Soil prokaryotic community structure is determined by a plant-induced soil salinity gradient rather than other environmental parameters associated with plant presence in a saline grassland

Journal of Arid Environments 176 (2020) 104100

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Soil prokaryotic community structure is determined by a plant-induced soil salinity gradient rather than other environmental parameters associated with plant presence in a saline grassland

T

Chikae Tatsumia,∗, Shogo Imadab,c, Takeshi Taniguchid, Sheng Due, Norikazu Yamanakad, Ryunosuke Tatenob a

Graduate School of Agriculture, Kyoto University, Oiwake, Kitashirakawa, Sakyo, Kyoto, 606-8502, Japan Field Science Education and Research Center, Kyoto University, Kyoto, 606-8502, Japan Institute for Environmental Sciences, 1-7 Ienomae, Obuchi, Rokkasho, Kamikita, Aomori, 039-3212, Japan d Arid Land Research Center, Tottori University, Tottori, 680-0001, Japan e State Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences, Yangling, Shaanxi, 712100, China b c

A R T I C LE I N FO

A B S T R A C T

Keywords: Diversity Drylands Nitrogen mineralization Prokaryotic community Saline soil Tamarisk

Soil microbial communities, which play important roles in biogeochemical cycling, are affected by plants in many ways. To understand how plants affect soil microbes, we compared soils under halophytic shrubs and herbs, whose effects on soil salinity (one of the strongest determining factors for soil microbes in saline grasslands) were different. We measured soil physicochemical properties, as well as prokaryotic abundance, community structure, diversity, and the quantified and predicted abundances of functional genes for nitrogen (N) cycling. As we intended, we found the soil salinity to be the highest in barren areas (BA), intermediate under a tamarisk crown (UC), and the lowest in herbaceous patches (HP), whereas other plant-related properties, e.g. soil organic matter content, differed between vegetated areas (UC and HP) and BA. Prokaryotic community structure and diversity changed along the soil salinity gradient, but not as a function of plant presence or absence. However, N transformation rate and N availability were not affected by the salinity gradient alone. The dominant prokaryotic groups that contributed the most to changes in prokaryotic community structure and diversity were likely not those primarily responsible for N mineralization.

1. Introduction Soil microbial communities play important roles in soil biogeochemical cycling through decomposition and nutrient transformation. Changes in microbial communities have been reported to be accompanied by changes in soil nutrient cycling, especially of nitrogen (N) (Hawkes et al., 2005; Fraterrigo et al., 2006; Iwaoka et al., 2018), which is the most limiting nutrient in many terrestrial ecosystems (Binkley and Hart, 1989). Soil microbial community structure and diversity are generally determined by soil pH, salinity, and temperature at the continental scale (Fierer and Jackson, 2006; Lozupone and Knight, 2007; Zhou et al., 2016). At the local scale, the presence and type of aboveground plant species (Berg and Smalla, 2009; Hovatter et al., 2011), as well as soil moisture, pH, and salinity (Sardinha et al., 2003; Drenovsky et al., 2004; Shen et al., 2013), is an important determinant of the soil microbial community's characteristics. Furthermore, at the ∗

micro-scale, plants strongly affect soil microbial communities and create communities with different structure beneath plants than in soil without plant coverage (Bachar et al., 2012; Philippot et al., 2013; Iwaoka et al., 2018). Thus, plant presence is an important factor determining the nature of the soil microbial community, and further affect its ability to function in N cycling at the local and micro scales. Plants affect soil microbial communities in various ways, and the effects were previously observed to be especially large in dryland ecosystems with less vegetation (Bachar et al., 2012; Iwaoka et al., 2018). For example, plants were found to affect soil microbes by supplying nutrients in the form of leaf and root litter (Kieft et al., 1998; Maestre et al., 2009) and secreting some signals or exudates from the roots to the microbes (Haichar et al., 2008, Doornbos et al., 2012). Plants also indirectly affect microbes by modifying soil moisture through absorbing water with their roots or suppressing water evaporation (Bertness et al., 1992; Singh et al., 1998). Furthermore, plants change soil salinity,

Corresponding author. E-mail address: [email protected] (C. Tatsumi).

https://doi.org/10.1016/j.jaridenv.2020.104100 Received 21 May 2019; Received in revised form 31 December 2019; Accepted 9 January 2020 0140-1963/ © 2020 Elsevier Ltd. All rights reserved.

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structure and its functional ability to cycle N were more strongly determined by plant presence alone or by a plant-induced salinity gradient. To this end, we compared soil prokaryotic abundances and community composition, as well as the predicted functional gene abundance and soil inorganic N dynamics, among the soil in barren areas, beneath tamarisk crowns, and in herbaceous patches in a dryland in China. We expected that there would be a plant-induced soil salinity gradient, with the salinity being the highest in barren areas, lower beneath tamarisks, and the lowest in herbaceous patches (Fig. 1). If the effects of plant presence were stronger than those of soil salinity, the soil microbial community was expected to be more different between barren areas and vegetated areas (beneath tamarisks and in herbaceous patches). On the other hand, if the effects of the modification of soil salinity by plants were stronger than those of plant presence alone, then we predicted that there would be gradients in microbial community characteristics from barren areas to those beneath tamarisk crowns, and then to herbaceous patches.

which was previously identified as one of the strongest determinants of soil microbial community structure in saline soils (Sardinha et al., 2003; Rietz and Haynes, 2003; Yuan et al., 2007; Iwaoka et al., 2018), by suppressing the evaporation of water, including that of water containing salts (Bertness et al., 1992). Thus, the natures of soil microbial communities can be determined by plants indirectly, as well as directly. It is difficult to identify how plants control soil microbial communities, since plant presence simultaneously changes many factors in the soil relative to their characteristics in the soil of barren areas (Ladenburger et al., 2006; Bachar et al., 2012; Iwaoka et al., 2018). To distinguish the effects of soil salinity from those of plant presence on soil microbial communities, soil microbial communities under two types of plants that grow at different salinity levels should be compared. Halophytic shrubs, such as tamarisks (Tamarix spp.), can increase the salinity of the soil under their crown because they can absorb salt through their roots and excrete it from their salt glands, and this salt is then washed off of the leaves by rainfall or falls naturally as dry deposits, producing litter with a high salt content (Di Tomaso, 1998; Imada et al., 2012, 2013). However, tamarisks can also lower soil salinity, since they intercept sunlight and prevent evaporation, which reduces the increased accumulation of salt in the soil surface below these plants in contrast to that in the soil of barren areas (Bertness et al., 1992). Herbs can also lower soil salinity by intercepting sunlight, but they cannot strongly increase soil salinity. As a result, salinity levels should be higher in soil under tamarisk shrubs than in herbaceous patches. Hence, we can distinguish the effects of plant presence and soil salinity on soil microbial communities by comparing the communities under tamarisk crowns, in herbaceous patches, and in barren areas (Fig. 1). Our previous study, that compared soils under tamarisk and in barren areas, could not clear which factor (salinity or tamarisk presence) determined the nature of the soil microbial community, especially its structure (Iwaoka et al., 2018). The present study was thus done to more clearly determine how the microbial community is controlled by plant presence in a saline grassland. Furthermore, recently developed methods of predictive metagenomics analysis using 16S rRNA-based community data (Langille et al., 2013) enable one to understand how soil microbial community have function to drive biogeochemical cycling, and this method of analysis has been applied to evaluate soil carbon and N cycling (Zhu et al., 2016; Isobe et al., 2018; Mushinski et al., 2018). Furthermore, the abundances of organisms with important functional genes such as for ammonia oxidization (amo A), which is the rate-limiting step in the nitrification process that produces nitrate N (Isobe et al., 2011; Prosser and Nicol, 2012), can now be quantified. This is important because such abundances have previously been difficult to evaluate due to the small numbers of genes present. Strong linkages have been found between soil N cycling and the predicted or quantified abundances of such functional genes (Di et al., 2009; Chen et al., 2013; Isobe et al., 2015, 2018; Zhu et al., 2016; Mushinski et al., 2018). Therefore, a deeper understanding of how soil N dynamics are determined by plants can be obtained by focusing on the analysis of such functional genes. In this study, we examined whether soil prokaryotic community

2. Material and methods 2.1. Study site This study was conducted in three tamarisk (Tamarix austromongolica Nakai) habitats in the northwestern Loess Plateau of China in October 2014. Tamarisks showed a patchy distribution in each site. The habitats were located between two cities, Dingbian (37° 59′ N, 107° 60′ E, 1372 m elevation) in Shaanxi Province and Yanchi (37° 79′ N, 107° 41′ E, 1345 m elevation) in the Ningxia Hui Autonomous Region. The vegetation type was categorized as that of a desert-steppe transitional zone (Yamanaka et al., 2014). According to Yanchi weather station (37° 80′ N, 107° 38′ E, 1356 m elevation) data, the total annual rainfall and mean annual air temperature of this site were 256.4 mm and 9.5 °C, respectively, in 2014 (Weather Underground, The Weather Company, Atlanta, GA, USA). Three tamarisk habitats with different soil water content due to them having different groundwater levels were chosen as our study sites. The study sites were located more than 2.5 km apart and were termed “Wet”, “Medium”, and “Dry” based on their water conditions. These study sites were described in more detail by Iwaoka et al. (2018). At each site, four individual tamarisks that were more than 5 m away from other individuals were selected. 2.2. Soil sampling and measurement of soil moisture and chemical properties Soil samples were taken at locations both under and away from the crowns of the four selected tamarisks at each site in October 2014. The ground under the crown was defined as the ‘under crown’ (UC) areas, while areas of bare ground and herbaceous patches at a distance of twice the crown radius from the UC areas were defined as the ‘barren areas’ (BA) and ‘herbaceous patches’ (HP), respectively (Fig. 1). As HP, we chose the vegetated areas covered mostly by the representative herbs in each site. HP in the Wet and Medium sites were mostly covered by Leymus secalinus. HP in the Dry site were mostly covered by Artemisia sp. Three soil samples (0–10 cm in depth) were collected at each place and then composited and mixed into one sample, obtaining 36 soil samples in total (4 tamarisks × 3 locations (BA or UC or HP) × 3 sites). Soil electric conductivity (EC) and pH were measured using a conductivity meter (ES-51, HORIBA, Kyoto, Japan) and pH meter (D-51, HORIBA), respectively, using fresh soil samples in a 1:2 soil:water suspension. Soil EC was used as an indicator of soil salinity. Soil water content was measured by drying soil samples at 105 °C for 3 days. After soil samples were dried and ground up, their content of total carbon (C) and N was measured using the dichromate oxidation method and the Kjeldahl method, respectively. After collection and incubation at 25 °C for 7 days, soil inorganic N was extracted from fresh soil samples with 2 M KCl at a 1:10 soil:extractant ratio. The amounts of nitrate and

Fig. 1. The sampling strategy. The allows show the salt dynamics through the water evaporation and deposition. 2

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normalized by the gene copy number, and the metagenome functional profiles were predicted using PICRUSt (ver. 1.1.1) to generate a table of Kyoto Encyclopedia of Gene and Genomes orthologs (KOs). We selected the genes coding enzymes commonly measured to evaluate soil N cycling (Saiya-Cork et al., 2002; Isobe et al., 2018) that included β1,4-Nacetylglucosaminidase (EC 3.2.1.52), leucine aminopeptidase (EC 3.4.11.1), chitinase (EC 3.2.1.14), and urease (EC 3.5.1.5). The genes coding for arginase (EC 3.5.3.1) were also searched for, but they were never or only very rarely counted, and thus, were not considered further herein.

ammonium N in the extracts were measured using a Bran + Luebbe AutoAnalyzer III (BLTEC, Tokyo, Japan) with the colorimetric method. Soil net N mineralization and nitrification rates were calculated based on the differences in inorganic N, which is the sum of nitrate and ammonium N content, and the differences in nitrate N, respectively, between before and after incubation. 2.3. Soil DNA extraction, quantification by quantitative real-time PCR, and metagenomics analysis by next-generation sequencing DNA was extracted from 0.25 g soil samples using the MoBio Powersoil DNA Isolation Kit (MoBio, Carlsbad, CA, USA) according to the manufacturer's instructions. The extracts were stored in a freezer (−20 °C) until further analysis. Quantitative real-time polymerase chain reaction (real-time qPCR) was performed in a Light Cycler Nano thermal cycler (Roche Diagnostics K.K., Mannheim, Germany) with an intercalating dye, SYBR Green I. Bacterial and archaeal 16S rRNA and amoA genes were targeted to estimate the population size of each soil microbial group. To do this, we followed the procedures described in detail by Iwaoka et al. (2018). We conducted next-generation sequencing analysis of the amplified 16S rRNA genes in the DNA extracts using U519f (Suzuki and Giovannoni, 1996)/U785r (Wang and Qian, 2009). Sequencing was performed on an Ion Personal Genome Machine (PGM) (Thermo Fisher Scientific, Waltham, MA, USA). The steps in this analysis were described in detail by Iwaoka et al. (2018). The computer analyses for the sequencing data were conducted using Quantitative Insights Into Microbial Ecology version (QIIME) 1.8.0 (Caporaso et al., 2011). Sequence data were deposited in the Sequence Read Archive of the National Center for Biotechnology Information (NCBI) under accession numbers DRA008377. We standardized the read numbers to 1124, using random pick-up based on the minimum read number. Any phylum whose relative abundance accounted for more than 10% of the reads was divided into classes, and only phyla or classes with relative abundances of more than 1% were considered further in the analysis.

2.5. Statistical analyses Linear mixed-effect models were used to analyze the effects of the factors ‘gradient’ (BA = −1, UC = 0, HP = 1) and ‘plant presence’ (BA = −1, UC and HP = 1) on soil properties (moisture, pH, total carbon and N content, and C:N ratio), inorganic N abundance, net N transformation rate, microbial abundance, relative abundances and numbers of OTUs corresponding to different prokaryote groups, and prokaryotic diversity index values. To focus on the differences created by plants, we defined site (Wet, Medium, or Dry) and the selected tamarisk individuals (4 individuals per site) as random effects that were included in the model. The objective variable was standardized in each of the linear mixed-effect model analyses. The standardization was conducted using mean and standard deviation (SD) values. The data for UC and BA sites can be found in Iwaoka et al. (2018). Nonmetric multidimensional scaling (NMDS) of community structure based on the Bray-Curtis dissimilarity index was performed using the metaMDS function in the vegan package (Oksanen et al., 2016) of R (version 3.4.4). The envfit function in the vegan package was used to illustrate significant correlations among soil properties and relative prokaryote group abundances, with the NMDS values of points overlaid as vectors on the NMDS ordination plot. Permutational multivariate analysis of variance (PerMANOVA) of the factors ‘gradient’ (BA = −1, UC = 0, HP = 1) and ‘plant presence’ (BA = −1, UC and HP = 1), with site included as a random blocking factor, was used to test for differences in soil prokaryotic community structure with the adonis function of the vegan package in R. To evaluate prokaryotic community diversity, the number of OTUs appearing in each set of 1124 reads was counted, and values of Fisher's α-diversity, Shannon's H′ index, and the Inverse Simpson's (1/D) index were calculated. In all of these tests, the significance level was defined as P < 5%, and P < 1 and 0.1% were considered as highly significance.

2.4. Prediction of functional gene abundances based on the sequencing data We searched for specific predicted functions of the prokaryote taxa found based on the 16S rRNA gene using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) pipeline (Langille et al., 2013). To perform PICRUSt searches, the sequences were reselected at a 97% similarity level with reference to the Greengenes database (version 13_05), and the operational taxonomic units (OTUs) found were referred to by their Greengenes IDs. The number of each sample was standardized to 845 reads, with reads not corresponding to the reference database eliminated; the OTU table was

Table 1 Soil electric conductivity (EC), pH, water content, total nitrogen (N) and carbon (C) content, C:N ratio, ammonium N and nitrate N content, and net N mineralization and nitrification rates. Values represent means ± standard deviations (SDs). The rightmost columns show standardized estimates and P-values (*P < 0.05) based on linear mixed-effect model analyses of the ‘gradient’ (BA (barren areas) = −1, UC (under tamarisk crown) = 0, HP (herbaceous patches) = 1) and ‘plant presence’ (BA = −1, UC and HP = 1) factors. The models were tested with site and the selected tamarisk included as random variables. Mean ± SDs

Electric conductivity (S m−1) pH Water content (%) Total C content (g kg−1) Total N content (g kg−1) Total C: N ratio Ammonium N content (mg kg−1) Nitrate N content (mg kg−1) Net N mineralization rate (mg kg−1 day−1) Net nitrification rate (mg kg−1 day−1)

Estimates

BA

UC

HP

Gradient

Plant presence

0.196 ± 0.155 9.08 ± 0.62 14.9 ± 5.1 448 ± 182 39.9 ± 16.0 11.2 ± 0.5 7.68 ± 3.66 0.81 ± 0.39 1.20 ± 0.61 0.40 ± 0.16

0.141 ± 0.099 8.69 ± 0.52 14.6 ± 6.4 612 ± 264 56.2 ± 27.0 11.2 ± 0.9 9.94 ± 4.64 1.13 ± 0.62 2.12 ± 1.41 0.30 ± 0.14

0.072 ± 0.086 8.51 ± 0.34 14.4 ± 6.3 599 ± 229 53.7 ± 21.3 11.2 ± 0.8 7.95 ± 5.93 0.92 ± 0.52 2.71 ± 1.34 0.30 ± 0.18

−0.55* −0.33 −0.05 −0.06 −0.11 0.00 −0.42 −0.39 0.45 0.00

0.05 −0.19 0.00 0.38 0.42* −0.03 0.44 0.50* 0.13 −0.29

3

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Table 2 Log10-abundance of soil bacterial and archaeal 16S rRNA and amoA genes. Values represent means ± standard deviations (SDs). The rightmost columns show standardized estimates and P-values (*P < 0.05, **P < 0.01, ***P < 0.001) based on linear mixed-effect model analyses of the ‘gradient’ (BA = −1, UC = 0, HP = 1) and ‘plant presence’ (BA = −1, UC and HP = 1) factors. The models were tested with site and the selected tamarisk included as random variables. Mean ± SDs BA Bacterial Archaeal Bacterial Archaeal

16S rRNA 16S rRNA amoA amoA

8.46 6.91 6.17 6.08

Estimates UC

± ± ± ±

0.36 0.38 0.38 0.29

8.47 6.55 6.24 6.02

HP ± ± ± ±

0.47 0.47 0.53 0.35

8.74 6.47 6.28 6.36

± ± ± ±

0.31 0.40 0.34 0.19

Gradient

Plant presence

0.35 −0.17 −0.33 0.96*

−0.04 −0.26 0.40 −0.47

3. Results 3.1. Soil moisture and chemical properties Soil EC significantly changed along the expected salinity gradient (Fig. 1), decreasing from BA to UC, and then further decreasing from UC to HP (Table 1). Total N content was significantly changed by plant presence, in that it was higher in soil under vegetation than in BA (Table 1). Soil pH, moisture, total C content, and C:N ratio did not significantly differ along the salinity gradient or as a result of plant presence.

3.2. Soil prokaryotic community Soil total bacterial and archaeal abundance did not significantly differ along the salinity gradient or as a result of plant presence (Table 2). PerMANOVA results showed that prokaryotic community structure clearly differed along the salinity gradient (F = 6.51, R2 = 0.12, P < 0.001), but not depending on plant presence (F = 1.40, R2 = 0.03, P = 0.142). On the NMDS plot, there was a gradient in the separation and placement of samples from BA to UC, and then from UC to HP (Fig. 2). Rhodothermi and Alphaproteobacteria, of which relative abundances are 14.6% and 14.1% (on average), respectively, were the two most abundant groups found in the soil at all sites. Relative abundances of Rhodothermi and Alphaproteobacteria significantly changed along the salinity gradient, decreasing (23.7–4.5%) and increasing (8.6–21.0%), respectively, along the gradient from BA to HP (Fig. 3a; Supplementary Material 1a). On the NMDS plot, these two groups’ abundances were strongly correlated with the soil EC (Fig. 2). Relative abundances of Saprospirae (5.7%), Deltaproteobacteria (5.2%), Acidimicrobiia (12.1%), Nitriliruptoria (1.5%), Acidobacteria (1.4%), and Crenarchaeota (2.4%) also significantly changed along the salinity gradient. The relative abundance of Saprospirae was also significantly changed by plant presence. Within the Deltaproteobacteria, only the relative abundance of Myxococcales (2.6%) changed along the salinity gradient (Supplementary Material 2). The alpha diversity indices (Fisher's α, Shannon's H′, and Inverse Simpson's (1/D) index, and the number of OTUs observed) of the prokaryotic community significantly changed along the salinity gradient, increasing from BA to UC, and then again from UC to HP (Fig. 3b; Supplementary Material 3). The number of OTUs in Alphaproteobacteria (32.1–58.8), Deltaproteobacteria (28.5–38.4), and Saprospirae (7.8–29.3) increased as the relative abundances of these groups increased along the gradient from BA to HP. The number of OTUs in Rhodothermi (27.4–13.2) and Nitriliruptoria (6.9–2.2) decreased as the relative abundances of these groups decreased along the gradient from BA to HP. The number of OTUs found in Rhodothermi and Nitriliruptoria were lower than the number of OTUs in Alphaproteobacteria, Deltaproteobacteria, and Saprospirae (Fig. 3; Supplementary Material 1).

Fig. 2. Nonmetric multidimensional scaling (NMDS) based on Bray-Curtis dissimilarities of prokaryotic community structures among sampling sites. The fill type and color of the symbols represent their location (BA, UC, or HP) and site (Wet, Medium, or Dry), respectively, as follows: □, BA; ■, UC; ☒, HP; and blue, green, and red symbols refer to the Wet, Medium, or Dry site, respectively. Only significant vectors are shown on the ordination. Black, dark gray, and light gray vectors represent soil properties, relative abundances of the main prokaryote phyla and predicted abundances of the genes involved in N degradation, and relative abundances of the main classes of prokaryotes, respectively. WC, water content; TN, total N content; TC, total C content; MR, net N mineralization rate. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

3.3. Soil N dynamics and predicted or quantified abundance of prokaryotic genes for N transformation Soil nitrate N content was significantly changed by plant presence, and it was higher under vegetation than in BA (Table 1). Soil ammonium N content, net N mineralization rate, and net nitrification rate did not change along the salinity gradient or depending on plant presence. The predicted abundances of genes for chitinase significantly differed along the salinity gradient, decreasing from BA to HP (Table 3). The abundances of genes for β-1,4-N-acetylglucosaminidase, leucine aminopeptidase, and urease did not significantly differ along the salinity gradient or depending on plant presence. The sum of the abundances of the prokaryotic groups (class Flavobacteriia, class Gammaproteobacteria, class Deltaproteobacteria, and phylum Verrucomicrobia) that are significantly positively correlated with N mineralization rate in this region based on the results of Iwaoka et al. (2018) did not differ along the salinity gradient or depending on plant presence (Supplementary Material 4). The abundance of ammonia-oxidizing bacteria did not differ along the salinity gradient or depending on plant presence 4

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Fig. 3. Relative abundances (%) (a) and the number of OTUs in 1124 reads of the main prokaryotic phyla and classes (b). Phyla with relative abundances > 10% (Bacteroidetes, Proteobacteria and Actinobacteria) are separated into classes. Phyla and classes with relative abundances < 1% are subsumed in the ‘other bacteria’ or ‘other archaea’ categories. The letters in parentheses after a phylum or class name indicate significant results of the linear mixed-effect model analyses for the ‘gradient’ (BA = −1, UC = 0, HP = 1) and/or ‘plant presence’ (BA = −1, UC and HP = 1) factors as follows: G means that the salinity gradient significantly affected the relative abundance of that taxon, and P means that it was significantly affected by plant presence. The number above the bar in (b) indicates the averaged Shannon's H diversity index.

mediated the effects of plants on the community structure because the moisture and pH did not significantly differ between the three locations. The relative abundances of the two dominant prokaryote groups, Alphaproteobacteria and Rhodothermi, changed markedly along the salinity gradient and were correlated with the soil EC. These results agreed with previous reports that abundances of Alphaproteobacteria decreased in response to treatments that increased the soil salinity (Abed et al., 2015; Yaish et al., 2016). Alphaproteobacteria have often been thought to be associated with plants (Fierer et al., 2007; Bachar et al., 2012; Angel et al., 2016), but the relative abundance of Alphaproteobacteria changed along the salinity gradient rather than depending on plant presence in the present study. The species Rhodothermus marinus within the Rhodothermi was previously defined as a thermohalophilic bacterium (Bjornsdottir et al., 2006), and has been observed in hot spring and deep-sea hydrothermal vent environments (Alfredsson et al., 1988; Nakagawa et al., 2003). However, little information is currently available about the Rhodothermi inhabiting soil environments. The relative abundance of Rhodothermi changed along the salinity gradient in the present study, suggesting that salinity is likely an important factor affecting Rhodothermi. Plants determined which prokaryotic groups were dominant in the soil indirectly by modifying soil salinity, resulting in clear prokaryotic community gradients from BA to UC, and then from UC to HP. Surprisingly, there were no phyla or classes whose relative abundances were consistently higher or lower in vegetated than in barren areas, regardless of their location along the gradient. Although the direct effects of plant presence should be important to soil microbial communities, as other studies have suggested (Haichar et al., 2008; Doornbos

(Table 2). However, the abundance of ammonia-oxidizing archaea was significantly affected by the salinity gradient, increasing from BA and UC to HP. 4. Discussion 4.1. Effects of plants on soil prokaryotic community structure were mediated by soil salinity In agreement with our expectations, the soil beneath the crowns of tamarisk shrubs had a medium salinity level, which created the salinity gradient that we predicted and observed. However, the total N content in the soil did not change along the salinity gradient but rather differed as a result of plant presence or absence. The litter supply to the soil should be higher under vegetation cover, resulting in higher total N content in UC and HP than in BA. Although the variation in soil physicochemical properties and organic matter content among these study sites was large (Iwaoka et al., 2018), all sites had common trends in the soil EC and in total N content among the three locations (BA, UC, and HP). We could, thus, successfully see the separate effects of plant presence and soil salinity on the soil prokaryotic community by using this experimental design. The differences of soil prokaryotic community structure between the three locations (BA, UC and HP) was caused by the salinity gradient, rather than by plant presence alone, suggesting that salinity is very important in our study site, as previous studies suggested (Rietz and Haynes, 2003; Sardinha et al., 2003; Yuan et al., 2007). Soil moisture and pH, which also strongly affected the community structure, unlikely

Table 3 The predicted abundances of genes coding for enzymes involved in N degradation. Values represent means ± standard deviations (SDs). The rightmost columns show standardized estimates and P-values (*P < 0.05, **P < 0.01, ***P < 0.001) based on linear mixed-effect model analyses of the ‘gradient’ (BA = −1, UC = 0, HP = 1) and ‘plant presence’ (BA = −1, UC and HP = 1) factors. The models were tested with site and the selected tamarisk included as random variables. Mean ± SDs BA β-1,4-N-acetylglucosaminidase Leucine aminopeptidase Chitinase Urease

537 397 189 281

Estimates UC

± ± ± ±

54 119 45 88

525 434 187 286

HP ± ± ± ±

60 75 44 57

5

551 381 155 295

± ± ± ±

79 67 42 46

Gradient

Plant presence

0.41 −0.60 −0.70* 0.15

−0.30 0.51 0.32 −0.04

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soil there. As a result, the net N mineralization rate did not change along the salinity gradient in this study site. This finding is consistent with our previous study, in which we found that not only the soil salinity but also nutrient levels were important determinants of the net N mineralization rate in the soil (Iwaoka et al., 2018). Nitrate N content differed depending on plant presence, and was higher in vegetated than in barren areas (Table 1), despite the fact that most of the differences in microbial community characteristics observed in this study occurred along the salinity gradient. A higher nitrate N content has often been reported in soil under vegetation than that in barren areas (Perroni-Ventura et al., 2010; Ochoa-hueso et al., 2017). However, our previous study did not find significant differences in this variable between BA and UC in June (Iwaoka et al., 2018). The deposition of fresh litter in October may have contributed to the higher nitrate N content observed in soil under vegetation in the present study. The abundance of ammonia-oxidizing archaea changed along the salinity gradient, increasing from BA to HP, probably due to decreasing soil salinity (Mosier et al., 2012). However, the change in the abundance of ammonia-oxidizing archaea likely did not contribute to changes in soil nitrate N content and nitrification rate in this study site. Ammoniaoxidizing bacteria were generally more important to the soil nitrification rate than ammonia-oxidizing archaea in previous studies (Di et al., 2009; Chen et al., 2013), except in acidic soils (Isobe et al., 2015). Plants’ indirect effects may not be as important as other soil properties to the nitrification process.

et al., 2012; Wang et al., 2014), the indirect effects can be stronger than the direct ones when plants strongly affect soil chemical properties. 4.2. Effects of plants on soil prokaryotic alpha diversity were also mediated by soil salinity As observed for the prokaryotic community structure, the prokaryotic alpha diversity (e.g. Shannon diversity index) also changed along the salinity gradient. The number of OTUs found in the groups whose relative abundances increased with salinity along the gradient (Alphaproteobacteria, Deltaproteobacteria, Saprospirae, and Nitriliruptoria) was much higher than that of the OTUs whose relative abundances decreased along the gradient (Rhodothermi and Acidimicrobiia). This appeared to contribute to the increase in prokaryotic community diversity observed along the gradient from BA to UC and then from UC to HP. High soil salinity has often been reported to reduce the diversity of prokaryotic communities (Hollister et al., 2010; Ibekwe et al., 2010). Low salinity levels would be advantageous to the growth of various non-halophytic groups, such as Alphaproteobacteria and Saprospirae (Yaish et al., 2016; Lijuan et al., 2017). Deltaproteobacteria were previously reported to prefer relatively high-salinity soil (Foti et al., 2008; Ibekwe et al., 2010; Iwaoka et al., 2018). However, in the present study, the relative abundance and the number of OTUs of Deltaproteobacteria increased with decreasing salinity gradient from BA to HP. Within the Deltaproteobacteria, the abundances of the most dominant group detected, Myxococcales, increased along the decreasing salinity gradient from BA to HP. This agreed with the results of a previous study, which reported that the abundance of Myxococcales increased under conditions of low soil salinity (César et al., 2009). This suggests that Deltaproteobacteria prefer low-salinity soil in some conditions, such as those in which Myxococcales was a dominant group herein. In non-saline soils, the microbial diversity observed in previous studies tended to be lower in the rhizosphere or in soils beneath plants (Berg et al., 2006; Hein et al., 2008; Ochoa-hueso et al., 2017). These reports contrasted with the result of our study, in which BA had the lowest community diversity, suggesting that the low microbial diversity observed in BA was not attributed to plant absence, but rather to the high soil salinity in these areas. Thus, we suggest that plants determine soil prokaryotic diversity by changing soil salinity rather than by having direct effects in saline grasslands.

5. Conclusion The effects of plants on prokaryotic community structure and alpha diversity were mediated more by the soil salinity gradient than by plant presence or absence in the studied sites. However, the quantified or predicted prokaryotic N-cycling genes mostly did not differ among locations. As a result, the N transformation rate and N availability did not change along the salinity gradient. The dominant prokaryotic groups at this site that contributed the most to changes in prokaryotic community structure and diversity were likely not responsible for the N cycling. Thus, a mixture of plant indirect effects and direct effects, including litter supply, is likely important in determining the available N production of the soil. Our study suggested halophytic plants strongly determine soil prokaryotic community structure and diversity indirectly by changing soil salinity, but their direct effect is also important for soil N cycling.

4.3. The soil salinity was not only the determinant factor for soil N cycling CRediT authorship contribution statement Unlike soil prokaryotic community structure and alpha diversity such as Shannon index, the abundance of most of the genes predicted to code for enzymes involved in N degradation did not change along the salinity gradient. The relative abundances of the two dominant prokaryote groups, Rhodothermi and Alphaproteobacteria, were not significantly correlated with the net N mineralization rate in the soil of our study sites (Iwaoka et al., 2018), although such a correlation was found in other studies of Alphaproteobacteria (Fierer et al., 2007; Weedon et al., 2012). This may suggest that the dominant groups that contributed the most to the change in community structure and diversity detected were not responsible for N degradation in our study sites. Only the abundance of genes for chitinase significantly changed along the salinity gradient, decreasing from BA to HP (especially, BA and UC were higher than in HP). The relative abundance of Actinobacteria also decreased from BA to HP (especially, BA and UC were higher than in HP), and most Actinobacteria can produce chitinase (Kawase et al., 2004). Actinobacteria are known to be competitive against other taxa when the soil organic matter content is low (Bastian et al., 2009; Bell et al., 2013), like in the BA in the present study. The increased abundance of Actinobacteria and the resultant increase in chitinase production potential in BA may have prevented the decrease in the net N mineralization rate that should have been caused by the low N content of the

Chikae Tatsumi: Conceptualization, Investigation, Formal analysis, Writing - original draft, Writing - review & editing. Shogo Imada: Investigation, Writing - review & editing. Takeshi Taniguchi: Investigation, Writing - review & editing. Sheng Du: Investigation, Writing - review & editing. Norikazu Yamanaka: Investigation, Writing - review & editing. Ryunosuke Tateno: Conceptualization, Investigation, Supervision, Writing - review & editing. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgment We greatly thank to members of the Institute of Soil and Water Conservation of Chinese Academy of Science (CAS), the Arid Land Research Center (ALRC) of Tottori University, the Field Science, Education and Research Center (FSERC) of Kyoto University, for cooperation and logistics in both of field survey and laboratory analysis. 6

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We also greatly thank to Dr. Kazuya Kobayashi, Dr. Takahito Yoshioka and Mr. Masataka Nakayama for helpful information. This study was financially supported in part by JSPS-KAKENHI (Grant No.15H05113), JSPS-NSFC Bilateral Joint Research Projects (Co-Principal Investigators: Norikazu Yamanaka and Du Sheng, NSFC Grant No. 41411140035, 41171419), Grant-in-Aid for JSPS Research Fellow (Grant No. 17J07686, 12J04309) and Fund of Joint Research Program of Arid Land Research Center, Tottori University.

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