environmental changes remain scarce. In a recent study, examining the impact of co-occurring climatic changes on N 2 O fluxes in an upland grassland, [START_REF] Cantarel | Effects of climate change drivers on nitrous oxide fluxes in an upland temperate grassland[END_REF] found that N 2 O fluxes responded equally strongly to both warming alone and the combination of summer drought or elevated CO 2 and warmed conditions. Results from laboratory incubations suggest that interactions between soil moisture and temperature can generate complex patterns of N 2 O emissions under controlled conditions [START_REF] Avrahami | N2O emission rates in a California meadow soil are influenced by fertilizer level, soil moisture and the community structure of ammonia-oxidizing bacteria[END_REF], but the importance of multiple climate changes for field N 2 O emissions remains unclear.

In addition to direct climate-induced changes in microbial activities, climate change drivers can impact N transformations and N 2 O emissions via indirect effects on the abundance of different microbial populations, and microbial community structure. Variation in soil N 2 O emissions may reflect differences in terms of abundances and/or composition of AOB (ammonium oxidizing archea seem to be not involved in N 2 O emission; [START_REF] Di | Nitrous oxide emissions from grazed grassland as affected by a nitrification inhibitor, dicyandiamide, and relationships with ammonia oxidizing bacteria and archaea[END_REF] and denitrifying microorganisms [START_REF] Avrahami | N2O emission rates in a California meadow soil are influenced by fertilizer level, soil moisture and the community structure of ammonia-oxidizing bacteria[END_REF][START_REF] Philippot | The ecological coherence of high bacterial taxonomic ranks[END_REF][START_REF] Brown | Effects of multiple global change treatments on soil N2O fluxes[END_REF]. To date, only AOB community structure has been studied for grasslands subjected to complex, multiple climate change treatments [START_REF] Horz | Ammoniaoxidizing bacteria respond to multifactorial global change[END_REF]. Horz et al. found that abundance of AOB decreased in response to combined elevated CO 2 and increased precipitation, but these effects appeared to be buffered under elevated temperature conditions. To our knowledge, no study has yet focused on changes in denitrifiers community structure under climate change. Hence, the potential impact of multiple climatic variables on the microbial community structure, and the respective contributions of AOB and denitrifying microorganisms to N 2 O fluxes on terrestrial ecosystems remain poorly understood.

In the present study, we investigated the relationship between field N 2 O fluxes and soil microbial parameters under three key climate change drivers at the Clermont Climate Change Experiment facility [START_REF] Bloor | Effects of warming, summer drought and CO 2 enrichment on aboveground biomass production, flowering phenology and community structure in an upland grassland ecosystem[END_REF]. This long-term grassland climate change facility manipulates air temperature (+3.5 °C), atmospheric CO 2 (+200 ppm) and summer drought (À20% summer rainfall) in an additive experimental design. The aims of our study were to determine impacts of sustained single and combined climate change treatments on N 2 O fluxes, nitrification, denitrification, abundance of microorganisms (AOB and nitrite reducers), denitrifiers community structure and to estimate the existing relationships between N 2 O fluxes, abiotic parameters and microbial parameters. Specifically, we asked: (1) How do nitrification, denitrification, abundances and composition of microbial nitrifiers/denitrifiers respond to multiple and simultaneous climate changes? (2) Are variations in field N 2 O fluxes mirrored by changes in microbial activities, abundance or community structure of specific microbial functional groups?

Materials and methods

Experimental design and climate treatments

The studied ecosystem was an upland permanent grassland in the French Massif Central region (45°43′N, 03°01′E, 850 m a.s.l.), characterized by a Cambisol soil (59.5% sand, 19.7% silt, 20.8% clay, pH 6.2), and a grass-dominated plant community (Festuca arundinaceae, Elytrigia repens, Poa pratensis; described in [START_REF] Bloor | Effects of warming, summer drought and CO 2 enrichment on aboveground biomass production, flowering phenology and community structure in an upland grassland ecosystem[END_REF]. The study area has a mean annual temperature of 8.7 °C and a mean annual rainfall of 780 mm.

The Clermont Climate Change Experiment was established in 2005, manipulating air temperature, summer rainfall and atmospheric CO 2 in line with IPCC projections for the study area in 2080 (ACACIA A2 scenario, IPCC, 2007; see [START_REF] Bloor | Effects of warming, summer drought and CO 2 enrichment on aboveground biomass production, flowering phenology and community structure in an upland grassland ecosystem[END_REF] for full details). In brief, the experimental design consisted of 80 grassland monoliths (0.5 9 0.5 9 0.4 m in size), excavated from the study grassland site and allocated at random to one of four climate treatments; C (control), T (+3.5 °C), TD (+3.5 °C, 20% reduction in summer rainfall) and TDCO 2 (+3.5 °C, 20% reduction in summer rainfall, CO 2 levels of 600 ppm). Each experimental treatment comprised of five experimental units (or repetitions), formed by grouping four monoliths together in specially prepared cavities in the ground. Elevated temperatures were achieved by transporting monoliths to a nearby lower-altitude site (Clermont-Ferrand, 350 m a.s.l.). Summer drought was established by the use of rain screens and reduced watering regimes during June, July and August. Enrichment of atmospheric CO 2 was obtained by Mini-FACE (Free Air Carbon dioxide Enrichment) technology; the target CO 2 concentration was only operational during daylight hours.

Meteorological measurements were achieved using a Campbell Scientific automatic weather station and logged to a CRX-10 data logger (Campbell scientific Inc., Utah, USA) at 30 min intervals for both the upland and lowland sites. Volumetric soil moisture (0-20 cm) was recorded hourly using ECH 2 O-20 probes (Dielectric Aquameter; Decagon Devices, Inc., Pullman, WA, USA). To stimulate the management prior to monolith extraction (i.e., low-intensity sheep grazing and no fertilization), vegetation in all experimental units was cut to a height of 5 cm at 6 month intervals (April and October). Monoliths were not fertilized throughout the study, in keeping with extensive management practices.

N 2 O flux measurements and soil sampling N 2 O fluxes were determined on four dates between May and November 2009, using medium-size, closed and non-vented manual chambers on one monolith per experimental unit (following [START_REF] Cantarel | Effects of climate change drivers on nitrous oxide fluxes in an upland temperate grassland[END_REF]. During each N 2 O measurement campaign, chambers were fixed onto a permanent base for each target monolith and gas samples were taken at 520 min intervals using a quick release pneumatic connector (TST Tansam Inc, Kocaeli, Turkey) and a PTFE-Teflon tube connected to an INNOVA 1412 photoacoustic multi-gas analyzer (INNOVA AIR Tech Instruments, Ballerup, Denmark). The INNOVA gas analyzer was encased in an air-conditioned box maintained at 20-25 °C to avoid confounding effects of temperature on analyzer measurements. N 2 O fluxes were calculated by linear regression of N 2 O in the chamber against time; flux data were rejected if the statistic P-value was above 0.05 and r² < 0.95 [START_REF] Cantarel | Effects of climate change drivers on nitrous oxide fluxes in an upland temperate grassland[END_REF]. Soil temperature in the topsoil layer (2-5 cm) was recorded by thermocouples (TC S.A., Dardilly, France) during N 2 O measurement campaigns. Immediately following in situ N 2 O measurements, three soil cores (diameter 1.5 cm) were taken from the top layer (0-10 cm) of each target monolith, pooled together and sieved at 4 mm. Soils were stored for less than 5 days at 4 °C before carrying out assays for nitrification and denitrification enzyme activity (NEA, DEA respectively). A subsample of ca. 2 g fresh soil was frozen at À18 °C for subsequent molecular analyses.

Denitrifying and nitrifying enzyme activities

Denitrification enzyme activity (DEA) was measured in fresh soils from each monolith following the protocol described in [START_REF] Patra | Effects of management regime and plant species on the enzyme activity and genetec structure of N-fixing, denitrifying and nitrifying bacterial communities in grassland soils[END_REF]. Two sub-samples (10 g equivalent dry soil) from each soil sample were placed into 150 ml plasma flasks, and 7 ml of solution containing KNO 3 (50 lg NO 3 À N g À1 dry soil), glucose (0.5 mg C g À1 dry soil) and glutamic acid (0.5 mg C g À1 dry soil) were added. Additional distilled water was provided to achieve 100% water holding capacity and optimal conditions for denitrification. Nitrification enzyme activity (NEA) was determined following the protocol described in [START_REF] Dassonville | Niche construction by the invasive Asian knoweeds (species complex Fallopia): impact on activity, abundance and community structure of denitrifiers and nitrifiers[END_REF]. Briefly, subsamples of fresh soil (3 g equivalent dry soil) were incubated with 6 ml of a solution of N-NH 4 (50 lg N-(NH 4 ) 2 SO 4 g À1 dry soil). Distilled water was adjusted in each sample to achieve 24 ml of total liquid volume in flasks. The flasks were sealed with Parafilm ® (Pechiney Plastic Packaging, Menasha, WI, USA) and incubated at 28 °C with constant agitation (180 rpm). During incubation, 1.5 ml of soil slurry was sampled at 1, 2h30, 4, 5h30 and 7h, filtered (0.2 lm pore size). Samples were stored at À20 °C until analysis of NO 2 À /NO 3 À concentrations on an ionic chromatograph (DX120 Dionex, Salt Lake City, USA). A linear rate of NO 2 À + NO 3 À production with time was always observed, and the rates of NEA were determined from the slope of this linear regression. The intercept was used to estimate pools of soil nitrate (NO 3 À ).

Soil DNA extraction and quantitation of AOB, nirK and nosZ abundances

DNA was extracted for each frozen soil subsample (0.5 g equivalent dry soil) using the 96 Well Soil DNA Isolation Kit (MO BIO Laboratories, Carlsbad, CA, USA) and manufacturer protocols. The quantity of the DNA extraction was checked using the Quant-iT TM PicoGreen ® method (Quant-iT TM PicoGreen ® dsDNA Assay kit; Molecular Probes Inc., Eugene, OR, USA). All gene quantitations were obtained by qPCR, using a Lightcycler 480 (Roche Diagnostics, Meylan, France). The abundance of b-proteobacterial AOB, that represented known AOB in soil, and which are potentially implied in N 2 O emissions [START_REF] Wrage | Distinguishing sources of N 2 O in European grasslands by stable isotope analysis[END_REF] was measured by qPCR targeting 16S rRNA gene sequences specific to this group [START_REF] Hermansson | Quantification of ammonia-oxidizing bacteria in arable soil by real-time PCR[END_REF]. The final qPCR reaction volume was 20 ll, with 0.5 lM of a 2 : 1 ratio of primer CTO189fA/B (GGAGR AAAGCAGGGGATCG) and CTO189fC (GGAGGAAAGT AGGGGATGC; [START_REF] Kowalchuk | Analysis of ammonia-oxidizing bacteria of the b subdivision of the class Proteobacteria in coastal sand dunes by denaturing gradient gel electrophoresis and sequencing of PCR-amplified 16S ribosomal DNA fragments[END_REF], 0.5 lM of RT1r primer (CGTCCTCTCAGACCARCTACTG; [START_REF] Hermansson | Quantification of ammonia-oxidizing bacteria in arable soil by real-time PCR[END_REF], 0.5 lM of TPM1 probe (CAACTAGCTAATCAGR CATCRGCCGCTC), 0.4 mg ml À1 bovine serum albumin (BSA), 10 ng of sample DNA or standard DNA with known number of copies. The samples were run as follow: 10 min at 95 °C; 45 cycles at 95 °C for 10 s, 58 °C for 20 s, and 1 s at 72 °C; and 30 s at 40 °C.

The abundance of nirK genes was determined using SYBR Green as the detection system in a reaction mixture of 20 ll, with 10 ll of SYBR Green PCR master mix, including HotStar Taq TM DNA polymerase, QuantiTec SYBR Green PCR buffer, dNTP mix with dUTP SYBR Green I, ROX and 5 mM MgCl 2 (QuantiTect TM SYBR ® Green PCR Kit; Qiagen, Courtaboeuf, France), 1 lM of nirK876 primer (ATYGGCGGVAYGGCGA), 1 lM of nirK1040 primer (GCCTCGATCAGRTTRTGGTT), according to Henry et al., 2006, 0.4 lg of T4 gene protein 32 (QBiogene, France), 5 ng of soil DNA and Rnase-free water to complete the 20 ll volume. The conditions for nirK qPCR were 15 min at 95 °C for denaturation; 45 cycles at 95 °C for 15 s, 63 °C for 30 s and 72 °C for 30 s for amplification; 1 s at 95 °C and 20 s at 68 °C for acquisition step and 10 s at 40 °C to finish analysis.

For nosZ gene quantitation, the primers nosZ2F (5′-CGCRACGGCAASAAGGTSMSSGT-3′) and nosZ2R (5′-CAK-RTGCAKSGCRTGGCAGAA-3′), according to [START_REF] Henry | Quantitative detection of the nosZ gene, encoding nitrous oxide reductase, and comparison of the abundances of 16S rRNA, narG, nirK, and nosZ genes in soils[END_REF] were used. The final volume 25 ll PCR mix contained: QuantitTect SybrGreen PCR Master Mix 1X (Qiagen), 0.1 lg of T4 gene protein 32 (QBiogene), 1 lM of each primer, and 5 ng of soil DNA extract or 5 ll of ten-fold standard serial dilution ranging from 10 7 to 10 2 nosZ copies of genomic DNA from Pseudomonas aeruginosa PA14. Thermal cycling was carried out by an initial enzyme activation step at 95 °C for 10 min followed by 55 cycles of denaturation at 95 °C for 15 s, annealing at 68 °C for 30 s with a touchdown of À1 °C by cycle until reach 63 °C and elongation at 72 °C for 30 s.

Characterization of nirK community by cloningsequencing

Characterization of nirK community was achieved on DNA extracted from samples taken at the beginning and at the end of flux measurements, i.e., May and November 2009 with/ without field N 2 O fluxes respectively. We used the conditions described by [START_REF] Wertz | Maintenance of soil functioning following erosion of microbial diversity[END_REF] to amplify partial nirK gene sequences prior to cloning procedures. Briefly, PCR was performed using the primers Copper 583F (5′-TCATGGT GCTGCCGCGKGACGG-3′) and Copper 909R (5′-GAAC TTGCCGGTPGCCCAGAC-3′) according to [START_REF] Liu | Molecular diversity of denitrifying genes in continental margin sediments within the oxygen-deficient zone off the pacific coast of Mexico[END_REF] and 30 ng of extracted DNA. The final reagent concentrations for PCR were 1 lM primers, 200 lM of each dNTP, 1.75 U of Taq (Qbiogene, Carlsbad, USA), and 0.5 lg of T4 protein in 50 ll of 10 mM Tris-HCl, 50 mM KCl, 0.1% Triton X-100, 1.5 mM MgCl2, pH 9. Thermal cycling was carried out by an initial step at 94 °C for 5 min followed by 30 cycles of denaturation at 94 °C for 30 s, annealing at 72 °C for 1 min with a touchdown of À1 °C by cycle until reach 67 °C and elongation at 72 °C for 1 min and a final elongation cycle at 72 °C for 7 min. PCR products were purified using the NucleoSpin ® Extract II kit (Macherey-Nagel, Du ¨ren, Germany) and were cloned using the pGEM T-Easy vector system (Promega Ltd., Southampton, UK) and DH10B electrocompetent Escherichia coli cells (Fisher Scientific-Invitrogen, Illkirch, France). For each treatment, three clone libraries were constructed from three (out of the four) randomly selected replicates. From each clone library, at least 28 clones were randomly picked and their vector sent for purification and sequencing (LGC Genomics, Berlin, Germany). Nucleotide sequences have been deposited in GenBank under the following accession numbers: JQ770451-JQ771049.

DNA sequence process, phylogentetic assessment and comparison of microbial communities

Vector and primer sequences were trimmed from the raw sequence dataset. Chimera formations were detected using ChimeraCheck [START_REF] Cole | The Ribosomal Database Project (RDP-II): sequences and tools for high-throughput rRNA analysis[END_REF] and sequences shorter than 358 bp were removed from the original dataset. From a total of 631 remaining 'clean' sequences random normalization of sample sizes was carried out using the Daisy Chopper tool [START_REF] Gilbert | The seasonal structure of microbial communities in the Western English Channel[END_REF], based on the smallest sample i.e., 25 sequences. This subsampled dataset was then aligned together, included the outgroup sequence of the nirS gene of Dechloromonas aromatica using MUSCLE [START_REF] Edgar | MUSCLE: multiple sequence alignment with high accuracy and high throughput[END_REF]) and the resulting alignment was manually checked using Seaview [START_REF] Galtier | SEAVIEW and PHYLO_WIN: two graphic tools for sequence alignment and molecular phylogeny[END_REF]. From the resulting optimized alignment, a maximum likelihood phylogenetic tree was inferred using RAxML [START_REF] Stamatakis | RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models[END_REF] under a GTR + Gamma + Invariable model of sequence evolution (Appendix 1). The resulting tree was then imported into the UNIFRAC on-line tool [START_REF] Lozupone | UniFracan online tool for comparing microbial community diversity in a phylogenetic context[END_REF] for comparison of community composition and detection of lineages specific to the various treatments. The RAMI tool was used to measure accurate branch lengths and distances between nodes containing [START_REF] Pommier | RAMI, a tool for identification and characterization of phylogenetic clusters in microbial communities[END_REF]. Proportional abundances of selected nodes were depicted using the KRONA tool (figure 2, [START_REF] Ondov | Interactive metagenomic visualization in a Web browser[END_REF].

Statistical analyses

Effects of climate treatment on N 2 O, NEA, NO 3 À , N 2 O DEA , N 2DEA , and abundance of gene copies (16S rRNA of AOB, nirK, nosZ) were analyzed using mixed model repeated measures analysis of variance (ANOVA) with both treatment and date as fixed factors [START_REF] Zar | Biostatistical Analysis[END_REF]. Effects of individual climate change drivers (temperature, drought, and CO 2 ) were analyzed using orthogonal contrasts [START_REF] Gilligan | Use and misuse of the analysis of variance in plant pathology[END_REF]. Effects of warming were determined by comparing the C and T treatment; effects of summer drought under elevated temperature by comparing T and TD; effects of elevated (CO 2 ) under elevated temperature and drought by comparing TD and TDCO 2 ; effects of simultaneous application of warming, summer drought, and CO 2 enrichment (2080 climate scenario) were investigated by the C vs. TDCO 2 comparison. All data used were checked for normality and non-normal data were log transformed to conform with assumptions of normality and homogeneity of variances. Relationships between field N 2 O fluxes, potential activities and gene abundances were examined using Spearman correlation coefficients. All analyses were carried out using Statgraphics Plus 4.1 ® (Statistical Graphics Corp., Rockville, Maryland, USA). We performed a restricted maximum likelihood method (REML) with the software JMP8 ® (SAS Institute Inc., SAS Campus Drive, NC, USA) considering monoliths as a random factor to determine, which variables (among soil temperature, WFPS, NO 3 -and NH 4+ contents, abundances, activities and composition of denitrifiers) were significantly related to in situ N 2 O fluxes in May and November (i.e., dates with diversity analyses). To compare field measures of N 2 O fluxes to denitrification activities measured in the laboratory, which differed in experimental temperature, we linearly transformed the denitrification values (N 2 O DEA corr) as suggested by their known linear correlation between 4 and 25 °C [START_REF] Braker | Influence of temperature on the composition and activity of denitrifying soil communities[END_REF].

Structural equation modeling (SEM) was performed using Amos18 ® (Amos Development Corporation, Crawfordville, FL, USA) with the data from May and November to explore the causal links between denitrification, microbial community structure, abiotic factors and the in situ N 2 O fluxes, using the following parameters: soil temperature, WFPS, pool of NO 3 -, N 2 O DEA , N 2 O DEA corr, percentage of sequences in nirK lineages A and B (Appendix 2). In a SEM, a v² test is used to determine whether the covariance structures implied by the model adequately fit the actual covariance structures of the data. A non-significant chi-squared test (P > 0.05) indicates adequate model fit. The coefficients of each path as the calculated standardized coefficients were determined using the analysis of correlation matrices. Paths in this model were considered significant with a P-value <0.1. These coefficients indicate by how many standard deviations the effect variable would change if the causal variable was changed by one standard deviation.

Results

Characteristics of climate treatments

During the study period (May-November 2009), the difference in mean monthly temperature between control and elevated temperature treatments was 3.4 ± 0.03 °C (Appendix 3). In summer (June, July, and August), the drought treatments (TD, TDCO 2 ) were subjected to a 21% reduction in rainfall compared with the no-drought treatments (C, T). Mean daily CO 2 differences between the TDCO 2 treatment and the ambient CO 2 treatments (C, T, and TD) were 193.3 ± 13.1 ppm (data not shown). Meteorological variables (i.e., soil moisture and soil temperature) recorded on days of N 2 O measurement indicated higher soil temperature in the warmed treatments (T, TD and TDCO 2 ) compared with the control (C). No significant differences in soil moisture between the C, T and TDCO 2 treatments were found for the four sampling dates (T-test, Table 1). However, the TD treatment showed lower soil moisture values than the T treatment in July and September. We found no significant difference between soil moisture and air temperature measured on the days of sampling and the averages recorded on the five previous days (all dates and treatments). This consistence between measurements allows considering measurements of each sampling date as representative of the preceding week. 23.5 ± 1.2 24.9 ± 1.1 17.3 ± 0.7 5.7 ± 0.7 TDCO 2 22.6 ± 1.2 22.9 ± 0.9 17.4 ± 0.8 6.7 ± 0.6

Effects

Changes in nitrifying and denitrifying enzyme activities

Over the course of the study, climate treatments had a significant effect on NEA, N 2 O DEA and soil nitrate pools (Table 2). Warming and combined climate treatments had a positive impact on nitrification (NEA; F 1,38 = 5.7, P < 0.05 and F 1,16 = 6.9, P < 0.05 respectively) and on NO 3 À , which is the product of the nitrifi- product ratio showed a significant effect of measurement date (Table 2). NO 3 À and N 2DEA showed a continuous increase over time (r² = 37.7, P < 0.001 and r² = 23.8, P < 0.001 respectively), whereas N 2 O DEA and denitrification product ratio showed a progressive decrease overtime (r² = 18.3, P < 0.001 and r² = 49.6, P < 0.001 respectively).

Changes in the abundances of AOB, nirK and nosZ

Responses of gene abundances to climate change treatments varied depending on the gene considered (Table 3). Both warming and combined climate change had a positive effect on abundance of N 2 O reducers (nosZ; F 1,16 = 6.1, P < 0.05 and F 1,16 = 6.4,

N 2 O F L U X E S A N D M I C R O B I A L E C O L O G Y 2525 P < 0.
01 respectively) at all measurement dates (no significant Treatment 9 Date interaction). In addition, nosZ gene abundance was found to increase over time (Table 3, r² = 12.5, P < 0.05). Climate treatment also had significant effects on the abundance of AOB sequences, but treatment effects varied overtime (significant treatment 9 date interaction, Table 3). In general, numbers of AOB copies were significantly higher in November compared with those in May and July. Warming had a positive effect on the abundance of AOB in May and November (F 1,16 = 6.3, P < 0.05 and F 1,16 = 9.1, P < 0.01 respectively), whereas combined climate and elevated CO 2 alone was only associated with an increase in AOB in November (F 1,16 = 8.5, P < 0.05 for combined climate and F 1,16 = 16.5, P < 0.001 for elevated CO 2 ). Unlike nos Z and AOB, abundance of nirK genes showed no significant response to climate treatments over the four measurement dates.

Relationship between microbial activities, microbial population abundances and abiotic factors

Across treatments, field N 2 O fluxes showed a positive correlation with denitrification product ratio, which is consistent with its positive correlation with the denitrification enzyme activity producing N 2 O (N 2 O DEA ) and a negative correlation with the reduction of N 2 O to N 2 *,*** indicates significant differences at P < 0.05 and 0.01, respectively and italic indicates marginal differences (0.05 < P < 0.1). (N 2DEA ) during the study period (Table 4). This pattern was mirrored by N 2 O fluxes in the control treatment. In addition, N 2 O fluxes in the C treatment showed a significant negative correlation with NEA (Table 4). Variation in gene abundances played a relatively more important role for N 2 O fluxes under warmed conditions compared with the control. In the T treatment, N 2 O fluxes showed a significant negative correlation with both NEA and the nosZ/nirK ratio (Table 4). In the TD treatment, N 2 O fluxes were positively correlated with the denitrification product ratio, but negatively correlated with nosZ abundance (Table 4). Finally in the TDCO 2 treatment, N 2 O fluxes showed a positive correlation with the denitrification product ratio, but a negative correlation with N 2DEA and the nosZ/nirK ratio. No significant relationships were observed between N 2 O fluxes and gene abundances across treatments (Table 4).

Changes in nirK community and diversity structure

On the basis of the complete maximum likelihood tree (Appendix 1), both the Unifrac significance and the P-test significance indicated that the nirK community sampled in May was significantly different in its structure from the community sampled in November (P < 0.03). In addition, the sequences from the November samples had a significant number of unique branches compared to the rest of the tree (P 0.01). When clus-tering the environments according to the full tree topology (Appendix 4) the nirK communities from the treatments T and TDCO 2 shared the most sequences, and were closer to the control nirK community than to the TD community. All pairwise comparisons of the treatments showed significant differences (Jackknife analysis, P < 0.05). At a branch threshold of 0.05, two lineages showed significant biases toward specific treatments (Fig. 2). Lineage A showed significant dominance in the TDCO 2 treatment (dominance 18 observed while 8.5 expected) and the C treatment (recession 2 observed instead of 8.5 expected). Lineage B showed significant dominance in all elevated temperature treatments compared with control (C = 3; T = 22; TD = 20; TDCO 2 = 19; expected = 16). Compared to the rest of the tree, the sequences belonging to both lineages showed significant differences in high-GC% sequences (mean GC % = 62.92% for lineages A and B; GC% = 62.03% for all other sequences; Kruskal-Wallis, X 2 = 32.5, P < 0.001). However, neither soil temperature nor WFPS measured on the day of sampling were related to changes in nirK community structure (Fig. 3, Appendix 5). SEM performed with nitrification (i.e., NEA and AOB gene abundances) were not significant (P < 0.05; data not shown) implying the weak effect of nitrifiers-related parameters on field N 2 O fluxes.

Microbial drivers of N

Similarly, SEM performed with gene abundances of denitrifiers (nirK and nosZ) were not significant (P < 0.05; data not shown), implying uncoupled responses of gene abundances and the denitrification process.

Discussion

Global changes are known to enhance soil N 2 O fluxes [START_REF] Cantarel | Effects of climate change drivers on nitrous oxide fluxes in an upland temperate grassland[END_REF][START_REF] Carter | Effects of elevated atmospheric CO2, prolonged summer drought and temperature increase on N 2 O and CH 4 fluxes in a temperate heathland[END_REF][START_REF] Niboyet | Global change could amplify fire effects on soil greenhouse gas emissions[END_REF]. In the present study, we aimed to improve the mechanistic understanding of soil microbial functioning and the processes contributing to the emissions of N 2 O for grasslands subjected to sustained climate change.

Warming and all combined climate change drivers induced strong modifications in field N 2 O fluxes and microbial functioning

Throughout our study, we found that N 2 O fluxes and microbial activities responded more strongly to warming and combined climate changes (simultaneous application of warming, summer drought and elevated CO 2 ) than to summer drought or elevated CO 2 under warmed conditions. Flux data from the present study confirms the importance of warming as a key driver of climate-induced changes for N 2 O-N losses in grassland ecosystems [START_REF] Cantarel | Effects of climate change drivers on nitrous oxide fluxes in an upland temperate grassland[END_REF]. Accordingly, we found positive effects of warming on N 2 O fluxes recorded during the growing season, but no significant warming effects for the winter sampling date [START_REF] Cantarel | Effects of climate change drivers on nitrous oxide fluxes in an upland temperate grassland[END_REF] due to an insufficient warning to compensate for the winter temperature. Such seasonal variation may reflect interactions between soil temperature and soil moisture on microbial processes [START_REF] Flechard | Effects of climate and management intensity on nitrous oxide emissions in grassland systems across Europe[END_REF], as well as variation in root exudation and soil nutrient availability. Moreover, both nitrification enzyme activity (NEA) and the in situ nitrate pool increased in response to elevated temperature, in agreement with previous results observed in well-aerated soils [START_REF] Barnard | Global change, nitrification, denitrification: a review[END_REF]. Warming was found to have a positive impact on AOB abundance in May, whereas combined warming, drought and elevated CO 2 had a positive impact on AOB abundance in November. Given the relatively limited changes in gene abundances observed, and their transient nature, it is likely that the increase of AOB abundances was probably a result of indirect effects, most likely mediated by the plant community [START_REF] Horz | Ammoniaoxidizing bacteria respond to multifactorial global change[END_REF]. AOB are believed to be inferior competitors for nutrient resources [START_REF] Belser | Population ecology of nitrifying bacteria[END_REF], and temporal changes in AOB community size may reflect a shifting competitive hierarchy for nutrient resources (mainly NH 4 + ) between AOB, heterotrophic microbes and plants.

Irrespective of measurement date, combined climate change (TDCO 2 ) was found to increase N 2 O DEA and N 2 O + N 2 emissions in the laboratory measurements (N 2 O tot ). Surprisingly, these two variables did not show a significant response to warming alone. This lack of response did not result from the confounding effects of soil moisture during the measurement campaigns, since similar soil moisture conditions were observed in the C and T treatments. [START_REF] Barnard | Global change, nitrification, denitrification: a review[END_REF] recently reported that denitrification enzyme activity (DEA) was generally less responsive to temperature in field experiments compared with laboratory studies, a phenomenon attributed to acclimation of DEA to ambient environmental conditions over time [START_REF] French | Elevated temperatures and carbon dioxide concentrations: effects on selected microbial activities in temperate agricultural soils[END_REF]. Denitrifying bacteria harboring nosZ genes also carry nirK or nirS genes, though denitrifying bacteria may solely harbor nirK and/or nirS genes [START_REF] Jones | Phylogenetic analysis of nitrite, nitric oxide, and nitrous oxide respiratory enzymes reveal a complex evolutionary history for denitrification[END_REF]. Therefore, shifts in nosZ community may not always reflect nirK and/or nirS community changes. In our study, the abundances of nosZ denitrifiers increased more than those of nirK denitrifiers in response to warming and combined climate changes, suggesting a shift in nirK and/or nirS community structure. Between nirK and nirS communities, the former have been shown to respond to environmental changes [START_REF] Hallin | Relationship between N-cycling communities and ecosystem functioning in a 50-year-old fertilization experiment[END_REF][START_REF] Szukics | Nitrifiers and denitrifiers respond rapidly to changed moisture and increasing temperature in a pristine forest soil[END_REF]. Herein, changes in nirK community structure were found under warming and combined climate change treatments. Indeed, we found two deeply branching lineages with significant biases for warmed treatments. This result suggests a selective process under warmed treatments; it is noteworthy that the sequences included in these two lineages harbored a higher GC-content than the other sequences on the tree, consistent with bacteria adapted to higher temperatures [START_REF] Madigan | Brock Biology of Microorganisms[END_REF].

Low variation in soil water status modifies microbial community structure but does not affect N-related microbial activities and abundance Combined summer drought and warming had no significant effect on microbial parameters (enzymatic activities and gene abundances) compared with warming alone. Previous study indicates that decreases in soil moisture are often associated with a decrease in DEA and an increase in NEA products [START_REF] Barnard | Global change, nitrification, denitrification: a review[END_REF][START_REF] Bateman | Contributions of nitrification and denitrification to N 2 O emissions from soils at different water-filled pore space[END_REF]. In our study, the variation in soil water status across T and TD treatments on measurement dates was weak, despite a 20% reduction of summer precipitation. Consequently, the limited effects of drought treatment on soil moisture conditions may have diminished the impact of experimental drought on soil processes. Another explanation could be that changes in nitrite community structure under warmed and drought treatment mitigated drought responses in enzymatic activities and gene abundances. Irrespective of the sampling dates (May or November), phylogenetic analysis of nirK sequences indicated a strong divergence between the nitrate reducer community in the TD treatment and those communities found in the other climate change treatments. This suggests a key selective process linked to drought under warmed conditions, which could explain why no difference was found in denitrification activities in the various treatments.

Elevated CO 2 was expected to increase soil water status due to reduced plant stomatal aperture and transpiration rates [START_REF] Schulze | Carbon-dioxide and water-vapor exchange in response to drought in the atmosphere and in the soil[END_REF], which can have indirect consequences on denitrification by releasing the soil O 2 partial pressure [START_REF] Smith | Exchange of greenhouse gases between soil and atmosphere: interactions of soil physical factors and biological processes[END_REF]. Although we measured significantly higher soil moisture conditions in July and September in the TDCO 2 treatment compared with the TD treatment (37% vs. 29% and 47% vs. 31% for July and September respectively), we found no impact of elevated CO 2 on enzymatic activities and N 2 O fluxes. The lack of response to drought and elevated CO 2 observed here mirrors the patterns of N 2 O fluxes recorded in 2007-08 at the same site [START_REF] Cantarel | Effects of climate change drivers on nitrous oxide fluxes in an upland temperate grassland[END_REF] and suggests that N 2 O-related microbial processes may also be insensitive to minor variations in soil water content. Moreover, drought and elevated CO 2 did not highly modify the relationship between field N 2 O fluxes and microbial activities and gene abundances. The maintenance of soil functioning in combined warming, drought and elevated CO 2 conditions despite substantial modifications in the bacterial community structure, agrees with other studies which show high-functional redundancy of microbial communities [START_REF] Wertz | Decline of soil microbial diversity does not influence the resistance and resilience of nitrifiers and denitrifiers following a model disturbance[END_REF][START_REF] Cabrol | Bacterial dynamics in steady-state biofilters: beyond the functional stability[END_REF].

Relationships among microbial parameters and field N 2 O emissions

N 2 O flux variations were better explained by the denitrification product ratio (N 2 O DEA /[N 2 O DEA + N 2DEA ]) both across treatments and under cool, wet conditions (control site). Changes in DEA products over time (i.e., decrease of N 2 O DEA and increase of N 2DEA ) were correlated with a decrease in field N 2 O fluxes. This may result from continuous N losses via N 2 fluxes in our grassland ecosystem even when no N 2 O fluxes were detected. The relative importance of microbial activities and microbial population size was modified under warmed conditions, with stronger correlations between field N 2 O fluxes and gene abundances in the T, TD and TDCO 2 treatments. Nevertheless, field N 2 O fluxes showed a stronger correlation with enzymatic activities than with community abundance across climate treatments. Links between N 2 O fluxes and microbial abundances are known to be elusive, and may depend on soil properties or ecosystem type [START_REF] Ma | Relationship between nitrifier and denitrifier community composition and abundance in predicting nitrous oxide emissions from ephemeral wetland soils[END_REF]. Furthermore, the activity of a given enzyme may be uncoupled from the size of the corresponding functional gene pool due to subsequent enzyme regulation. Additional study is needed to examine the relative importance of other denitrifying and nitrifying genes on patterns of microbial activities and associated field N 2 O fluxes under future climate conditions.

A challenge in this study was to link in situ N 2 O fluxes and functioning microbial ecosystem, and particularly the nirK denitrifiers community. The SEM supported the importance of changes in abiotic conditions (i.e., soil temperature) toward in situ N 2 O fluxes. However, additional significant path coefficients suggested that other factors e.g., changes in denitrification activities and community structure were important in determining field N 2 O fluxes. Moreover, the availability of NO 3 À pool influenced in situ N 2 O fluxes indirectly by providing substrate for denitrification and impacting the nirK community structure (lineage A). The observed direct and indirect influences of nirK diversity suggest that the mechanisms driving field N 2 O fluxes are subtler than simple warming effects on denitrifying enzymatic activities. Taken together, our results strongly suggest that the combined effects of soil temperature, denitrifier community structure and activity, provide a much better predictor of N 2 O fluxes than nitrifier-related parameters.

Further study coupling automated N 2 O measurements with more frequent soil sampling over the course of the year is required to confirm these findings, and improve our understanding of climate change impacts on annual N 2 O fluxes and N-related microbial functioning.

  of climate change drivers on in situ N 2 O fluxes During the four measurement dates, N 2 O fluxes ranged from À5 to 369 lg N 2 O-N.m À2 .hr À1 across treatments.N 2 O fluxes showed significant climate treatment effects for measurement dates during the growing season, but no response to climate treatments in November (significant treatment 9 date interaction; F 1,9 = 2.16, P < 0.05; Fig.1). This significant interaction was driven by very low N 2 O fluxes in November across all climate treatments. With the exception of the November sampling date, warming had a positive effect on N 2 O emissions (C vs. T comparison; F 1,16 = 23.1, P < 0.001; F 1,16 = 6.6, P < 0.05 and F 1,16 = 14.6, P < 0.01 respectively for May, July and September). This pattern of response was also found for the combined climate change drivers (C vs. TDCO 2 ) in May and July. Unlike warming and combined climate change, summer drought (T vs. TD) and elevated CO 2 (TD vs. TDCO 2 ) had little impact on N 2 O fluxes. However, drought was associated with a significant negative effect on N 2 O fluxes in September (F 1,16 = 15.5, P < 0.01; Fig.1).

Fig. 1

 1 Fig. 1 Effects of climate manipulations on N 2 O fluxes for measurement dates in spring, summer and autumn 2009. Treatments are given by: C, control; T, elevated temperature (+3.5 °C); TD, elevated temperature and summer drought (+3.5 °C, À20% rainfall); TDCO 2 , elevated temperature, summer drought and CO 2 enrichment (+3.5 °C, À20% rainfall, +200 ppm CO 2 ). Means and standard errors are presented per treatment and measurement date (n = 5).

Fig. 2

 2 Fig.2Maximum likelihood tree based on GTR-GAMMA model of substitution. All but two nodes were collapsed to illustrate significant biases toward climate (lineage A in dark grey and lineage B in light grey). Bar legend indicates 0.1 substitutions/nucleotide.

Fig. 3

 3 Fig. 3 Structural equation model results for effects of nirK denitrifiers on in situ N 2 O fluxes. Path coefficients (values indicated next to the arrows) correspond to the standardized coefficients calculated based on the analysis of correlation matrices. Tests of path significance are given in Appendix 5.

Table 1

 1 Mean soil moisture (WFPS, %) and soil temperature recorded during each N 2 O measurement date for experimental climate treatments. Means and SEs are presented (n = 5)

			23rd	28th
		29th May 27th July	September	November
	WFPS (%)
	C	31.9 ± 0.0 35.9 ± 0.0 50.1 ± 0.0	52.9 ± 0.3
	T	29.3 ± 1.1 32.7 ± 1.6 45.2 ± 2.9	52.6 ± 2.3
	TD	32.4 ± 0.8 29.4 ± 0.3 30.6 ± 0.3	48.8 ± 0.2
	TDCO 2 29.5 ± 0.8 37.3 ± 1.8 47.1 ± 1.7	51.8 ± 1.5
	Soil temperature (°C)

C 17.3 ± 0.4 17.3 ± 0.4 14.3 ± 0.2 4.2 ± 0.3 T 22.7 ± 1.5 23.8 ± 0.6 17.1 ± 0.5 6.3 ± 0.6 TD

Table 2

 2 Effects of climate change treatment on (a) nitrifying enzyme activities (NEA), (b) nitrates (NO 3

	À ), (c) potential N 2 O fluxes

*,**,*** indicates significant differences at P < 0.05; 0.01 and 0.001, respectively and italic indicates marginal differences (0.05 < P < 0.1).

© 2012 Blackwell Publishing Ltd, Global Change Biology, 18, 2520-2531

Table 3

 3 Effects of climate change treatments on the (a) AOB, (b) nirK and (c) nosZ gene abundances (means and standard errors are shown; n = 5). Results from repeated measures ANOVA testing the effects of climate change treatments, measurement dates and their interaction are presented (significant P-values are shown in bold)

		Climate treatments			Repeated measures ANOVA
									Treatments
		C	T	TD	TDCO 2	P	Treatments	Dates	9 dates
	(a) Mean copy numbers of ammonia-oxidizing bacteria (10 6 copy per g of dry soil)
	May	3.97 ± 0.15	5.58 ± 0.67	4.34 ± 0.80	4.13 ± 0.15	***	0.083	<0.001	0.021
	July	4.84 ± 0.70	4.95 ± 0.46	4.77 ± 0.69	5.32 ± 0.76
	September	5.14 ± 0.39	4.73 ± 0.50	4.40 ± 0.66	5.01 ± 0.32
	November	5.12 ± 0.43	6.40 ± 0.59	7.23 ± 0.54	8.36 ± 0.86
	(b) Mean copy numbers of Cu nitrite reductors nirK (10 7 copy per g of dry soil)
	May	1.70 ± 0.23	1.93 ± 0.12	1.48 ± 0.14	1.64 ± 0.18	ns	0.371	0.051	0.740
	July	2.10 ± 0.38	2.06 ± 0.35	2.31 ± 0.37	2.22 ± 0.33
	September	1.62 ± 0.25	2.20 ± 0.33	1.82 ± 0.31	1.94 ± 0.19
	November	1.36 ± 0.23	1.76 ± 0.26	1.98 ± 0.24	2.03 ± 0.84
	(c) Mean copy numbers of nitrous oxide reductors nosZ (10 7 copy per g of dry soil)
	May	2.24 ± 0.39	2.46 ± 0.38	2.54 ± 0.90	2.57 ± 0.85	*	0.017	0.048	0.942
	July	1.49 ± 0.26	2.76 ± 0.28	3.76 ± 0.91	2.98 ± 0.84
	September	1.64 ± 0.13	2.78 ± 0.38	3.22 ± 0.91	3.56 ± 0.94
	November	2.33 ± 0.12	3.93 ± 0.45	4.13 ± 0.66	4.71 ± 0.97
	C, control treatment; T, elevated temperature treatment; TD, temperature and drought treatment; TDCO 2 , temperature, drought,
	and elevated CO 2 .

Table 4

 4 Correlation coefficients (Spearman) between field N 2 O fluxes, microbial activities and gene abundances pooled across experimental climate treatments and dates (n = 80), and for each climate treatment pooled across dates (n = 20). Significant P values (P < 0.05) are shown in bold and marginal P values (0.1 > P > 0.05) are in italic

		Microbial activities			Gene abundances
					Denitrification
	N 2 O fluxes	NEA	N 2 O DEA	N 2DEA	product ratio	AOB	nirK	nosZ	nosZ/nirk
	Pooled treatments	0.016	0.335	À0.291	0.411	À0.179	0.117	À0.166	À0.216
	C	À0.462	0.477	À0.734	0.768	À0.205	0.281	0.017	À0.211
	T	À0.428	0.221	À0.321	0.398	0.006	0.359	À0.177	À0.426
	TD	0.206	0.275	À0.356	0.475	À0.314	À0.252	À0.447	À0.327
	TDCO 2	À0.101	0.305	À0.393	0.599	À0.346	0.120	À0.264	À0.394

© 2012 Blackwell Publishing Ltd, Global Change Biology, 18, 2520-2531

Acknowledgements

The authors would like to thank Alexandre Salcedo and Laurent Gaumy for assistance with soil sampling and chamber measurements, to Robert Falcimagne and Patrick Pichon for maintenance at the mini-FACE site. The authors acknowledge the financial support of the French Ministry of Education and Research for the doctoral fellowship to AAMC and of the EC FP6 'NitroEurope-IP' project and of the French ANR VMCS 'VALIDATE' project. Quantitative PCR were carried out at the platform DTAMB (IFR 41, Universite ´Lyon 1). Nitrification and denitrification measurements were performed at the Chromatography platform (UMR5557-USC1193). The authors declare that there is no conflict of interest in the present manuscript.

Supporting Information

Additional Supporting Information may be found in the online version of this article: Appendix S1. Maximum likelihood tree based on GTR-GAMMA model of substitution. Bar legend indicates 0.1 substitutions/nucleotide. Appendix S2. The initial structural equation models. Appendix S3. Daily air temperature (a), rainfall (b) and soil moisture (c) recorded during the study period (April-November 2009). The control, upland site is given by gray line, whereas the warmer, lowland site is presented by black line (T, full line; TD, dashed line; TDCO 2 , dotted line). Arrows represented day of N 2 O measurement and soil sampling. Appendix S4. Clustering environments according to the full tree topology. Appendix S5. Full SEM results for each path in the model. UnStd Est, unstandardized path coefficient estimates; SE, standard error of the unstandardized path estimate; CR, critical ratio for regression weight (UnStd Est/SE); P-value, test of significance of path estimate; Std., standardized path coefficient estimates.


Martin A Grandjean

A Conceptual Framework for the Analysis of Multilayer Networks in the Humanities

A Conceptual Framework for the Analysis of Multilayer Networks in the Humanities Digital Humanities 2020, Ottawa Martin Grandjean, University of Lausanne, Switzerland If network analysis has made its way into the humanities toolbox, and especially in history (Düring et al. 2016), it is because it helps to grasp the complexity of the objects of these disciplines. However, to understand the multidimensionality of the data requires a consequent reflection on its modeling. This paper seeks to be part of a series of publications aimed at making advanced network analysis concepts more accessible to the humanities scholars: from ontological questions [START_REF] Langmead | Towards Interoperable Network Ontologies for the Digital Humanities[END_REF] to the necessary discussion of the integration of temporality in graphs [START_REF] Lemercier | Taking time seriously. How do we deal with change in historical networks[END_REF][START_REF] Conroy | Visualizing Networks and Temporality[END_REF], the development of typologies of uses (Grandjean 2017a) or attempts to provide aids to interpretation [START_REF] Grandjean | Translating Networks: Assessing Correspondence Between Network Visualisation and Analytics[END_REF]. The question of multilayer networks becomes especially more and more important, whether in a general way [START_REF] Mcgee | Towards Visual Analytics of Multilayer Graphs for Digital Cultural Heritage[END_REF] or applied to the humanities (eg. Vugt 2017; Grandjean 2017b). Our purpose is to discuss a unifying conceptual framework allowing the transition between a current formal multilayer model [START_REF] Kivelä | Multilayer networks[END_REF][START_REF] Knudsen | Unifying the Framework of Multi-Layer Network and Visual Analytics[END_REF]) and the language of the humanities. This framework is expressed by a visual representation that contains a multiplicity of layers that synthesizes and clarifies the different possible networks and facilitate the appropriation of the model by researchers.

Description of the framework

The key element of this concept is the distinction between "layers" and "levels": within a horizontal level, 1-mode graphs all have the same set (and type) of nodes (visualized with a fixed position or not, depending on our will to represent the different structures or to help keeping the "mental map" between each other). A level is therefore a multiplex system of layers. The levels can be connected together by edges that express the relation between two types of nodes (2-mode). It is to be noted that the formal model does not imply a hierarchy between layers. Here, this verticality is only a visual artifact to facilitate the expression of all kind of humanities datasets. Each level represents one entity type and relationships within. It can have many layers organized by "aspects" and include edges between these layers. The inter-level relations can be represented on a layer that contains a 2-mode network (which can also be deployed according to different aspects). The visual expression of this framework (fig. 1) looks quite similar to an OLAP Graph Cube [START_REF] Zhao | Graph Cube: on Warehousing and OLAP Multidimensional Networks[END_REF] or a Space-Time Cube Visualization [START_REF] Bach | A Review of Temporal Data Visualizations Based on Space-Time Cube Operations[END_REF]. The proposed framework is however not a cube: this well aligned appearance is a convenient way of representing things to make them less abstract so that they can be used to discuss modeling issues. Not all levels necessarily have the same dimensions and do not need to be aligned. Moreover, there can be more than three axes since it is an intellectual tool and not a graphic construction.

Use cases

This visual convention allows to design "scenarios" where the model coincides with data from applied research to make the layers within them explicit:

Fig. 1

 1 Fig. 1 Multilayer network system made of three 1-mode networks organised on three layers set ("levels") connected by two 2-mode networks. At each level, this representation explicit the possibility of developing the graph depending on (at least) two aspects.

A.

  Affiliation networks (fig. 2) based on interlocking data with multiple companies, institutions, prosopographic categories (eg. David & Westerhuis 2014).

Fig. 2

 2 Fig. 2 Modeling example of an affiliation network.B. Character networks (fig.3) based on theatre plays with addition of information on groups, families, temporality (eg.[START_REF] Xanthos | Visualising the Dynamics of Character Networks[END_REF]).

Fig. 3

 3 Fig. 3 Modeling example of a character network. C. Circulation networks (fig. 4) with multiple transportation means, places, goods, routes, letters (eg. Orengo & Livarda 2016).

Fig. 4

 4 Fig. 4 Modeling example of a circulation network.