# THE ROLE OF MICROBIAL COMMUNITIES IN TROPICAL ECOSYSTEMS

EDITED BY: Silvia Pajares, Brendan J. M. Bohannan and Valeria Souza PUBLISHED IN: Frontiers in Microbiology

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ISSN 1664-8714 ISBN 978-2-88945-067-1 DOI 10.3389/978-2-88945-067-1

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# **THE ROLE OF MICROBIAL COMMUNITIES IN TROPICAL ECOSYSTEMS**

Topic Editors:

**Silvia Pajares,** National Autonomous University of Mexico, Mexico **Brendan J. M. Bohannan,** University of Oregon, USA **Valeria Souza,** National Autonomous University of Mexico, Mexico

The Calakmul Biosphere Reserve in Yucatan contains the archaeological site of Calakmul, one of the largest-known Maya sites, and the largest expanse of seasonal tropical forests remaining in Mexico. The reserve is a ecological gradient conecting the Peten (Guatemala) humid forest with the xeric forest to the north. The function of this ecocline has been challenged by ongoing forest disturbance. Understanding the consequences of land use changes of this biological corridor on soil diversity and microbiallymediated processes is a major conservation frontier. Photo credit: S. Pajares

Tropical ecosystems are different in important ways from those of temperate regions. They are a major reservoir of plant and animal biodiversity and play important roles in global climate regulation and biogeochemical cycling. They are also under great threat due to the conversion of tropical ecosystems to other uses. Thus, in the context of global change, it is crucial to understand how environmental factors, biogeographic patterns, and land use changes interact to influence the structure and function of microbial communities in these ecosystems.

The contributions to this Research Topic showcase the current knowledge regarding microbial ecology in tropical ecosystems, identify many challenges and questions that remain to be addressed and open up new horizons in our understanding of the environmental and anthropological factors controlling microbial communities in these important ecosystems.

**Citation:** Pajares, S., Bohannan, B. J. M., Soza, V., eds. (2016). The Role of Microbial Communities in Tropical Ecosystems. Lausanne: Frontiers Media. doi: 10.3389/978-2-88945-067-1

# Table of Contents

*06 Editorial: The Role of Microbial Communities in Tropical Ecosystems* Silvia Pajares, Brendan J. M. Bohannan and Valeria Souza

#### **FACTORS CONTROLLING SOIL MICROBIAL COMMUNITIES IN TROPICAL SYSTEMS:**

Silvia Pajares and Brendan J. M. Bohannan

### **IMPACT OF TROPICAL FOREST CONVERSION ON SOIL MICROBIAL COMMUNITY:**

Kshitij Ranjan, Fabiana S. Paula, Rebecca C. Mueller, Ederson da C. Jesus, Karina Cenciani, Brendan J. M. Bohannan, Klaus Nüsslein and Jorge L. M. Rodrigues

#### **IMPACT OF LAND MANAGEMENT PRACTICES ON SOIL MICROBIAL COMMUNITY:**

*111 Tree Plantation Systems Influence Nitrogen Retention and the Abundance of Nitrogen Functional Genes in the Solomon Islands*

Frédérique Reverchon, Shahla H. Bai, Xian Liu and Timothy J. Blumfield

## **NOVEL METHANOTROPHS FROM TROPICAL SOIL:**

*141 Acid-Tolerant Moderately Thermophilic Methanotrophs of the Class*  **Gammaproteobacteria** *Isolated From Tropical Topsoil with Methane Seeps* Tajul Islam, Vigdis Torsvik, Øivind Larsen, Levente Bodrossy, Lise Øvreås and Nils-Kåre Birkeland

## **TROPICAL FROG SKIN MICROBIOTA:**

### *153 Panamanian frog species host unique skin bacterial communities*

Lisa K. Belden, Myra C. Hughey, Eria A. Rebollar, Thomas P. Umile, Stephen C. Loftus, Elizabeth A. Burzynski, Kevin P. C. Minbiole, Leanna L. House, Roderick V. Jensen, Matthew H. Becker, Jenifer B. Walke, Daniel Medina, Roberto Ibáñez and Reid N. Harris

# Editorial: The Role of Microbial Communities in Tropical Ecosystems

Silvia Pajares 1 \*, Brendan J. M. Bohannan2 and Valeria Souza3

1 Unidad Académica de Ecología y Biodiversidad Acuática, Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México, Mexico City, Mexico, 2 Department of Biology, Institute of Ecology and Evolution, University of Oregon, Eugene, OR, USA, 3 Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México, Mexico City, Mexico

Keywords: microbial ecology, tropical ecosystem, environmental factors, land use change, microbial processes and diversity

**The Editorial on the Research Topic**

#### **The Role of Microbial Communities in Tropical Ecosystems**

Microorganisms represent the largest proportion of the Earth's biodiversity and play an essential role in ecosystem processes, providing functions that ultimately sustain all of life (Falkowski et al., 2008; Prosser, 2012). Understanding the link between ecosystem functioning and the distribution of microbial diversity is essential to predict ecosystem responses to a changing environment (de Vries and Shade, 2013; Logue et al., 2015). With the rapid development of molecular-based techniques, a new interest in understanding the distribution patterns and functional traits of microbial communities has emerged (Fuhrman, 2009; Strickland et al., 2009; Bradford and Fierer, 2012; Krause et al., 2014). However, most of this research has been focused in temperate regions, and the principal mechanisms controlling microbial community variation within the tropics are poorly known.

#### Edited by:

Paul Bodelier, Nederlands Instituut voor Ecologie, Netherlands

> Reviewed by: Sascha M. B. Krause, University of Washington, USA

> > \*Correspondence: Silvia Pajares<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Terrestrial Microbiology, a section of the journal Frontiers in Microbiology

Received: 11 October 2016 Accepted: 27 October 2016 Published: 11 November 2016

#### Citation:

Pajares S, Bohannan BJM and Souza V (2016) Editorial: The Role of Microbial Communities in Tropical Ecosystems. Front. Microbiol. 7:1805. doi: 10.3389/fmicb.2016.01805

Tropical ecosystems are different in important ways from those of temperate regions. They are a major reservoir of plant and animal biodiversity and play important roles in global climate regulation and biogeochemical cycling (Gibson et al., 2011; Townsend et al., 2011). They are also under great threat due to the conversion of tropical ecosystems to other uses (Bawa et al., 2004; Stork et al., 2009). Thus, in the context of global change, it is crucial to understand how environmental factors, biogeographic patterns, and land use changes interact to influence the structure and function of microbial communities in these ecosystems.

It is possible that different rules apply to microbial life in tropical ecosystems. For instance, elevated nitrogen deposition by anthropogenic activities may exacerbate phosphorus deficiency in tropical regions, in ways uncommon in temperate ecosystems (Vitousek et al., 2010). However, it is poorly understood how phosphorus availability affects soil microbes (Heuck et al., 2015), or how microbial processes interact with nitrogen deposition in tropical ecosystems (Hietz et al., 2011). Moreover, the distribution of microbes not only is related to environmental factors, but also can vary in relation to temporal and spatial scale (Martiny et al., 2006; Shade et al., 2012). These factors influence biodiversity patterns of larger organisms, but their role in microbial diversity remains unclear (Barberán et al., 2014), especially in tropical systems. In addition, little is known about microbial community responses to disturbance in the tropics. Land use is one of the main drivers of biodiversity alteration in plant and animal communities, especially in tropical areas, where natural ecosystems such as forests are being rapidly altered by conversion to agriculture and other uses (Barnes et al., 2014). Understanding the effects of land use change on soil microorganisms is also a major conservation frontier.

The primary aim of this Research Topic is to explore the environmental and anthropological factors controlling the composition and functional diversity of microbial communities in tropical systems. For example, Fanin et al. reported that temporal differences in nitrogen and phosphorus availability in tropical forest soils are critical for the activity and the structure of microbial decomposer communities. Tripathi et al. observed that within tropical rainforests there is a strong degree of ecological differentiation in soil and, as a consequence soil properties together with bacterial and fungal communities varied significantly between forest types. Lee et al. documented the distribution of methanotrophs and methanogens along a depth gradient in a tropical rice paddy, and looked for patterns with oxygen, methane, and organic carbon concentrations in these soils. The influence of environmental and anthropogenic factors, such as nitrogen deposition and land-use change, on the key players in the nitrogen cycle was also assessed in a general review Pajares and Bohannan. This review highlights the large gaps in our understanding of microbially mediated nitrogen processes in tropical forest soils and identifies important areas for future research.

Due to ongoing and widespread deforestation of tropical ecosystems for pasture and crop cultivation, there is an urgent need to evaluate their soil biological diversity. This Research Topic presents new findings on the impact of rainforest conversion on soil microbial community structure, diversity and ultimately functional traits Schneider et al. Particularly in the wet season, land use change from rainforest to agriculture reduced the abundance of different functional microbial groups related to the soil carbon and nitrogen cycles Lammel et al. There is limited knowledge regarding the diversity of important groups of microorganisms in the tropics. Thus, other contributions studied how abundant lineages in soils, such as Acidobacteria Navarrete et al., and Verrucomicrobia Ranjan et al., responded to forest-topasture conversion. These studies demonstrate that such groups can be useful as indicators of agricultural impact on tropical ecosystems. Furthermore, the isolation and characterization of two novel moderately thermophilic and acid-tolerant obligate methanotrophs recovered from a tropical methane seep topsoil habitat were described Islam et al.

#### REFERENCES

Some contributions to this Research Topic have focused on how land management practices impact microbially-mediated processes in tropical soils through the alteration of microbial communities. Reverchon et al. showed the influence of different tree plantation systems on nitrogen retention and the abundance of nitrogen functional genes, while Wood et al. concluded that the direct effect of farm management (rather than the indirect effect through changes in the taxonomic and functional diversity of microbial communities) was the dominant control of nutrient loss from smallholder tropical agriculture. Both studies emphasize the necessity of further research on incorporating microbial dynamics into plantation management to improve productivity while mitigating soil fertility loss. Xiong et al. observed that sustainable agricultural management regimes, such as crop rotation, of commercially important crops in tropical regions significantly increased soil fungal diversity and might be a meaningful strategy to prevent vanilla Fusarium wilt disease.

Finally, one of the biggest threats facing frog populations in montane tropical systems is a lethal skin disease caused by a chytrid fungus. The composition of the frog skin microbiota can strongly influence many facets of frog health and disease resistance. Field survey data published as part of our Research Topic has demonstrated that the frog skin microbiota is producing broadly similar sets of anti-fungal metabolites across different host species and sites Belden et al.

In summary, the contributions to this Research Topic showcase the current knowledge regarding microbial ecology in tropical ecosystems, identify many challenges and questions that remain to be addressed and open up new horizons in our understanding of the environmental and anthropological factors controlling microbial communities in these important ecosystems.

#### AUTHOR CONTRIBUTIONS

SP produced the first draft of the editorial, and all authors edited the editorial.

on the map. Nat. Rev. Microbiol. 4, 102–112. doi: 10.1038/nrmic ro1341

forest carbon cycle. Front. Ecol. Environ. 9, 9–17. doi: 10.1890/1 00047

Vitousek, P. M., Porder, S., Houlton, B. Z., and Chadwick, O. A. (2010). Terrestrial phosphorus limitation: mechanisms, implications, and nitrogen-phosphorus interactions. Ecol. Appl. 20, 5–15. doi: 10.1890/08-0127.1

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Pajares, Bohannan and Souza. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# (A)synchronous Availabilities of N and P Regulate the Activity and Structure of the Microbial Decomposer Community

*Nicolas Fanin1\*†, Stephan Hättenschwiler1, Paola F. Chavez Soria2 and Nathalie Fromin1†*

## *Edited by:*

*Brendan J. M. Bohannan, University of Oregon, USA*

#### *Reviewed by:*

*Anthony Yannarell, University of Illinois at Urbana-Champaign, USA Bruce A. Hungate, Northern Arizona University, USA*

> *\*Correspondence: Nicolas Fanin<EMAIL_ADDRESS>
#### *†Present address:*

*Nicolas Fanin, Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden; Nathalie Fromin, Procédés, Matériaux et Energie Solaire – Centre National de la Recherche Scientifique, 7 rue du Four Solaire, F66120 Odeillo, France.*

#### *Specialty section:*

*This article was submitted to Terrestrial Microbiology, a section of the journal Frontiers in Microbiology*

*Received: 14 September 2015 Accepted: 14 December 2015 Published: 06 January 2016*

#### *Citation:*

*Fanin N, Hättenschwiler S, Chavez Soria PF and Fromin N (2016) (A)synchronous Availabilities of N and P Regulate the Activity and Structure of the Microbial Decomposer Community. Front. Microbiol. 6:1507. doi: 10.3389/fmicb.2015.01507*

*1 Centre d'Ecologie Fonctionnelle et Evolutive, UMR 5175, CNRS, Université de Montpellier, Université Paul-Valéry Montpellier, Montpellier, France, 2 University of Toulouse III Paul Sabatier, Toulouse, France*

Nitrogen (N) and phosphorus (P) availability both control microbial decomposers and litter decomposition. However, these two key nutrients show distinct release patterns from decomposing litter and are unlikely available at the same time in most ecosystems. Little is known about how temporal differences in N and P availability affect decomposers and litter decomposition, which may be particularly critical for tropical rainforests growing on old and nutrient-impoverished soils. Here we used three chemically contrasted leaf litter substrates and cellulose paper as a widely accessible substrate containing no nutrients to test the effects of temporal differences in N and P availability in a microcosm experiment under fully controlled conditions. We measured substrate mass loss, microbial activity (by substrate induced respiration, SIR) as well as microbial community structure (using phospholipid fatty acids, PLFAs) in the litter and the underlying soil throughout the initial stages of decomposition. We generally found a stronger stimulation of substrate mass loss and microbial respiration, especially for cellulose, with simultaneous NP addition compared to a temporally separated N and P addition. However, litter types with a relatively high N to P availability responded more to initial P than N addition and *vice versa*. A third litter species showed no response to fertilization regardless of the sequence of addition, likely due to strong C limitation. Microbial community structure in the litter was strongly influenced by the fertilization sequence. In particular, the fungi to bacteria ratio increased following N addition alone, a shift that was reversed with complementary P addition. Opposite to the litter layer microorganisms, the soil microbial community structure was more strongly influenced by the identity of the decomposing substrate than by fertilization treatments, reinforcing the idea that C availability can strongly constrain decomposer communities. Collectively, our data support the idea that temporal differences in N and P availability are critical for the activity and the structure of microbial decomposer communities. The interplay of N, P, and substrate-specific C availability will strongly determine how nutrient pulses in the environment will affect microbial heterotrophs and the processes they drive.

Keywords: decomposition, microbial limitations, multiple element limitation, nutritional constraints, nutrient availability, successive fertilization, temporal variability

## INTRODUCTION

Nutrient availability often exerts strong limitations on ecological processes in different ecosystem types and across distinct biomes (Vitousek and Howarth, 1991; Elser et al., 2007). Nitrogen (N) and phosphorus (P) play a particularly important role because of their high requirements for building living biomass, and because they determine growth processes and biological activity to a large degree (Sterner and Elser, 2002; Hartman and Richardson, 2013). An important source of N and P, especially for P with ongoing ecosystem and soil development (Walker and Syers, 1976), is dead organic matter of mostly plant origin. Due to the large variability in leaf C: N: P stoichiometry from high to low latitudes (McGroddy et al., 2004), and among phylogenetic groups, life forms or plant species (Hättenschwiler et al., 2008; Yuan and Chen, 2009), microbial decomposers are exposed to substrates of a considerable range in C: N: P (Cleveland and Liptzin, 2007). Recently, Fanin et al. (2013) proposed that C: N: P of leaf litter leachates also control the stoichiometry and structure of the microbial decomposer community. This finding highlights the importance of considering the relevant substrate used by microbial decomposers during the initial stage of decomposition, with leachate C: N: P stoichiometry varying from 169:2:1 to 8280:96:1 among different litter species in the studied tropical rainforest of French Guiana (Fanin et al., 2014). Thus, besides the commonly measured stoichiometry of bulk leaf litter material, the variability of N and P in soluble compounds is likely critical for assessing the effects of any alterations in nutrient availability on microbial communities.

Simultaneous additions of N and P in tropical forests were shown to strongly stimulate litter mass loss and microbial functioning during decomposition (Hobbie and Vitousek, 2000; Reed et al., 2011; Barantal et al., 2012; Fanin et al., 2012). These results support the increasing evidence that ecosystem processes are often co-limited by N and P rather than by N or P alone (Vitousek et al., 2010; Sundqvist et al., 2014). In contrast to the simultaneous application of N and P during fertilization experiments, these two nutrients are unlikely to be available in balanced amounts at the same time in most ecosystems due to their distinct quantities and chemical bonds in dead organic matter and because of their different pathways during biogeochemical cycling. For example, in contrast to N that forms relatively strong chemical bonds with C atoms, ester bonds between P and C atoms are comparatively easier to break, allowing microorganisms to mineralize P from organic molecules with less energy than is required to mineralize N (McGill and Cole, 1981). Thus, although P availability is relatively in excess initially, it may become quickly limiting during the decomposition process. Nutrient availability may also vary throughout the year, with local leaf litter input changing significantly in N: P stoichiometry and in the amount of dissolved organic C between seasons (Wood et al., 2005; Cleveland and Townsend, 2006; Cleveland et al., 2006). Similarly, N or P deposition originating from fossil fuel use, agricultural activity, or from Saharan dust are projected to rise in tropical biomes (Galloway et al., 1994; Okin et al., 2004), but these nutrient inputs are likely to vary substantially over time. For example, P deposition is strongly dependent on Saharan climate and global circulation patterns and has been shown to vary by almost twofold from 20 to 35 kg ha−1 year−1 (Okin et al., 2004). Despite the large temporal variability in the relative availability of C and nutrients over quite short time spans, its consequences for the structure and function of soil microbial communities remain poorly studied.

Because plant leaf litter represents the most important source of C and nutrients for microbial decomposers, any modification of substrate stoichiometry and nutrient availability can influence extracellular enzyme production (Allison and Vitousek, 2005; Mooshammer et al., 2012; Sinsabaugh et al., 2015), and elementuse efficiency of decomposer communities (Manzoni et al., 2012; Mooshammer et al., 2014a). Recently, empirical and theoretical studies showed that shifts in the microbial community structure and in the stoichiometry of the microbial biomass could be one way microbes respond to stoichiometric imbalance with their substrates (Fanin et al., 2013; Kaiser et al., 2014). Because bacteria and fungi present different stoichiometric requirements and constraints (Keiblinger et al., 2010; Strickland and Rousk, 2010), any changes in N and P availability may strongly control the relative proportion of these groups within the community (Krashevska et al., 2010). For instance, Güsewell and Gessner (2009) showed in a microcosm experiment that bacteria were relatively more abundant when decomposition of cellulose was N limited, whereas the relative abundance of fungi increased when cellulose decomposition was P limited. However, whether the timing of N and P availability is critical for the structure and functioning of microbial communities have received only scant attention.

Here we examined how microbial communities respond to successive N and P additions, and whether these responses differ among leaf litter substrates with distinct leachate C: N: P stoichiometry and cellulose as a nutrient free substrate. We specifically addressed the following question: Do successive N and P additions (first N and then P, and *vice-versa*) have different effects compared to simultaneous N and P additions? Although we anticipated that a combined NP addition would stimulate microbial activity more than a temporally separated N and P addition, we hypothesized that this stimulation would be dependent on the substrate quality. Specifically, we anticipated that the effects of sequential nutrient supply would depend on the N: P ratio of the soluble fraction of the litter substrates: (i) litter that is relatively rich in soluble N or P would respond stronger when P or N is added first, respectively, (ii) litter that is relatively well balanced in soluble N and P would not show a particular response to either N or P added first, and (iii) cellulose as a nutrient-free substrate would display the strongest effects when both nutrients are added simultaneously (Hypothesis 1). Because these two nutrients show distinct release patterns from decomposing litter, we hypothesized that the order in the relative N- and P-availability would determine microbial process rates, with: (i) P added first and then N resulting in the weakest effect because P is relatively available early during the decomposition process, (ii) N added first and then P showing an intermediate effect as P is relatively more limiting later during the decomposition process, and (iii) a simultaneous NP addition having the strongest effects because of synergistic responses when both elements are available in excess (Hypothesis 2). Finally, as a result of different nutrient requirements between fast-growing bacteria and more slowly growing fungi (Güsewell and Gessner, 2009; Keiblinger et al., 2010), we hypothesized that the relative abundance of these different microbial groups would be altered in a predictable way by N and P additions, with: (i) P added first and then N leading first to an increased proportion of bacteria that decreases again when N is added (ii) N added first and then P increasing the relative proportion of fungi in a first time which decreases again once P is added, (iii) simultaneous NP addition stimulating both fungal and bacterial biomasses with no net change in their relative abundance (Hypothesis 3). We tested our hypotheses with a laboratory microcosm experiment by measuring litter mass loss, microbial activity (substrateinduced respiration, SIR), and microbial community structure (phospholipid fatty acids, PLFAs) in the litter and the underlying soil during the initial stage of decomposition.

### MATERIALS AND METHODS

### Soil and Litter Material

The soil used for our experiment was collected from the topsoil (0–10 cm) in the Amazonian rainforest, at Paracou, French Guiana (5◦15 N, 52◦53 W). It is classified as a nutrient-poor acrisol developed over a Precambrian metamorphic formation called the Bonidoro series with a mean pH of 4.4 in the top soil and a soil texture with 20% clay, 6% silt and 74% sand, with a total C of 22.1 g kg−1, a total N of 1.5 g kg−1 soil and a total P of 0.10 g kg−1 soil (see Fanin et al., 2011 for more details). The soil was dried at 25◦C, sieved at 2 mm, homogenized, and stored dry until use.

Air-dried leaf litter of three different tree species occurring at our research site [*Goupia glabra* (Aublet), *Simarouba amara* (Aublet) and *Vochysia tomentosa* (G. Mey.)] as well as cellulose paper (filter paper qualitative 410, VWR, Fontenay-sous-Bois, France) were used as substrates for decomposition. In addition to leaf litter as a naturally occurring substrate for microbial growth, we used cellulose as a widely accessible C source for a large part of the heterotrophic microbial community that contains no other elements than C, O and H. Cellulose served as an extreme end point along the stoichiometric gradient covered by the three litter species and also to add a single C compound compared to the litter species composed of diverse and distinct C compounds (**Table 1**). The three litter species were selected based on their distinct C: N: P stoichiometry of the soluble fraction (**Table 1**) that has previously been shown to have a major impact on litter and soil microbial communities (Fanin et al., 2013, 2014). The three species also showed contrasting responses to combined N and P fertilization in a previous decomposition experiment in the field, with a very small effect on *V. tomentosa*, an intermediate effect on *S. amara,* and a strong effect on *G. glabra* (Fanin et al., 2012).

Freshly fallen leaf litter was collected with 25 m × 25 m large litter traps installed in mono-specific stands of an experimental plantation adjacent to the Paracou natural forest station (Roy et al., 2005), air-dried immediately upon collection, pooled across sampling dates, and stored dry. Only intact leaves without signs of herbivory, galls or fungal attack were kept (see Fanin et al., 2012 for further details on leaf litter collection).

### Experimental Design

For testing the impact of the changing relative availability of N and P through time on decomposition and microbial decomposer communities, we defined three different fertilization treatments. The first treatment consisted in the combined addition of N and P at the same time (denoted as '*NP*' in the following). In the second treatment we applied first N and later P (denoted as '*Np*' in the following), and in the third treatment we applied first P and later N (denoted as '*Pn*' in the following). A control treatment without any fertilizer application was also included. Nutrients were added either at the beginning of the experiment (d0), and/or 36 days after the start of experiment (d36) depending on the treatment. We added N as KNO3 and NH4NO3, and P as KH2PO4 at the same total quantities of N and P as used in a fertilization experiment in the field at the research site in Paracou (Barantal et al., 2012), i.e., 0.055 g KNO3 and 0.069 g NH4NO3 per microcosm, and 0.074 g KH2PO4 per microcosm. We used KNO3 in addition to NH4NO3 to keep the added K constant between the N and P treatments. The total amounts of added N and P correspond to 130 kg N ha−1 y−1, and 69 kg ha−1 y−1, respectively, which is comparable to the quantities used in other fertilization experiments in tropical forests (see Hobbie and Vitousek, 2000; Cleveland et al., 2006; Kaspari et al., 2008).

The microcosms consisted of 30 cm wide × 15 cm long × 10 cm tall plastic boxes that were covered with a plastic cap. Each microcosm received 80 g of homogenized air dry soil and 4 g (oven dried-corrected weight) of air dry leaf litter material or cellulose paper that was placed on the top of the soil. Leaf litter and cellulose paper were cut in 1 cm × 1 cm squares in order to homogenize leaf margin: total surface area ratio across all substrates. For each microcosm, we soaked the 4 g-substrate aliquotes in 40 mL of distilled water (for the *control* treatment) or in 40 mL of the corresponding fertilizer solution (for the *NP*, *Np,* and *Pn* treatments) while shaking for 24 h in order to ensure complete and homogeneous rewetting of the substrates before their addition to the microcosms. Along with the substrates we also added the soaking solution to each individual microcosm at d0, which corresponded to the volume of water needed to reach 80% of water holding capacity of the soil in the microcosm. For the second fertilization event at d36, 10 mL distilled water or fertilizer solutions (four times more concentrated than at d0 in order to add the same quantity of nutrients while keeping soil humidity close to 80% of water holding capacity) were added to all microcosms of the respective treatments. During the entire experimental duration of 74 days, the microcosms were kept at constant temperature of 30◦C and under water saturated atmosphere, and microcosms were sprinkled to their original weight with distilled water every 4 days in order to keep soil and litter humidity close to constant.

We constructed a total of 12 microcosms for each combination of litter material (*G. glabra, S. amara, V. tomentosa* and cellulose paper) and fertilization treatment (control, *NP*, *Np,* and *Pn*), resulting in a total of 192 microcosms. Three microcosms for each treatment combination were harvested after 18 (d18) and 36 (d36) days, just before the second fertilization event, and after 54 (d54) and 74 days of incubation (d74).

#### Response Variables

At each sampling date and for each microcosm, the remaining litter material was carefully retrieved, cleaned from soil particles, and weighed for fresh remaining mass. A litter subsample was dried for 48 h at 65◦C for dry mass conversion and the calculation of litter mass loss in each microcosm. Additional subsamples of litter and of homogenized soil were immediately frozen after pooling the three replicates of each treatment for further PLFA analyses (*n* = 1 for each litter × treatment combination). The remaining material was air-dried at 25◦C.

Substrate induced respiration (SIR), as an integrative indicator of the overall microbial activity (Nannipieri et al., 2003), was determined for soil and litter material as previously described (Fanin et al., 2012). Briefly, 10 g of air dry soil were placed in a sealed 150 mL flask and received a solution of glucose in order to reach 80% of the soil water holding capacity and to add 1.5 mg C g−1 of soil. For litter materials, 2 g of air dry litter was incubated in the same way, but adding glucose solution to reach 20 mg C g−1 of litter. The flasks were incubated at 25◦C for 6 h, a time span that is considered as short enough to avoid *de novo* enzyme synthesis. Two hundred micro liter headspace air samples were analyzed after 2 and 6 h for CO2 concentration using a gas chromatograph with catharometric detection (Varian CP-4900, Walnut Creek, CA, USA). The amount of CO2 released during the 4 h time lapse was used to calculate SIR rate expressed in μg C-CO2 g−1 soil or litter h−1.

Microbial community structure was determined by analyzing group-specific PLFAs from a representative subsample of 1 and 10 g fresh weight frozen litter material and soil, respectively, using the protocol described in Fanin et al. (2014). Branched and saturated, mono-unsaturated and cyclopropyl PLFAs i15:0, a15:0, i16:0, i17:0, a17:0 16:1ω7c, cy17:0, 18:1ω7c, cy19:0 were used to characterize bacteria while the 18:1ω9, 18:2ω6,9 PLFAs were used as biomarkers of fungi (e.g., Frostegård and Bååth, 1996; Zelles, 1999; Bååth, 2003).

### Data Analysis

We analyzed litter mass loss, litter SIR, and soil SIR at the different sampling dates using repeated measures ANOVAs in which litter material, fertilizer treatment, and sampling date were treated as fixed effects and were permitted to interact. Microcosm identity was included as a random factor to account for the repeated sampling over time. For each litter material, we also compared the differences between treatments for each sampling date, followed by a *post hoc* test of Tukey-HSD (α = 0.05). Using Bray–Curtis dissimilarity matrices on the selected 23 PLFAs markers, we used permutational multivariate ANOVAs (PERMANOVA) to test the effects of treatment, litter material and sampling date on microbial community structure. Following repeated measure ANOVA, we used the % sum of square as a comparative

measure of % variation explained by the different factors (substrate identity, fertilization treatment, sampling date and their interactions). We run non-metric multidimensional scaling (NMDS) with vector fitting, based on multiple linear regressions using the coordinates on the first two principal axes as the explanatory variables, and the variable of interest (e.g., relative abundance of microbial groups) as the dependent variable. Finally, to visualize the effect of N and P availability on microbial community structure, we compared the fungi to bacteria ratio (fungi:bacteria) across all substrate types as a function of the different fertilization treatments at d36 and d74. Cellulose was excluded from this analysis because of the large differences in microbial community responses compared to the three other species. All statistical tests were performed with the R software (version 2.11.1).

### RESULTS

### Fertilization and Substrate Identity Effects on Litter Mass Loss

Sampling date (28.4% of the variation explained), fertilization treatment (16.1%), and substrate identity (6.1%) all had significant effects on substrate mass loss (**Table 2A**). The interaction between substrate identity and fertilization treatment also explained 11.3% of the observed variation in substrate mass loss, i.e., a higher amount of variation than the substrate identity alone. Of all substrates, cellulose showed the largest variation in mass loss among the fertilization treatments, ranging from 22.5% of initial mass lost on average in the control treatment to 57.4% of initial mass lost in the *NP* treatment at the final harvest after 74 days of decomposition (**Figure 1A**). The other two fertilization treatments where N and P were added successively showed intermediate final mass loss of 32.6% for *Pn* and 40.6% for *Np*. Despite the marginally significant interaction between sampling date and fertilization treatment (**Table 2A**), cellulose mass loss in response to N or P addition (as the first phase of the *Np* or *Pn* treatments, respectively) did not differ from the control treatment during the first 36 days (**Figure 1A**). After the additional fertilization event (i.e., when the second nutrient was added), cellulose mass loss was faster in both treatments (*Np* and *Pn*) compared to the control treatment, although still remaining lower as compared to cellulose fertilized with both nutrients simultaneously. Opposite to cellulose, *V. tomentosa* leaf litter was not affected by any of the fertilization treatments (**Figure 1D**). *G. glabra* and *S. amara* leaf litters showed intermediate responses to the fertilization treatments compared to cellulose and *V. tomentosa* leaf litter. After 74 days, mass loss in the *NP* treatment was 13.7 and 19.4% higher than in the control for *G. glabra* and *S. amara*, respectively (**Figures 1B,C**). Adding P first clearly enhanced decomposition of *S. amara* but not that of *G. glabra* during the first part of the experiment. In both, *Np* and *Pn* treatments, the addition of the second nutrient at d36 influenced further decomposition only little, and the differences to mass loss in the control treatment got overall rather smaller with ongoing experimental duration.

## Fertilization and Substrate Identity Effects on SIR

Substrate induced respiration in decomposing material varied between 1.8 and 258.6 μg C-CO2 g−1 across all substrates and all sampling dates (**Figure 2**). In contrast to litter mass loss, litter SIR was primarily influenced by substrate identity and its interaction with sampling date, explaining 31.8 and 10.8% of the overall variation, respectively (**Table 2B**). A significant, though lower amount of variation in litter SIR was explained by fertilization treatment (10.6%) and its interaction with sampling date (8.5%). Prior to the second fertilization event, SIR rates were highest in cellulose in the NP treatment (49.2 μg C-CO2 g−1 at d36), while SIR rates following the supply of N or P alone did not differ from the control treatment between 0 and 36 days (**Figure 2A**). When the second nutrient was added, SIR increased to 94.0 and 66.8 μg C-CO2 g−1 at d54 in the *Pn* and *Np* treatments, respectively, reaching values similar to those when cellulose was fertilized with N and P at the same time (*NP* treatment). Without any nutrient addition (control treatment), SIR rates on cellulose were null at all of the sampling dates. Similar to cellulose, *G. glabra* showed a substantial stimulation of litter SIR following the first fertilization event when N and P were added together, reaching 223.9 μg C-CO2 g−1 at d36 on average (**Figure 2B**). After the second fertilization, SIR rates of *G. glabra* in *Pn* and *Np* treatments were higher than in the control and did not differ to those observed in *NP*. But on d74 the stimulation of SIR was highest in *NP* and intermediate in *Np* and *Pn* as compared to the control. Similar to cellulose and *G. glabra*, SIR rates of *V. tomentosa* and *S. amara* leaf litter were also higher at d36 with a combined *NP* fertilization than with N supplied alone, and also with higher SIR rate when P was added compared to N (**Figures 2C,D**). Following the second fertilization, and as observed for cellulose and *G. glabra*, SIR rates at d54 increased following P supply in the *Np* treatments for both litter species, and with N supply in the *Pn* treatment on *S. amara*, and reached similar values to those observed when both resources were added simultaneously (*NP* treatment). But at the final d74 sampling, the stimulation of SIR rates of *V. tomentosa* following *Np* did not persist.

Substrate induced respiration measured in the soil underneath the decomposing substrates was generally distinctively lower than that measured in decomposing material, varying between 0.70 and 6.59 μg C-CO2 g−1 soil (**Figure 3**). The hierarchy of factors explaining soil SIR over the incubation period were similar to those observed for litter SIR, with a large part of the variation explained by substrate identity (31.3%), fertilization treatment (11.3%) and sampling date (5.6%) as well as interactions between factors (**Table 2C**). Overall, *NP* addition and *P* only addition treatments showed higher soil SIR rates at d36 compared to N only addition or control treatment, except for cellulose where *NP* did not differ from the control (**Figure 3A**). The addition of N in the *Pn* treatment did not show any influence on the soil SIR rates under *V. tomentosa*, *G. glabra* and cellulose, and even a trend for a negative effect under *S. amara* (**Figure 3C**). Opposite to this pattern, the addition of P in the *Np* treatment stimulated soil SIR rates, especially under *G. glabra* with an increase from 2.40 to 5.28 μg C-CO2 g−1 between d36 and d54 (**Figure 3B**). Differences between treatments were, however, smaller at the end

*Repeated measures ANOVAs were performed to examine the relative influence of litter material, fertilization treatment, and time, as well as interactions between the three explanatory variables in accounting for the variation in microbial activity over the course of the incubation experiment.*

of the incubation, except for *V. tomentosa* with a higher soil SIR rate of 5.19 in *NP* compared to 2.05 in the control (**Figure 3D**).

### Fertilization and Substrate Identity Effects on the Microbial Community Structure

The structure of the microbial community (i.e., relative abundance of the 23 group-specific PLFA markers) differed strongly among the decomposing substrates, with contrasted NMDS plots mostly driven by cellulose that showed a distinct PLFA pattern compared to leaf litter along the second NMDS axis (**Figure 4A**). Such differences in microbial community structure between leaf litter and cellulose were clearly related to lower fungal lipid markers in leaf litter than in cellulose that translated into lower litter fungi:bacteria (see Supplementary Table S1 for more details). PERMANOVA to test for the effects of the different treatments on PLFA markers revealed that substrate identity accounted for 42.1% of the overall variation of the microbial community structure, which is roughly as much as that accounted for by all the remaining factors and interactions (**Table 3A**). To a lower extent, the structure of the microbial community in the decomposing substrates also differed across the fertilization treatments and their interactions with sampling date and substrate identity (6.4, 20.1, and 13.8% of explained variation, respectively). When omitting the cellulose substrate from the comparison, the effects of fertilization treatments were particularly apparent on the leaf litter fungi:bacteria (**Figure 4B**). The addition of N only strongly decreased the relative proportion of bacterial lipid markers by 49% whereas the addition of P only increased it by 57% on average. Such change resulted in contrasting fungi:bacteria of 7.1 (with N only) or 1.7 (with P only), respectively, compared to 2.8 in the control treatment. After complementary P addition in the *Np* treatment or N addition in the *Pn* treatment, the fungi:bacteria shifted to almost similar values. No significant difference was observed among the fertilization treatments at d74.

Microbial community structure in the soil was affected by different factors compared to that in decomposing substrates (**Table 3B**). Neither substrate identity nor fertilization treatment and their interactions explained the PLFA-based microbial community structure in the soil. In contrast, sampling date and its interaction with fertilization treatment accounted for 18.7 and 10.7% of the total variation observed in soil microbial community structure, respectively. We did not find any clear effect of the substrate identity on the soil community structure in the NMDS plot (**Figure 5A**). By contrast to the litter microbial community and even after omitting the cellulose substrate, we did not find any clear effects of fertilization or sampling date on the PLFAbased soil microbial community structure (**Figure 5B**).

gray square, solid line; *Pn*: dark gray diamond, solid line). Microcosms received the first dose of fertilizer (N, P, or NP) at d0, and the second dose of fertilizers (i.e., P, N, or NP respectively) just after d36 sampling. Stars indicate significant differences in the mass loss for each date considered separately, followed by a *post hoc* Tukey-HSD test (α = 0.05), with different letters indicating contrasted effects among the different fertilization treatments.

#### DISCUSSION

In line with the increasing number of studies demonstrating that the combined NP additions increase ecosystem processes more than P or N added singly (e.g., Vitousek et al., 2010), we found an overall stronger stimulation of substrate mass loss and litter microbial activity with a simultaneous NP supply compared to a temporally separated N and P addition. The fact that N and P together constrain the activity of microbial communities was particularly clear for litter SIR rates that on average varied sevenfold between the control and the *NP* treatments throughout the incubation period (**Figure 2**). These results are in line with previous studies showing that the decomposition of labile C substrates (Nottingham et al., 2012, 2015) or more complex leaf litter (Hobbie and Vitousek, 2000; Barantal et al., 2012; Fanin et al., 2012) were predominantly limited by concomitant

*amara*, (D) *Vochysia tomentosa*. Fertilization treatments are shown with distinct symbols and curves (control: open triangle, dotted line; *NP*: black circles, solid line; *Np*: light gray square, solid line; *Pn*: dark gray diamond, solid line). Stars indicate significant differences in the mass loss for each date considered separately, followed by a *post hoc* Tukey-HSD test (α = 0.05), with different letters indicating contrasted effects among the different fertilization treatments.

NP additions. Because the stoichiometry of N and P supply and demand is generally in close balance in most terrestrial ecosystems (Vitousek et al., 2010), N is rarely available in great excess relative to P, and a slight addition of P can rapidly generate a N limitation or *vice-versa*: N and P limitation may thus alternate in multiple incremental steps, ultimately producing a synergistic effect when both nutrients are added together (Davidson and Howarth, 2007). In contrast to the 'Liebig world' view, which states that the nutrient in the shortest supply will be limiting, we demonstrate here that both elements can constrain litter decomposition and are required by microbial communities for maintaining their activity.

Beyond evaluating the effects of simultaneous NP additions, we addressed how the temporal variability in N and P availability affects litter decomposition and the activity of decomposer organisms. As expected following our first hypothesis, the responses of microbial parameters to a temporally separated N and P addition varied strongly among litter substrates (**Table 2**). We found that P supply showed a higher effect on SIR rates for the litter species with the highest soluble N content (i.e.,

followed by a *post hoc* Tukey-HSD test (α = 0.05), with different letters indicating contrasted effects among the different fertilization treatments.

*S. amara* and *V. tomentosa*), whereas the respective effects of N or P additions were similar when these two elements are relatively well balanced in litter leachates (i.e., *G. glabra*), or when they are completely absent (i.e., cellulose) (**Figure 2**). These results suggest that the relative imbalance between N and P in litter leachates can predict relatively well which of these two elements will be limiting during the initial phase of decomposition and may thus be used as a general indicator of nutritional constraints regulating the microbial activity. In other words, because the timing of nutrient availability in a given ecosystem can vary, depending for example on seasonal differences such as dry *versus* wet season in tropical forests (e.g., Turner et al., 2015), considering N and P in the soluble fraction of plant leaf litter may contribute to the understanding of temporal variability in soil processes.

Interestingly, after adding the second nutrient in the middle of the course of the experiment (i.e., N to the *Pn* treatment, and P to the *Np* treatment), SIR generally increased to similar rates as those observed in the *NP* treatment (**Figure 2**). In

contrast to our second hypothesis, the similar responses of microbial processes regardless of whether N or P was added first suggest that the order in relative N- and P-availability does not determine final microbial process rates, and confirms that litter decomposition and microbial activity are limited by multiple elements in interaction (Townsend et al., 2011; Fanin et al., 2015). In parallel, we observed a lower relative abundance of bacteria compared to fungi in response to N fertilization, in line with our expectation of enhanced fungal biomass following N additions (Koranda et al., 2014). Such a shift in the fungi to bacteria ratio was reversed following further addition of P later in the *Np* treatment (**Figure 4**), suggesting

TABLE 3 | Permutational multivariate ANOVA evaluating the role of substrate identity, fertilization treatment and sampling dates on microbial community structure in (A) litter, and (B) soil.

*Permutational multivariate ANOVAs were performed to examine the relative influence of litter material, fertilization treatment, and time, as well as interactions between the three explanatory variables in accounting for the variation in microbial community composition over the course of the incubation experiment.*

that P is a more critical element for the fast-growing bacterial community compared to the relatively slower growing fungal community (Güsewell and Gessner, 2009). Thus, although the PLFA results should be interpreted with caution because of lack of replication, they suggest that any change in the relative availability of N and P can substantially alter the fungi:bacteria ratio, with bacteria dominating at low N: P ratios as it has been observed previously (Krashevska et al., 2010; Fanin et al., 2013).

Furthermore, the difference in the effect size between litter mass loss and litter SIR after simultaneous N and P additions indicate that the addition of a labile C source during SIR measurement may have also masked a potentially non-negligible C co-limitation on litter decomposition. For instance, the *NP* effect on litter mass loss was the strongest for cellulose (relatively accessible C substrate), intermediate for *G. glabra* or *S. amara* (both litter species relatively rich in DOC), and null for *V. tomentosa* (DOC-poor litter) (**Figure 1**). These results highlight that C availability may modulate the *NP* responses during litter decomposition, confirming that litter C leachates provide the microorganisms with the required energy to efficiently use available nutrients (Cleveland et al., 2006; Fanin et al., 2012). Accordingly, the community structure of microbial decomposers was more dependent on substrate identity than on fertilization treatments (**Table 3**). Cellulose decomposition appears to be mainly driven by fungi, while the leachate C-rich *G. glabra* leaf litter supported the highest proportion of bacteria relative to fungi of all the substrates. Such a C control on microbial community was even stronger in the soil, where substrate identity explained almost three times as much variation in soil SIR (59.4%) than did the fertilization treatments (21.5%) (**Table 2**). The fact that the nature of substrate leachates reaching the soil has a greater impact on soil microbial communities than nutrient fertilization is a rather surprising result (**Figure 5**). However, in line with our finding, Heuck et al. (2015) recently demonstrated that even in P-poor soils, the microbial biomass was primarily limited by C availability, suggesting that P is only secondarily limiting. Collectively, these results suggest that soil microorganisms are strongly limited by easily accessible C compounds originating from fresh litter material rather than by nutrients. C rather than nutrient limitation can in part be a consequence of the better balanced soil C: N: P stoichiometry compared to leaf litter with much wider C: N: P (Fanin et al., 2014; Mooshammer et al., 2014b). Perhaps even more important is the permanent high energy requirement of microorganisms in tropical soils with constantly high temperatures compared to soils at higher latitudes, which may reinforce C limitation in tropical rainforests (Hättenschwiler et al., 2011).

In summary, our study provides clear evidence that shifts in the relative availability of N and P through time regulate the structure and activity of the microbial decomposer community. Collectively, our data suggest that litter mass loss, microbial respiration and microbial community structure can vary temporally along with shifts in the relative availability of nutrients. These changes are rapid and reversible in response to changing nutrient availability. In fact, when both N and P are in excess in the environment, the order in the relative N and P availability did not determine final microbial process rates. The impact of successive N and P additions depended on the identity of decomposing litter material and their nutrient and C status of the soluble fraction, suggesting intimate interactions between exogenous nutrient availability and substrate-specific C and nutrient leachates. Hence, the interactive effects of exogenous N and P availability depend in addition on litter-derived labile C with an even stronger impact on microbial communities in the soil compared to those in the litter layer. The consequences of N and P pulses on microbial communities and their activities in nutrient-poor tropical forests therefore depend to a large extent on litter-specific C quality, and thus, on tree species composition.

#### AUTHOR CONTRIBUTIONS

NiF formulated the idea, NiF and NaF established the analytical methods, conceived and designed the experiment, PS and NiF performed the analyses and collected the data, NiF and NaF analyzed data, NiF, NaF, and SH wrote the manuscript.

### REFERENCES

#### ACKNOWLEDGMENTS

We thank Bruno Buatois and Raphaëlle Leclerc (UMR CEFE) for their help with laboratory analyses. This work has benefited from an 'Investissement d'Avenir' grant from the Agence Nationale de la Recherche (CEBA, ref ANR-10-LABX-25-01). This research was funded through a CNRS 'PIR Amazonie II' grant to SH and a CNRS-EC2CO grant to NaF. We also thank the Anthony Yannarell and Bruce A. Hungate for important suggestions to the manuscript.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal*.*frontiersin*.*org/article/10*.*3389/fmicb*.* 2015*.*01507

and function in an Amazonian rain forest. *Funct. Ecol.* 29, 140–150. doi: 10.1111/1365-2435.12329

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2016 Fanin, Hättenschwiler, Chavez Soria and Fromin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Distinctive Tropical Forest Variants Have Unique Soil Microbial Communities, But Not Always Low Microbial Diversity

Binu M. Tripathi1† , Woojin Song1,2, J. W. F. Slik3 , Rahayu S. Sukri3 , Salwana Jaafar3 , Ke Dong1 and Jonathan M. Adams1 \*

1 Department of Biological Science, College of Natural Sciences, Seoul National University, Seoul, South Korea, 2 Seoul Zoo, Seoul, South Korea, 3 Faculty of Science, Universiti Brunei Darussalam, Gadong, Brunei Darussalam

#### Edited by:

Silvia Pajares Moreno, Universidad Nacional Autónoma de México, Mexico

#### Reviewed by:

Daniel Renato Lammel, University of São Paulo, Brazil Ederson Da Conceicao Jesus, Embrapa, Brazil

#### \*Correspondence:

Jonathan M. Adams<EMAIL_ADDRESS>
#### †Present address:

Binu M. Tripathi Arctic Research Center, Korea Polar Research Institute, Incheon, South Korea

#### Specialty section:

This article was submitted to Terrestrial Microbiology, a section of the journal Frontiers in Microbiology

Received: 14 November 2015 Accepted: 08 March 2016 Published: 05 April 2016

#### Citation:

Tripathi BM, Song W, Slik JWF, Sukri RS, Jaafar S, Dong K and Adams JM (2016) Distinctive Tropical Forest Variants Have Unique Soil Microbial Communities, But Not Always Low Microbial Diversity. Front. Microbiol. 7:376. doi: 10.3389/fmicb.2016.00376 There has been little study of whether different variants of tropical rainforest have distinct soil microbial communities and levels of diversity. We compared bacterial and fungal community composition and diversity between primary mixed dipterocarp, secondary mixed dipterocarp, white sand heath, inland heath, and peat swamp forests in Brunei Darussalam, Northwest Borneo by analyzing Illumina Miseq sequence data of 16S rRNA gene and ITS1 region. We hypothesized that white sand heath, inland heath and peat swamp forests would show lower microbial diversity and relatively distinct microbial communities (compared to MDF primary and secondary forests) due to their distinctive environments. We found that soil properties together with bacterial and fungal communities varied significantly between forest types. Alpha and beta-diversity of bacteria was highest in secondary dipterocarp and white sand heath forests. Also, bacterial alpha diversity was strongly structured by pH, adding another instance of this widespread pattern in nature. The alpha diversity of fungi was equally high in all forest types except peat swamp forest, although fungal beta-diversity was highest in primary and secondary mixed dipterocarp forests. The relative abundance of ectomycorrhizal (EcM) fungi varied significantly between forest types, with highest relative abundance observed in MDF primary forest. Overall, our results suggest that the soil bacterial and fungal communities in these forest types are to a certain extent predictable and structured by soil properties, but that diversity is not determined by how distinctive the conditions are. This contrasts with the diversity patterns seen in rainforest trees, where distinctive soil conditions have consistently lower tree diversity.

Keywords: biodiversity, microbial communities, soil pH, Southeast Asia, tropical rainforest

### INTRODUCTION

Ecologists are fascinated by patterns in the biodiversity of tropical rainforests, including how and why communities differ from one another, and why diversity is greater in some places than others. In terms of known biodiversity, Southeast Asian tropical rainforests are one of the most diverse terrestrial ecosystems on Earth (Corlett, 2014), with numerous poorly studied habitat types within them. The major equatorial rainforest types in Southeast Asia are mixed dipterocarp forest (MDF), heath forest and peat swamp forest (Whitmore, 1984). Heath and peat swamp forests occur on

acidic sandy and wet peaty soils, respectively, and support lower plant diversity compared to MDF (Bruenig and Droste, 1995; Davies and Becker, 1996; Slik et al., 2009). However, compared to macro-organisms, the community composition and diversity of microorganisms in these rainforest habitats is largely unknown.

Soil microorganisms constitute the largest proportion of the world's biodiversity and are important to terrestrial ecosystem functioning (Prosser, 2012). Thus, understanding their biodiversity patterns and the major divers of these patterns in natural habitats may be important for prediction of ecosystem responses to a changing environment (Jing et al., 2015). Previous studies of tropical soils have indicated that land use changes in tropical regions influence soil microbial communities, which are mainly driven by changes in soil chemical properties (Jesus et al., 2009; Tripathi et al., 2012; Lee-Cruz et al., 2013; Rodrigues et al., 2013; Kerfahi et al., 2014). Soil pH is becoming recognized as one of the most import drivers of microbial community structure and diversity in tropical soils at various scales (Jesus et al., 2009; Tripathi et al., 2014). There have also been some studies which compared the soil microbial community composition and diversity in different rainforest habitat types (Satrio et al., 2009; Araujo et al., 2012; Miyashita et al., 2013; Pacchioni et al., 2014; Pupin and Nahas, 2014). However, most of these studies were concentrated in Neotropical regions.

The present study concentrates on variation in soil bacterial and fungal community composition and diversity in several different types of lowland tropical rainforest habitat within Brunei Darussalam, Northwest Borneo, in Southeast Asia. The MDF forests dominate the lowland forests of Borneo (Ashton, 1988; Slik et al., 2003, 2009), whereas tropical heath and peat swamp forests are relatively distinctive habitats compared to MDF forests in Borneo in terms of species composition and diversity (Brünig, 1974; MacKinnon, 1996; Cannon and Leighton, 2004). Though the above ground diversity is wellstudied in these various rainforest types, it is still unclear whether different rainforest habitats have distinct microbial community composition and diversity, analogous to the distinct plant community composition and diversity levels of these habitats (Bruenig and Droste, 1995; Davies and Becker, 1996).

The present study was conducted in Brunei Darussalam, Northwest Borneo, in Southeast Asia. Across Brunei, the major rainforest types are MDF primary, MDF secondary and peat swamp forests, with smaller scattered areas of heath forests (Whitmore, 1984). This concentration of a range of different rainforest types in close proximity provides an opportunity to study the soil microbial community composition and diversity under common climatic conditions while also diminishing the potential effect of dispersal limitation, meaning that detected differences can most likely be ascribed to differences in the soil and plant community only. We used 16S rRNA gene and ITS1 region amplicon sequencing using Illumina MiSeq platform to address the following questions:

(1) How do different rainforest habitats influence the OTU composition of soil bacteria and fungi, and what are the major soil properties linked to bacterial and fungal community structure?

(2) What are the dominant higher level bacterial and fungal taxa in each rainforest habitat type, and how does their relative abundance vary with respect to different rainforest habitats? (3) How does the alpha and beta-diversity of bacteria and fungi vary across different rainforest habitats?

We hypothesized that white sand heath, inland heath and peat swamp forests would show lower alpha- and beta-diversity of bacteria and fungi, with relatively distinct microbial communities compared to MDF primary and secondary forests due to distinctive conditions of these environments.

### MATERIALS AND METHODS

#### Site Description and Sample Collection

Five different lowland tropical rainforest types in Brunei Darussalam, Northwest Borneo were selected for this study (**Figure 1**). These forest types were MDF primary, mixed dipterocarp secondary, white sand heath, inland heath, and peat swamp forests. The MDF primary forest is dominated by large tree species in the family Dipterocarpaceae and the forest structure is complex and multi-layered. The sampled MDF secondary forests were aged around 60 years (Davies and Becker, 1996). Previously, the secondary forest sampling area was covered with primary forest (Davies and Becker, 1996). MDF secondary forest is characterized by similar plant species composition to the MDF primary forest, but differing by the dominance of pioneer tree species such as Macaranga, Vitex, and Dillenia species. The secondary MDF is also has a more open structure, consisting of a complex mosaic of near- mature and regenerating forest patches with contrasting plant compositions and micro-climates. The white sand and inland heath forests differ considerably from MDF forest both in plant species and structure (having a low and uniform single-layered canopy with dense undergrowth full of shrubs, herbs, pitcher plants, etc.). The main difference between the two heath forest types we sampled is that inland heath forest has low drainage capacity compared to white sand heath forest, which means that the white sand heath forest is being more susceptible to drought, while the inland heath forest can be flooded for part of the year. The peat swamp forest sampled in this study is dominated by a single canopy species of even aged/sized trees of Shorea albida (Dipterocarpaceae), while general plant diversity is much lower than in MDF, although the overall forest structure can be quite similar.

Field sampling was carried out during the month of June 2014, during a time with characteristic climate conditions in which afternoon rainstorms occurred about every other day (Becker, 1992). Brunei has a seasonal climate, with two drier periods February/March and July/August (Becker, 1992), and a mean annual rainfall above 2300 mm (David and Sidup, 1996). Three clusters of samples were taken in each forest type within a 3 km transect (**Figure 1**). Within each cluster, three quadrats (10 m × 10 m in size) were collected at least 30 m apart along a smaller scale linear transect (**Figure 1**). Each individual sample consisted of five pooled samples (each approximately 50 g from the four corners and one center point of the quadrat).

The top 10 cm of soil was collected in a sterile sampling bag after removing the litter layer. A total of 45 samples were collected from five different forest types (nine samples from each forest type). The collected soil samples were homogenized by sieving (2 mm sieve), and stored at −20◦C until DNA extraction.

### Soil Properties Analysis

Geographical co-ordinates and soil temperature at 5 cm depth were measured using a GPS device and a soil thermometer at each sampling quadrat during field sampling. Soil pH, gravimetric water content, organic matter content, total nitrogen and available phosphorus concentrations, and soil texture were measured at Universiti Brunei Darussalam using the standard methods (Allen, 1989). Total nitrogen content was determined by Kjeldahl method. Soil available phosphorus was extracted using Bray's reagent (0.025 M hydrochloric acid and 0.03 M ammonium fluoride), and the phosphorus concentration in the extracts was then determined using a UV-spectrophotometer (UV-1800, Shimadzu, Kyoto, Japan). Soil organic matter content was determined after incineration in a muffle furnace at 550◦C for 2 h, according to the methodology described by Allen (1989).

### DNA Extraction, PCR, and Illumina Sequencing of 16S rRNA Gene and ITS1 Region

Soil DNA was extracted from each of the collected samples using the PowerSoil DNA extraction kit (MO BIO Laboratories, Carlsbad, CA, USA) following manufacturer's instructions, and DNA samples were sent to Macrogen Incorporated (Seoul, Korea) for PCR amplification and sequencing. The extracted DNA samples were amplified for V3 and V4 region of 16S rRNA gene using the primer pairs Bakt\_341F (50 -CCTACGGGNGGCWGCAG-30 ) and Bakt\_805R (50 - GACTACHVGGGTATCTAATCC-30 ) for characterizing the bacterial communities (Herlemann et al., 2011). The fungal internal transcribed spacer (ITS) region 1 was amplified using ITS1F (50 -CTTGGTCATTTAGAGGAAGTAA-30 ) and ITS2 (50 -GCTGCGTTCTTCATCGATGC-30 ) primer pairs (White et al., 1990; Gardes and Bruns, 1993). The resulting 16S rRNA gene and ITS1 amplicons were sequenced using paired-end (2 × 300 nt) Illumina Miseq system (Illumina, USA).

### Sequence Processing

The paired-end sequences of 16S rRNA gene and ITS1 amplicons were assembled using PANDAseq assembler (Masella et al., 2012). The initial sequence processing steps such as quality filtering and sequence alignment were performed using mothur (Schloss et al., 2009). The 16S rRNA gene sequences were aligned against a SILVA alignment1 . Chimeric 16S rRNA gene and ITS1 sequences were identified using 'chimera.uchime' command implemented in mothur in de novo mode (Edgar et al., 2011), and removed. Taxonomic assignments of all the high quality 16S rRNA gene and ITS1 sequences were performed in mothur using the EzTaxone database2 (Kim et al., 2012), and UNITE database (Abarenkov et al., 2010), respectively. To determine the ectomycorrhizal (EcM) fungi we matched the fungal taxonomic assignments with known EcM lineages (Tedersoo et al., 2010). The operational taxonomic units (OTUs) were assigned for 16S rRNA gene and ITS 1 sequences using mothur and QIIME implementation of UCLUST (Caporaso et al., 2010; Edgar, 2010), respectively, with a threshold of ≥97% sequence similarity. The entire singleton OTUs were removed prior to analysis. All the 16S rRNA gene and ITS1 sequences used in this study are deposited to metagenomic-RAST server (Meyer et al., 2008) under the project ID 148753 .

### Statistical Analysis

Prior to statistical analysis, a random subset of 3,352 and 4,207 sequences per sample was generated for 16S rRNA gene and ITS 1 sequences, respectively, to correct for the differences in number of reads. To assess the differences in soil properties among different forest types, we used analysis of variance (ANOVA) or Kruskal–Wallis tests for normal and non-normal data, respectively. Furthermore, parametric (Tukey's HSD test) or non-parametric (pairwise Wilcox test) post hoc tests were

1http://www.arb-silva.de/

2http://eztaxon-e.ezbiocloud.net/

3http://metagenomics.anl.gov/linkin.cgi?project=14875

used in case of significant results of ANOVA or Kruskal– Wallis tests, respectively. We used the Benjamini-Hochberg correction to assess pairwise comparisons (P < 0.05; Benjamini and Hochberg, 1995). A principal components analysis (PCA) was performed on the correlation matrix of soil properties data of each sample in Canoco 5.0 (Biometrics, Wageningen, The Netherlands). We used permutational multivariate analysis of variance (PerMANOVA, 'adonis' function in vegan R package) to test the effect of forest type on a Euclidean distance matrix of normalized soil properties data with 9999 random permutations.

Cluster analysis was performed on Bray–Curtis distance matrices of bacterial and fungal OTUs by using an unweighted pair group mean (UPGMA) algorithm implemented in the 'hclust' function of vegan R package (Oksanen et al., 2007). As 16S rRNA genes are suitable for phylogenetic analysis, a unweighted UniFrac distance matrix was also generated for bacteria (Lozupone et al., 2011). Bray–Curtis and unweighted UniFrac distance matrices were further visualized by non-metric multidimensional scaling (NMDS) plots. Furthermore, PerMANOVA was used to evaluate the effect of forest type on Bray–Curtis and unweighted UniFrac distance matrices with 9999 random permutations. To detect possible associations between bacterial and fungal community structure and soil properties, the vectors of significant soil properties (P < 0.05) were fitted onto ordination space using the 'envfit' function of the vegan R package with 999 random permutations.

The significant differences in composition and diversity of bacterial and fungal taxa in different forest types were analyzed by ANOVA or Kruskal–Wallis tests as described above. To test the relationship between soil properties and the relative abundance of dominant bacterial and fungal phyla, we used the Spearman rank correlation test. We performed linear regression analysis to test for differences in alpha-diversity (Shannon index) in relation to soil properties. We used the betadisper function of 'vegan' R package to assess the differences in beta-diversity among different forest types, and significance of this test was determined using 999 permutations.

### RESULTS

#### Soil Properties among Forest Types

All the measured soil properties varied significantly among different forest types, except for total nitrogen and silt concentrations (**Table 1**). PCA of the different soil properties measured indicated that peat swamp forest sites were clearly distinct from other forest types (Supplementary Figure S1); however, sites from other forest types were not well-separated from each other (Supplementary Figure S1). The first two axis of the PCA explained about 71% of the total variance, with axis 1 and 2 explaining 51.6 and 19.4% of the total variance, respectively. The PerMANOVA analysis revealed a statistically significant effect of forest type on soil properties (P < 0.001, 9999 permutations).

### Bacterial and Fungal Community Composition among Forest Types

The UPGMA clustering analysis based on Bray–Curtis distance showed that bacterial and fungal community compositions were largely separated by forest types (Supplementary Figure S2). The bacterial and fungal communities in white sand heath forest were most distinct from all other forest types (Supplementary Figure S2). Whereas, MDF primary and secondary forests had most similar bacterial and fungal community composition. However, in the MDF secondary forest there were two and four extreme bacterial and fungal communities, respectively. The bacterial communities of inland heath and peat swamp forests were at the same distance level to the MDF primary and secondary forest communities, whereas, bacterial communities in these forests were in turn at the same distance level to those in the white sand heath forest. In the case of fungi, inland heath forest communities are closer in composition to MDF forests than to peat swamp and white sand heath forests communities. The UPGMA clustering results were further corroborated by the NMDS ordination plot, which also showed that bacterial and fungal community compositions were segregated by forest type (**Figures 2A,B**). The PerMANOVA analyses indicated that forest type explained 36.1 and 37.8% variation in bacterial and fungal community composition, respectively (P < 0.001, 9999 permutations). The phylogenetic community composition of bacteria, based on unweighted UniFrac distance also displayed similar pattern as that of bacterial OTU composition (Supplementary Figure S3), and also significantly influenced by forest type (PerMANOVA, P < 0.001, 9999 permutations).

To further investigate the effect of soil properties on bacterial and fungal community structure, the vectors of environmental variables were fitted onto ordination space. The environmental fitting analysis indicated that of the measured soil properties, soil pH, organic matter content, gravimetric water content, available phosphorus, temperature, sand and clay content were strongly correlated with bacterial and fungal community structure (Supplementary Figure S3 and **Figures 4A,B**).

#### Dominant Bacterial and Fungal Taxa

A total of 150,840 good quality bacterial 16S rRNA gene sequences were obtained (3,352 randomly selected reads per sample). Proteobacteria was the most dominant bacterial phylum (40.6% of all bacterial sequences) followed by Acidobacteria (37.2%), Planctomycetes (7.1%), Actinobacteria (3.5%), Verrucomicrobia (3.4%), and Chloroflexi (2.9%; **Figure 3A**). Except Planctomycetes, the relative abundance of these phyla varied significantly (P < 0.05) among forest types (**Table 2**). For fungal ITS1 sequences, a total of 189,315 high quality sequences were obtained from 45 samples (4,207 randomly selected reads per sample). The most abundant fungal phylum detected across all samples was Ascomycota (54.1% of all fungal sequences) followed by Basidiomycota (15.4%), and 30.1% of the detected sequences were unclassified (**Figure 3B**). The relative abundance of these most abundant fungal phyla varied significantly in relation to different forest types (**Table 2**). The relative abundance of Ascomycota was higher in white sand and

Means on the same row with different letters were significantly different (P < 0.05) based on Tukey's HSD test (1) or pairwise Wilcox test (2) followed by Benjamini-Hochberg correction for multiple comparisons.

inland heath forests (**Table 2**), whereas the relative abundance of Basidiomycota was higher in MDF primary and secondary forests (**Table 2**).

A total of 18,460 sequences belonged to known EcM fungal genera, representing around 9.7% of the total detected fungal sequences. The relative abundance of the detected EcM fungal genera varied significantly between forest types, with highest and lowest relative abundance observed in MDF primary forest and white sand heath forest, respectively (P < 0.0001; **Table 2**). The most abundant EcM fungal genus was Russula (78% of total EcM sequences), followed by Amanita, Thelephora, and Tomentella. The relative abundance of Russula also varied significantly between forest types, and showed similar pattern to that of total EcM fungi (Supplementary Table S1).

The relative abundance of Proteobacteria, Acidobacteria, and Chloroflexi was significantly correlated with gravimetric water content, soil pH, and organic matter content (**Table 3**). Whereas, the reactive abundance of Planctomycetes and Actinobacteria correlated with clay content (**Table 3**). Additionally, soil temperature and sand content was also found to be correlated with the relative abundance of Acidobacteria and Actinobacteria, respectively. The relative abundance of Verrucomicrobia was not correlated with any of the environmental variables measured (**Table 3**). The relative abundance of the most dominant fungal phylum Ascomycota was significantly correlated only with clay content, whereas the relative abundance of Basidiomycota the other dominant fungal phylum was significantly correlated with gravimetric water content and organic matter content.

### The Alpha and Beta-Diversity of Bacteria and Fungi

The alpha-diversity index (OTU richness and Shannon index) of both bacteria and fungi also varied significantly among forest types (**Table 4**). The lowest average bacterial alpha diversity was observed in inland heath and peat swamp forest, whereas fungal alpha diversity was lowest in peat swamp forest only, although due to high variation in diversity values, considerable overlap in diversity existed between some forest types (**Table 4**). Bacterial Shannon diversity index correlated positively with soil temperature and pH (**Figure 4A**), whereas gravimetric water content and organic matter content of the soils displayed negative correlation with bacterial diversity indices (**Table 3**). Available

FIGURE 3 | Relative abundance of dominant (A) bacterial phyla observed in 16S rRNA gene sequences and (B) fungal phyla in ITS1 sequences in different forest types.

Different letters represent means that were significant different (P < 0.05) based on Tukey's HSD test (1) or pairwise Wilcox test (2) followed by Benjamini-Hochberg correction for multiple comparisons.

phosphorus and soil clay content were negatively correlated with the Shannon index of the fungi (**Figure 4B**), while sand content was found to be positively correlated with fungal Shannon index (**Table 3**). The Whittaker beta-diversity of bacterial and fungal communities, measured as the average distance of all samples to the centroid in each forest type varied significantly among forest types (**Figure 5**). The MDF secondary and white sand heath forests having highest bacterial beta-diversity, whereas MDF

TABLE 3 | Spearman rank correlations between soil properties and the relative abundance of dominant bacterial and fungal phyla, and alpha diversity indices.

∗P < 0.05; ∗∗P < 0.01; ∗∗∗P < 0.001.

TABLE 4 | The alpha diversity indices (OTU richness and Shannon index) of bacteria and fungi in different forest types.

Values with different letters were significantly different (P < 0.05) based on Tukey's HSD test followed by Benjamini-Hochberg correction for multiple comparisons.

primary and secondary forests had highest fungal beta-diversity (**Figure 5**).

### DISCUSSION

### Distinct Soil Conditions amongst the Different Forest Types

The soil conditions varied significantly among different forest types (**Table 1**), reflecting the broad scale mosaic of environments within the lowland forests of Brunei (Moran et al., 2000; Din et al., 2015). The primary dipterocarp forest was toward the more acidic end of the normal range of pH for lowland terra firme rainforest (about pH 3.7–5.5), with typical available P and total N content (Sukri et al., 2012). The secondary forest had somewhat higher pH, but similar available P and total N levels. Soil temperature in the secondary forest at the time of sampling was somewhat higher on average, likely due to the more open canopy allowing greater daytime heating of the soil surface. Organic matter content was higher in both white sand and inland heath forests compared to MDF primary and secondary forest. The water logged environment of heath forests with limited oxygen levels might lead to accumulation of organic matter content (Moran et al., 2000). The sand content of both heath forest types was similar to the MDF primary and secondary forests. Our own examination of these soils before analyses showed that the white quartz grain component of the soils in both these heath forest types was unusually fine grade at these sites, and probably it ended up classified as silt grade. The inland heath forest was very acidic and high in soil gravimetric water content, showing swampy conditions. Swampy areas in heath forests are quite common in this region (Moran et al., 2000; Din et al., 2015). The peat swamp forest soils were very acidic, with higher organic matter and available P contents than the other forest types, and its gravimetric water content was much higher – reflecting the high water table and the abundant spongy peat. The higher level of nutrients in peat swamp forest is not unusual, as the peat soil is generated from the accumulation of partially decayed organic matter due to water logged conditions with limited oxygen supply (Andriesse, 1988; Satrio et al., 2009).

regressions were used to test the correlation between Shannon index and soil properties.

## Are There Distinct Microbial Communities on Different Rainforest Types?

The results of our comparison of soil bacterial and fungal communities across Brunei forests revealed that there are distinct community types in different types of rainforest. The community composition of fungi and bacteria in each forest type was significantly different from all of the others, but the most distinct community is that of the peat swamp forest. The distinct nature of these communities suggests that there is strong environmental and perhaps evolutionary selection for both bacterial and fungal OTUs better adapted in each environment. The clustering patterns of bacterial and fungal communities were very similar to one another, and influenced by soil pH, organic matter content, gravimetric water content, available phosphorus, temperature, sand and clay content. Previous studies on tropical soils have also shown that microbial community composition is influenced by variations in underlying soil properties due to land use change (Jesus et al., 2009; Tripathi et al., 2012, 2013; Lee-Cruz et al., 2013; Kerfahi et al., 2014).

The most abundant bacterial phyla detected across the samples were Proteobacteria and Acidobacteria, which is consistent with the results of previous studies on rainforest soils (Kanokratana et al., 2011; Tripathi et al., 2012; Lee-Cruz et al., 2013). The relative abundance of Proteobacteria was significantly lower in white sand heath and peat swamp forests, and this result could be due to restricted nutrient availability in oxygen-limited waterlogged environments of white sand heath peat swamp forests (Moran et al., 2000; Page et al., 2006). It has been shown that the relative abundance of major proteobacterial subphyla increases with nutrient additions (Leff et al., 2015). However, the relative abundance of Acidobacteria was significantly higher in peat swamp forests and negatively correlated with soil pH – a result which is to be expected as peat soils had very low pH, and most of the acidobacterial lineages are shown to dominate acidic soil environments (Jones et al., 2009). The relative abundance of Actinobacteria was highest in white sand heath forest, and positively correlated with the sand content of soil. These results are in agreement with previous observations that the members of phylum Actinobacteria are generally abundant in sandy forest soils (Russo et al., 2012; Pacchioni et al., 2014). The dominance of photosynthetic bacterial phylum Chloroflexi in secondary forests could be explained by that, due to more open canopy secondary forest soils are exposed to sunlight to a greater extent than other forest soils (Nacke et al., 2014).

At the broad taxonomic level, the relative abundance of fungal taxa detected in this study is similar to the soils of other tropical regions, where Ascomycota and Basidiomycota are also the most predominant phyla (Kerfahi et al., 2014; McGuire et al., 2014). Compared to MDF primary forest, there is an increased proportion of Ascomycota in other forest types. Ascomycota are often found at higher abundance in stressful environments (De Beeck et al., 2015), and the communities here appears to reflect this pattern. However, the lower relative abundance of Basidiomycota in white sand heath forest, inland heath forest and peat swamp forest reflect the distinctive conditions in these forest types compared to MDF forests, as numerous Basidiomycota fungi tend to be slow-growing, late-successional fungi that are sensitive to physical and chemical perturbations (Frankland, 1998; Osono, 2007).

### Are Distinctive Conditions Associated with Lower Fungal and Bacterial Diversity?

We expected to find lower alpha- and beta-diversity of bacteria and fungi in the more distinctive environments of the heath and peat swamp forests. This would be due to a combination of the low likelihood of lineages acquiring the evolutionary adaptations necessary to live in the conditions of low pH and water logged environments with limited oxygen supply. However, the observed diversity patterns did not follow these predictions. Alpha diversity of bacteria was higher in the white sand heath forest than in MDF primary forest, and similar to MDF secondary forest, while inland heath and peat swamp forest had almost similar level of alpha diversity to the MDF primary forest. When samples across all the forest types were compared in relation to soil parameters, pH emerged as overwhelmingly the strongest predictor of bacterial alpha diversity (**Figure 4A**). This result gives further confirmation of the generality of the pattern observed in other contexts around the world (Fierer and Jackson, 2006; Lauber et al., 2009; Tripathi et al., 2012), that bacterial alpha diversity increases toward neutral pH. Bacterial beta-diversity was highest in the white sand heath forest and in the MDF secondary forest – the same pattern as for bacterial alpha diversity. The other forest types had almost similar levels of bacterial beta diversity to one another. It appears that in this case tree species diversity has no bearing on the beta-diversity, perhaps reflecting the generally looser relationships between soil bacterial diversity and particular tree hosts (Millard and Singh, 2010).

Fungal alpha diversity was the same in all of the forest types except peat swamp forest. Thus, despite the apparently extreme conditions of two types of heath forests, fungal alpha diversity is no lower than in MDF primary or secondary forests. Only the peat swamp forest, perhaps because of waterlogged conditions, had lower fungal diversity. Beta diversity of fungi was, however, greater in the MDF primary and secondary forests than in the heath and peat swamp forests. This might be explicable in terms of the lower plant species diversity of these other non-terra firme forest types (Davies and Becker, 1996). Fungi often are involved in direct interactions with plants (Broeckling et al., 2008; Millard and Singh, 2010), and mycorrhizal fungi are specialized to grow under direct symbiotic relationships with plants (Gao et al., 2013). The greater beta diversity in the two terra firme forest types might then reflect the greater tree species diversity of these, with different samples able to reflect the range of host-tree-specific fungal communities that are present. An important role of the woody plant cover is also supported by the dominance of EcM fungi in MDF forests, EcM fungal groups are often dominant in Southeast Asian dipterocarp forests (Peay et al., 2010; Brearley, 2012; McGuire et al., 2014). Also importance might be a greater range of different saprotrophic fungal communities resulting from the input of different litter types from a more diverse assemblage of tree species.

Overall, this study confirmed our expectations that within the tropical rainforest, there is a strong degree of ecological differentiation in soil bacterial and fungal communities. However, the patterns in soil microbial diversity that we found amongst the various forest types in Brunei do not closely conform to our predictions that distinctive environments would show lower alpha- and betadiversity of bacteria and fungi. There is need for further theoretical consideration to try to explain why the apparently distinctive and geologically 'ephemeral' environments of heath and peat swamp forests are about as diverse, or more diverse, than terra firme MDF primary and secondary forests.

#### AUTHOR CONTRIBUTIONS

fmicb-07-00376 April 5, 2016 Time: 12:44 # 10

BT, JS, RS, and JA designed the study, BT, WS, JS, RS, and SJ completed fieldwork in Brunei, BT, WS, and SJ processed samples in the laboratory, BT and KD completed data processing and analysis, BT and JA produced the first draft of the manuscript, and all authors edited the manuscript.

#### FUNDING

This work was supported by a grant from the National Research Foundation (NRF) funded by the Korean government, Ministry

#### REFERENCES

of Education, Science and Technology (MEST; NRF-0409- 20150076).

#### ACKNOWLEDGMENTS

We thank the Brunei Forestry Department and the Biodiversity Research and Innovation Centre for entry and export permits respectively, and Universiti Brunei Darussalam for permission to conduct research.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb. 2016.00376

community by metagenomic analysis. Microb. Ecol. 61, 518–528. doi: 10.1007/s00248-010-9766-7

a plant and soil ecotone. New Phytol. 185, 529–542. doi: 10.1111/j.1469- 8137.2009.03075.x

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Tripathi, Song, Slik, Sukri, Jaafar, Dong and Adams. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# High resolution depth distribution of Bacteria, Archaea, methanotrophs, and methanogens in the bulk and rhizosphere soils of a flooded rice paddy

Hyo Jung Lee1 , Sang Eun Jeong1 , Pil Joo Kim2 , Eugene L. Madsen3 and Che Ok Jeon1 \*

*1 Department of Life Science, Chung-Ang University, Seoul, South Korea, 2 Division of Applied Life Science, Gyeongsang National University, Jinju, South Korea, 3 Department of Microbiology, Cornell University, Ithaca, NY, USA*

#### Edited by:

*Brendan J. M. Bohannan, University of Oregon, USA*

#### Reviewed by:

*Marc Gregory Dumont, Max Planck Institute for Terrestrial Microbiology, Germany Hiroyuki Imachi, Japan Agency for Marine-Earth Science and Technology, Japan*

#### \*Correspondence:

*Che Ok Jeon, Department of Life Science, Chung-Ang University, 84, Heukseok-ro, Dongjak-gu, Seoul 156-756, South Korea<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Terrestrial Microbiology, a section of the journal Frontiers in Microbiology*

Received: *01 April 2015* Accepted: *12 June 2015* Published: *25 June 2015*

#### Citation:

*Lee HJ, Jeong SE, Kim PJ, Madsen EL and Jeon CO (2015) High resolution depth distribution of Bacteria, Archaea, methanotrophs, and methanogens in the bulk and rhizosphere soils of a flooded rice paddy. Front. Microbiol. 6:639. doi: 10.3389/fmicb.2015.00639* The communities and abundances of methanotrophs and methanogens, along with the oxygen, methane, and total organic carbon (TOC) concentrations, were investigated along a depth gradient in a flooded rice paddy. Broad patterns in vertical profiles of oxygen, methane, TOC, and microbial abundances were similar in the bulk and rhizosphere soils, though methane and TOC concentrations and 16S rRNA gene copies were clearly higher in the rhizosphere soil than in the bulk soil. Oxygen concentrations decreased sharply to below detection limits at 8 mm depth. Pyrosequencing of 16S rRNA genes showed that bacterial and archaeal communities varied according to the oxic, oxic-anoxic, and anoxic zones, indicating that oxygen is a determining factor for the distribution of bacterial and archaeal communities. Aerobic methanotrophs were maximally observed near the oxic-anoxic interface, while methane, TOC, and methanogens were highest in the rhizosphere soil at 30–200 mm depth, suggesting that methane is produced mainly from organic carbon derived from rice plants and is metabolized aerobically. The relative abundances of type I methanotrophs such as *Methylococcus*, *Methylomonas*, and *Methylocaldum* decreased more drastically than those of type II methanotrophs (such as *Methylocystis* and *Methylosinus*) with increasing depth. *Methanosaeta* and *Methanoregula* were predominant methanogens at all depths, and the relative abundances of *Methanosaeta*, *Methanoregula*, and *Methanosphaerula*, and GOM\_Arc\_I increased with increasing depth. Based on contrasts between absolute abundances of methanogens and methanotrophs at depths sampled across rhizosphere and bulk soils (especially millimeter-scale slices at the surface), we have identified populations of methanogens (*Methanosaeta*, *Methanoregula*, *Methanocella*, *Methanobacterium*, and *Methanosphaerula*), and methanotrophs (*Methylosarcina, Methylococcus, Methylosinus,* and unclassified *Methylocystaceae*) that are likely physiologically active *in situ*.

Keywords: methanogen, methanotroph, flooded rice paddy, depth distribution, methane, bulk and rhizosphere soil

### Introduction

Methane (CH4) is the second most important greenhouse gas in the atmosphere, after carbon dioxide. It is produced by methanogens in the biosphere, but a large part of the released methane is metabolized by methanotrophs before it reaches the atmosphere (Bridgham et al., 2013). Therefore, methane emission from the biosphere into the atmosphere is mainly determined by methanogenic and methanotrophic activities, and numerous prior studies have focused on methanogens and methanotrophs to gain a better understanding of methane metabolism in the biospheric carbon cycles (Semrau et al., 2010; Bridgham et al., 2013; Costa and Leigh, 2014). Among methanogenic groups, members of the orders Methanomicrobiales, Methanobacteriales, Methanococcales, Methanosarcinales, and Methanocellales have been identified from rice paddies (Sakai et al., 2009; Ma et al., 2012; Lee et al., 2014; Liu et al., 2015). Although anaerobic methanotrophs such as the anaerobic methanotrophic archaea (ANME), "Candidatus Methylomirabilis oxyfera," and "Candidatus Methanoperedens nitroreducens" have recently been reported as putatively important players in methane oxidation (Ettwig et al., 2010; Biddle et al., 2012; Haroon et al., 2013; Shen et al., 2014), it has been suggested that methane is metabolized mainly by aerobic methanotrophs in rice paddies (Groot et al., 2003; Ma et al., 2010, 2013; Lee et al., 2014). Based on their physiological characteristics and phylogeny, the aerobic methanotrophs are generally divided into two groups, types I and II, corresponding to the family Methylococcaceae (Gammaproteobacteria) and the families Methylocystaceae and Beijerinckiaceae (Alphaproteobacteria), respectively (Semrau et al., 2010; Lüke and Frenzel, 2011). In addition, atypical methanotrophs belonging to the family Methylacidiphilaceae of the phylum Verrucomicrobia were reported to be aerobic (Op den Camp et al., 2009).

Rice, one of the most important crop plants worldwide with a total cultivation area of 155 million hectares, is traditionally grown under flooded or wet conditions during most of the cultivation period (Ma et al., 2010). Because the waterlogged soil of rice paddies provides ideal conditions for methanogenesis, rice field is considered an important anthropogenic methane source accounting for 5–19% of the global methane emission to the atmosphere (IPCC, 2007; Ma et al., 2010). Methane emission from rice paddies is the result of complicated processes, including hydrolysis and fermentation of organic matter, methanogenesis, and methane transport and oxidation, which involve complex consortia of hydrolytic, fermenting, methanogenic, and methanotrophic microorganisms (Liesack et al., 2000; Conrad, 2002, 2007). Previous studies have shown that different methanotrophic and methanogenic taxa display varying sensitivities and responses to oxygen concentrations (Gilbert and Frenzel, 1998; Yuan et al., 2009, 2011; Krause et al., 2010; Ma et al., 2012; Reim et al., 2012). Indeed, the abundance and distribution of methanogenic and methanotrophic communities both respond to and create the physical and chemical gradients that occur with depth in flooded rice paddy soils (Lüdemann et al., 2000; Noll et al., 2005; Krause et al., 2010; Reim et al., 2012; Ma et al., 2013).

Because it is well known that organic matter derived from detritus and root exudates of rice plants is an important driver of methane production and that oxygen influx occurs through the aerenchyma of rice roots, the abundance and structure of the methanogenic and methanotrophic communities along a depth gradient are expected to differ between the bulk and rhizosphere soils of planted and unplanted rice paddies. However, previous studies of microbial communities in rice paddies have largely relied upon terminal-restriction fragment length polymorphism (T-RFLP) analysis of 16S rRNA genes (Lüdemann et al., 2000; Noll et al., 2005; Reim et al., 2012). Although T-RFLP has been successfully used by some research groups to analyze methanogenic and methanotrophic communities (Ma and Lu, 2011; Ma et al., 2013), T-RPLP can deliver only limited insights into complex communities. Pyrosequencing is a more powerful technique to explore the composition of microbial communities in natural habitats (Roesch et al., 2007), and recently pyrosequencing has been used to analyze methanogen and methanotroph communities in rice paddies (Lee et al., 2014; Lüke et al., 2014; Breidenbach and Conrad, 2015). However, it has not yet been applied to samples gathered at millimeter resolution.

In this study, we combined the millimeter-scale sampling with pyrosequencing-based community characterization of communities, especially methanotrophs and methanogens, in bulk and rhizosphere soils of a planted rice paddy. Furthermore, we determined the oxygen, methane, and total organic carbon (TOC) concentrations in the corresponding depths. Thus, the findings reported here expand the current knowledge and understanding of methane production, oxidation, and transport in rice paddies.

### Materials and Methods

#### Rice Paddy Description and Soil Sampling along a Depth Gradient

Soil cores were samples from a rice paddy located in Sacheon, South Korea (35◦ 10′ 90′′N, 128◦ 11′ 84′′E). The rice paddy has a silt loam soil texture (20% clay, 55% silt, 25% sand) and has been tilled once a year for the last several decades. All farming practices, including tillage, transplanting of the Korean rice cultivar Dongjinbyeo (Oryza sativa, Japonica type), water irrigation, and chemical fertilization, were performed according to the same procedures described previously (Lee et al., 2014). Soil core sampling in the flooded rice paddy was performed on September 3, 2013, approximately 90 days after ricetransplanting (flowering and heading stages of rice growth), as methane emission reaches a maximal level during this phase of the rice cultivation period (Lee et al., 2014). Prior to the soil core sampling, oxygen concentrations along a depth gradient in the bulk and rhizosphere soils of the rice paddy were measured in situ using the Fibox 3 LCD trace system with the Pst3 sensor (PreSens GmbH, Rogensburg, Germany). Soil cores (3-cm diameter and 45-cm length) were collected from the bulk soil (at the middiagonal point separating individual plants placed at 30 × 15 cm spacings) and from the rhizosphere soil (∼3 cm from the base of rice plants) within the flooded rice paddy (Supplementary Figure S1), using polyvinyl chloride soil core samplers sealed with rubber stoppers.

The sampled tubes were immediately frozen in a dry ice/ethanol bath, and then stored at −80◦C until further analysis.

For the analysis of the microbial communities and methane concentrations along a depth gradient, 1.0-mm-thick sections were cut from the surface of the frozen soil cores to a 10-mm depth using a microtome cryostat (HM 505E, Microm, Germany) as described previously (Reim et al., 2012). In addition, soil samples corresponding to 30, 60, 100, 200, 300, and 400 mm depth were cut from the frozen soil cores using the microtome cryostat. Half of each frozen soil subsample was transferred into a 10-ml serum bottle, which was immediately sealed with rubber stoppers for the analysis of the methane concentration. The remaining half of soil subsamples were stored at −80◦C until the analysis of the microbial community structure and abundance.

#### Analysis of Methane and TOC Concentrations

To measure methane concentrations along a depth gradient in the rice paddy, the 10-ml serum bottles containing the soil samples were warmed to 25◦C and shaken for 1 min after injection of 1 ml of distilled water (DW). Headspace gas samples (0.5 ml) were taken from the serum bottles using a gas-tight syringe (Hamilton, USA) and methane concentrations were measured with a 6890N gas chromatograph (GC, Agilent Technologies, USA) equipped with a flame-ionization detector and an HP-5 capillary column (0.32 mm × 30 m, 0.25µm film thickness, J & W Scientific, USA) as described previously (Herman and Roberts, 2006). The methane concentrations in the rice paddy soil were calculated on a fresh weight basis by measuring the weights of the soil subsamples used for methane analysis. The measurements were performed in triplicate.

To measure TOC concentrations along a depth gradient, seven soil samples corresponding to 0–10, 25–35, 55–65, 95– 105, 195–205, 295–305, and 395–405 mm depths in the bulk and rhizosphere soils of the rice paddy were collected from the frozen soil cores using a small handsaw. Twenty-five milliliters of DW were added to 5 g of the moist soil samples and the mixtures were agitated for 60 min using a shaker at 250 rpm. The mixtures were centrifuged at 8000 rpm (∼7200 g) for 10 min and the supernatants were filtered using 0.45-µm pore-size filters. The TOC concentrations of the filtrates were measured using a TOC analyzer (TOC-VCPH, Shimadzu, Japan). The TOC concentrations in the rice paddy were calculated on a dry weight basis by measuring the dry weight of the precipitates after centrifugation. The measurements were performed in triplicate.

#### Quantitative Real-time Polymerase Chain Reaction (qPCR)

To estimate the bacterial and archaeal abundances along a depth gradient in the rice paddy, qPCR was performed as described previously with some modifications (Lee et al., 2012, 2014). Briefly, genomic DNA was extracted from the remaining half of frozen soil subsamples using a Fast DNA spin kit (MP Biomedicals, Solon, OH) according to the manufacturer's instructions. Two qPCR primer sets, bac1114F (5′ -CGG CAA CGA GCG CAA CCC-3′ )/bac1275R (5′ -CCA TTG TAG CAC GTG TGT AGC C-3′ ) and arch349F (5′ -GYG CAS CAG KCG MGA AW-3′ )/arch806R (5′ -GGA CTA CVS GGG TATC TAA T-3′ ), were used to amplify the 16S rRNA genes of Bacteria and Archaea, respectively (Takai and Horikoshi, 2000; Denman and McSweeney, 2006). The qPCR amplifications were performed in triplicate and gene copy numbers were estimated by using two standard curves generated from pCR2.1 vectors (Invitrogen, USA) carrying bacterial (Chloroflexi) and archaeal (a methanogenic archaeon) 16S rRNA genes as described previously (Jung et al., 2011; Lee et al., 2012). Bacterial and archaeal 16S rRNA gene copy numbers in the rice paddy were calculated on a fresh weight basis by measuring the weight of the soil samples used for DNA extraction.

#### Analysis of the Bacterial and Archaeal Community Structure using Pyrosequencing

For the analysis of bacterial and archaeal community structures along a depth gradient in the rice paddy, composite genomic DNA samples were prepared by mixing equal amounts of the genomic DNA extracted from the same depths of three different cores. The hypervariable regions of bacterial and archaeal 16S rRNA genes were amplified using primer sets Bac9F/Bac541R and Arc344F/Arc927R with unique 7–11 mer barcode sequences, respectively (Supplementary Table S1). The PCR products were pooled for pyrosequencing using a 454 GS-FLX Titanium system (Roche, Mannheim, Germany) at Macrogen (Seoul, Korea) as described previously (Lee et al., 2014).

The pyrosequencing data were processed using RDPipeline (http://pyro.cme.msu.edu; Cole et al., 2009). The pyrosequencing reads were sorted to the specific samples based on their unique barcodes, after which the barcode and primer sequences were trimmed. Reads with more than two undetermined nucleotides and/or read lengths shorter than 300 bp were excluded from subsequent analysis, and potential chimeric reads were discarded by using the UCHIME chimera slayer (Edgar et al., 2011) in RDPipeline. To compare the microbial diversity among the soil samples, the bacterial and archaeal read numbers of each sample were normalized to those of the sample with the smallest number of reads by random removal of sequencing reads using the sub.sample command of the Mothur program (Schloss et al., 2009). Operational taxonomic units (OTUs), Shannon–Weaver (Shannon and Weaver, 1963), and Chao1 biodiversity (Chao, 1987) indices and evenness for the normalized sequencing reads were computed with RDPipeline at a 97% identity cutoff value.

The bacterial and archaeal communities along a depth gradient in the bulk and rhizosphere soils of the rice paddy were compared using the UniFrac analysis (Lozupone and Knight, 2005). Representative sequences were selected by aligning the normalized reads and clustering them at a 97% nucleotide identity cutoff in RDPipeline, and singletons were removed as described by Zhou et al. (2011). The representative sequences without singletons were aligned in RDPipeline and neighborjoining (NJ) trees were constructed based on the Kimura two-parameter model using the PHYLIP software (ver. 3.695) (Felsenstein, 2002). The NEXUS files of the NJ trees were used as input files for weighted hierarchical clustering and principal coordinate analysis (PCoA).

Taxonomic classification was performed for all high-quality sequences of Bacteria and Archaea at the phylum, class, and genus levels based on the SILVA database (v.102), using the nearest-neighbor method within the mothur program (Pruesse et al., 2007). The absolute abundances of methanotrophic and methanogenic groups along a depth gradient in the bulk and rhizosphere soils of the rice paddy were estimated by multiplying the relative abundances of methanotrophic and methanogenic groups that were classified at the genus level and the gene copy numbers of bacterial and archaeal 16S rRNA genes that were obtained by the qPCR analysis.

#### Nucleotide Sequence Accession Number

The pyrosequencing data of the bacterial and archaeal 16S rRNA genes are publicly available in the NCBI Short Read Archive (SRA) under accession no. SRP052852 (NCBI BioProject PRJNA273696).

#### Results

#### Vertical Profiles of Oxygen, Methane, and TOC Concentrations

The concentration profiles of oxygen, methane, and TOC along a depth gradient in the flooded rice paddy are shown in **Figure 1**. The oxygen concentration in the surface floodwater of the paddy soil was approximately 8.8 ppm, but the concentration decreased very sharply to below the detection limit at 8-mm (**Figure 1A**). This result suggested that the oxic-anoxic interface representing the penetration limit of oxygen from the soil surface was located at the depth range of approximately 6–10-mm, which was slightly different from previously reported results in which the oxic-anoxic interfaces were located at approximately 2-mm depth (Revsbech et al., 1999; Reim et al., 2012). This difference might be explained by the high oxygen concentration in the floodwater in this study. No clear difference was observed in the vertical oxygen profiles of the bulk and rhizosphere soils. The methane concentrations were extremely low in the aerobic surface soil (<0.1µg/g-soil) (**Figure 1A**). Simultaneous depletion of oxygen and methane in the 4–10 mm oxic-anoxic interface zone is characteristic of aerobic methanotrophy. Below this interface zone, where aerobic methanotrophy certainly occurred in situ, methane concentration increased steadily with depth to maximum levels of 2.7 and 3.3µg/g-soil at 60–100 mm in the bulk and rhizosphere soils, respectively; methane then decreased to approximately 0.4µg/g-soil at the 400-mm depth in both soils (**Figure 1A**). Interestingly, the methane concentration at a depth of 9 mm in the rhizosphere soil was nearly double that at the same depth in the bulk soil. The TOC concentrations increased to maximum levels of 8.7 and 14.7 mg/g-soil at approximately 100-mm depth in the bulk and rhizosphere soils, respectively (**Figure 1B**). The vertical profiles of TOC corresponded well with those of methane concentrations. As expected, both the methane and TOC concentrations were generally higher in the rhizosphere than in the bulk soil of the rice paddy.

#### Abundances of Bacteria and Archaea along a Depth Gradient

To estimate the bacterial and archaeal abundances along a depth gradient in the bulk and rhizosphere soils of the rice paddy, a qPCR approach targeting the 16S rRNA genes was used (**Figure 2**). The total numbers of 16S rRNA gene copies of

Bacteria were approximately 4.1 × 108 and 8.9 × 108 copies/gsoil at the surface of the bulk and rhizosphere soils, respectively. The copy numbers increased sharply to a maximum of 1.5 × 109 copies/g-soil at 3–4-mm depth and 2.0 × 109 copies/g-soil at 7–8-mm depth in the bulk and rhizosphere soils, respectively, and then rapidly decreased to numbers similar to those at the surface, at 10-mm depth (**Figure 2A**). These results are consistent with the hypothesis that organic carbon, including methane, may be supporting the in situ growth (hence high populations) of Bacteria at the oxic-anoxic interface of the rice paddy. Below 10-mm depth, which corresponds to anaerobic conditions, the bacterial 16S rRNA gene copy numbers remained relatively constant at approximately 7.0 × 108 copies/g-soil in both the bulk and rhizosphere soils. The 16S rRNA gene copy numbers of Archaea were approximately 2.2 × 106 and 4.3 × 106 copies/gsoil at the surface of the bulk and rhizosphere soils, respectively (**Figure 2B**). The copy numbers increased to a maximum of 2.4 × 107 and 4.5 × 107 copies/g-soil at 60–100-mm depth in the bulk and rhizosphere soils, respectively, and then gradually decreased below 100-mm depth with increasing depth in the rice paddy. The 16S rRNA gene copy numbers of Bacteria were higher in the rhizosphere than in the bulk soil at 0–30-mm depth, while those of Archaea were higher in the rhizosphere than in the bulk soil at all sampled depths.

#### Bacterial and Archaeal Community Composition along a Depth Gradient

To investigate the phylogenetic structure of the bacterial and archaeal communities along a depth gradient in the bulk and rhizosphere soils of the rice paddy, a parallel pyrosequencing approach was applied. After the removal of low-quality and putative chimeric reads, and trimming of the barcoded PCR primers, a total of 163,731 and 116,773 high-quality reads with corresponding average read lengths of 474 and 495 bases were obtained for the bacterial and archaeal communities, respectively (Supplementary Table S2). Because the read numbers influence the statistical diversity indices, especially the Chao1 and Shannon–Weaver indices, the numbers of the bacterial and archaeal reads in each sample were normalized to 3031 and 2122 reads, respectively, and the statistical diversity indices were calculated based on the normalized samples (Table S2). The Chao1 and Shannon–Weaver indices indicated that the bacterial diversities were relatively constant over the first 10-mm in both the bulk and rhizosphere soils, but they decreased below the 30 mm depth (**Figure 3A**), suggesting that the oxygen concentration is an important factor for bacterial diversity in the rice paddy. The archaeal diversities decreased steadily in both soils between the surface and 400-mm depth with increasing depth (**Figure 3B**). However, no obvious differences in the bacterial and archaeal diversities along a depth gradient were observed between the bulk and rhizosphere soils of the rice paddy.

The composition of the bacterial and archaeal communities along a depth gradient in the bulk and rhizosphere soils of the rice paddy was statistically compared using weighted UniFrac clustering and PCoA. Because Zhou et al. (2011) demonstrated that singletons can cause data distortion, these were removed from the normalized data before clustering and PCoA (**Figure 4**). The sequences of Bacteria were clustered three groups according to depths of 0–6, 6–10, and 30–400 mm (**Figure 4A**), corresponding with the oxic zone, the oxic-anoxic interface, and the anoxic zone, respectively, based on the vertical oxygen profiles as shown in **Figure 1A**. The sequences of Archaea were clustered into three groups according to depth; however, the clusters were less tightly related to the vertical oxygen profiles than that of the bacterial community data, especially in the oxic-anoxic interface of the rhizosphere soil

(**Figure 4B**). The PCoA results confirmed that the bacterial and archaeal communities could be divided into three groups corresponding with the vertical oxygen profiles (**Figures 4C,D**). However, the bacterial and archaeal communities were not clearly differentiated between the bulk and rhizosphere soil of the rice paddy. Taken together, these results showed that the composition of the bacterial and archaeal communities of the rice paddy differed along a depth gradient and suggested that the oxygen concentration might be a determining factor in this.

Next, the high-quality bacterial and archaeal sequences were taxonomically classified at the phylum and class levels, respectively, using the SILVA reference database (**Figure 5**). The classification showed that the bacterial and archaeal communities were not distinctly differentiated between the bulk and rhizosphere soils of the rice paddy. Predominant Bacteria belonged to the phyla, Proteobacteria, Cyanobacteria, Chloroflexi, Bacteroidetes, Acidobacteria, Actinobacteria, and Firmicutes at all depths in both the bulk and rhizosphere soils (**Figures 5A,B**), although their relative abundances were slightly different along a depth gradient. The relative abundances of Cyanobacteria and Bacteroidetes decreased, while those of Chloroflexi, Acidobacteria, and Actinobacteria increased with depth in both soils. The taxonomic classification of the archaeal sequences showed that methanogens, including Methanomicrobia, Methanobacteria, and Methanococci, which belong to the phylum Euryarchaeota were predominant at all depths in both the bulk and rhizosphere soils (**Figures 5C,D**). The relative abundance of the methanogens, especially Methanomicrobia, increased with depth, while non-methanogenic Halobacteria, which were predominant at the surface layer, sharply decreased with depth and formed a minority at the deep soil layer. These results suggested that Halobacteria in the rice paddy are aerobic, which was consistent with previously reported results that archaeal isolates belonging to Halobacteria are aerobic (Wainø et al., 2000; Shimane et al., 2011). In addition, Soil\_Crenarchaeotic\_Group (SCG) and Group\_C3 belonging to the phylum Crenarchaeota were detected at high relative abundance and their relative abundances gradually increased with depth in both the bulk and rhizosphere soils (**Figures 5C,D**). The changes in the bacterial and archaeal communities were more pronounced at the surface layer (∼10 mm) associated with a sharp oxygen gradient, suggesting that oxygen might be a determining factor for the taxonomic composition of the microbial communities.

We sought deeper insights into the paddy soil microbial communities by moving from phylum- and class-level analyses (**Figure 5**) to the more rarified genus level. Here we focused on bacterial and archaeal sequences specifically involved in methane metabolic processes—methanogens and aerobic methanotrophs along the depth gradient in the rice paddy soil (**Figures 6**, **7**). Only 0.25–3.27% of the bacterial sequences were assigned as methanotrophs, while 37.3–88.1% of the archaeal sequences were recognized as methanogens (**Figures 6A–D**, respectively); these proportions of methanotrophs and methanogens were consistent with previously reported results (Lee et al., 2014). The relative abundances of the methanotrophs in the bulk soil increased in the surface layer and their maximum abundance was observed at 3–4-mm depth, but they gradually decreased with depth (**Figure 6A**). Whereas, the relative abundances of the methanotrophs in the rhizosphere soil showed a maximum level at the surface layer (1–3 mm) and then gradually

decreased with depth (**Figure 6B**). Members of Methylosinus, Methylocystis, Methylococcus, Methylomonas, and Methylosarcina were detected as dominant methanotrophs residing in both the bulk and rhizosphere soils, and their relative abundances differed along a depth gradient. The relative abundance of type II methanotrophs was nearly constant or decreased slowly with depth in both the bulk and rhizosphere soils, while that of type I methanotrophs, in particular the type Ib Methylococcus, decreased more sharply with depth. In contrast to the methanotrophs, the relative abundances of the methanogens increased with depth, and a pronounced increase was observed around the oxic-anoxic interface (**Figures 6C,D**). Methanosaeta, Methanoregula, Methanocella, Methanosarcina, Methanobacterium, Methanosphaerula, and GOM\_Arc\_I (Gulf of Mexico\_Archaea\_I) were the dominant methanogens in both soils, and their relative abundances differed along a depth gradient. The relative abundances of the genera Methanosaeta, Methanoregula, Methanosphaerula, and GOM\_Arc\_I, which belong to the class Methanomicrobia, increased with depth. In particular, GOM\_Arc\_I was one of the predominant members at the deep soil layer (400 mm). Species of the ANME group were hardly detected at all depths of the rice paddy, which was consistent with previously reported results (Lee et al., 2014).

Trends in the rice paddy methane profile in **Figure 1A** (drastic methane depletion at the 4–10 mm depths in both bulk and rhizosphere soils and a methane concentration at 9 mm in the rhizosphere soil nearly double that that of the bulk soil) were not readily interpretable based on the relative abundances of methanotrophs and methanogens shown in **Figure 6**. With the goal of identifying populations potentially involved in in situ methane production and consumption, we next estimated the absolute abundances (16S rRNA gene copies) of methanotrophic and methanogenic populations. To accomplish this, we multiplied the relative abundances of each group of populations with their 16S rRNA gene copy numbers (**Figure 7**). Absolute abundances of total methanotrophs and methanogens were ∼107 16S rRNA gene copies/g-soil in both the bulk and rhizosphere soils. This result was in agreement

with previously reported results, in which the abundances of methanotrophs and methanogens were analyzed by using qPCR of the methyl-coenzyme M reductase (mcrA) and particulate methane monooxygenase (pmoA) genes, which encode key enzymes of methanogens and methanotrophs, respectively (Yuan et al., 2009; Shrestha et al., 2010; Lee et al., 2014).