Ecological clusters based on responses of soil microbial phylotypes to precipitation explain ecosystem functions

Journal Pre-proof Ecological clusters based on responses of soil microbial phylotypes to precipitation explain ecosystem functions Ying Wu, Jianping Wu, Muhammad Saleem, Bing Wang, Shuijin Hu, Yongfei Bai, Qingmin Pan, Dima Chen PII:

S0038-0717(20)30014-6

DOI:

https://doi.org/10.1016/j.soilbio.2020.107717

Reference:

SBB 107717

To appear in:

Soil Biology and Biochemistry

Received Date: 27 September 2019 Revised Date:

26 December 2019

Accepted Date: 9 January 2020

Please cite this article as: Wu, Y., Wu, J., Saleem, M., Wang, B., Hu, S., Bai, Y., Pan, Q., Chen, D., Ecological clusters based on responses of soil microbial phylotypes to precipitation explain ecosystem functions, Soil Biology and Biochemistry (2020), doi: https://doi.org/10.1016/j.soilbio.2020.107717. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

Graphical Abstract

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Ecological clusters based on responses of soil microbial phylotypes to precipitation explain

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ecosystem functions

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We constructed a 3-year experiment with nine levels of artificial precipitation (100-500 mm) in a

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typical semi-arid steppe. Ecological clusters based on the relationships between the relative

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abundance of phylotypes and dry and wet gradients were correlated with soil C or N mineralization

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rates; these ecological clusters explained 15-24% of the total variance in soil C and N mineralization

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rates. In contrast, soil C or N mineralization rates were not correlated with the commonly measured

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properties (e.g., biomass and diversity) of plant, soil bacterial, and soil fungal communities. Our

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findings indicate that the grouping of soil microorganisms into ecological clusters based on responses

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to precipitation gradients can provide insights into the relationships between soil organisms and

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ecosystem functions.

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Type of paper: Research Paper

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Running Title: Microbial phylotypes and functions

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Ecological clusters based on responses of soil microbial phylotypes to precipitation explain ecosystem

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functions

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Ying Wu1,2,3, Jianping Wu1, Muhammad Saleem4, Bing Wang3,5, Shuijin Hu6, Yongfei Bai3,5, Qingmin

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Pan3*, and Dima Chen2,3*

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Complete affiliations:

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China

State key Laboratory for Conservation and Utilization of Bio-Resources, Yunnan University, Kunming,

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of Sciences, Beijing, China

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*Corresponding author:

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Prof. Dima Chen

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College of Biological and Pharmaceutical Sciences, China Three Gorges University, Yichang, China

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Tel: (+86)-10-6283-6923; Fax: (+86)-10-8259-5771; E-mail: [email protected]

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or Prof. Qingmin Pan

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State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of

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Sciences, Beijing, China

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Tel: (+86)-10-6283-6977; Fax: (+86)-10-8259-5771; E-mail: [email protected]

College of Biological and Pharmaceutical Sciences, China Three Gorges University, Yichang, China State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy

Department of Biological Sciences, Alabama State University, Montgomery, Alabama University of Chinese Academy of Sciences, Beijing, China Department of Entomology & Plant Pathology, North Carolina State University, Raleigh, NC, USA

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Abstract

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Ecological classification has been proposed as a way to more tightly link microbial communities and

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ecosystem functions, but few studies have attempted to relate ecological classifications of microbial

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communities with specific ecosystem functions. Here, we conducted a 3-year experiment with nine

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levels of artificial precipitation (100-500 mm) in a typical semi-arid steppe. The first five levels (≤ 300

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mm) were considered a “dry” gradient, and the last five (≥ 300 mm) were considered a “wet”

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gradient. Increases in precipitation under dry and wet gradients did not alter the alpha diversities of

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soil bacterial, soil fungal, or plant communities, except that increases in precipitation under the dry

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gradient decreased bacterial alpha diversity. Increases in precipitation under the dry and wet

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gradients altered the composition of the soil bacterial community but did not alter the composition

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of the fungal or plant communities. Ecological clusters (ECs) based on the relationships between the

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relative abundance of phylotypes and dry and wet gradients were correlated with soil C or N

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mineralization rates; these ECs explained 14-28% of the total variance in soil C and N mineralization

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rates. In contrast, soil C or N mineralization rates were not correlated with the commonly measured

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properties (e.g., biomass and diversity) of plant, soil bacterial, and soil fungal communities. Our

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findings indicate that the grouping of soil microorganisms into ECs based on responses to

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precipitation gradients can provide insights into the relationships between soil organisms and

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ecosystem functions.

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Keywords: alpha diversity, asymmetrical pattern, beta diversity, precipitation change, precipitation

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gradient, soil function

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Introduction In many terrestrial biomes, precipitation is the primary constraint of soil C and nutrient cycling

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and other ecosystem functions (Knapp and Smith, 2001; Bell et al., 2014; Liu et al., 2016). This is

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especially true for semi-arid ecosystems, which cover 17.7% of Earth's terrestrial surface (Ahlström et

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al., 2015). However, the responses of these ecosystems to shifts in precipitation regimes are difficult

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to predict because climate models suggest that most semi-arid regions will experience highly variable

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precipitation patterns and frequent extreme precipitation and drought events (Savo et al., 2016). It is

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well documented that alterations in precipitation may greatly affect aboveground net primary

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productivity (ANPP), plant diversity, and ecosystem C and N cycling in semi-arid regions (Knapp and

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Smith, 2001; Huxman et al., 2004). Furthermore, soils harbor highly diverse microbial communities

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that are crucial for regulating multiple ecosystem processes (Delgado-Baquerizo et al., 2018;

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Glassman et al., 2018; Saleem et al., 2019). Although past studied have increased our understanding

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of how alterations in precipitation affect soil microbial communities and thereby affect ecosystem

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processes (Bell et al., 2014; Zhou et al., 2018; Chen et al., 2019a), these past studies have seldom

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investigated the effects of precipitation on ecosystem processes based on ecological clusters (ECs) of

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soil microbial phylotypes (Singh et al., 2010; Fierer et al., 2012). The ECs have recently been used to

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reveal how environmental conditions influence the ecological properties of soil microorganisms

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(Philippot et al., 2010; Evans and Wallenstein, 2014; Delgado-Baquerizo et al., 2018). It may therefore

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be useful to determine whether assessment of ECs can increase our understanding of the responses

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of soil microbial communities to climate change and to the relationships between soil microbial

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communities and ecosystem processes (Fierer et al., 2007; Delgado-Baquerizo et al., 2018).

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The responses of soil microbial communities to precipitation change remain highly uncertain.

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Several studies have shown that increased or decreased precipitation does not affect soil microbial

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communities (Angel et al., 2010; Santonja et al., 2017), but other studies indicate that soil microbial

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composition and structure are responsive to altered precipitation (Castro et al., 2010; Hawkes et al., 3

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2011; De Vries et al., 2018). There are several reasons for this inconsistency. First, the response of soil

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microbial communities to environmental changes may be over- or underestimated depending on

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which method is used to assess the microbial communities (Lok, 2015; Delgado-Baquerizo et al.,

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2018). Second, understanding soil microbial responses to precipitation change is often difficult, in

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part because the effects of precipitation change on the microbial taxa can be taxon-specific and

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difficult to interpret when microorganisms are assessed only at the community level (Fierer et al.,

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2007; Singh et al., 2010; Lennon and Jones, 2011). Third, most previous research concerning the

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effects of precipitation on soil microbial communities has been conducted at a regional or continental

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scale along precipitation gradients (e.g., Angel et al., 2010; Hawkes et al., 2011; Maestre et al., 2015),

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which makes it difficult to tease apart the direct effect of precipitation from other coupled

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environmental variables. Fourth, most previous precipitation manipulation experiments have

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included only a few levels of precipitation; manipulation experiments that include multiple levels of

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precipitation (i.e., gradients) are needed to discern the effects of precipitation on soil biodiversity and

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ecosystem functioning (Kreyling et al., 2018).

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The ecological responses to alteration in precipitation can be “symmetrical” (with similar

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responses to the precipitation gradient under drier and wetter conditions) or “asymmetrical” (with

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different responses to the precipitation gradient under drier and wetter conditions) (Kreyling et al.,

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2018; Zhou et al., 2018). Observational studies or manipulation experiments have indicated that the

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ANPP in semi-arid grasslands is more sensitive to increases in precipitation in wet years than dry

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years (Knapp and Smith, 2001) or under wet treatments than dry treatments (Wilcox et al., 2017), i.e.,

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the responses are asymmetrical. Another study found an asymmetrical response, i.e., soil respiration

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was more sensitive to increases in precipitation at wet sites than at dry sites (Liu et al., 2016).

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Although ecologists have proposed that the sensitivities of ecosystem functions to changes in

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precipitation may be quite different under dry vs. wet conditions, whether the responses of soil

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microbial communities to precipitation are symmetrical or asymmetrical under dry vs. wet conditions 4

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has seldom been assessed (Castro et al., 2010; Zhou et al., 2018). Limited reports showed that the

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responses of soil microbial composition to altered precipitation are asymmetrical (Hawkes et al.,

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2011; Zhou et al., 2018). These asymmetrical changes in the compositions of soil fungal and bacterial

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communities may have strong effects on ecosystem functions (e.g., C and N cycling) (Fierer, 2017).

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Understanding the ecological attributes of microbial phylotypes by identifying ECs could increase

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our ability to predict how soil processes will respond to precipitation change (Singh et al., 2010; Evans

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and Wallenstein, 2014; Delgado-Baquerizo et al., 2018). Bacterial taxa were divided into three life

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strategies after a dry-rewetting experiment (Evans and Wallenstein, 2014), and the possibility of

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predicting bacterial distribution at a global scale was verified by associating soil environment

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characteristics with bacterial taxa and by grouping these taxa into ECs (Fierer et al., 2007; Fierer et al.,

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2012; Delgado-Baquerizo et al., 2018). The identification of ECs was recently proposed as a way to

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more tightly link microbial communities to ecosystem functions (Fierer et al., 2007;

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Delgado-Baquerizo et al., 2018). The consideration of such ECs can increase our understanding of the

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relationships of microorganisms with soil and plant attributes (Fierer et al., 2007; Singh et al., 2010).

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The linking of microbial ECs to important soil functions such as soil C and N mineralization could be

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especially useful because microbial communities associated with soil mineralization are variable

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(Barnard et al., 2015; Chen et al., 2019a). Moreover, some studies that have linked microbial

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communities with soil mineralization suggest that only a subset of microbial taxa are correlated with

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or respond to substrate mineralization (Philippot et al., 2013; Banerjee et al., 2018), which once again

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suggests that the identification of soil microbial ECs could be useful for understanding the effects of

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soil microorganisms on soil ecosystem functions.

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Here, we determined how soil bacterial and fungal communities responded to increases in

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precipitation under dry conditions (≤ 300 mm of precipitation per growing season) and wet

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conditions (≥ 300 mm of precipitation per growing season) and whether the responses are associated

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with ecosystem functions in a typical semi-arid steppe. We attempted to answer three questions: 1) 5

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How do the composition and structure of soil bacterial and fungal communities at different taxa

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levels respond to increases in precipitation under dry and wet conditions? 2) How is soil microbial

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community composition related to plant community composition under dry and wet conditions? and

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3) If soil microorganisms are assigned to ECs based on their responses to increasing levels of

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precipitation under dry vs. wet conditions, are the clusters related to ecosystem functions (i.e., C or N

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mineralization rates)?

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Materials and methods

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Study site and experimental design

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A precipitation manipulation experiment was initiated at the Inner Mongolian Grassland

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Ecosystem Research Station (IMGERS, 43°38′N, 116°42′E, 1200 m a.s.l) in 2013. The station is located

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in a region with a typical semi-arid continental climate; over the last 40 years in the region, the mean

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annual precipitation (MAP) has been 334 mm, and the mean annual temperature (MAT) has been 0.9

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o

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indicated that the mean precipitation was 292 mm, the maximum precipitation was 455 mm, and the

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minimum precipitation was 146 mm (Fig. 1). Based on this pattern of natural precipitation over a

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recent 31-year period, we simulated nine levels of precipitation during the growing season: 100, 150,

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200, 275, 300, 350, 400, 450, and 500 mm. We defined the five levels (≤ 300 mm) as a “dry gradient”

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and the last five levels (≥ 300 mm) as a “wet gradient”. We selected 300 mm as the level that

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separated the two gradients because 300 mm is near the mean of growing-season precipitation (292

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mm) at the site.

C. Data collected during the growing season at the IMGERS for a recent 31-year period (1982-2013)

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To attain these levels of precipitation, we constructed six rainout shelters with arched, steel

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frames (Fig. 1). Each shelter was 40 m long and 10 m wide, and included a high strength, transparent

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polyethylene film that was raised to cover the shelter in the presence of natural precipitation and

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that was lowered in the absence of natural precipitation. Each shelter functioned as a block in the

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experimental design and contained nine 4-m × 4-m plots, i.e., one randomly assigned plot per 6

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precipitation level. This yielded a total of 54 plots (nine levels × six randomized blocks). The nine plots

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in a shelter were separated by 1-m walkways, and each plot was surrounded by a galvanized sheet

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that extended 100 cm into the soil and 5 cm above the soil. The plant community was natural and

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uniform among all 54 plots and consisted of the following species: Leymus chinensis (Trin.) Tzvel.,

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Stipa grandis P. Smirn., Agropyron cristatum (L.) Gaertn., Achnatherum sibiricum (L.) Keng,

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Cleistogenes squarrosa (Trin.) Keng, Carex korshinskyi Kom., Chenopodium aristatum L., Salsola

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collina Pall., and Chenopodium glaucum L.

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The experiment began at the start of the growing season in 2013 and ended at the end of the

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growing season in 2015, i.e., the experiment was conducted for three consecutive growing seasons.

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Water was added throughout each growing season to each plot to attain the desired total level of

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precipitation. To simulate the natural precipitation, the timing of each artificial precipitation was

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based on the growing-season precipitation pattern for 30 years (Fig. 2). Specifically, water was added

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to each plot 23 times (eight times during May-June, 10 times during July-August, and five times

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during September-October) using mobile water spray systems. From May to October in 2015,

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volumetric soil water content at 10 cm depth was recorded about every 10 days with a portable soil

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moisture probe (Diviner 2000, Sentek Pty Ltd., Balmain, Australia). The results showed that the

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average soil moisture content for the entire growing season significantly increased with the increases

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in precipitation under both dry or wet gradients (Fig. 1).

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Plant and soil measurements

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In late August of 2015 (at the end of the third growing season), the aboveground biomass of

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plants was sampled in a 1.0-m × 0.5-m quadrat in each plot. These samples were separated by

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species, oven-dried at 65 oC for 48 h, and weighed. Four soil cores (2 cm in diameter and 15 cm in

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depth) were randomly collected from each plot and combined to form one composite soil sample per

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plot. All soil samples were gently mixed and passed through a 2-mm-mesh sieve. One subsample of

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soil was air-dried and used to determine soil organic C (SOC) by the Walkley-Black modified 7

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acid-dichromate FeSO4 titration method and to determine total soil N based on colorimetric

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determination with a 2300 Kjeltec Analyzer Unit (Chen et al., 2019b). One subsample of moist soil

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was incubated for 21 days to determine C and N mineralization rates as indicated in the next

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paragraph. A 50-g moist subsample of each soil sample was stored at -80 °C and was used for

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molecular analysis as described in the next section.

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Soil C and N mineralization rates were determined using the aerobic incubation method (Chen

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et al., 2019b). In brief, a 20-g subsample of fresh soil from each sample was incubated in a 250-ml jar

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with a lid at 25°C for 21 days in the dark. Before incubation, an acclimatization period of 1 week

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reduced the microbial activity resulting from changes in temperature and moisture contents during

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soil mixing. Thereafter, soil moisture in each jar was maintained at 60% of field capacity by weighing

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and watering daily. CO2 released from the soil was measured after 1, 2, 3, 7, 10, 15, and 21 days of

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incubation by gas chromatography (Agilent HP 5890 SERIES II, USA). The C mineralization rate was

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the average CO2 released during the whole incubation period. NH4+-N and NO3−-N were extracted

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from the soil before and after incubation with 50 ml of 2 mol L-1 KCl; their concentrations were

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measured with a 2300 Kjeltec Analyzer Unit (FOSS, Höganäs, Sweden). The soil N mineralization rate

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was the difference in total inorganic N concentration between the start and the end of the incubation.

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Because soil mineralization rates are strongly affected by SOC content, the rates were standardized to

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SOC, i.e., the rates were expressed as mg g-1 dry SOC day-1 (Wang et al., 2014).

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Soil DNA extraction, amplification, and sequencing

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Microbial DNA was extracted from 0.5-g soil subsamples using the FastDNA® Spin Kit for Soil

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(MP Biomedical, Solon, OH). A NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific,

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Wilmington, USA) was used to measure the DNA concentration and purity, and 1% agarose gel

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electrophoresis was used to check DNA quality. The V3-V4 region of the bacterial 16S rRNA gene was

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amplified by PCR using primers 338F and 806R, and the fungal ITS sequence of the 18S rRNA gene

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(O'Dwyer et al., 2012) was amplified using primers ITS1F and ITS2 and a themocycler PCR system 8

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(GeneAmp 9700, ABI, USA). PCR reactions were conducted as follows: 3 min of denaturation at 95 °C,

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27 cycles for bacteria and 35 cycles for fungi of 30 s at 95 °C, 30 s at 55 °C, and 45 s at 72 °C, with a

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final extension at 72 °C for 10 min.

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Purified amplicons were sequenced by Shanghai Majorbio Bio-pharm Technology (Shanghai,

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China) using an Illumina MiSeq platform (San Diego, CA, USA). Raw fastq files were demultiplexed,

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quality-filtered by Trimmomatic, and merged by FLASH with the following criteria: i) Reads were

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truncated at any site receiving an average quality score <20 over a 50-bp sliding window; ii) Primers

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were exactly matched allowing 2-nucleotide mismatching, and reads containing ambiguous bases

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were removed; and iii) Sequences with overlaps longer than 10 bp were merged according to their

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overlap sequence.

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Operational taxonomic units (OTUs) or phylotypes were clustered with 97% similarity cutoff

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using UPARSE (version 7.1, http://drive5.com/uparse/), and chimeric sequences were identified and

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removed using UCHIME. The taxonomy of each gene sequence was analyzed by the RDP Classifier

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algorithm (http://rdp.cme.msu.edu/) against the Silva database (Silva 128) for bacteria and the Unite

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database (Unite 7.0) for fungi using a confidence threshold of 70%.

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Alpha and beta diversity analysis for microbial and plant communities

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All of the pre-processes for the alpha and beta diversity of soil bacterial and fungal communities

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were calculated by using the script in QIIME (http://qiime.org/scripts/). We excluded rare OTUs by

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filtering the phylotypes with relative abundance lower than 0.01% within each sample, and the new

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filtered OTU tables were used for diversity analysis (Galand et al., 2009). First, the filtered OTU table

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was rarefied by using the single_rarefaction.py script (Weiss et al., 2015). Values of the

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Shannon-Wiener index (an indicator of alpha diversity) of soil bacterial and fungal communities were

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calculated using the alpha_diversity.py script. Second, values for Bray-Curtis similarity (used as an

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indicator of beta diversity) of soil bacterial and fungal communities were calculated from cumulative

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sum scaling transformed OTU abundances using the normalize_table.py script and beta_diversity.py 9

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script (Paulson et al., 2013). In addition, values for the Shannon-Wiener index and Bray-Curtis

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similarity of plants were calculated with the vegan 2.4-6 package in R version 3.3.2 (R Development

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Core Team, 2016). The beta diversities of microbial and plant communities along precipitation

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gradients were visualized by principal coordinate analysis (PCoA) using the cmdscale function. We

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used permutational analyses of variance (PERMANOVA) based on Bray-Curtis similarity to determine

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the difference in beta diversity between dry and wet gradients. Before conducting the PERMANOVA,

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we tested the data for homogeneity by multivariate dispersions (PERMDISP). The PERMANOVA and

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PERMDISP were performed using vegan package (Oksanen et al., 2010). We found no significant

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PERMDISP differences within the dry or wet gradients (Supporting Information, Table S1), indicating

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that the results were not affected by dispersion differences within the gradients.

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Classification of ecological clusters

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The relationships between the relative abundance of microorganisms at the OTU level under dry

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and wet gradients were analyzed by linear mixed-effect models, separately. Possible relationships

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between the abundance of each OTU under dry gradients vs. wet gradients are illustrated in the

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hypothetical plots (Fig. 2). Using these plots, we defined hypothetical symmetrical and asymmetrical

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ECs in the following way. If the responses (in terms of change in relative abundance) of a specific

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microbial taxon to the dry and wet precipitation gradients are similar, i.e., both are significantly (P <

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0.05) positive (+/+) or negative (-/-) or non-significant (0/0), then that taxon and similarly responding

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taxon are grouped into a symmetrical EC. If, however, the responses of a specific microbial taxon to

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dry and wet precipitation gradients are different, then that taxon and similarly responding taxon are

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grouped into an asymmetrical EC (Fig 2). Using this approach, we defined nine hypothetical ECs

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representing distinct responses (based on relative abundance) of microbial taxa (OTUs) to the

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simulated precipitation gradients. Three of the ECs were symmetrical, and six were asymmetrical (Fig.

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2). Each EC was further divided into groups based on taxonomic level (phylum, class, order, family,

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genus, and species). We also constructed phylogenetic trees of soil bacteria and fungi (Guindon et al., 10

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2010) and visualized ECs at the OTU level using the interactive Tree Of Life (iTOL) (Letunic and Bork,

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2016).

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Statistical analyses

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All statistical analyses were performed using R version 3.3.2 (R Development Core Team, 2016).

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The effects of the dry and wet gradients on the alpha and beta diversity (first principal coordination

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of PCoA) of soil microbial and plant communities were analyzed by linear mixed-effect models. The

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relationships in Bray-Curtis matrices between soil microbial and plant communities were modeled

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using the Mantel test in vegan (Oksanen et al., 2010). We also used linear mixed-effect models to test

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the relationships between soil mineralization rates and the relative abundance of ECs of soil microbial

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communities under the dry and wet gradients. Linear mixed-effect models were also used to test the

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relationship between the biomass, relative abundance, alpha diversity, and beta diversity of plant and

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soil microbial communities under the dry and wet gradients. Finally, we used variation partitioning

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analyses to determine how soil C or N mineralization rates were affected by variables that were found

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to be significant by linear mixed-effect models; this was done with the calc.relimp function in the

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“relaimpo” package (Grömping, 2006).

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Results

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Effects of dry and wet gradients on the diversity of the soil microbial community

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After extremely rare OTUs were filtered, the data set included 1565 bacterial and 774 fungal

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OTUs. The soil bacterial communities were dominated by the phyla Actinobacteria, Acidobacteria,

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Proteobacteria, Chloroflexi, and Bacteroidetes (Table S2). The soil fungal communities were

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dominated by the classes Sordariomycetes, Agaricomycetes, Dothideomycetes, and Eurotiomycetes

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(Table S2). The alpha diversity (indicated by the Shannon-Wiener index) of the soil bacterial

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community decreased as precipitation increased in the dry gradient (precipitation levels ≤ 300 mm)

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but was not significantly altered by precipitation in the wet gradient (precipitation levels ≥ 300 mm)

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(Fig. 3a). The alpha diversities of the soil fungal and plant communities were not changed by 11

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precipitation in the dry or wet gradient, although the alpha diversity of the plant community

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increased with precipitation across all nine precipitation levels (r=0.381, P<0.01) (Fig. 3b-c). Under

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the wet but not the dry gradient, the plant alpha diversity was positively related to soil bacterial

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alpha diversity and to soil fungal alpha diversity (Fig. S1).

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The effects of dry and wet gradients on the beta diversities of soil bacterial and fungal

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communities were investigated by principal coordinate analysis (PCoA) based on Bray-Curtis

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dissimilarity at the OTU level (Fig. 4). The PCoA1 of the bacterial community was significantly altered

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by precipitation under both gradients (Fig. 4a-b). The PCoA1 of the soil fungal community, in contrast,

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was not significantly associated with precipitation under the dry or wet gradient, but was significantly

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associated with precipitation across all nine precipitation levels (r=-0.484, P<0.001) (Fig. 4c-d).

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Surprisingly, the composition of the plant community was not altered by precipitation in the dry or

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wet gradient (Fig. 4e-f), although precipitation increased ANPP under both the dry and wet gradients

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(Fig. S2). In addition, the Mantel test showed that there was no relationship in beta diversity between

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plant and soil bacterial communities or between plant and soil fungal communities under the dry or

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wet gradient, except for a positive relationship between plant and fungal communities under the wet

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gradient (Fig. S3).

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Ecological clusters of soil microbial communities at different taxon levels

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As indicated earlier, the OTUs were classified into nine ECs based on the relationships between

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the relative abundance of OTUs and precipitation gradients under the dry vs. the wet gradient. The

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percentages of OTUs represented by the nine ECs differed among bacterial phyla and fungal classes

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(Fig. 5). A very large percentage of the OTUs (1146 bacterial OTUs and 635 fungal OTUs) belonged to

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symmetrical EC2, for which relative abundance was unrelated to the precipitation level under either

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the dry or wet gradient (Fig. 5). The total percentage of OTUs that belonged to other symmetrical ECs

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(EC6 and EC7) was 2.66% for bacteria and 5.46% for fungi (Fig. 5). The total percentage of OTUs that

295

belonged to asymmetrical ECs (EC1, EC3, EC4, EC5, EC8, and EC9) was 25% for bacteria and 22% for 12

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fungi (Fig. 5).

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The percentages of each EC differed among taxon levels (Fig. S4). For example, at the bacterial

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phylum level, 5 of 21 phyla belonged to asymmetrical clusters. Among these 5 asymmetrical phyla,

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the relative abundances of Bacteroidetes, Proteobacteria, and Parcubacteria showed no response to

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increases in precipitation under the dry gradient but increased in response to precipitation under the

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wet gradient (EC1; Table S2). The relative abundance of other asymmetrical phyla (Actinobacteria and

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Nitrospirae) increased in response to increased precipitation under the dry gradient but showed no

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response to increased precipitation under the wet gradient (EC5; Table S2). At the fungal class level, 3

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in 16 classes were asymmetrical, i.e., their responses to precipitation differed across the dry vs. the

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wet gradient. Among the 3 asymmetrical classes, the relative abundance of Agaricostilbomycetes and

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Microbotryomycetes showed no response to increases in precipitation under the dry gradient but

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decreased in response to increased precipitation under the gradient (EC3; Table S2). The relative

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abundance of the asymmetrical class Leotiomycetes increased in response to increased precipitation

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under the dry gradient conditions but showed no response to increases in precipitation under the

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wet gradient (EC5; Table S2).

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Linking ecological clusters with ecological functions

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The linear mixed regression model showed that the soil C and N mineralization rates were not

313

associated with the increases in precipitation in the dry or wet gradient (Fig. S2). The linear mixed

314

regression model also showed that commonly measured properties for plant communities (biomass,

315

alpha diversity, and beta diversity) or soil microbial communities (alpha diversity, beta diversity, and

316

relative abundance at the phylum or class level) were not associated with soil C or N mineralization

317

rates (Table 1 and Table S3). Moreover, although ANPP increased, neither the C nor the N

318

mineralization rate changed across the precipitation gradients (Table 1). However, the relative

319

abundance of five ECs were correlated with soil C or N mineralization rates (Fig. 6). Soil C

320

mineralization rates increased as the relative abundance of the EC2 cluster of fungi increased but 13

321

decreased as the relative abundance of the EC8 cluster of fungi increased (Fig. 6a-b); these two

322

fungal ECs explained 14% of the total variance in the soil C mineralization rate (Fig. 6g). The relative

323

abundance of bacterial EC4 was positively correlated with the soil N mineralization rate, and the

324

relative abundances of bacterial EC8 and fungal EC7 and EC8 were negatively correlated with the soil

325

N mineralization rate (Fig. 6c-f); these four bacterial and fungal ECs explained 28% of the total

326

variance in the soil C mineralization rate (Fig. 6g).

327

Discussion

328

Effects of simulated precipitation on the alpha diversity of soil microbial communities

329

Previous reports have documented that the effects of simulated precipitation on ANPP, net

330

ecosystem exchange, and soil respiration were "asymmetrical" in that the effects differed under wet

331

conditions vs. dry conditions; more specifically, the effects were greater under wet than dry

332

conditions (Liu et al., 2016; Wilcox et al., 2017). The effects of simulated precipitation on soil bacterial

333

and fungal communities, however, have not been previously compared under wet and dry conditions

334

in a semi-arid steppe. Consistent with a study concerning the response of a microbial community to

335

drought (Carson et al., 2010), the current results indicate that the alpha diversity of bacteria was

336

positively related to increases in precipitation under dry conditions, suggesting that increased

337

precipitation under drought could enhance the diversity of soil bacteria.

338

A lack of response of bacterial or fungal alpha diversity to increased precipitation under wet

339

conditions, as found in the current study, was also reported in other studies (Hartmann et al., 2017; Li

340

et al., 2018). Although the failure of microbial communities to respond to increased precipitation

341

under wet conditions was based on data from only three growing seasons in the current study, this

342

may reflect the conditions on the Mongolia steppe, because such extreme precipitation events (dry

343

to 100 mm or wet to 500 mm) have not lasted for three consecutive years based on precipitation

344

data during the past 30 years (Fig. 1). Our results also indicated a lack of robust relationships in alpha

345

diversity between plant communities and soil bacterial communities or fungal communities under 14

346

the dry gradient (Fig. S1), which is consistent with a previous report (Prober et al., 2015) but is

347

inconsistent with several recent studies at local and global scales (Yang et al., 2017; Guo et al., 2019).

348

Our results, however, indicated that plant alpha diversity was positively related to soil bacterial alpha

349

diversity and to soil fungal alpha diversity under the wet gradient (Fig. S1). This inconsistency

350

between the dry and the wet gradient may be due to scale effects or to specific abiotic and biotic

351

factors affecting the relationships between the alpha diversities of plant and soil microbial

352

communities (Prober et al., 2015; Li et al., 2016; Guo et al., 2019). Our results suggest that drought

353

may decouple the positive relationships between the alpha diversities of plant and soil microbial

354

communities, and that dry conditions might have a longer lasting effect on the soil microbial

355

community than wet conditions in a semi-arid grassland.

356

Effects of simulated precipitation on the beta diversity of soil microbial communities

357

We found that the responses of beta diversity to increases in precipitation under wet or dry

358

condition were greater for the bacterial community than for the fungal community. A strong

359

response of bacterial communities was also observed in several previous studies in which bacterial

360

community composition was significantly altered by changes in precipitation under wet or dry

361

conditions (Castro et al., 2010; Hawkes et al., 2011; De Vries et al., 2018). These

362

precipitation-induced changes in the composition of microbial communities could be due to

363

simulated precipitation directly changing the physiological status of specific microbial taxa and

364

indirectly changing the quality and quantity of plant-derived resources by increasing plant

365

productivity (Castro et al., 2010; Maestre et al., 2015; Li et al., 2016); in the latter case, however,

366

there is an ongoing debate about whether plant community composition is strongly associated with

367

microbial community composition (Prober et al., 2015; Guo et al., 2019). Compared with bacterial

368

communities, fungal communities were more tolerant and resistant to wet or dry conditions in the

369

current study, which was consistent with several previous studies (Barnard et al., 2013). Moreover, a

370

recent experiment showed that recovery of fungi and bacteria from drought was differentially 15

371

governed by plant physiological responses to drought (De Vries et al., 2018).

372

Although the simulated precipitation had only small effects on microbial communities, the

373

observed changes in bacterial and fungal community structure caused by simulated precipitation may

374

be due to different responses among different taxon levels. Some studies have suggested that many

375

high taxonomic levels of bacteria demonstrate “ecological coherence” (Philippot et al., 2010). For

376

example, the relative abundance of Bacteroidetes and Proteobacteria increased as precipitation

377

increased under the wet gradient but not under the dry gradient (an asymmetrical response) in our

378

study (Table S2), and such positive correlations between Bacteroidetes and Proteobacteria and

379

precipitation gradients were also observed in several other studies (Bachar et al., 2010; Barnard et al.,

380

2013). This suggests that these phyla may develop faster than others when sufficient resources are

381

available (Hartmann et al., 2017). In addition, we classified the phylum Actinobacteria as an

382

asymmetrical type because the abundance of this phylum did not respond to increased precipitation

383

across the wet gradients but increased with increased precipitation across the dry gradient in the

384

current study (Table S2). The bacteria in this phylum increased in response to drought in several

385

previous studies (Clark et al., 2009; Schimel, 2018), but in a short-term experiment, the relative

386

abundance of Actinobacteria did not differ between wet and dry conditions (McHugh and Schwartz,

387

2016). At the phylum level, 75% of bacterial phyla showed no response to increases in precipitation

388

under the wet or dry gradient (Table S2). Such a high percentage of symmetrical types (horizontal line

389

in response to dry and wet gradients) could have at least two explanations. First, water availability

390

might not be a limiting factor for these phyla. For instance, the abundance of the phylum

391

Acidobacteria is more tightly correlated with pH and temperature than with water availability (Lauber

392

et al., 2009; Yergeau et al., 2012). Second, although our 3-year experiment simulated natural

393

precipitation, the experiment may have been too short to assess the responses of these phyla to wet

394

or dry conditions.

395

Compared with bacteria, fungi are generally more resistant to changes in moisture conditions 16

396

(Ho et al., 2017). In a recent 15-week study, for instance, 15 of 16 fungal classes were resistant to

397

simulated rapid shifts in moisture conditions (Schmidt et al., 2018). The three fungal classes that

398

were found to be asymmetrical types in the current study (Agaricostilbomycetes, Leotiomycetes, and

399

Microbotryomycetes) are saprophytes (Hartmann et al., 2017), which may be more tightly associated

400

than parasites with simulated precipitation-induced changes in plant biomass and litter. In addition,

401

that a majority of bacterial phyla and fungal classes did not respond to changes in simulated

402

precipitation in the current study may be due to trade-offs between different OTUs (i.e., some were

403

enhanced and others were suppressed by increases in precipitation) within the same phylum or class.

404

This possibility was supported by our finding that the percentage of asymmetrical types was higher at

405

the OTU level than at the phylum or class level (Fig. S4). These findings challenge the previous

406

viewpoint that prokaryotic traits are conserved at higher phylogenetic levels (Philippot et al., 2010;

407

Hartmann et al., 2017) and suggest that classification based on higher taxonomic levels might mask

408

heterogeneous responses at lower taxonomic levels.

409

Linking ecological clusters to ecosystem functions

410

The ECs, which were classified based on responses of phylotypes to simulated precipitation,

411

were significantly related to soil C and N mineralization rates. We found no correlation, however,

412

between commonly measured properties (biomass, relative abundance, and diversity) of plant or soil

413

microbial communities and soil C and N mineralization rates, although it is well established that C and

414

N mineralization rates are tightly related to the diversity or biomass of plant and soil microbial

415

communities (Zak et al., 2003; Delgado-Baquerizo et al., 2016). This lack of significant relationships

416

between these commonly measured properties of plant or soil microbial communities and soil

417

mineralization rates might be due to the short duration and small spatial scale of our experiment.

418

That soil C and N mineralization rates were significantly correlated with the relative abundance of five

419

ECs suggests that our classification based on OTU response to simulated precipitation is ecologically

420

meaningful in semi-arid grasslands. Although the microorganisms in the ECs that were closely linked 17

421

with ecosystem function were not abundant, these low-abundance ECs may have disproportionately

422

large effects on ecosystem functions and services (Lynch and Neufeld, 2015). Some bacterial phyla

423

with low relative abundance in clusters EC4 and EC8, such as Burkholderiales and Rhizobiles, are

424

dominant members of the rhizosphere microbiota and are considered keystone taxa for ecosystem

425

functioning (Philippot et al., 2013; Banerjee et al., 2018). The abundance of fungi in the order

426

Mortierellales, which had low relative abundances in clusters EC7 and EC8 of the current study,

427

declined with N deposition in a previous study (Dean et al., 2014), and many genera in the

428

Mortierellales have the ability to decompose complex organic substrates (Wagner et al., 2013). We

429

also found that the ECs for fungi explained more of the variation in the mineralization rates of soil C

430

and N than ECs for bacteria, perhaps because fungi are more important than bacteria for the

431

decomposition of non-labile soil organic matter (Boer et al., 2005; Glassman et al., 2018). Our results

432

indicate that bacterial communities might be less functionally redundant than fungal communities

433

with regards to decomposition of soil organic matter. These results differ from those from a recent

434

grassland mesocosm study, which showed that changes in bacterial communities were more closely

435

associated with soil functioning during recovery from drought than changes in fungal communities

436

(De Vries et al., 2018).

437

It is important to note that our approach of linking microbial community structure to specific

438

functions includes some uncertainties. First, regarding the identity of microbial taxa in each EC,

439

different soils may have different types of microbial taxa, and it might therefore be challenging to

440

identify and classify ECs at lower taxonomic levels, i.e., at genus, species, or genotype levels. Second,

441

C and N mineralization rates were measured under standardized in vitro conditions (with constant

442

moisture contents) and therefore may not reflect in situ mineralization rates in response to

443

precipitation gradients. Third, in vitro rates of C and N mineralization may deviate from in situ rates as

444

a result of the indirect effects of precipitation treatments; precipitation, for instance, may affect

445

mineralization rates by affecting plant primary productivity and thus plant-derived inputs to soil. 18

446

Fourth, the recovery of soil taxa by DNA-based methods does not necessarily mean that they

447

contribute to soil functions in part because a large proportion of the soil microbial community is

448

inactive (Lennon and Jones, 2011). Our understanding of structure-function relationships would be

449

increased by the functional metatranscriptomic profiling of microbial communities, for instance,

450

using RNA-SIP (stable isotope probing) or other approaches to dissect the physiological and genetic

451

basis of diversity-functioning relationships. Despite these limitations, our results suggest that it may

452

be time to reevaluate the relative roles of bacterial and fungal communities in soil functioning under

453

climate change. To our knowledge, the current study is the first to classify ECs based on taxon levels

454

under simulated precipitation gradients and to describe the relationships of such clusters with

455

ecosystem functions. The results indicate that the identification of ECs provides useful insights into

456

the relationships between microbial communities and ecosystem functions.

457

Acknowledgements

458

We thank Huasong Chen for his help in soil analysis. We also thank editor and two anonymous

459

reviewers for their valuable comments on the earlier version of this paper. This study was supported

460

by the Chinese National Key Development Program for Basic Research (2016YFC0500804), the

461

National Natural Science Foundation of China (31570450 and 31630010), and the Youth Innovation

462

Promotion Association of the Chinese Academy of Sciences (2015061).

463

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Zhu, K., Luo, Y., 2017. Asymmetric responses of primary productivity to precipitation extremes: A

619

synthesis of grassland precipitation manipulation experiments. Global Change Biology 23,

620

4376-4385. 25

621

Yang, T., Adams, J.M., Shi, Y., He, J.s., Jing, X., Chen, L., Tedersoo, L., Chu, H., 2017. Soil fungal diversity

622

in natural grasslands of the Tibetan Plateau: associations with plant diversity and productivity.

623

New Phytologist 215, 756-765.

624

Yergeau, E., Bokhorst, S., Kang, S., Zhou, J., Greer, C.W., Aerts, R., Kowalchuk, G.A., 2012. Shifts in soil

625

microorganisms in response to warming are consistent across a range of Antarctic environments.

626

The ISME Journal 6, 692-702.

627 628 629 630

Zak, D.R., Holmes, W.E., White, D.C., Peacock, A.D., Tilman, D., 2003. Plant diversity, soil microbial communities, and ecosystem function: are there any links? Ecology 84, 2042-2050. Zhou, Z.H., Wang, C.K., Luo, Y.Q., 2018. Response of soil microbial communities to altered precipitation: A global synthesis. Global Ecology and Biogeography 27, 1121-1136.

631

Supporting Information

632

Table S1 PERMDISP for bacterial, fungal, and plant communities within the gradients under dry and

633

wet conditions.

634

Table S2 Classification of ECs at the phylum level for bacteria and at the class level for fungi.

635

Table S3 Relationships between dominant bacterial phyla or fungal classes in soil microbial

636

communities and soil mineralization rates under dry and wet precipitation gradients.

637

Figure S1 Relationships between the alpha diversity of the plant community and the alpha diversity

638

of soil microbial communities under dry and wet precipitation gradients.

639

Figure S2 Effects of dry and wet precipitation gradients on the ANPP and soil mineralization rates.

640

Figure S3 Relationships between the beta diversity of plant communities and the beta diversity of soil

641

microbial communities under dry and wet precipitation gradients.

642

Figure S4 Percentage of bacterial and fungal taxa numbers at different taxonomic levels.

643

26

644

Table 1 Relationships between commonly measured properties of plant and microbial communities

645

and soil C and N mineralization rates under dry and wet precipitation gradients. Regressions were

646

estimated using a mixed-effect model, and regression statistics are indicated (r). Significant

647

regressions (P < 0.05) are in bold. C mineralization rate -1 -1 (mg g SOC d )

Parameter

N mineralization rate -1 -1 (mg g SOC d )

Dry (≤300 mm)

Wet (≥300 mm)

Dry (≤300 mm)

Wet (≥300 mm)

Precipitation gradient (mm)

0.051

0.001

0.031

0.001

Soil water content (v/v%)

0.065

0.061

0.009

0.006

ANPP (g m )

0.017

0.010

0.057

0.000

Plant Shannon-Wiener index

0.001

0.033

0.197*

0.008

Plant PCoA1

0.007

0.000

0.012

0.012

Plant PCoA2

0.062

0.012

0.002

0.037

Bacterial Shannon-Wiener index

0.078

0.029

0.002

0.018

Bacterial PCoA1

0.013

0.068

0.030

0.017

Bacterial PCoA2

0.001

0.006

0.003

0.011

Fungal Shannon-Wiener index

0.002

0.074

0.030

0.001

Fungal PCoA1

0.006

0.074

0.016

0.014

Fungal PCoA2

0.049

0.006

0.004

0.000

-2

648 649

27

650

Figure Legends

651

Figure 1 Background information on the simulated precipitation experiment. The six rainout shelters

652

minus transparent film covering (a), plots in one rainout shelter minus transparent film covering (b),

653

precipitation distribution from 1982 to 2012 (c), and effects of the dry precipitation gradient (≤ 300

654

mm of precipitation per growing season) and the wet precipitation gradient (≥ 300 mm of

655

precipitation per growing season) on soil water content (d). Regressions were estimated using a

656

mixed-effect model, and regression statistics are indicated (***, P < 0.001).

657

Figure 2 Nine potential ecological clusters (ECs) based on the relationships between the relative

658

abundance of specific microbial taxa under dry and wet precipitation gradients. If the responses of a

659

specific microbial taxon to dry and wet precipitation gradients are similar (+/+, both are significantly

660

positive; -/-, both are significantly negative; 0/0, both are non-significant), the response of the taxon

661

is considered to be symmetrical (S). If the responses of a specific microbial taxon to dry and wet

662

precipitation gradients are different, the response of the taxon is considered to be asymmetrical (A).

663

Taxa with similar responses are grouped in one of nine ECs. The regressions were estimated using a

664

linear mixed-effect model.

665

Figure 3 Effects of a dry precipitation gradient and a wet precipitation gradient on the alpha diversity

666

(indicted by the Shannon-Wiener index) of soil bacterial (a), soil fungal (b), and plant (c) communities.

667

Regressions were estimated using a mixed-effect model, and regression statistics are indicated (ns, P >

668

0.05; *, P < 0.05).

669

Figure 4 Effects of dry and wet precipitation gradients on the beta diversity of soil bacterial (a-b), soil

670

fungal (c-d), and plant (e-f) communities. Beta diversity was estimated by principal coordinate

671

analysis (PCoA) based on Bray-Curtis similarities. The Bray-Curtis similarities were based on the

672

relative abundance of OTUs for soil bacterial and fungal communities and on the relative biomass of

673

plant species. Differences in the composition of communities between dry and wet gradients were

674

assessed using permutational analysis of variance (PERMANOVA) (ns, P > 0.05; **, P < 0.01). 28

675

Relationships between the first principal coordination (PCoA1) and precipitation gradients under dry

676

conditions and wet gradients were estimated using a mixed-effect model, and regression statistics are

677

indicated (ns, P > 0.05; *, P < 0.05).

678

Figure 5 Phylogenetic distribution of OTUs and ecological clusters (ECs) in bacterial (a) and fungal (b)

679

communities. The ring indicates ECs for all filtered OTUs (1565 for bacteria and 774 for fungi) based

680

on relationships between relative abundance of taxa and dry and wet precipitation gradients as

681

explained in Figure 2. Phyla or classes are indicated by different branch colors, and pie charts show

682

the percentages represented by the indicated ECs.

683

Figure 6 Relationships between relative abundance of ecological clusters (ECs) and soil C (a-b) and

684

soil N (c-f) mineralization rates in the simulated precipitation experiment. The relative abundance of

685

each EC was the sum of the relative abundances of OTUs. Regressions were estimated using a

686

mixed-effect model, and regression statistics are indicated (*, P < 0.05). Plot (g) indicates the

687

percentage of the variation in mineralization of soil C and N explained by the relative abundance of

688

ECs. Note that the significant relationship in plot (c) was disappeared after removing the four points

689

on right hand side.

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Highlights Precipitation under dry and wet gradients did not alter alpha diversities of biotic communities. Precipitation under dry and wet gradients only altered beta diversity of soil bacterial community, but not for soil fungi or plant. Commonly measured properties (e.g., biomass and diversity) of biotic communities were not correlated with ecosystem functions. Ecological clusters based on responses of soil microbial phylotypes to precipitation explain ecosystem functions.

Type of contribution: Research Paper Date of preparation: December 24, 2019 Number of text pages: 35; Number of tables: 1; Number of figures: 6 Title: Ecological clusters based on responses of soil microbial phylotypes to precipitation explain ecosystem functions Names of authors: Ying Wu1,2,3, Jianping Wu1, Muhammad Saleem4, Bing Wang3,5, Shuijin Hu6, Yongfei Bai3,5, Qingmin Pan3*, and Dima Chen2,3* Complete affiliations: 1

State key Laboratory for Conservation and Utilization of Bio-Resources, Yunnan University, Kunming,

China 2

College of Biological and Pharmaceutical Sciences, China Three Gorges University, Yichang, China

3

State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy

of Sciences, Beijing, China 4

Department of Biological Sciences, Alabama State University, Montgomery, Alabama

5

University of Chinese Academy of Sciences, Beijing, China

6

Department of Entomology & Plant Pathology, North Carolina State University, Raleigh, NC, USA

*Corresponding author: Prof. Dima Chen College of Biological and Pharmaceutical Sciences, China Three Gorges University, Yichang, China Tel: (+86)-10-6283-6923; Fax: (+86)-10-8259-5771; E-mail: [email protected] or Prof. Qingmin Pan State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China Tel: (+86)-10-6283-6977; Fax: (+86)-10-8259-5771; E-mail: [email protected]

Dear Chief Editor Timothy Clough, The authors declare no competing financial interests. All authors have read and approved this version of the manuscript, and due care has been taken to ensure the integrity of the work. The accompanying manuscript constitutes original unpublished work and is not under consideration for publication elsewhere. Thank you for consideration of our manuscript. Please let us know if any further information would be helpful.

Ying Wu, Jianping Wu, Muhammad Saleem, Bing Wang, Shuijin Hu, Yongfei Bai, Qingmin Pan, and Dima Chen