Plant And Soil Microbial Communities Regulate Soil Respiration In Response To Precipitation And Land Use In An Inner Mongolian Grassland

Purpose: Changes in precipitation amount and land use are expected to greatly impact soil respiration (R s ) of grassland ecosystems. However, little is known about whether they can interactively impact R s and how plant and soil microbial communities regulate the response of R s . Methods: Here, we investigated the impacts of altered precipitation amount (–50%, ambient and +50%) and land-use regime (fencing, mowing and grazing) on R s with a eld experiment in the Inner Mongolian grassland. Results: We found that altered precipitation amount impacted R s and its components across the 3-year study period, while land-use regime alone or its interaction with precipitation amount impacted them in certain years. In addition, changed soil microclimate, especially soil moisture, under altered precipitation amount and land-use regime can impact the components of R s either directly or indirectly via inuencing plant and soil microbial communities. Conclusions: Integrating changing precipitation amount and land-use regime within experiment can produce more accurate insights into grassland R s , and chronically shifted plant and soil microbial communities under these changes may result in distinct long-term impacts on R s .

In grassland ecosystems, grazing and mowing are widely used management practices and can strongly impact R s (Cao et al. 2004;Jia et al. 2006Jia et al. , 2007. For example, experimental studies and syntheses have shown that R s often decreases with grazing intensity (Cao et  and mowing may affect R s via processes that are climate dependent, such as changing plant biomass or altering abiotic conditions, the effects of grazing and mowing may depend on climatic conditions. For example, grazing or mowing may in uence soil moisture and consequently affect R s , but such effects may be stronger in arid regions than in humid regions (Wang et al. 2020d; Han et al. 2012). As a result, understanding the grazing and mowing effects on R s in grassland requires consideration of the potential interactive effect of climate and land use regime (Xu et al. 2015; Wang et al. 2020d).
Changes in precipitation and land use regime may interact because they both in uence many of the same abiotic and biotic drivers of R s . Observational (Geng et (Piao et al. 2010, Huang et al. 2015. While previous work in this region has examined the effects of land use and precipitation individually, it remains unclear how land use and precipitation may interact to affect soil respiration (Gong et al. 2014;Yang et al. 2020). Here, we performed an in situ experiment with altered precipitation and land use regimes (i.e. mowing and grazing, Figure S1) to investigate their interactive effects on R s and its components. We also quanti ed soil microclimate, plant and soil microbial communities to elucidate the mechanisms through which the interactive effects occur.

Study site
Our study was conducted at the Maodeng Grassland Ecosystem Research Station of Inner Mongolia University (44°10' N, 116°28' E, 1101 m a.s.l.) located in the Xilingol region of Inner Mongolia, China ( Figure S1a). This area has a temperate semi-arid climate, with a short and cool growing-season (normally starts in May and ends in October). During the 3 years of study (2017-2019), the mean growing-season and annual air temperature was 10.7 °C and -1.2 °C, respectively. Annual growing-season precipitation ranged from 144 to 252 mm yr -1 and accounted for ~90% of total annual precipitation (ranged from 168-278 mm, Figure S2). The natural vegetation is a typical steppe dominated by perennial grasses such as Leymus chinensis, Stipa baicalensis and Cleistogenes squarrosa. Soil developed is a Calcic-Orthic Aridisol according to the US soil taxonomy classi cation system with a mean pH of 8.1 at 0-20 cm soil depth (Wang et al. 2020b).

Experimental design
We established a eld experiment with treatments of precipitation amount and land-use regime using a randomized complete block design with split plot ( Figure S1b). Within each block, we arranged land use treatments in plots and precipitation amount treatments in subplots. Speci cally, in 2011, we established three 100 m × 100 m blocks with 3 m in between. Each block was divided into nine 33.3 m × 33.3 m plots. We applied three land use treatments to each block with 3 plots randomly chosen for each treatment. The three land use treatments are no grazing or mowing (wire fence enclosure), grazing (with 6 sheep in July and August, once per month until residual height of plants reached 6 cm), and mowing (to 6 cm, once per year in August). The intensity of grazing and mowing treatments in our experiment represented a moderate land-use intensity in this region (Baoyin et al. 2014). In 2016, we established precipitation amount treatments at the end of the growing season. Brie y, three 3 m × 5 m subplots with 2 m in between were established within each plot and each subplot was randomly assigned to one of the three precipitation treatments. Consistent with a network of world-wide precipitation amount manipulation experiments (www.drought-net.org), we implemented three levels of precipitation treatment: 50% reduction (Dry), ambient (Amb), and 50% increase (Wet). Reduction in precipitation was achieved by installing ten transparent Panlite sheet channels over the subplot (25 cm wide and 340 cm long). These channels were installed at a ~10° angle above each subplot and covered 50% of its area, resulting in removing 50% of rainwater ( Figure S1b). Removed rainwater was collected and immediately sprinkled to a wet treatment subplot within the same plot after the rain event, resulting in adding 50% of rainwater.
During growing seasons of the 3-year study period (2017-2019), the amounts of precipitation received by subplots of these precipitation treatments ranged from 68-378 mm, representing 75% of the range observed during the last 10 years under natural condition (ranged from 136-454 mm) ( Figure 1).

Soil respiration measurement
We manually measured R s biweekly in each subplot. R s was measured with a LI-8100 Automated Soil CO 2 Flux systems (Li-Cor Inc., Lincoln, NE, USA) on a shallow polyvinyl chloride (PVC) collar (20 cm in diameter and 10 cm in height) installed to a soil depth of 5 cm. We also partitioned the R s into heterotrophic (R h ) and autotrophic (R a ) components. Heterotrophic respiration was measured by installing a deep PVC collar (20 cm in diameter and 45 cm in height) to a soil depth of 40 cm and removing all above-ground vegetation inside these collars. The installation depth is su cient to exclude most organic matter input from plants because > 80% of the belowground biomass is distributed in the top 30 cm ( Figure S3). Because installing deep collars could arti cially increase dead roots input into soil and thus in uence R h , we installed these deep collars 9 months prior to R h measurements to eliminate experimental artifacts (Zhou et al. 2007;Wang et al. 2020c). Autotrophic respiration was calculated as the difference between R s and R h . Concurrent with soil respiration measurements, we measured soil temperature (ST) and moisture (SM) at 5 cm depth using 6000-09TC and GS-1 probes attached to the LI-8100. We also obtained daily air temperature and precipitation from a nearby weather station (~100 m).

Plant and soil sampling
We measured the aboveground (ANPP) and belowground (BNPP) net primary productivities with a harvest method and a root ingrowth-core method (Xu et al. 2015; Liu et al. 2018), respectively. To measure ANPP, we prepared three 1 m × 1 m quadrats in each subplot and randomly selected one of them before each growing season. The selected quadrates within grazing plots were protected with cages.
Although this method overlooks potential plant compensatory growth in response to grazing or mowing within the sampled growing season, the compensatory growth is limited under light and moderate intensity of grazing (Irisarri et al. 2016; Milchunas et al. 2008). Thus, we believe the ANPP measurements are representative of this system. In early August, all green plant tissues within selected quadrats were harvested, oven dried and weighed to obtain ANPP. To measure BNPP, we rst took three soil cores (7 cm in diameter) of 0-50 cm along the diagonal of each subplot at the end of growing season in 2017, sieved (2 mm mesh) soils to remove roots and re lled soil cores with root-free soils collected from the same depth. During the following two years (2018 and 2019), we resampled soils from the same cores at the same time of ANPP measurement, sieved (2 mm mesh) soil samples to obtain roots, oven dried and weighed them to calculate BNPP. Residual root-free soils were put back for BNPP measurements the next year.
In early August of 2019, we sampled three additional soil cores along the diagonal of each precipitation amount treatment subplot and measured microbial biomass carbon (MBC) and nitrogen (MBN) using the chloroform fumigation-extraction method (Vance et al. 1987). Speci cally, we mixed three cores of soils collected from the same subplot, sieved (2 mm mesh) to remove plant tissues, and then weighed 6 aliquots (3 g equivalent). Subsequently, 3 of them were fumigated with ethanol-free CHCl 3 at 25 °C in the darkness for 48 h and the other 3 aliquots were unfumigated. The fumigated and unfumigated samples were extracted with 0.5 M K 2 SO 4 (12 ml) for 30 min on a shaker. Carbon and nitrogen in the K 2 SO 4 extracts were analyzed with a total organic C/N analyzer (Elementar vario TOC, Elementar Co., Germany) and the differences between fumigated and unfumigated samples were converted to MBC and MBN with a conversion factor of 0.45 (Brookes et al. 1985;Liu et al. 2009).

Statistical analysis
In our statistical analyses, measured abiotic (i.e. ST and SM) and biotic (i.e. ANPP, BNPP, MBC and MBN) driving factors of respiration (or their annual mean values) were directly used in analyses, while R s and its components (R h and R a ) were rst natural logarithm transformed and then analyzed. Speci cally, we analyzed the effects of precipitation and land use treatments on ST, SM, R s and its components (R h and R a ) as well as annually measured plant and soil microbial parameters (ANPP, BNPP, MBC and MBN) using linear mixed-effects models. In these models, treatments of precipitation amount, land-use regimes, and date (or year for ANPP and BNPP) of measurement were treated as categorical xed effects, and block, plot and subplot were hierarchically arranged as random effects. The effects of treatments were analyzed in each year separately as well as combined over the 3-year study period (MBC and MBN were measured and analyzed only in 2019). Subsequently, we used structural equation models (SEMs) with annual mean values of variables to quantify direct and indirect impacts of abiotic (ST and SM) and biotic (plant and soil microbial parameters) factors on heterotrophic and autotrophic components of R s . We rst  Table S1). For SM, it had been signi cantly impacted by precipitation amount and land use as well as their interaction in the overall study period (Figure 2e-h, Table S1). For precipitation amount treatments alone, growing-season SM decreased by 22.3% and increased by 30.5% on a 3-year average under dry and wet treatments, respectively (Figure 2h). The effect of wet treatment was less pronounced under mowing. Compared with fencing, grazing and mowing under ambient precipitation, dry treatment respectively decreased SM by 24.0%, 22.9% and 20.4% and wet treatment respectively increased SM by 38.6%, 36.4% and 17.8% (Figure 2h). For land use treatments alone, grazing and mowing treatments respectively increased SM by 8.8% and 7.1% on a 3-year average compared with fencing ( Figure 2h). When separately investigated in different years, the SM was only signi cantly impacted by precipitation amount in each year and signi cantly or marginally signi cantly affected by land-use regime and its interaction with precipitation amount in certain years (Figure 2e-g, Table S1).
Effects of precipitation and land use on above-and below-ground productivity Precipitation amount signi cantly in uenced ANPP, showing a decrease of 72.4% and an increase of 47.4% on average over the three years under dry and wet treatments, respectively (Figure 2l, Table S1). The direction of the precipitation effect is consistently across the years and can all be detected statistically when data from each year were analyzed separately (Figure 2i-k, Table S1). Land-use regime alone had signi cant effects on ANPP in certain years (Figure 2i-k, Table S1), but had no effect on ANPP when integrated over the 3-year period (increased by 6.0% and decreased by 11.6% under grazing and mowing, respectively) (Figure 2l). In addition, we detected a marginally signi cant interaction between precipitation amount and land-use regime in 2018 as the effect of wet treatment was less pronounced under mowing. Compared with fencing, grazing and mowing under ambient precipitation, dry treatment respectively decreased ANPP by 70.7%, 90.3% and 82.4%, while wet treatment respectively increased ANPP by 72.1%, 85.4% and 25% (Figure 2j). The BNPP was signi cantly affected by precipitation amount and land-use regime but not by their interaction when separately considered in different years or integrated over the study period (Figure 2n-p, Table S1). Speci cally, dry and wet treatments respectively resulted in a decrease of 16.1% and an increase of 14.3% in BNPP, while grazing and mowing respectively reduced BNPP by 11.7% and 1.0% on average over the study period (Figure 2p).
For the soil microbial community, precipitation amount signi cantly impacted MBC and MBN, while landuse regime only signi cantly impacted MBC (Figure 3, Table S1). Speci cally, MBC and MBN were respectively reduced by 17.8% and 24.4% under dry treatment and respectively increased by 4.1% and 9.9% under wet treatment (Figure 3). Compared with fencing, grazing decreased MBC by 5.5% and increased MBN by 4.5%, while mowing increased MBC by 8.9% and increased MBN by 9.2% (Figure 3). We also detected signi cant or marginally signi cant interactive effects of precipitation and land use on MBC and MBN as the more pronounced dry effects under grazing (Figure 3). For MBC, dry treatment respectively decreased it by 10.3%, 33.0% and 10.4% under fencing, grazing and mowing compared with those of ambient precipitation, while wet treatment respectively increased it by 0.7%, 4.3% and 7.1% ( Figure 3a). For MBN, dry treatment respectively decreased it by 8.0%, 48.8% and 14.5% under fencing, grazing and mowing compared with those of ambient precipitation, while wet treatment respectively increased it by 0.2%, 21.4% and 6.9% (Figure 3b).

Effects of precipitation and land use on soil respiration and its components
Precipitation amount signi cantly impacted R s and its components in different years and the overall study period (Figure 4, Table S2). On a 3-year average, R s and its autotrophic and heterotrophic components were reduced by 35.9%, 41.7% and 33.1% under dry treatment and increased by 28.5%, 36.0% and 25.0% under wet treatment (Figure 4d, h and l). In addition, land use impacted R s and its components (R a and R h ) across the three years (Figure 4d, h, l, Table S2). Compared with fencing treatment, grazing respectively reduced R s and R a by 1.1% and 5.3% but increased R h by 1.0%, while mowing respectively reduced them by 7.2%, 10.7% and 5.5% on a 3-year average. For the overall study period, precipitation and land use had no interactive impacts on R s and R a , but interactively impacted R h with a marginal signi cance as wet treatment had a more pronounced impact under grazing ( Figure  4l). Compared with fencing, grazing and mowing under ambient precipitation, dry treatment respectively decreased R h by 31.9%, 33.5% and 34.0%, while wet treatment respectively increased R h by 23.2%, 30.6% and 21.2% (Figure 4l). In certain years, land use only signi cantly or marginally signi cantly impacted R s and R h , but had no effect on R a (Figure 4a-c, e-g, i-k, Table S2). In addition, we also detected interactive effects of precipitation and land use on R s , R a and R h in certain years (Figure 4a, 4f and 4i, Table S2).
We then used SEMs to separately explore the direct and indirect effects of abiotic (ST and SM) and biotic (plant and soil microbial parameters) factors on R a and R h (see Figure S4-S6 for results of regressions used to construct initial SEMs as well as Figure S7 and Table S3-S4 for further details of initial SEMs). For R a , our nal SEM model con rmed positive direct in uences of SM and BNPP, as well as a positive indirect impact of SM via BNPP and a negative indirect impact of ST via BNPP (Figure 5a, Table S5). In addition, the nal SEM for R h con rmed positive direct impacts of SM and MBN and a positive indirect impact of SM via MBN (Figure 5b, Table S6).

Discussion
We conducted a three-year manipulative experiment to investigate the effects of altered precipitation amount and land use regime on R s and its components. We found that increasing precipitation generally increases autotrophic, heterotrophic and total R s , but the precipitation effect depends on land use regime. uxes through their in uence on abiotic and biotic factors. We suggest that precisely predicting the consequence of climate change and land use should incorporate these interactions.

Effects of precipitation amount and land-use regime on soil respiration and its components
Our study found that precipitation treatment exerted a more substantial in uence on soil respiration than land-use treatment (Figure 4). This is consistent with the observation that precipitation amount had stronger impacts on both soil moisture and soil temperature than land-use regime (Figure 2a-h). The effects of precipitation on soil respiration is multifaceted. On the one hand, precipitation amount had positive effect on soil moisture (Figure 2e-h). High soil moisture due to precipitation increase presumably led to increase in R s and its components as observed in many previous studies ( A previous precipitation manipulation experiment in our region found that increased soil moisture under high precipitation amount can improve the biomass of soil microbes, and therefore, stimulating R h and R s (Liu et al. 2009). This pattern is consistent with the current study. Moreover, our SEM model further showed that soil moisture had a stronger direct effect on R h than soil microbial biomass (nitrogen) (Figure 5b). This result suggests that the impact of soil moisture on substrate diffusion might be more important than its impact on microbial biomass in regulating R h .
The R s can also be strongly regulated by plant productivity as a productive plant community can allocate more photosynthate to belowground, stimulating R a and thus R s . At global and regional scales, previous studies have shown that R s can be positively impacted by precipitation and/or soil moisture because of their regulation on plant productivity (Geng et al. 2012;Peng et al. 2013), which is consistent with the current study (Figure 5a). In addition, the SEM for R a showed that the direct effect of BNPP on R a was as strong as that of soil moisture (Figure 5a). The regulation of BNPP on R a may result from the capacity of plants to physiologically adapt to water stress via adjusting stomatal conductance (Chaves 2002), changing the vertical distribution of roots and using groundwater of deeper soils (Jackson et al. 2000).
For example, a recent 5-year precipitation manipulation experiment showed that decreasing precipitation amount by 50% results in more plant roots distributed in deeper soils (Liu et al. 2018). Because changing precipitation amount and land-use regime can in uence plant community productivity via chronically shifting its structure in the long term (Xu et al. 2015;Liu et al. 2018), the discovered regulation of plant productivity on R a suggests that the long-term impacts of these changes on R a and R s may differ from the short-term impacts.

Implications And Conclusions
Our 3-year eld experiment combining altered precipitation amount and land-use regime (Baoyin et al. 2014) showed that they can interactively affect R s and its components in certain years ( Figure 4). The detected interactive impact is consistent with but weaker than a recent study in a meadow steppe of northeast China, showing persistent interactive effects of adding precipitation and a heaver intensity of grazing than ours on R s and its autotrophic component over a 2-year study period (Wang et al. 2020d).
These results suggest that the interactive effects of precipitation and land use may be a general phenomenon in Inner Mongolian temperate grasslands. Thus, a precise prediction of ecosystems carbon cycling in response to climate change in this region should consider the land use context. As Inner  Effects of altered precipitation amount (P) and land-use regime (L) on soil temperature (a-d) and moisture (e-h) at 5 cm depth, aboveground (ANPP, i-l) and belowground (BNPP, n-p) net primary productivities. The box plots showed the mean and median (solid and dashed black lines in the boxes), interquartile ranges (boxes) and 10th and 90th percentiles (short black lines). Black cycles represent actual mean values. Results (F values) of the analysis of variance are shown in gure and indicated by *** when P < 0.001, ** when P < 0.01, * when P < 0.05, # when P < 0.10 and n.s. (not statistically signi cant) when P > 0.10.  Effects of altered precipitation amount (P) and land-use regime (L) on soil respiration (Rs, a-d) and its autotrophic (Ra, e-h) and heterotrophic (Rh, i-l) components. The box plots showed the mean and median (solid and dashed black lines in the boxes), interquartile ranges (boxes) and 10th and 90th percentiles (short black lines). Black cycles represent actual mean values. Results (F values) of the analysis of variance are shown in gure and indicated by *** when P < 0.001, ** when P < 0.01, * when P < 0.05, # when P < 0.10 and n.s. (not statistically signi cant) when P > 0.10. Figure 5