Sihang Yang
email: yangyf@tsinghua.edu.cn

Qiaoshu Zheng

Yunfeng Yang

Mengting Yuan

Xingyu Ma

Nona R Chiariello

Kathryn M Docherty

Christopher B Field

Jessica L M Gutknecht

Bruce A Hungate

Audrey Niboyet

Xavier Le Roux

Jizhong Zhou
email: jzhou@ou.edu

Fire affects the taxonomic and functional composition of soil microbial communities, with cascading effects on grassland ecosystem functioning

Keywords: Fire, Microbial communities, Climate change, Californian grasslands, high-throughput sequencing

Introduction

Anthropogenic activities, and in particular changes in land use and management, have considerably altered fire regimes [START_REF] Andersson | Tropical savannah woodland: effects of experimental fire on soil microorganisms and soil emissions of carbon dioxide[END_REF]. Climate change is likely to also affect fire regimes because antecedent wet periods could provide herbaceous fuels for fire, while drought and warming could extend weather patterns that are conducive to fire [START_REF] Bowman | Fire in the Earth System[END_REF]. As a consequence, recent years have witnessed increased fire frequencies in many areas, and it is anticipated that fire frequency and intensity will further increase in the coming century [START_REF] Westerling | Warming and earlier spring increase western US forest wildfire activity[END_REF]. It is likely that fire in California would burn more rapidly, with higher intensity, under warmer climate scenarios [START_REF] Fried | The impact of climate change on wildfire severity: a regional forecast for northern California[END_REF].

Fire affects plant diversity by increasing resource availability for subordinate and early-successional species [START_REF] Pickett | The ecology of natural disturbance and patch dynamics[END_REF]. Fire also has a short-term stimulatory effect on N availability to plants but a long-term negative effect due to increased N losses [START_REF] Reich | Fire and vegetation effects on productivity and nitrogen cycling across a forest-grassland continuum[END_REF]. Fire can stimulate fine root production and aboveground net primary production in the first post-fire growing season in grasslands [START_REF] Docherty | Soil microbial responses to fire and interacting global change factors in a California annual grassland[END_REF][START_REF] Strong | Experimental fire increases soil carbon dioxide efflux in a grassland long-term multifactor global change experiment[END_REF]. Changes in aboveground communities could indirectly affect belowground systems via altering nutrient inputs, increasing solar warming and surface soil temperatures, and changing evapotranspiration and soil moisture (Hart, DeLuca, Newman, MacKenzie, & Boyle,

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This article is protected by copyright. All rights reserved 2005; [START_REF] Strong | Experimental fire increases soil carbon dioxide efflux in a grassland long-term multifactor global change experiment[END_REF]. Fire can also induce higher greenhouse gases emissions, including CO 2 [START_REF] Strong | Experimental fire increases soil carbon dioxide efflux in a grassland long-term multifactor global change experiment[END_REF] and N 2 O [START_REF] Niboyet | Global change could amplify fire effects on soil greenhouse gas emissions[END_REF]. Responses of soil C dynamics to fire vary between ecosystems. For instance, soil respiration was stimulated shortly after fire and then decreased in a tropical savanna woodland [START_REF] Andersson | Tropical savannah woodland: effects of experimental fire on soil microorganisms and soil emissions of carbon dioxide[END_REF]) and a permafrost forest [START_REF] Tas | Impact of fire on active layer and permafrost microbial communities and metagenomes in an upland Alaskan boreal forest[END_REF], but CO 2 efflux rates decreased after fire in a ponderosa pine forest [START_REF] Sullivan | Wildfire reduces carbon dioxide efflux and increases methane uptake in ponderosa pine forest soils of the southwestern USA[END_REF] and an arid grassland [START_REF] Vargas | Precipitation variability and fire influence the temporal dynamics of soil CO2 efflux in an arid grassland[END_REF]. In a grassland ecosystem, three mechanismsincreases in soil temperature, fine root production and aboveground net primary production -were shown to explain changes in soil respiration rates post fire [START_REF] Strong | Experimental fire increases soil carbon dioxide efflux in a grassland long-term multifactor global change experiment[END_REF].

Fire plays an important role in structuring and altering underground microbial communities. Fire can directly kill microbes by heating the soil upper layer (Delmas et al. 1995) and indirectly affect microbial community composition by altering plant community composition and plant-driven organic matter inputs to soil [START_REF] Docherty | Soil microbial responses to fire and interacting global change factors in a California annual grassland[END_REF][START_REF] Hart | Post-fire vegetative dynamics as drivers of microbial community structure and function in forest soils[END_REF], as well as soil environmental conditions. In boreal forests, fire decreased microbial biomass and dissolved organic C in surface organic horizons five years post fire [START_REF] Waldrop | Interactive effects of wildfire and permafrost on microbial communities and soil processes in an Alaskan black spruce forest[END_REF]. In contrast, fire stimulated microbial growth by increasing soil ammonium (NH 4 + ) and dissolved organic C concentrations by 20 -52% in a tropical savannah woodland [START_REF] Andersson | Tropical savannah woodland: effects of experimental fire on soil microorganisms and soil emissions of carbon dioxide[END_REF]. However, microbial N immobilization and plant N uptake were reduced by fire. In grasslands, enzyme activities related to C degradation and P cycling decreased, the effect being mild in the first growing season after fire, higher in the second growing season after fire, but subsidizing in the third growing season after fire [START_REF] Gutknecht | Inter-annual variation in soil extra-cellular enzyme activity in response to simulated global change and fire disturbance[END_REF]. In contrast, microbial enzyme activities related to organic N degradation have been shown to either decrease [START_REF] Gutknecht | Inter-annual variation in soil extra-cellular enzyme activity in response to simulated global change and fire disturbance[END_REF] or increase [START_REF] Ajwa | Changes in enzyme activities and microbial biomass of tallgrass prairie soil as related to burning and nitrogen fertilization[END_REF], after fire, likely due to site specific differences. As fire could strongly affect soil nutrients, responses of microbial functional groups to pulse increases of nutrient availability could cause long-term changes in ecosystem. For example, fire significantly decreased soil microbial biomass and changed compositions of nitrogen-fixing and ammonia-oxidizing communities in a mixed conifer forest, linking to fire-related increases in soil NH 4 + or soil pH [START_REF] Yeager | Changes in nitrogen-fixing and ammonia-oxidizing bacterial communities in soil of a mixed conifer forest after wildfire[END_REF]. This can change nitrification rates, NO and N 2 O flux in post-fire ecosystems [START_REF] Serça | Comparison of nitrogen monoxide emissions from several African tropical ecosystems and influence of season and fire[END_REF]. Overall, some inconsistencies among fire effects on soil N processes have been reported, which reflects differences in soil type, fire

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Microbe-mediated soil bioprocesses are considered to be one of the important drivers of ecosystem recovery from fire [START_REF] Neary | Fire effects on belowground sustainability: a review and synthesis[END_REF].

Although many studies have investigated the responses of below-and above-ground communities to fire in grasslands, studies examining the fire effects on soil microbes have largely focused on microbial population sizes and microbial activity [START_REF] Ajwa | Changes in enzyme activities and microbial biomass of tallgrass prairie soil as related to burning and nitrogen fertilization[END_REF][START_REF] Andersson | Tropical savannah woodland: effects of experimental fire on soil microorganisms and soil emissions of carbon dioxide[END_REF][START_REF] Gutknecht | Inter-annual variation in soil extra-cellular enzyme activity in response to simulated global change and fire disturbance[END_REF][START_REF] Waldrop | Interactive effects of wildfire and permafrost on microbial communities and soil processes in an Alaskan black spruce forest[END_REF][START_REF] Yeager | Changes in nitrogen-fixing and ammonia-oxidizing bacterial communities in soil of a mixed conifer forest after wildfire[END_REF]. In contrast, how fire affects the total soil microbial community composition largely remains to be studied in grassland ecosystems, and it is unclear which assembly processes control post-fire microbial community dynamics. Moreover, it is needed to analyze fire effects not only on the taxonomic diversity of soil microbial community, but also on its functional diversity, particularly the range of microbial functional groups linked to soil C and N dynamics, is required to expand our understanding of the effects of fire on soil function in grassland ecosystems.

Here, we studied fire effects in an annual Mediterranean grassland in California, USA, as part of the long-term Jasper Ridge Global Change Experiment (JRGCE) [START_REF] Henry | Interactive effects of elevated CO 2 , N deposition and climate change on extracellular enzyme activity and soil density fractionation in a California annual grassland[END_REF]. In particular, we examined fire effects on the soil microbial community nine months after the prescribed burn in July 2011. We used bacterial 16S rRNA gene amplicon sequencing (Illumina MiSeq) and functional gene array (GeoChip 4.6) analyses to examine the effects of fire on the taxonomic and functional diversities of soil microbial communities. These techniques allow for high-resolution profiling of community composition for both facets of microbial diversity. We also examined whether the fire would had cascading effects on ecosystem functioning such as soil CO 2 efflux.

Given that fire typically increases soil C and N inputs, we hypothesized that there would be a shift of microbial taxonomic composition, and more particularly that functional genes associated with labile C degradation would be stimulated, leading to higher soil CO 2 efflux. Since previous results at our study site showed that fire significantly increased soil NH 4 + concentrations [START_REF] Docherty | Soil microbial responses to fire and interacting global change factors in a California annual grassland[END_REF] in the first growing season post 2003 fire, and soil N 2 O emission [START_REF] Niboyet | Global change could amplify fire effects on soil greenhouse gas emissions[END_REF] in the second and third growing seasons after a 2003 fire, we also hypothesized that functional microbial groups associated with N cycling would be stimulated.

Materials and methods

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Site description

The Californian annual grassland ecosystem experiences a Mediterranean-type climate, in which the growing season is cool and wet from November to May, and the rest of the year is hot and dry. The plant community comprises non-native annual grasses, perennial native grasses, native annual forbs and non-native forbs [START_REF] Henry | Interactive effects of elevated CO 2 , N deposition and climate change on extracellular enzyme activity and soil density fractionation in a California annual grassland[END_REF]

DNA extraction, purification and quantification

DNA was extracted from 5 g of soil samples with a freeze-grinding mechanical lysis method (Zhou, Bruns, & Tiedje, 1996). DNA was purified by gel electrophoresis with a 0.5% low-melting-point agarose, then extracted with phenol and chloroform and finally precipitated with butanol. DNA quality was assessed by ratios of light absorbance at A 260 /A 280 (>1.8) and A 260 /A 230 (>1.7), using a Nanodrop (NanoDrop Technologies Inv., Wilmington, DE, USA). The final DNA concentration was quantified using a PicoGreen® method using a FLUOstar Optima microplate reader (BMG Labtech, Jena, Germany) (S. [START_REF] Liu | The interactive effects of soil transplant into colder regions and cropping on soil microbiology and biogeochemistry[END_REF].

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A PCR amplification targeting the V4 hypervariable region of 16S rRNA genes was carried out to examine the bacterial community diversity, using the primers F515

(5'GTGCCAGCMGCCGCGG3') and R806 (3'TAATCTWTGGGVHCATCAG5'). The Taxonomic annotation was assigned to OTUs based on representative sequences with RDP's 16S Classifier. The number of sequences retrieved from each sample might differ, and this reflects different efficiency of the sequencing process rather than actual biological variation [START_REF] Weiss | Normalization and microbial differential abundance strategies depend upon data characteristics[END_REF]. We thus standardized the number of sequences across samples.

GeoChip hybridization and raw data processing

To study functional genes of bacterial, archaeal and fungal communities, DNA hybridization with GeoChip 4.6 was carried out as previously described (Y. [START_REF] Yang | Responses of the functional structure of soil microbial community to livestock grazing in the Tibetan alpine grassland[END_REF]. Microbial DNA extracted from soil was labeled with Cy-3 fluorescent dye using random priming method, and then purified with a DNA purification kit (Qiagen, Valencia, CA, USA). After drying at 45°C with SpeedVac (ThermoSavant, Milford, MA, USA), labeled DNA was hybridized to GeoChip 4.6 at 42 o C for 16 h in a MAUI hybridization system (BioMicro, Salt Lake City, UT, USA). After washing the slides with buffers provided by NimbleGen, the hybridized DNA was scanned using a

NimbleGen MS 200 Microarray Scanner (Roche, Basel, Switzerland).

Raw GeoChip data were processed as follows: (i) remove the spots with a signal-to-noise ratio (SNR) less than 2.0; (ii) perform log-transformation on the signal

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This article is protected by copyright. All rights reserved intensity for each gene and divide them by the mean intensity in the sample; and (iii) remove genes detected only in one out of the four replicates from the same treatment.

Soil abiotic measurements

To characterize the microclimate of the soil where samples were taken, soil temperature at the depth of 2 cm was measured hourly during 2012 with in situ thermocouples. The average values measured during April, 2012, (the month when soils were sampled), were calculated. One soil core (5 cm diameter x 7 cm deep) per quadrant was sampled and stored at -20 o C prior to soil abiotic measurements. Soil moisture was measured by drying 10 g of freshly collected soil at 105 o C for one week.

Soil pH was measured using 5 g of soil dissolved in distilled water. Soil ammonium (NH 4 + ) and nitrate (NO 3 -) concentrations were measured by suspending 5 g of soil in a 2 M KCl solution, filtering the extracts, and analyzing them colorimetrically using a SEAL Automated Segmented Flow analyzer in the Loyola University, Chicago, IL, USA. Soil CO 2 efflux of each sample was measured three times by a closed-chamber method in April 2012 as previously described [START_REF] Strong | Experimental fire increases soil carbon dioxide efflux in a grassland long-term multifactor global change experiment[END_REF], where a LiCOR LI-6400 portable photosynthesis system and LI6400-09 Soil Flux Chamber (LiCOR, Lincoln, NE, USA) were used.

Vegetation variable measurements

Aboveground plant biomass was collected from 141 cm 2 of each quadrant on the sampling in April 2012. Individual plant species were separated into functional groups (annual grasses, perennial grasses, annual forbs, perennial forbs, and litter), and the biomass was oven-dried at 70°C and weighed. Root biomass was estimated by separating roots from two soil cores (2.5 cm diameter by 15 cm depth) from the same area of the aboveground biomass collection, and the root biomass was oven-dried at 70°C and weighed.

Statistical analyses

Unpaired t-tests were used to determine fire effects on relative abundances of microbial taxa, functional genes and soil and vegetation variables. P values were adjusted by the Benjamini-Hochberg method with sequentially modified Bonferroni Correction, using the p.adjust function in the vegan package in R version 3.5.0.

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Permutational multivariate ANOVA (PERMANOVA) was implemented to rank global change drivers of the composition of environmental variables, using adonis function in the vegan package. Analysis of variance (ANOVA) was performed to rank global change drivers of each environmental variable, using aov function in the vegan package. Three nonparametric dissimilarity tests (Multi-response permutation procedure (mrpp), Analysis of similarities (anosim) and Permutational multivariate analysis of Variance (adonis) in the vegan package in R) were used to compare differences of microbial community composition. Shannon Index (H') was used to calculate the α-diversity of the microbial community in the unburned and burned plots, using the alpha.g function in the ieggr package in R.

Microbial community assembly could be shaped by interspecies competition for resources and cooperative interactions (Zelezniak et al., 2015). Metabolically interdependent microbial groups are strongly coupled through mutualistic interactions which support species growth: thus these groups form persistent sub-networks within the soil microbial communities (Zelezniak et al., 2015). Such high degree of connectivity among keystone taxa can dampen soil microbial compositional changes in response to environmental changes and disturbances [START_REF] Herren | Keystone taxa predict compositional change in microbial communities[END_REF]Zelezniak et al., 2015). Therefore, we used linear mixed model to examine whether the connectivity among keystone taxa, measured by cohesion metrics, could explain microbial compositional changes. Cohesion metrics measure the instantaneous connectivity of a microbial community and contain two metrics showing the strength of positive and negative relationships between taxa. Bray-Curtis dissimilarity was used to measure community compositional changes at OTUs level. The regression analysis using the most highly connected taxa was conducted to examine the relationship between Bray-Curtis dissimilarity and cohesion metrics. The R 2 of each model was recorded. Highly connected taxa would be better predictors of the whole bacterial community than randomly selected taxa since the former has disproportionate influence in determining bacterial community dynamics. Regression analyses using random subsets of taxa were repeated 500 times to generate a distribution of model R 2 . We compared the R 2 value of a model using the most highly connected taxa with that of models using random taxa: if the former is higher than the 95 th percentile of the latter, then the model using the most highly connected taxa was considered to be significantly different from the model using random taxa, suggesting that interactions among keystone taxa indeed have an important role regarding

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This article is protected by copyright. All rights reserved compositional changes of the bacterial community. In addition, we used the pnull function in the ieggr package in R to calculate the β-nearest taxon index (βNTI), which measures the degree to which microbial community composition is governed by environmental filtering based on OTUs' ecological niches [START_REF] Stegen | Quantifying community assembly processes and identifying features that impose them[END_REF].

Using the psem function in the piecewiseSEM package in R, structural equation models (SEM) were also fitted to calculate the direct and indirect effects of soil variables, soil microbial community composition and plant biomass on soil CO 2 efflux rates. Model fits were measured by a non-significant Shipley's test of d-separation, with Fisher's P > 0.05 indicating that no paths were missing, and low Akaike information criterion (AIC) being used to choose among competing models [START_REF] Jing | The links between ecosystem multifunctionality and above-and belowground biodiversity are mediated by climate[END_REF]. The first principal components (PCo1) of microbial taxonomic community were used to represent the microbial community composition, using the pco function in the vegan package in R.

Results

The fire impact on the soil bacterial community did not vary largely across different levels of CO 2 , nitrogen deposition, warming and precipitation (Table S1). Furthermore, of over 26 possible interactive effects tested between fire and other global climate changes, only one was statistically significant -the interaction between fire and nitrogen deposition on soil nitrate concentration (Table S2 &S3). Since the responses to fire across the sixteen factorial combinations of CO 2 , nitrogen deposition, warming and precipitation (n=64) were similar to those for fire as a sole treatment (n=4) (Table S1), data were pooled across all the other treatment levels following a previous example [START_REF] Reich | Nitrogen limitation constrains sustainability of ecosystem response to CO2[END_REF]. The n=64 samples increased the statistical power and identified trends valid across the range of climate change scenarios tested at the Jasper Ridge Global Change Experiment.

Changes of taxonomic composition of bacterial communities by fire

A total of 8,882,825 sequences were obtained, ranging from 29,275 to 92,183 reads per sample. After re-sampling with 29,275 sequences per sample, 87,275 operational taxonomic units (OTUs) were obtained. Singletons were removed, which might represent inherently unreplicated data possibly resulting from sequencing errors [START_REF] Dickie | Insidious effects of sequencing errors on perceived diversity in molecular surveys[END_REF], leading to a total of 64,515 OTUs. Using three non-parametric statistical analyses (Anosim, Adonis, and MRPP), we found that fire significantly

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This article is protected by copyright. All rights reserved changed taxonomic composition (P ≤ 0.003 for all three methods, Table 1). However, the α-diversity of the bacterial community, based on Shannon index, was similar between the unburned (8.16±0.02) and burned (8.24±0.02) sites (P = 0.270). 4,503

unique OTUs were detected in unburned plots, and 6,744 unique OTUs in burned plots, while 53,268 OTUs were found in both plots (Fig. S1a). The relative abundance of Proteobacteria was significantly higher in burned plots (P = 0.047), while the relative abundances of Planctomycetes (P = 0.047), OD1 (P = 0.009), Chlorobi (P = 0.009), OP11 (P = 0.007) and Spirochaetes (P = 0.006) were significantly lower in burned plots (Fig. S1b). At finer taxonomic levels, relative abundances of 19 genera (such as Agromyces, Ferruginibacter and Pasteuria) were significantly decreased, whereas those of 16 genera (such as Actinoplanes, Bdellovibrio and Flavisolibacter)

were significantly increased (Table S4).

Cohesion metrics were significant predictors of compositional changes in burned sites (P = 0.017 and P < 0.001) (Table S5), with both the negative and positive cohesion metrics related to the compositional changes (P < 0.001) (Fig. S2). The R 2 values of models using the most highly connected taxa remained unchanged when the least-connected taxa were removed, whereas the R 2 values of models containing random subsets of taxa decreased when more taxa were removed (Fig. 1a). The explanatory power of these models increased to 13% when a small subset (46) of highly connected taxa was used to calculate the cohesion metrics, suggesting that biotic interactions among taxa could partially explain the community compositional change. These keystone taxa included 13 genera from Proteobacteria (such as Rudaea, Bauldia and Microvirga), 9 genera from Acidobacteria (Gp1-Gp7, Gp16 and Gp17), 6 genera from Actinobacteria (such as Rugosimonospora, Catenulispora and Agromyces), 6 genera from Bacteroidetes (sucha as Terrimonas, Adhaeribacter and Ferruginibacter), 4 genera from Planctomycetes (Gemmata, Planctomyces, Singulisphaera and Isosphaera), and 7 genera from Armatimonadetes, Chloroflexi, Firmicutes, Verrucomicrobia and WS3 (Table S6).

In burned sites, the models with highly connected taxa showed significantly higher performance than those with randomly selected taxa (Fig. 1a). As the negative cohesion was a better predictor of compositional changes of the bacterial community than positive cohesion (Fig. S2), we focused on 46 genera showing the strongest negative associations with others. Among them, 7 genera of Gp5 (P = 0.039),

Agromyces (P = 0.010), Ferruginibacter (P = 0.039), Pasteuria (P = 0.001),

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This article is protected by copyright. All rights reserved Planctomyces (P = 0.001), Isosphaera (P = 0.041) and Verrucomicrobium (P < 0.001) decreased in relative abundances compared to control (Fig. S3). Many of those highly connected genera had low relative abundances. For example, Rudaea was the 9 th most connected genus but its relative abundance ranked only the 144 th among all the genera, with a mean relative abundance across all treatments of 0.03% (Table S6). In unburned sites, the explanatory power of models constructed using either randomly selected or most connected taxa remained very low (< 2%, Fig. 1a), suggesting that there was no keystone taxa effect in the unburned sites.

The β-nearest taxon index (βNTI), indicating the degree to which bacterial community composition is governed by environmental filtering, increased from 3.28 ± 0.025 in unburned plots to 3.74 ± 0.027 in burned plots (P < 0.001) (Fig. 1b), suggesting that the bacterial community was more strongly governed by environmental filtering in burned plots.

Changes of microbial functional potentials by fire

A total of 68,695 microbial functional genes were detected by GeoChip. Fire caused significant changes in the overall functional composition of soil microbial communities according to the three non-parametric statistical analyses conducted (P < 0.001) (Table 1) and significantly decreased microbial functional α-diversity.

For functional genes associated with C degradation, the signals detected (hereafter called relative abundances) for 16 relevant genes all declined in response to fire (see Fig. 2). In contrast, only camDCAB had higher abundances for burned compared to unburned plots. For methane cycling, the abundances of the mcrA gene related to methanogenesis and mmoX genes related to methane oxidation decreased in burned sites (Fig. S4). Other methane-oxidation genes (pmoA and hdrB) remained unchanged.

Similar to C cycling genes, a number of N cycling genes also declined in response to fire (see Fig. 3). In contrast, the abundances of the two genes nifH and amoA were not significantly altered by fire (Fig. 3). The P cycling genes encoding phytase, polyphosphate kinase (ppk) and exopolyphosphatase (ppx) also decreased in response to fire (Fig. S5).

Fire influence on soil and vegetation variables

After nine months post fire, aboveground plant biomass (+25.8%, P = 0.034) and

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This article is protected by copyright. All rights reserved belowground biomass increased (+24.6%, P = 0.080) but litter mass decreased (-86.6%, P < 0.001) (Table S7). Soil temperature also increased (+1 o C, P < 0.001) (Table S7). Soil NO 3 -decreased from 346 mg L -1 to 189 mg L -1 (P = 0.029), and soil NH 4 + from 834 mg L -1 to 389 mg L -1 (P = 0.034) (Table S7). Soil CO 2 efflux significantly increased in the burned plots (+ 37.1%, P < 0.001) (Table S7). Other soil variables such as soil moisture, pH and TN remained unchanged by fire (Table S7).

Drivers of changes in soil functioning and bacterial communities

In the unburned sites, soil CO 2 efflux was correlated to root biomass (β = 0.436, P = 0.003) and to a similar extent to bacterial community composition (β = 0.421, P = 0.006) (Fig. 4a). In contrast, soil CO 2 efflux in burned sites was only and strongly correlated to root biomass (β = 0.936, P = 0.044, Fig. 4b). In these burned sites, bacterial community composition was related to aboveground plant biomass (β = 0.205, P = 0.011) and soil moisture (β = 0.171, P = 0.043), though soil pH (β = 0.610, P < 0.001) had stronger effects on microbial community (Fig. 4b).

Discussion

Fire significantly altered the taxonomic composition of the bacterial community. The change could be predicted by cohesion metrics revealing the existence of 'keystone' taxa, which exert disproportionate influence on community composition through strong biotic interactions [START_REF] Herren | Keystone taxa predict compositional change in microbial communities[END_REF]. Biotic interactions can include metabolite exchanges among taxa, which enhances self-reinforcing modules within microbial communities that can stabilize community composition [START_REF] Levy | Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules[END_REF]. More particularly, stronger negative cohesion was related to lower levels of compositional change in the burned sites (Table S5). Those strong negative cohesion values represented the negative feedback loops formed by biotic interactions among taxa, which had an important role in stabilizing community composition and mitigating the effects of fire. Similar results were reported for other environmental disturbances [START_REF] Coyte | The ecology of the microbiome: networks, competition, and stability[END_REF][START_REF] Konopka | Dynamics in microbial communities: unraveling mechanisms to identify principles[END_REF]. This is also consistent with recent studies reporting that negative interactions among taxa, such as competition and predation, prevail in microbial communities [START_REF] Herren | Keystone taxa predict compositional change in microbial communities[END_REF]. For instance, a previous study focusing on bacterial strains isolated from a rainwater pool showed that negative interactions accounted for a great majority of pairwise taxa interactions, owing to potential resource competition among

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This article is protected by copyright. All rights reserved microbes [START_REF] Foster | Competition, not cooperation, dominates interactions among culturable microbial species[END_REF]. Since microbial diversity is typically high, investing in positive interactions often specific to a few other species could bring little evolutionary benefit, which may explain the dominance of negative interactions [START_REF] Foster | Competition, not cooperation, dominates interactions among culturable microbial species[END_REF].

Higher βNTI values in burned sites indicated more phylogenetically clustered bacterial assemblages than expected by chance and suggested that fire increased the relative influence of deterministic environmental filtering for shaping bacterial communities (S. [START_REF] Yang | Variations of soil microbial community structures beneath broadleaved forest trees in temperate and subtropical climate zones[END_REF]. This result leads us to propose an explanation to the observed decreased microbial functional potentials, especially those related to C degradation and N cycling. As fire stimulated above-and below-ground plant growth, plant-bacterial competition for soil inorganic N might be enhanced, possibly imposing higher deterministic environmental filtering on bacterial communities. This would explain the higher βNTI values and is consistent with previous works on microbial assemblages in salt marsh, desert, agricultural fields, aquatic and forest ecosystems [START_REF] Caruso | Stochastic and deterministic processes interact in the assembly of desert microbial communities on a global scale[END_REF][START_REF] Dini-Andreote | Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession[END_REF][START_REF] Mendes | Taxonomical and functional microbial community selection in soybean rhizosphere[END_REF][START_REF] Wang | Phylogenetic beta diversity in bacterial assemblages across ecosystems: deterministic versus stochastic processes[END_REF][START_REF] Yang | Variations of soil microbial community structures beneath broadleaved forest trees in temperate and subtropical climate zones[END_REF].

In particular, [START_REF] Jurburg | Autogenic succession and deterministic recovery following disturbance in soil bacterial communities[END_REF] reported that a heat disturbance imposed a strong selective pressure on the community composition of active soil bacteria. In this case, phylogenetic turnover patterns indicated that the community experienced stronger deterministic selection during recovery post heat shock. Overall, significantly increased βNTI values suggested higher deterministic environmental filtering on soil bacterial communities following fire and provided an insight into the mechanisms structuring community composition.

In sharp contrast to higher above-and belowground plant biomass in response to fire (Table S7), most of the microbial genes related to C degradation were significantly decreased (Fig. 2). Among all the 16,326 C degradation genes detected in our study, 1.9% were archaeal genes, 14.7% were fungal genes and 83.3% were bacterial genes. Archaeal, fungal and bacterial C degradation genes were affected by fire to similar extent, approximately one-third of each category being significantly changed. Fire largely consumed the standing dead plant biomass, which likely explains that litter mass was reduced by 86.6% in burned plots (Table S7). In the unburned plots, litter mass was probably not only comprised of dead biomass produced in the nine months period since fire but also from a longer time frame.

Therefore, it is likely that the decrease in microbial C degradation genes in burned

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This article is protected by copyright. All rights reserved plots was driven by a strong decrease in both fresh and old aboveground plant material inputs to soil. Consistently, there was significant decrease in the relative abundance of the amyX and glucoamylase genes associated with degradation of labile C in burned plots. The reduced access to labile C by microbial communities could be aggravated by the formation of severely or partly charred plant necromass (i.e., charcoal) by fire, which effectively absorbs soil organic C, especially plant root exudates [START_REF] Mackenzie | Charcoal and shrubs modify soil processes in ponderosa pine forests of western Montana[END_REF]. Charcoal has indeed a polycyclic aromatic structure that makes it chemically and biologically stable [START_REF] Hart | Post-fire vegetative dynamics as drivers of microbial community structure and function in forest soils[END_REF]. However, we also observed decreases in the abundances of genes related to the degradation of chemically recalcitrant C, i.e., chitin and lignin (Fig. 2). Several mechanisms could offer explanations. First, absorbed soil organic C can block charcoal pores, preventing degradation of the charcoal surface by microbial enzymes as well as desorption of charcoal components into the solution (Zimmerman, Gao, & Ahn, 2011).

Consistently, grass charcoal showed a negative priming effect in soil with low organic matter, reducing C mineralization rates (Zimmerman et al., 2011). Second, charred C is more resistant to degradation owing to the formation of mineral-organic aggregates [START_REF] Czimczik | How surface fire in Siberian Scots pine forests affects soil organic carbon in the forest floor: Stocks, molecular structure, and conversion to black carbon (charcoal)[END_REF][START_REF] Jastrow | Mechanisms controlling soil carbon turnover and their potential application for enhancing carbon sequestration[END_REF].

Third, charcoal contains many toxic substances, such as dioxins, furans, phenols and ethylene, that reduce microbial activities, allowing charred C to reside in soil for a long time (Zimmerman et al., 2011).

However, we observed an increase of soil CO 2 efflux by 37% (Table S7), which was similar to the observation after the 2003 fire at the JRGCE during the second and third growing seasons post fire [START_REF] Niboyet | Global change could amplify fire effects on soil greenhouse gas emissions[END_REF]. Post-fire increases of soil CO 2 efflux ranging from 10% to 33% were observed in many grassland systems [START_REF] Knapp | Determinants of soil CO2 flux from a sub-humid grassland: effect of fire and fire history[END_REF][START_REF] Munoz-Rojas | Soil respiration dynamics in fire affected semi-arid ecosystems: Effects of vegetation type and environmental factors[END_REF][START_REF] Xu | Water-and plant-mediated responses of soil respiration to topography, fire, and nitrogen fertilization in a semiarid grassland in northern China[END_REF]. In our grassland, this effect was attributed to increased plant root biomass (Fig. 4b), consistent with previous studies indicating that plants mediated the increased CO 2 efflux post-fire in grassland ecosystems [START_REF] Strong | Experimental fire increases soil carbon dioxide efflux in a grassland long-term multifactor global change experiment[END_REF][START_REF] Xu | Water-and plant-mediated responses of soil respiration to topography, fire, and nitrogen fertilization in a semiarid grassland in northern China[END_REF]. This could be attributed to reduced early season shading by litter removal, and to a fertilization effect as litter combustion increased N and P inputs to soil [START_REF] Henry | Interactive effects of fire, elevated carbon dioxide, nitrogen deposition, and precipitation on a California annual grassland[END_REF]. Increased respiration rates from the increased root biomass and higher soil temperatures in the burned plots could also contribute to this plant-mediated mechanism [START_REF] Strong | Experimental fire increases soil carbon dioxide efflux in a grassland long-term multifactor global change experiment[END_REF].

Alternatively, increased CO 2 efflux can be attributed to increased soil moisture

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This article is protected by copyright. All rights reserved [START_REF] Knapp | Determinants of soil CO2 flux from a sub-humid grassland: effect of fire and fire history[END_REF][START_REF] Munoz-Rojas | Soil respiration dynamics in fire affected semi-arid ecosystems: Effects of vegetation type and environmental factors[END_REF][START_REF] Xu | Water-and plant-mediated responses of soil respiration to topography, fire, and nitrogen fertilization in a semiarid grassland in northern China[END_REF], though it could be more important in the short than medium and long terms. In contrast, when soil moisture was limited during the warm growing season, [START_REF] Vargas | Precipitation variability and fire influence the temporal dynamics of soil CO2 efflux in an arid grassland[END_REF] also found that soil CO 2 efflux rates in an arid grasslands decreased by nearly 70% in 2-year post fire.

Bacterial community composition, as represented by the first principal component of bacterial community, was not significantly correlated with changes of soil respiration in burned sites (Fig. 4b), reflecting a decreased microbial C degradation potential (Fig. 2). In contrast, soil bacterial community composition was significantly correlated with plant aboveground biomass in burned sites (Fig. 4b). Similarly, a previous study found that plant aboveground biomass and the flux of C assimilated to roots drove both microbial community composition and soil respiration [START_REF] Litton | Soil-surface carbon dioxide efflux and microbial biomass in relation to tree density 13 years after a stand replacing fire in a lodgepole pine ecosystem[END_REF]. The apparent lack of stimulated degradation of older soil C by microbial community, as shown by decreased soil C degradation potentials (Fig. 2), suggests that there is a negative feedback to stabilize recalcitrant C after fire.

Fire is a strong controller of soil mineral N availability and plant community in semi-arid grassland ecosystems. At the JRGCE, the 2003 fire increased soil NH 4 + concentration [START_REF] Docherty | Soil microbial responses to fire and interacting global change factors in a California annual grassland[END_REF] but decreased the N:P ratio of plant tissue [START_REF] Henry | Interactive effects of fire, elevated carbon dioxide, nitrogen deposition, and precipitation on a California annual grassland[END_REF] in the first growing season post fire. At the second year after the 2003 fire, there was a significant decrease of soil NH 4 + concentration, as well as a decreasing trend of gross N mineralization rates [START_REF] Niboyet | Global change could amplify fire effects on soil greenhouse gas emissions[END_REF]. We found that the 2011 fire significantly decreased both soil NH 4 + and NO 3 -concentrations (Table S7). Due to the low microbial activity and removal of aboveground plants, leaching likely occurred during the rainfalls after fire, leading to a substantial loss of inorganic N. In accordance, C. L. [START_REF] Turner | Soil N and plant responses to fire, topography, and supplemental N in tallgrass prairie[END_REF] showed that fire reduced soil net N mineralization rates. In contrast, total plant production nine months after the 2003 fire also increased in the burned plots supplied with nitrate [START_REF] Henry | Interactive effects of fire, elevated carbon dioxide, nitrogen deposition, and precipitation on a California annual grassland[END_REF], while 33 months after the 2003 fire, aboveground and belowground plant biomasses were indistinguishable between the unburned and burned plots [START_REF] Docherty | Soil microbial responses to fire and interacting global change factors in a California annual grassland[END_REF]. Both aboveground and belowground plant biomasses were increased by fire (Table S7). Therefore, the stimulatory effects of fire on plant biomass, possibly through the elimination of detrital shading and increase of soil temperature (Table S7), can offset the negative effects derived from reduced N supply. Our results suggest that post-fire controls of soil N cycling and plant biomass may operate independently,

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This article is protected by copyright. All rights reserved leading to a large decoupling between an increased plant growth and decreased N cycling and availability in burned grasslands [START_REF] Reich | Fire and vegetation effects on productivity and nitrogen cycling across a forest-grassland continuum[END_REF][START_REF] Turner | Soil N and plant responses to fire, topography, and supplemental N in tallgrass prairie[END_REF].

Many microbial genes related to N cycling were decreased in response to fire, which was likely due to the substantial decrease of litter biomass (from 166 to 22 g/m 2 ) and might also explain the reduced soil net N mineralization rate often observed post fire [START_REF] Reich | Fire and vegetation effects on productivity and nitrogen cycling across a forest-grassland continuum[END_REF][START_REF] Turner | Soil N and plant responses to fire, topography, and supplemental N in tallgrass prairie[END_REF]. Consistent with decreased soil mineralization rates in burned northern forest ecosystems [START_REF] Smithwick | Postfire Soil N Cycling in Northern Conifer Forests Affected by Severe, Stand-Replacing Wildfires[END_REF], we found that ureC -which is related to ammonification-was decreased (Fig. 3). Although the pyrolysis of organic matter and the deposition of N in ash may have led to a short-lived pulse of soil NH 4 + after fire [START_REF] Smithwick | Postfire Soil N Cycling in Northern Conifer Forests Affected by Severe, Stand-Replacing Wildfires[END_REF], microbial genes related to N cycling tended to decrease in the longer term. For example, the relative abundances of genes associated with denitrification (e.g., nirS

and nosZ) and dissimilatory nitrite reductase decreased steadily throughout several years post fire [START_REF] Cobo-Diaz | Metagenomic assessment of the potential microbial nitrogen pathways in the rhizosphere of a mediterranean forest after a wildfire[END_REF][START_REF] Liu | Soil environmental factors rather than denitrification gene abundance control N2O fluxes in a wet sclerophyll forest with different burning frequency[END_REF], which was attributed to reduced soil moisture and availability of C and N substrates (X. [START_REF] Liu | Soil environmental factors rather than denitrification gene abundance control N2O fluxes in a wet sclerophyll forest with different burning frequency[END_REF][START_REF] Smithwick | Postfire Soil N Cycling in Northern Conifer Forests Affected by Severe, Stand-Replacing Wildfires[END_REF]. However, almost three years after the 2003 fire disturbance, soil N 2 O efflux and denitrifying enzyme activity remained higher in our burned plots [START_REF] Niboyet | Global change could amplify fire effects on soil greenhouse gas emissions[END_REF]. This inconsistency might be caused by different sampling time after fire disturbance.

Although fire did not change the abundance of amoA gene associated with nitrification, the abundance of hao gene -also associated with nitrification-was significantly decreased by 7.2% (Fig. 3), which was concomitant with lower soil NH 4 + concentration (Table S7). Several mechanisms might contribute to those results. First, increased soil temperatures could stimulate NH 4 + to volatilize into atmosphere [START_REF] Romanyà | Short-term effects of fire on soil nitrogen availability in Mediterranean grasslands and shrublands growing in old fields[END_REF]. Indeed, fire caused a loss of 10-40 kg N ha -1 yr -1 across a forest-grassland continuum by volatilization [START_REF] Reich | Fire and vegetation effects on productivity and nitrogen cycling across a forest-grassland continuum[END_REF]. Second, nitrification may be inhibited by charcoal (M. G. [START_REF] Turner | Inorganic nitrogen availability after severe stand-replacing fire in the Greater Yellowstone ecosystem[END_REF]. Third, soil P could affect the competition between nitrifiers and other soil organisms. Although we did not measure the concentration of available P in soil, the significant decrease of P cycling genes in relative abundance (Fig. S5) suggested a decreased microbial potential for P cycling. Fourth, there might be an increased competition for soil N between plants and microbes, including nitrifiers, thus leading to lower nitrification rates (Fig. 3 &4).

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Our findings highlight how fire affects microbial communities and soil biogeochemical cycling potentials nine months post fire, reflecting the recovery of the ecosystem during the first growing season after fire. First, our results add to growing evidence from multiple grassland ecosystems that fire increases growing-season soil CO 2 efflux, suggesting that altered fire regimes could affect grassland C turnover rates and increase greenhouse gas emissions from grasslands to atmosphere. As the grasslands in our study are used to frequent fires [START_REF] Docherty | Soil microbial responses to fire and interacting global change factors in a California annual grassland[END_REF], the plant community is likely to be highly resilient to infrequent, high-intensity fires by developing adaptive plant traits to fire regimes [START_REF] Keeley | Fire-driven alien invasion in a fire-adapted ecosystem[END_REF][START_REF] Keeley | Fire as an evolutionary pressure shaping plant traits[END_REF]. This might contribute to higher post-fire soil CO 2 efflux than that in grasslands that are not well adapted to frequent fire regimes [START_REF] Knapp | Determinants of soil CO2 flux from a sub-humid grassland: effect of fire and fire history[END_REF]. Second, we demonstrate that negative biotic interactions and deterministic filtering have a key role regarding fire effects on the taxonomic composition of the bacterial community. Third, our study shows that fire influence on soil microorganisms and plants induces a decoupling between increased plant growth both above-and below-ground, and decreased soil N cycling potentials and availability. Therefore, fire might exacerbate the competition for soil N between plants and soil microorganisms, then altering the competitive balance between them.

Fire-induced N limitation in this grassland could also constrain the responses of plant and soil microorganisms to other global change factors, such as elevated CO 2 , warming and precipitation.

Tables

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This article is protected by copyright. All rights reserved to the number of taxa excluded from cohesion calculations under unburned or burned conditions. For each number of taxa on the X-axis, cohesion values were calculated for the most connected taxa (red lines) and for a random subset of taxa (blue lines).

The solid blue lines show the median adjusted model R 2 for randomly selected subsets, while the dotted blue lines give the 5% and 95% intervals. When 46 genera were used to calculate cohesion under burned conditions, models using the most highly connected taxa had significantly higher model R 2 than models using random taxa, suggesting that biotic interactions among taxa could partially explain the community compositional changes after fire. The panel on the right (b) indicates β-nearest taxon index (β-NTI) in unburned and burned sites.

Fire

  affects the taxonomic and functional composition of soil microbial communities, with cascading effects on grassland ecosystem functioning Sihang Yang, Qiaoshu Zheng, Yunfeng Yang, Mengting Yuan, Xingyu Ma, Nona R. Chiariello, Kathryn M. Docherty, Christopher B. Field, Jessica L. M. Gutknecht, Bruce A. Hungate, et al. To cite this version: Sihang Yang, Qiaoshu Zheng, Yunfeng Yang, Mengting Yuan, Xingyu Ma, et al.. Fire affects the taxonomic and functional composition of soil microbial communities, with cascading effects on grassland ecosystem functioning. Global Change Biology, 2020, 26 (2), pp.431-442. 10.1111/gcb.14852. hal-02423302

  PCR products were sequenced by 2x250 bp paired-end sequencing with MiSeq instrument (Illumina, San Diego, CA, USA). Raw sequence data were processed on the Galaxy platform (http://zhoulab5.rccc.ou.edu:8080). The paired-end raw sequences were demultiplexed to remove PhiX sequences and then sorted to appropriate samples based on paired barcodes. These sequences were then trimmed with Btrim based on quality scores and paired-end reads were combined with FLASH into full-length sequences[START_REF] Magoc | FLASH: fast length adjustment of short reads to improve genome assemblies[END_REF]. Sequences less than 200 bp or containing ambiguous bases were removed and chimeric sequences were discarded based on prediction by Uchime using the reference database mode.Operational taxonomic units (OTUs) were clustered at the 0.97 identity level by UCLUST (R.C.[START_REF] Edgar | Search and clustering orders of magnitude faster than BLAST[END_REF][START_REF] Edgar | UCHIME improves sensitivity and speed of chimera detection[END_REF].

Figure captions Fig. 1 :

 captions1 Figure captionsFig. 1: The panel on the left (a) indicates relationships between the adjusted model R 2 values for the Bray-Curtis dissimilarity vs. cohesion regression analysis according

Fig. 2 :

 2 Fig. 2: Percentage changes in relative abundances of C degradation genes induced by fire, calculated as: 100*((mean value for burned samples/mean value for unburned samples) -1). The complexity of C is arranged from labile (on the left) to recalcitrant (on the right). Mean values and standard errors are presented (n = 64). Significant negative changes in the relative abundances of the genes by fire are indicated in blue, and significant positive changes in the relative abundances of the genes by fire are indicated in red. Asterisks indicate significant differences: *, P < 0.05; **, P < 0.01; ***, P < 0.005. P values were adjusted by the Benjamini-Hochberg method using sequentially modified Bonferroni Correction.

Fig. 3 :Fig. 4 :

 34 Fig. 3: Percent changes in relative abundances of N cycling genes induced by fire, calculated as: 100*((mean value in burned samples/mean value in unburned samples) -1). Average signal intensities of the genes in blue were significantly decreased by fire. Average signal intensities of the genes in black were not significantly altered by fire. Genes in grey were not targeted by GeoChip. Asterisks indicate significant differences: *, P < 0.05; **, P < 0.01; ***, P < 0.005. P values were adjusted by the Benjamini-Hochberg method using sequentially modified Bonferroni Correction.

Table 1 .

 1 Results of statistical analyses testing for a fire effect on the taxonomic and functional compositions of microbial communities. Significant effects are indicated in bold.

	Statistical Approaches		Taxonomic	Functional
	MRPP	Δ	0.479	0.287
		P	0.001	<0.001
	Anosim	R	0.069	0.117
		P	0.001	0.001
	Adonis	F	2.36	10.087
		P	0.003	<0.001

Acknowledgements

The authors wish to thank staffs of the Jasper Ridge Global Change Experiment for site preserving and sampling assistance. This study was funded by grants from the US Department of Energy, the US NSF (DEB-0092642/0445324), the National Natural Science Foundation of China (41430856, 41877048, and 41825016), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB15010102), the Packard Foundation, the Morgan Family Foundation, and the French CNRS/INSU -EC2CO Program (project INTERACT).

Data availability

GeoChip data are available online (www.ncbi.nlm.nih.gov/geo/) with the accession number GSE107168 and GSE126737. MiSeq data are available in NCBI SRA database with the accession number PRJNA419455.

Conflict of interest

The authors declare no conflict of interest.

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This article is protected by copyright. All rights reserved efflux rate for (a) unburned and (b) burned plots. Solid lines represent significant paths (P < 0.05, piecewise SEM), and dashed lines represent insignificant paths (P > 0.05, piecewise SEM). Blue arrows represent negative paths, and black arrows represent positive paths. NO 3 -, soil nitrate concentration; bacterial community composition: the first principal components (PCo1) of the principal co-ordinate analysis of soil bacterial community. Overall fit of piecewise SEM was evaluated using Shipley's test of d-separation, with Fisher's C statistics (if P > 0.05, then no paths are missing, and the model is a good fit) and Akaike information criterion (AIC).

For each variable, the percentage of the variance explained by the model is indicated