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Research Article

Effects of forest conversion on soil microbial communities depend on soil layer on the eastern Tibetan Plateau of China

  • Ruoyang He,

    Roles
    Investigation,

    Software,

    Writing – original draft

    Affiliation
    Institute of Ecology and Forest, Sichuan Agricultural University, Chengdu, China

  • Kaijun Yang,

    Roles
    Formal analysis,

    Investigation

    Affiliation
    Institute of Ecology and Forest, Sichuan Agricultural University, Chengdu, China

  • Zhijie Li,

    Roles
    Formal analysis

    Affiliation
    Institute of Ecology and Forest, Sichuan Agricultural University, Chengdu, China

  • Martin Schädler,

    Roles
    Writing – review & editing

    Affiliation
    Helmholtz Centre for Environmental Research-UFZ, Department of Community Ecology, Halle, Germany

  • Wanqin Yang,

    Roles
    Investigation

    Affiliation
    Institute of Ecology and Forest, Sichuan Agricultural University, Chengdu, China

  • Fuzhong Wu,

    Roles
    Methodology

    Affiliation
    Institute of Ecology and Forest, Sichuan Agricultural University, Chengdu, China

  • Bo Tan,

    Roles
    Investigation

    Affiliation
    College of Forestry, Sichuan Agricultural University, Chengdu, China

  • Li Zhang,

    Roles
    Software

    Affiliation
    Institute of Ecology and Forest, Sichuan Agricultural University, Chengdu, China

  • Zhenfeng Xu

    Roles
    Conceptualization,

    Funding acquisition,

    Investigation,

    Writing – original draft,

    Writing – review & editing

    * E-mail: [email protected]cau.edu.cn

    Affiliation
    Institute of Ecology and Forest, Sichuan Agricultural University, Chengdu, China

    ORCID logo


    http://orcid.org/0000-0002-9777-017X

Effects of forest conversion on soil microbial communities depend on soil layer on the eastern Tibetan Plateau of China

  • Ruoyang He, 
  • Kaijun Yang, 
  • Zhijie Li, 
  • Martin Schädler, 
  • Wanqin Yang, 
  • Fuzhong Wu, 
  • Bo Tan, 
  • Li Zhang, 
  • Zhenfeng Xu
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  • Published: October 5, 2017
  • https://doi.org/10.1371/journal.pone.0186053
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Abstract

Forest land-use changes have long been suggested to profoundly affect soil microbial communities. However, how forest type conversion influences soil microbial properties remains unclear in Tibetan boreal forests. The aim of this study was to explore variations of soil microbial profiles in the surface organic layer and subsurface mineral soil among three contrasting forests (natural coniferous forest, NF; secondary birch forest, SF and spruce plantation, PT). Soil microbial biomass, activity and community structure of the two layers were investigated by chloroform fumigation, substrate respiration and phospholipid fatty acid analysis (PLFA), respectively. In the organic layer, both NF and SF exhibited higher soil nutrient levels (carbon, nitrogen and phosphorus), microbial biomass carbon and nitrogen, microbial respiration, PLFA contents as compared to PT. However, the measured parameters in the mineral soils often did not differ following forest type conversion. Irrespective of forest types, the microbial indexes generally were greater in the organic layer than in the mineral soil. PLFAs biomarkers were significantly correlated with soil substrate pools. Taken together, forest land-use change remarkably altered microbial community in the organic layer but often did not affect them in the mineral soil. The microbial responses to forest land-use change depend on soil layer, with organic horizons being more sensitive to forest conversion.

Citation: He R, Yang K, Li Z, Schädler M, Yang W, Wu F, et al. (2017) Effects of forest conversion on soil microbial communities depend on soil layer on the eastern Tibetan Plateau of China. PLoS ONE 12(10):
e0186053.

https://doi.org/10.1371/journal.pone.0186053

Editor: Jorge Paz-Ferreiro, RMIT University, AUSTRALIA

Received: March 17, 2017; Accepted: September 25, 2017; Published: October 5, 2017

Copyright: © 2017 He et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper.

Funding: This research was financially supported by the National Key Research and Development Program of China (no. 2016YFC0502505-03) and the Natural Science Foundation of China (no. 31570601, 31570445, 31500509) and China Postdoctoral Science Foundation Special Financial Grant (no. 2014T70880).

Competing interests: The authors have declared that no competing interests exist.

Introduction

Soil microbes play an important role in forest ecosystems through decomposition of organic matter, carbon and nutrient cycling, humic compound incorporation into mineral soils, and linking plant and ecosystem functions [ 1 ]. Soil microbial communities are very sensitive to forest land-use changes [ 2 ]. It has been reported that different forests may select for distinct below-ground soil microbial communities [ 3 ]. Thus, forest type conversion could induce significant shifts in soil microbial community via biotic and abiotic factors, including species composition, above- and below-ground litter, and soil substrate quality and quantity, which are associated closely with soil microbial community [ 4 , 5 ]. A number of studies have suggested that the structure and function of microbial communities in forest soils are affected strongly by tree species and composition, implying an important link between above- and below-ground processes [ 3 , 6 ].

It is well known that boreal forests accumulate a large amount of organic material in the surface forest floor as a result of slow decomposition processes. An organic layer often includes various stages of decomposed organic matter, such as highly decomposed, septic; moderately decomposed, hemic, and minimally decomposed [ 7 ]. Compared to mineral horizons in the soil profile, they are rich in organic matter, with typically black or dark brown in color. In boreal forests, the organic layer is considered to be the most active interface where most of biological activities occur [ 8 ]. The organic layer and the mineral soil often have different substrate quality and availability for soil microbial growth and reproduction. Therefore, soil microbial communities could be significantly different between the two soil layers. Additionally, forest type conversion generally causes significant shifts in above- and below-ground litter type and production. Therefore, compared with mineral soils, microbial properties in the organic layer may be more vulnerable to forest type conversion because the organic layer is strongly controlled by litter production and decomposition.

The subalpine forests on the eastern Tibetan Plateau are typical alpine boreal forests at low latitude, with important consequences for regional carbon balance [ 9 ]. Over the last decades, natural coniferous forests on the eastern Tibetan Plateau were deforested and reforested with dragon spruce (Picea asperata Mast.) under national restoration programs. Currently, there are approximately one million hectares of spruce plantations in southwestern China [ 10 ]. Additionally, secondary birch forests have also regenerated in some clear-cutting lands [ 11 ]. In general, there is a thick organic layer in these forests. A large amount of soil organic matter is stored in the organic layer in addition to that in the mineral soil in the study area [ 8 , 12 ]. Forest land-use change often induces significant changes in overstory and understory vegetation composition and soil physicochemical properties [ 10 , 11 ], which in turn might alter soil microbial community, especially in the organic layer. However, soil microbial community in response to forest land-use change is still poorly understood in boreal forests. Therefore, in this study, soil microbial properties (e.g., soil microbial biomass, microbial respiration and PLFAs biomarkers) were assayed in two soil layers (organic layer vs. mineral soil) among three contrasting forest types (natural coniferous forest, secondary birch forest, and dragon spruce plantation). Specifically, we tested the following hypotheses: (1) forest type conversion would alter soil nutrient pools and microbial properties; (2) microbial responses to forest land-use change would vary between soil layers; and (3) variations in the soil microbial community would be correlated with the changes of nutrient pools induced by forest conversion.

Material and methods

Ethics statement

We received a permission from the Lixian Forestry Bureau to conduct this study in local forests in 2015. In this study, only limited soil samples were collected to study microbial properties and our work thus had negligible influences on the function of the broader ecosystem. In addition, this study was carried out in compliance with the laws of the People’s Republic of China. The research did not involve measurements of humans or animals, and no endangered or protected plant species were involved.

Study site and sampling

This study was conducted at the Long-term Research Station of Alpine Forest Ecosystems, which is located on the eastern Tibetan Plateau, China (102°53′-102°57’E, 31°14′-31°19′). Mean annual temperature decreased from 4°C to 2°C and mean annual precipitation increased from 820 mm to 850 mm with increasing elevation from 2700 m to 3600 m.

Since 1960s, pervasive logging for commercial use has largely reduced the forest cover on the Tibetan Plateau, especially in the eastern part (Wu and Liu, 1998). Some logging areas were reforested with dragon spruce under national restoration programs. Alternatively, secondary birch forests had also regenerated in some clear-cutting lands [ 11 ]. Currently, natural coniferous forest (NF), secondary birch forest (SF) and dragon spruce plantation (PT) are the three dominant forest types due to local forest management practice. According to the local Forestry Bureau, the dragon spruce plantation and birch secondary forest chosen in this study developed from logging operations during the 1950s and 1960s. No additional practices (e.g., fertilizer and irrigation) were used in either forests type. However, to mitigate the impacts of environmental degradation, China has been implementing large-scale conservation programs, including the Natural Forest Conservation Program and the Grain for Green Program over last decades [ 13 ]. All kinds of forests in this area, including spruce plantations established initially for commercial timber, were again used for ecological services, such as conservation of biodiversity and water, flood and erosion mitigation in addition to carbon sequestration [ 14 ]. Moreover, with the rapid growth of economies and populations, human disturbances (e.g., grazing and wild mushroom collection) have been increasing in the plantation. The understory was mainly dominated by grasses in the plantation. Conversely, the understory is dominated by mosses, woody shrubs (especially dwarf bamboos) and grasses in the natural coniferous and secondary forests. The basic conditions are shown in Table 1 . The soils at the three forest types are typical brown forest soils and classified as a Cambic Umbrisols according to the IUSS Working Group [ 15 ].

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Table 1. Basic description of three forest stands.

https://doi.org/10.1371/journal.pone.0186053.t001

In July 2015, three independent patches were established in each forest type having similar natural conditions (natural coniferous forest, secondary birch forest and dragon spruce plantation). Soil samples of the organic layer and the upper mineral soil (10 cm) were collected in each forest. The organic layer was identified from the mineral soil via its morphology (soil color, texture, and consistency) [ 8 , 16 ]. Nine cores (5 cm diameter) were taken randomly from each patch and nine samples from same patch were mixed to get one composite sample. Each composite sample was passed through a sieve (2 mm diameter), and any visible living plant material was removed manually from the sieved soil. The sieved soil was kept in the refrigerator at 4°C prior to the analysis of microbial properties. A sub-sample of each soil was air-dried and ground prior to chemical analysis.

Soil chemical analysis

Soil organic carbon (C) was measured by oxidation with K2CrO7 in an acid medium and titration of the excess dichromate with (NH4)2Fe(SO4)2. Soil nitrogen (N) was analyzed following the Kjeldahl digestion procedure. Soil phosphorus (P) was determined using the phosphomolybdenum-yellow colorimetric method. Soil pH was measured with a calomel electrode at 1:5 soil-to-water ratio. Soil microbial biomass C (MBC) and Soil microbial biomass N (MBN) were measured by fumigation-extraction method [ 17 ]. The released C and N were converted to MBC and MBN using kec− 0.38 and ken− 0.45, respectively.

Soil microbial respiration

Soil microbial respiration (MR) was estimated by determining CO2 production over a 4-week incubation period [ 10 ]. Briefly, fresh soil samples (100 g) of the organic layer and mineral layer were adjusted to 60% water holding capacity. The soil samples were incubated in 1 L jars at 20°C. Empty jars without soils were used as controls. CO2 production was measured 4 weeks after the incubation by using alkali absorption method. Soil microbial respiration (MR) was calculated per unit mass in the unit time for average rate.

PLFA analysis

The phospholipid fatty acids (PLFAs) were extracted and quantified using a modified method previously described by White [ 18 ]. Lipids from 2 g of fresh soil were extracted by a one-phase extraction technique using phosphate buffer, methanol and chloroform in a 0.8:2:1(v/v/v) ratio [ 19 ]. Phospholipids were transformed by alkaline methanolysis into fatty acid methyl esters (FAMEs), which were quantified by a gas chromatograph (GCMS-QP2010 Series, Shimadzu, Japan). The fatty acid nomenclature used in this study was that described by Frostegård et al. [ 19 ]. Bacteria markers were identified by the following PLFAs: 15:0, i15:0, a15:0, 16:0, i16:0, 17:0, i17:0, a17:0, 16:1w7c, 16:1w5t, 16:1w9c, 18:1w7c, 18:00, cy17:0, cy19:0 and 20:5 [ 19 , 20 ]. Polyunsaturated PLFAs, i.e., 18:3, 18:1w9c, 18:2w6, 9c and 20:1w9c, represented fungi biomass [ 21 , 22 ]. The PLFAs i15:0, a15:0, i16:0, i17:0 and a17:0 were used as gram-positive bacteria markers [ 23 , 24 ], and the PLFAs 16:1w7c, 16:1w9c, 18:1w7c, cy17:0 and cy19:0 were used as gram-negative bacteria markers [ 25 ].

Statistical analysis

Two-way ANOVA was employed to analyze the effects of forest type, soil layer and their interaction on all measured soil variables. For a given layer, one-way ANOVA with Fisher’s LSD test was used to identify significant differences in soil properties among forest types. For a given forest type, Student’s t-tests were used to compare the differences between soil layers. To describe the similarity or dissimilarity pattern of soil microbial composition in the two soil layers of three forest types. Moreover, redundancy analysis (RDA) was used to visualize the correlations between PLFAs profiles and soil properties (e.g., C, N, P, and pH) by using the CANOCO software (version 4.5, Microcomputer Power, Inc., Ithaca, NY). The statistical tests were considered significant at the p< 0.05. The statistical tests were performed using IBM SPSS Statistics 20.0.

Results

Soil chemical properties

Soil C, N and P were 2.9–4.7, 2.0–6.3 and 1.2–2.4 times higher in the organic layer than in the mineral soils among three forests, respectively ( Table 2 ). In the organic layer, soil C, N and P in both NF and SF were significantly higher than those in the PF. However, soil C, N and P concentrations were greatest in the SF in the mineral soil compared to the other two forest types ( Table 2 ). There were no significant differences in C:N and C:P ratios among forest types within a given layer. However, C:P ratio was higher in the organic layer in each forest type as compared to mineral soil ( Table 2 ). Soil pH in the mineral layer was higher for NF compared to SF and PT ( Table 2 ). The statistical analysis revealed a significant forest type × soil layer interaction effect for N, P and pH ( Table 2 ). Soil N and P were lower in the PT compared to NF and SF in the organic layer, but they peaked in SF in the mineral layer. The pH-values peaked in SF for the organic layer, but in PT for the mineral layer.

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Table 2. Soil chemical properties in the organic and mineral soils of three subalpine forests.

https://doi.org/10.1371/journal.pone.0186053.t002

Soil MBC, MBN and MR

Regardless of forest types, soil microbial biomass carbon (MBC), microbial biomass nitrogen (MBN) and MR were significantly lower in the mineral soil than in the organic layer ( Fig 1A, 1B and 1D ; Table 3 ). In the organic layer, MBC, MBN and MR all showed a trend of NF>SF>PT ( Fig 1A, 1B and 1D ). However, there were no significant differences in MBC, MBN and MR among forest types in the mineral soil ( Fig 1A, 1B and 1D ; Table 3 ). Forest type, soil layer and their interaction all had significant influence on MBC, MBN and MR, but did not affect MBC:MBN ratio ( Fig 1C ; Table 3 ).

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Fig 1. Soil microbial biomass carbon (MBC), nitrogen (MBN), MBC:MBN and microbial respiration in three contrasting subalpine forests on the eastern Tibetan Plateau.

Different uppercases denote significant differences between forest types in same soil layer. Different lowercases denote significant differences between soil layers in same forest type. NF: natural coniferous forest; PT: spruce plantation; SF: secondary birch forest. The values are means ± SD, n = 3.

https://doi.org/10.1371/journal.pone.0186053.g001

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Table 3. Results of two-way ANOVA showing the p values for responses of measured variables to forest type and soil layer.

https://doi.org/10.1371/journal.pone.0186053.t003

Soil microbial PLFAs characteristics

Forest type and soil layer generally had significant effects on PLFA variables ( Fig 2 ; Table 3 ). In the organic layer, total PLFAs, bacteria, fungi, gram-positive bacteria and gram-negative bacteria were higher in both natural forest types (NF and SF) than in the PT ( Fig 2 ; Table 3 ). In both NF and SF, total PLFAs, bacteria, fungi, gram-positive bacteria, gram-negative bacteria were higher in the organic layer than in the mineral soil ( Fig 2A, 2D, 2E, 2G and 2H ). However, in the PT forest, gram-positive bacteria and bacteria:fungi ratio were higher in the mineral soil relative to organic layer ( Fig 2F and 2G ). Moreover, there was significant interaction of forest type × soil layer on almost all measured microbial PLFAs parameters ( Table 3 ).

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Fig 2. The phospholipid fatty acid biomarker contents in three contrasting subalpine forests on the eastern Tibetan Plateau.

Different uppercases denote significant differences between forest types in same soil layer. Different lowercases denote significant differences between soil layers in same forest type. NF: natural coniferous forest; PT: spruce plantation; SF: secondary birch forest. The values are means ± SD, n = 3.

https://doi.org/10.1371/journal.pone.0186053.g002

Correlations between soil PLFAs and physicochemical variables

In the RDA analysis, the first and second axes accounted for 81.9% and 2.4%, respectively, of the variation in soil PLFAs ( Fig 3 ). Soil N (67.7%, p<0.01) was the most significant explanatory variable for soil PLFAs, and thereafter the most important ones were soil MBN (67.1%, p<0.01), MBC (66.8%, p<0.01), total P (64.0%, p<0.01) and TOC (61.9%, p<0.01) ( Fig 3 ). In addition, both soil C:P (40.2%, p<0.01) and C:N (22.2%, p<0.05) showed significant influences on soil PLFAs composition ( Fig 3 ). It should be noted that the strong correlations between PLFAs profiles and physicochemical variables could, in part, be influenced by the auto-correlations among PLFAs biomarkers and/or explanatory variables.

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Fig 3. Redundancy analyses between soil microbial PLFAs and chemical parameters.

Tbacteria: total bacteria PLFAs; Tfungi: total fungi PLFAs; Gram+: gram-positive bacteria; Gram: gram-negative bacteria; B:F: the ratio of bacteria to fungi; G+:G: the ratio of gram-positive bacteria to gram-negative bacteria. TOC: total organic carbon; TN: total nitrogen; TP: total phosphorus; MBC: microbial biomass carbon; MBN: microbial biomass nitrogen; C:N: the ratio of TOC to TN; C:P: the ratio of t to TP.

https://doi.org/10.1371/journal.pone.0186053.g003

Discussion

Small changes in the microbial biomass or community structure could affect organic matter turnover and nutrient cycling [ 26 , 27 ]. Therefore, soil microbial properties have been regarded as important indices reflecting the influences of forest land-use on soils [ 3 ]. It have been widely demonstrated that the composition and structure of the microbial community are strongly related to abiotic and biotic factors, such as climate factors (e.g., temperature and precipitation), soil substrate properties (e.g., C and N pools) and tree species composition and diversity [ 28 , 29 ]. Thus, forest type conversion (from natural to secondary or plantation forests) may affect soil microbial biomass, diversity and community structure through altering soil substrate conditions, such as tree species, litter type and quantity, soil C and nutrient availability.

A number of studies conducted in temperate and subtropical forests have found that the conversion from natural forest to secondary or plantation forest often lowered soil microbial biomass and diversity, and altered the composition and structure of microbial communities [ 30 , 3 ]. For instance, the secondary forest had higher microbial biomass as compared to larch plantation in Northeast China [ 31 ]. Moreover, total PLFAs were reduced following the conversion of natural forests to plantations in subtropical and tropical zones [ 32 , 5 ]. In this study, MBC, MBN and MR exhibited a clear tendency of NF>SF>PT in the organic layer, suggesting relatively poor microbial growth and activity in the PT. There were no significant differences in total PLFAs between NF and SF; however, microbial profiles in NF and SF were higher as compared to those in PT. This observation is consistent with our previous findings where topsoil (0–15 cm) MBC and MBN were lower in PT than in NF [ 10 ]. Additionally, soil PLFAs has been demonstrated to be correlated strongly with MBC, which was in line with the result observed in this study [ 33 ].

The pattern of soil microbial profiles among the three forest types could be attributed to the differences in soil C and nutrient pools following the forest type conversion. As stated in this study, soil C, N and P pools were lower in the PT compared to NF. This was also supported by the positive correlations between microbial properties and soil C and nutrients pools. Moreover, because soil microbes primarily rely on organic C for their growth, they are profoundly controlled by any change in C input in soils [ 34 ]. In forest ecosystems, C input results mainly from the decay of soil organic matter, such as root and leaf litter, woody plant debris and root exudates. The contribution of these components is substantially dependent on overstory and understory plant species [ 35 ]. The conversion from NF to PT generally causes a considerable loss of plant species diversity, which in turn induce a significant decrease in quality and quantity of plant debris entering the soil. The Shannon-Wiener diversity index of NF (1.9–2.5) was significantly higher than those of PT (0.3–1.4) in the overstory layer [ 36 ]. Moreover, litter biomass was also greater in the NF (1.92 t ha-2) than in the PT (1.55 t ha-2) [ 37 ]. Forest land-use changes accompanied by shifts in tree species may alter quality and quantity of leaf and root litter [ 38 ], consequently affecting the substrate quality and availability for microbial growth. High quality litter with lower C:N ratio and higher N concentration can often decompose faster, resulting in rapid decomposition of organic matter relative to low quality litter [ 39 ]. Our previous studies have found that the dominant leaf and root litters (dragon spruce) in the PT have higher C:N ratios and lower N content than the equivalent litters from the SF or multi-species NF [ 40 , 41 ]. Moreover, our prior studies have found that soil respiration and N mineralization rate, reflecting soil microbial activity, were also reduced markedly following the conversion from NF [ 9 , 10 ]. Previous studies have also observed that soil N availabilities (e.g., dissolved organic N and inorganic N pools) were substantially greater in the NF than in the PT [ 42 ]. This may partially contribute to the microbial discrepancy among contrasting forest ecosystems because soil microbes are easier to be limited by N in rich C soil. Lastly, prior studies found that bulk density was greater in the PT relative to NF, which may also partially cause microbial degradation in the PT [ 10 , 42 ].

The composition of the soil PLFAs was different among forest land-use types. The soil microbial community in the organic layer occupied different portions of ordination space in natural forests (NF and SF) and PT, indicating that the composition of the soil microbial community in NF and SF differed profoundly from that of PT. Our results found that soil microbial community was dominated by bacteria in each forest type regardless of soil layers. The bacteria groups accounted for ca. 90% of total PLFAs. Similar patterns were also observed in subtropical forests [ 32 ]. The high proportion of bacteria may attribute to high-quality substrate in subalpine forests. The lack of changes in bacteria:fungi ratios in the organic layer among three forest soils suggested that forest conversion had similar effects on the magnitude of both bacteria and fungi, resulting in similar bacteria-fungi ratios. The stability of bacteria-fungi soil food webs among different forest soils could favor subalpine forests being stable in response to forest land-use changes. However, the ratio of G+ to G bacteria was higher in the NF than in the PT, suggesting that the microbial bacteria community structure significantly shifted following forest conversion. The alterations in the size and structure of the soil microbial community imply low soil resource availability or high soil nutrient stress in the PT compared to NF.

In boreal forest ecosystems, there is a large amount of organic layer accumulated in the upper forest floor due to slow decomposition associated with low temperature. There are significant differences in soil substrate quality and availability between organic layer and mineral soil as a result of different rates of C input, accumulation, and turnover [ 43 , 16 ]. In both NF and SF, almost all components of the soil PLFA profiles were substantially higher in the organic layer as compared to mineral soil. This may because leaf litter input provides large amounts of fresh substrates and energy for microorganisms in the organic layer, and the abundance of microorganisms is strongly related to litter input. Similar patterns were observed in other boreal forests [ 44 ]. Another source of organic matter inputs derived from root activity (e.g., root exudation, root turnover) in the organic layer could also lead to a larger microbial community in the organic horizon, as it has been reported that most fine roots are distributed in the topsoil in the fir and birch forests of southwestern China [ 45 ]. Moreover, Tibetan forests have higher nutrient pools in the organic layer relative to mineral soil [ 42 ], which may also account for the higher microbial biomass and activity of organic layer. Collectively, forest management practice profoundly alters above- and below-ground litter inputs (primary source of soil C) which regulate substrate availability and quality for soil microbial growth and activity [ 46 ]. As expected, organic layer is much more vulnerable to forest land-use change as compared to mineral soils. Prior studies have found that tree species exerted a strong effect on PLFA profiles in the organic soil, while PLFA profiles were slightly influenced by tree species in the mineral soil [ 47 ]. Our studies have also found organic soil C and N mineralization rate significantly differed among three forests but the rates of mineral soils were slightly affected by forest conversion (unpublished data). The results mentioned above clearly indicated that forest type conversion caused significant effects on soil microbial biomass and structure in southwestern China; whereas the effect of forest land-use change was strongly dependent on soil layer.

Conclusions and implications

In summary, this study explored variations of soil microbial biomass, activity and community composition in the surface organic layer and subsurface mineral soil among three forest types. Irrespective of forest types and soil layers, soil microbial community was mainly dominated by bacteria groups in this area. Soil microbial community was higher in the natural forests (NF and SF) as compared to PT. Forest land-use change often altered microbial community profiles in the organic layer but show little or no effect in the mineral soil. The effect of forest land-use change on microbial properties depended strongly on soil layer, with organic horizon being much more sensitive to forest conversion. Moreover, microbial responses to forest land-use change could, in part, be complicated by human disturbances over past two decades.

The findings in this study have the following important implications. On the one hand, soil C and nutrient cycling could become slow following the forest type conversion as a result of low microbial biomass and activity in the PT. Moreover, it is important to note that a large amount of soil C and N were lost by deforestation in the organic layer in Tibetan forests. Therefore, protecting current natural forests is very vital to mitigate climate change. On the other hand, the differences between the organic and mineral layers in response to forest-type conversion highlight the importance and sensitivity of organic layer in Tibetan forests. Additionally, SF should be a better restoration approach in terms of soil microbial and nutrient pools as compared to PT. Future detailed work to focus on the microbial functions in the two layers would help us to better elucidate and understand the importance of soil microbial community to soil carbon and nutrient cycling in Tibetan boreal forests.

Acknowledgments

We thank the Long-term Research Station of Alpine Forest Ecosystems and Key Laboratory of Ecological Forestry Engineering.

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          Active microorganisms in forest soils differ from the total community yet are shaped by the same environmental factors: the influence of pH and soil moisture

          Karl J. Romanowicz

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          School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI 48109, USA


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            Karl J. Romanowicz, Zachary B. Freedman, Rima A. Upchurch, William A. Argiroff, Donald R. Zak; Active microorganisms in forest soils differ from the total community yet are shaped by the same environmental factors: the influence of pH and soil moisture, FEMS Microbiology Ecology, Volume 92, Issue 10, 1 October 2016, fiw149, https://doi.org/10.1093/femsec/fiw149

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          Predicting the impact of environmental change on soil microbial functions requires an understanding of how environmental factors shape microbial composition. Here, we investigated the influence of environmental factors on bacterial and fungal communities across an expanse of northern hardwood forest in Michigan, USA, which spans a 500-km regional climate gradient. We quantified soil microbial community composition using high-throughput DNA sequencing on coextracted rDNA (i.e. total community) and rRNA (i.e. active community). Within both bacteria and fungi, total and active communities were compositionally distinct from one another across the regional gradient (bacteria P = 0.01; fungi P < 0.01). Taxonomically, the active community was a subset of the total community. Compositional differences between total and active communities reflected changes in the relative abundance of dominant taxa. The composition of both the total and active microbial communities varied by site across the gradient (P < 0.01) and was shaped by differences in soil moisture, pH, SOM carboxyl content, as well as C and N concentration. Our study highlights the importance of distinguishing between metabolically active microorganisms and the total community, and emphasizes that the same environmental factors shape the total and active communities of bacteria and fungi in this ecosystem.

          bacteria , fungi , forest soil , RNA , soil moisture , soil pH

          INTRODUCTION

          Soils are highly diverse habitats that mediate biogeochemical processes of global importance, yet our understanding of how these functions are influenced by microbial biodiversity is only slowly advancing (Treseder et al. 2012 ; Powell, Welsh and Hallin 2015 ). Currently, microbial community composition is an important determinant of ecosystem process rates (Reed and Martiny 2007 ; Strickland et al. 2009 ), and identifying microbial community composition has become an essential component for predicting ecosystem responses to environmental change (Baldrian et al. 2012 ). However, before we can predict the ecosystem response to environmental change, we must first understand how the environment shapes microbial community composition. For example, soil moisture can influence microbial composition along topographic gradients (Morris and Boerner 1999 ), as well as in multiple forest ecosystems (Brockett, Prescott and Grayston 2012 ). Moreover, soil pH (Fierer and Jackson 2006 ), C (Garbeva and de Boer 2009 ) and N (Zak et al. 2008 ; Ramirez, Craine and Fierer 2012 ), as well as C:N ratios (Lauber et al. 2008 ), have also shaped microbial community composition. Additionally, changes in the chemical constituents of soil organic matter (SOM) such as aryl, alkyl, O/N-alkyl and carboxyl content can influence microbial community composition (Baumann et al. 2009 ; Ng et al. 2014 ). Nevertheless, it remains unclear whether the total microbial community, as well as the subset that is metabolically active, are similarly affected by environmental variation.

          Our understanding of how soil microbial communities are influenced by the environment has been derived mainly from ribosomal DNA (rDNA) analyses representing the total community. This approach cannot distinguish between dead, dormant or metabolically active microbial cells (Ostle et al. 2003 ; Lennon and Jones 2011 ). Conversely, ribosomal RNA (rRNA) has a short residence time in soil (Moeseneder, Arrieta and Herndl 2005 ) and can be used to identify microbial communities that may be metabolically active at the time of sampling (Prosser 2002 ; Anderson and Parkin 2007 ; Urich et al. 2008 ; Baldrian et al. 2012 ), which are more likely to be connected to soil functions (Nannipieri et al. 2003 ). Investigating the balance between total and active communities could provide novel insights into the dynamics regulating soil microbial composition (Lennon and Jones 2011 ; Baldrian et al. 2012 ; Zhang et al. 2014 ). Moreover, it is imperative that we distinguish the total microbial community from the community that is metabolically active as the relationship between the composition of the total and active microbial communities can be differentially affected by changes in the environment (McMahon, Wallenstein and Schimel 2011 ; Baldrian et al. 2012 ; Barnard, Osborne and Firestone 2013 ).

          Furthermore, it is essential to simultaneously study the bacterial and fungal communities in soil to understand microbial influences on ecosystem processes, because these communities mediate different ecological functions in soil (Baldrian et al. 2012 ). Previously, our research group has documented that the total communities (via rDNA) of soil bacteria and fungi are shaped by both temporal (e.g. barriers to dispersal) as well as environmental determinants (e.g. soil moisture, pH, C:N; see Cline and Zak 2014 ; Freedman and Zak 2015 ; Mueller et al. 2016 ) in northern hardwood forests in the Upper Great Lakes region of North America. Here, we utilized high-throughput DNA sequencing of coextracted rDNA (total community) and rRNA (active community) from forest floor to investigate how climatic and biogeochemical differences among these same forested study sites influence the composition of bacteria and fungi in both their total and metabolically active communities. We sought to understand how the total and active communities differ from one another and which environmental factors influence community composition. We hypothesized that total and active communities would be influenced by environmental factors across the regional gradient of northern hardwood forests. Specifically, we hypothesized that total community composition would be influenced by site differences in pH, C and N concentration as shown in our previous studies (Cline and Zak 2014 ; Freedman and Zak 2015 ), whereas active community composition would respond predominantly to differences in soil moisture as short-term changes in this factor have been shown to strongly influence the expression of active microbial communities (Evans, Wallenstein and Burke 2014 ). In this way, we sought to derive novel insight into how environmental factors influence the composition of total and active soil microbial communities.

          MATERIALS AND METHODS

          Study sites

          Forest floor samples (Oe/Oa horizon) were collected on 23–24 May 2013 (sites B-D) and 19 June 2013 (site A) from four sugar maple dominated (Acer saccharum Marsh.) northern hardwood forest stands that span a 500-km regional climate gradient across lower and upper Michigan, USA (Fig. S1, Supporting Information). Timing of sample collection across the regional gradient coincided with spring precipitation events to ensure that ample moisture was present to support high rates of microbial activity. The four forest stands encompass the north-south latitudinal range of the northern hardwood forest region (Braun 1950 ) and were selected from 31 candidate sites based on multivariate similarities of floristic and edaphic characteristics (Burton et al. 1991 ; MacDonald et al. 1991 ). The thin Oi horizon was composed of sugar maple leaf litter and a dense mat of fine roots interpenetrated the Oe/Oa horizons. Forest floor samples (Oe/Oa horizons) were collected at each site (n = 4) from three 30 m by 30 m replicate plots. In each plot (n = 12), 10 random 0.1 m by 0.1 m forest floor samples were collected after removing the Oi horizon. All samples were composited by plot, homogenized by hand in the field, and a subset of each sample was immediately flash frozen on liquid N2 to preserve microbial nucleic acids. An additional subset of homogenized sample was stored on ice prior to transport to the laboratory. All flash-frozen samples were subsequently stored at –80°C prior to nucleic acid extraction, and samples stored on ice were used for the determination of environmental characteristics.

          Physical, chemical and biogeochemical environmental characteristics

          A combination of physical, chemical and biogeochemical characteristics was analyzed to determine if changes in environmental factors across the regional gradient of northern hardwood forests differentially influence the composition of total and active microbial communities (Table  1 ). To quantify soil moisture, fresh samples were weighed, oven-dried overnight at 50°C and reweighed to determine the total dry mass. Forest floor pH was quantified with an Accumet Model 15 pH meter (Fischer Scientific, Waltham, MA, USA) using a forest floor: H2O slurry (1:30). To gain quantitative insight into chemical changes in forest floor across study sites, chemical characterizations were conducted by solid-state 13C nuclear magnetic resonance (NMR) spectroscopy at the Technische Universität Müchen, Germany. Detailed methods for solid-state 13C-NMR spectroscopy can be found in Steffens, Kölbl and Kögel-Knabner ( 2009 ). Prior to biogeochemical characterizations for C and N concentration, as well as C:N ratio, forest floor samples were ground to a fine powder by hand and analyzed in duplicate (0.4 g replicate−1) at the University of Michigan utilizing a TruMac C/N analyzer (LECO, St. Joseph, MI, USA).

          Table 1.

          Physical, chemical and biogeochemical forest floor characteristics for four forest stands in upper and lower Michigan, USA that span the north–south geographical range of northern hardwoods. Mean ± standard deviation values are presented with post-hoc analysis for site differences (P < 0.05 via ANOVA) denoted via lowercase letters.

           Site A Site B Site C Site D 
          Physical 
           Moisture (%) 24.2 ± 3.9a 66.3 ± 1.1b,c 69.9 ± 1.9b 63.6 ± 1.1c 
           pH (1:30 forest floor/H2O) 5.1 ± 0.1a 5.7 ± 0.3b 5.5 ± 0.1b 5.5 ± 0.1b 
          Chemical 
           Aryl (%) 15.9 ± 0.5a 15.6 ± 0.4a 14.5 ± 0.4b 14.4 ± 0.6b 
           Alkyl (%) 20.0 ± 1.2a 20.8 ± 1.6a 22.2 ± 0.6a 22.3 ± 1.8a 
           O/N-alkyl (%) 56.7 ± 0.9a 56.8 ± 1.5a 57.1 ± 1.0a 57.1 ± 2.8a 
           Carboxyl (%) 7.5 ± 0.3a 6.7 ± 0.5 a,b 6.2 ± 0.3b 6.3 ± 0.5b 
          Biogeochemical 
           C (g C kg−1 forest floor) 430.3 ± 24.5a 392.3 ± 13.7a 410.4 ± 15.8a 403.6 ± 8.6a 
           N (g N kg−1 forest floor) 13.6 ± 0.7a 17.3 ± 1.4b 16.6 ± 0.5b 14.9 ± 1.5a,b 
           C:N 31.6 ± 1.8a 22.8 ± 2.1b 24.7 ± 1.0b 27.3 ± 3.1a,b 
           Site A Site B Site C Site D 
          Physical 
           Moisture (%) 24.2 ± 3.9a 66.3 ± 1.1b,c 69.9 ± 1.9b 63.6 ± 1.1c 
           pH (1:30 forest floor/H2O) 5.1 ± 0.1a 5.7 ± 0.3b 5.5 ± 0.1b 5.5 ± 0.1b 
          Chemical 
           Aryl (%) 15.9 ± 0.5a 15.6 ± 0.4a 14.5 ± 0.4b 14.4 ± 0.6b 
           Alkyl (%) 20.0 ± 1.2a 20.8 ± 1.6a 22.2 ± 0.6a 22.3 ± 1.8a 
           O/N-alkyl (%) 56.7 ± 0.9a 56.8 ± 1.5a 57.1 ± 1.0a 57.1 ± 2.8a 
           Carboxyl (%) 7.5 ± 0.3a 6.7 ± 0.5 a,b 6.2 ± 0.3b 6.3 ± 0.5b 
          Biogeochemical 
           C (g C kg−1 forest floor) 430.3 ± 24.5a 392.3 ± 13.7a 410.4 ± 15.8a 403.6 ± 8.6a 
           N (g N kg−1 forest floor) 13.6 ± 0.7a 17.3 ± 1.4b 16.6 ± 0.5b 14.9 ± 1.5a,b 
           C:N 31.6 ± 1.8a 22.8 ± 2.1b 24.7 ± 1.0b 27.3 ± 3.1a,b 
          View Large
          Table 1.

          Physical, chemical and biogeochemical forest floor characteristics for four forest stands in upper and lower Michigan, USA that span the north–south geographical range of northern hardwoods. Mean ± standard deviation values are presented with post-hoc analysis for site differences (P < 0.05 via ANOVA) denoted via lowercase letters.

           Site A Site B Site C Site D 
          Physical 
           Moisture (%) 24.2 ± 3.9a 66.3 ± 1.1b,c 69.9 ± 1.9b 63.6 ± 1.1c 
           pH (1:30 forest floor/H2O) 5.1 ± 0.1a 5.7 ± 0.3b 5.5 ± 0.1b 5.5 ± 0.1b 
          Chemical 
           Aryl (%) 15.9 ± 0.5a 15.6 ± 0.4a 14.5 ± 0.4b 14.4 ± 0.6b 
           Alkyl (%) 20.0 ± 1.2a 20.8 ± 1.6a 22.2 ± 0.6a 22.3 ± 1.8a 
           O/N-alkyl (%) 56.7 ± 0.9a 56.8 ± 1.5a 57.1 ± 1.0a 57.1 ± 2.8a 
           Carboxyl (%) 7.5 ± 0.3a 6.7 ± 0.5 a,b 6.2 ± 0.3b 6.3 ± 0.5b 
          Biogeochemical 
           C (g C kg−1 forest floor) 430.3 ± 24.5a 392.3 ± 13.7a 410.4 ± 15.8a 403.6 ± 8.6a 
           N (g N kg−1 forest floor) 13.6 ± 0.7a 17.3 ± 1.4b 16.6 ± 0.5b 14.9 ± 1.5a,b 
           C:N 31.6 ± 1.8a 22.8 ± 2.1b 24.7 ± 1.0b 27.3 ± 3.1a,b 
           Site A Site B Site C Site D 
          Physical 
           Moisture (%) 24.2 ± 3.9a 66.3 ± 1.1b,c 69.9 ± 1.9b 63.6 ± 1.1c 
           pH (1:30 forest floor/H2O) 5.1 ± 0.1a 5.7 ± 0.3b 5.5 ± 0.1b 5.5 ± 0.1b 
          Chemical 
           Aryl (%) 15.9 ± 0.5a 15.6 ± 0.4a 14.5 ± 0.4b 14.4 ± 0.6b 
           Alkyl (%) 20.0 ± 1.2a 20.8 ± 1.6a 22.2 ± 0.6a 22.3 ± 1.8a 
           O/N-alkyl (%) 56.7 ± 0.9a 56.8 ± 1.5a 57.1 ± 1.0a 57.1 ± 2.8a 
           Carboxyl (%) 7.5 ± 0.3a 6.7 ± 0.5 a,b 6.2 ± 0.3b 6.3 ± 0.5b 
          Biogeochemical 
           C (g C kg−1 forest floor) 430.3 ± 24.5a 392.3 ± 13.7a 410.4 ± 15.8a 403.6 ± 8.6a 
           N (g N kg−1 forest floor) 13.6 ± 0.7a 17.3 ± 1.4b 16.6 ± 0.5b 14.9 ± 1.5a,b 
           C:N 31.6 ± 1.8a 22.8 ± 2.1b 24.7 ± 1.0b 27.3 ± 3.1a,b 
          View Large

          Nucleic acid coextraction and first-strand cDNA synthesis

          Coextraction of total nucleic acids from forest floor samples was conducted in triplicate (0.3 g replicate−1, total fresh weight) using the PowerLyzer PowerSoil DNA Isolation kit (MoBio Laboratories, Carlsbad, CA, USA), with a PowerLyzer 24 instrument (MoBio) following modified methods (Freedman et al. 2015 ). In brief, the following modifications from the manufacturer’s protocol allowed for simultaneous coextraction of rDNA and rRNA (i.e. total and active communities, respectively): initial addition of phenol:chloroform:isoamyl alcohol (25:24:1; pH 6.7); bead beating at 4000 rpm for 45 s; all centrifugation occurred at 4°C; and overnight ethanol precipitation at –20°C with linear acrylamide as an RNA carrier (5 mg mL−1). Extracted nucleic acids were purified using a PowerClean DNA Cleanup kit (MoBio) prior to separating the rDNA and rRNA fractions. To prevent DNA contamination of the rRNA sample, the RTS DNase kit (MoBio) was used following manufacturer’s protocol. The DNA-free rRNA fraction was reverse transcribed to gene-specific complimentary DNA (cDNA) using the SuperScript III First-Strand Synthesis System for RT-PCR (Life Technologies, Grand Island, NY, USA) using either the universal bacterial 16S rRNA gene primer 27F (Lane 1991 ) or the universal fungal 28S rRNA gene primer LR3 (Vilgalys and Hester 1990 ).

          PCR amplification and high-throughput sequencing

          All PCR reactions were performed in triplicate using the Expand High Fidelity PCR system (Roche, Indianapolis, IN, USA) on a Mastercycler ProS thermocycler (Eppendorf, Hauppauge, NY, USA). A ∼500 bp portion of the bacterial 16S rRNA gene (V1-V3 region) was amplified using barcoded universal bacterial primers 27F and 519R (Lane 1991 ) from both rDNA and rRNA fractions from each plot. All 16S rRNA gene PCR procedures followed methods in Freedman and Zak ( 2015 ). In brief, the master mix included 200 μM dNTP, 1.5 mM MgCl2, 400 μM primers and 2-U Expand high fidelity Taq polymerase (Roche). PCR conditions included an initial denaturation stage of 95°C for 10 min followed by 25 cycles of 95°C for 30 s, 55°C for 1 min and 72°C for 1 min, with a final extension at 72°C for 20 min. For the fungal 28S rRNA gene, a ∼700 bp region (D1 domain) was amplified using barcoded universal fungal primers LROR (Bunyard, Nicholson and Royse 1994 ) and LR3 (Vilgalys and Hester 1990 ) from both rDNA and rRNA fractions from each plot. The master mix included 0.01 mg BSA, 200 μM dNTP, 400 μM primers and 2-U Expand high fidelity Taq polymerase (Roche). PCR conditions included an initial denaturation stage of 95°C for 5 min followed by 20 cycles of 95°C for 30 s, 54°C for 30 s and 75 s at 72°C, with final extension at 72°C for 7 min. All PCR amplicons were purified using a MinElute PCR purification kit (Qiagen, Valencia, CA, USA). Purified barcoded PCR amplicons were then pooled from four experimental plots in equimolar concentration per SMRT cell and sequenced using the PacBio RS II system (Pacific Biosciences, Menlo Park, CA, USA) with C4 chemistry and standard protocols (Eid et al. 2009 ). For this study, we utilized PacBio circular consensus technology, which can generate DNA fragments upwards of 500 bp at >99.5% sequence accuracy (Travers et al. 2010 ). For more detailed information regarding the use of PacBio circular consensus technology in high-throughput amplicon sequencing, please refer to Fichot and Norman ( 2013 ).

          DNA sequence processing and microbial community composition

          Circular consensus sequences were generated in fastq format using the pbh5 tools package (Pacific Biosciences) and each fastq file used in this analysis was deposited to NCBI under project accession numbers SRR1944476 and SRR1944477. All subsequent bioinformatic processing was performed using MOTHUR (version 3.2.4; Schloss et al. 2009 ). In brief, initial quality control methods removed any sequence with a circular consensus fold-coverage <5, average quality score <25 (50 bp rolling window), anomalous length ±50 bp of 500 bp or 650 bp for bacterial or fungal communities, respectively, an ambiguous base >8 homopolymers, or a >1 bp mismatch to either the barcode or primer. The remaining high-quality sequences were aligned using k-mer searching (8mers) with Needleman–Wunsch global, pairwise alignment methods (Needleman and Wunsch 1970 ). For bacterial 16S rRNA genes, the Greengenes database was used as a template (version 13.5; DeSantis et al. 2006 ), whereas for fungal 28S rRNA genes, the Ribosomal Database Project (RDP) LSU training set (version 7; Cole et al. 2014 ) was used. All remaining sequences were screened for chimeras using UCHIME (Edgar et al. 2011 ) and assigned taxonomy using a Bayesian classifier (Wang et al. 2007 ) with a bootstrap cut-off of 80% against the Greengenes or RDP database for 16S or 28S rRNA gene fractions, respectively. All sequences from both the total and active communities were clustered together into operational taxonomic units (OTUs), such that OTU identities were shared between communities. The average neighbor algorithm in MOTHUR was implemented to pick OTUs with a cut-off value set at 0.03 or 0.01 for the bacterial or fungal communities, respectively, to achieve ‘species’ level classification. Sequence loss through quality control is summarized in Table S1 (Supporting Information).

          Statistical analyses

          Following OTU assignment, alpha diversity estimates were calculated via Chao 1 richness (Chao 1984 ). Significant differences between sites, communities (i.e. total and active) and their interaction were assessed by two-way analysis of variance (ANOVA) followed by Tukey’s HSD post hoc test, using the VEGAN package (version 2.3-5; Oksanen et al. 2007 ) in the R environment (version 3.2.4; R Core Team 2014 ). All microbial community composition datasets and environmental dataset were non-normally distributed and analyzed using non-parametric techniques. As such, beta diversity was assessed via two-way permutational multivariate analysis of variance (PERMANOVA; Anderson 2001 ) to determine the effects of site, community and their interactions on the composition of total and active microbial communities across the regional gradient. The Bray–Curtis dissimilarity was calculated for bacterial or fungal OTU abundance and analyzed via PERMANOVA (999 permutations) using the Adonis function in the VEGAN package in R. Differences in total and active microbial community composition were visualized through unconstrained non-metric multidimensional scaling (NMDS) ordination using the metaMDS function of the VEGAN package in R. Prior to calculating the Bray–Curtis dissimilarity for NMDS, OTU abundances for bacteria and fungi underwent a square-root transformation and Wisconsin double standardization using three dimensions to reduce stress (i.e. stress <0.20). Total and active bacterial and fungal communities were separated and assessed via two-way PERMANOVA to determine the effects of site on each community’s composition over the regional gradient. Again, the Bray–Curtis dissimilarity was calculated for each bacterial or fungal total and active community based on OTU abundance and analyzed via PERMANOVA (999 permutations) using the Adonis function in the VEGAN package in R. To determine which environmental factors shape each community’s composition over the regional gradient, distance-based linear models (DistLM; Legendre and Legendre 1998 ) were used in Primer (version 6; Primer-E Ltd, Plymouth, UK). The impact of individual environmental factors on total and active bacterial and fungal community OTU abundance was assessed using DistLM marginal tests (999 permutations). For DistLM, the Bray–Curtis dissimilarity was used to quantify microbial community composition and Euclidean distances were used to quantify climatic differences across the regional gradient. To determine whether changes in total and active microbial community composition could be attributed to changes in taxonomic affiliations, the mean relative abundance of individual bacterial orders or fungal classes was determined within total and active communities; significance was assessed via ANOVA in R. Further, to determine the influence of environmental factors on the change in relative abundance of microbial taxa within the total and active communities, correlations between the relative abundance of individual taxa identified to contribute to community effects and those individual environmental factors corresponding to site effects were investigated via Spearman’s rank correlations (rs) in R. Here, the calculated critical value for determining significance was |rs| >0.59 based on α <0.05 (n = 12, two-tailed test; Zar 1984 ). All environmental factors were tested for collinearity through Pearson’s correlation (r) following z-score transformation in R prior to use in DistLM marginal tests or Spearman’s rank correlations. In the circumstance where two factors were considered collinear (|r| >0.80; Tabachnik and Fidell 1996 ), one factor was removed from all subsequent analyses. All statistical analyses were considered significant at P <0.05. Input files and rendered code for all statistical analyses utilizing the R environment (version 3.2.4) are available at http://zaklab-soils.github.io/MI_Gradient_Romanowicz_2016/ .

          RESULTS

          Microbial sequencing of total and active communities

          Sequencing of coextracted bacterial 16S rDNA (total community) and metabolically active rRNA (active community) amplicons produced 47 496 total and 39 100 active unique sequences following quality control (see Table S1, Supporting Information). In all, the combined total and active bacterial communities were rarified to 2139 sequences for each plot. These sequences were clustered into 45 057 OTUs at 97% sequence similarity to compose a species-level classification.

          Sequencing of fungal 28S rDNA and rRNA amplicons resulted in 36 583 total and 41 084 active unique sequences following quality control (see Table S1, Supporting Information). The combined total and active fungal communities were rarified to 1523 unique sequences for each plot and clustered into 20 183 OTUs at 99% similarity to represent a species-level classification.

          Environmental characteristics between sites

          Forest floor moisture and pH were significantly lower in the northern-most site A than values recorded at sites B-D, with moisture also significantly higher at site C than site D (P <0.05; Table  1 ). The N concentration at site A was also lower than sites B and C (P <0.05; Table  1 ), but not different from site D (P = 0.53). Conversely, the carboxyl content of site A was greater than sites C and D (P <0.05; Table  1 ), but not different from site B (P = 0.20). The chemical components, alkyl and O/N-alkyl, were not different across sites (P >0.05; Table  1 ), nor was C concentration of the forest floor (P >0.05).

          Richness of total and active microbial communities

          Richness estimates (Chao 1) between total and active bacterial communities revealed a site by community interaction (P = 0.05). Total bacterial richness was greater in site B than in any other site (P <0.05; Table  2 ). However, active bacterial richness was not different between sites (P >0.05; Table  2 ). The differences in mean richness between total and active bacterial communities across sites were examined, with only site B having a significant reduction in active community richness compared to the total community (–71 667; P = 0.03).

          Table 2.

          Alpha diversity Chao 1 richness for bacterial and fungal total and active communities across sites. Mean ± standard deviation values are presented with post-hoc analysis for site differences (P < 0.05 via ANOVA) denoted via lowercase letters.

           Site A Site B Site C Site D 
          Bacteria 
           Total 44 395 ± 21 017a 135 943 ± 42 170b 67 797 ± 20 669a 45 872 ± 5332a 
           Active 35 649 ± 11 111a 64 276 ± 34 446a 67 047 ± 5591a 41 874 ± 12 922a 
          Fungi 
           Total 34 051 ± 17 222a 34 047 ± 6896a 7799 ± 3426b 5900 ± 1627b 
           Active 13 719 ± 885a 10 987 ± 623a 19 304 ± 12 705a 9128 ± 3300a 
           Site A Site B Site C Site D 
          Bacteria 
           Total 44 395 ± 21 017a 135 943 ± 42 170b 67 797 ± 20 669a 45 872 ± 5332a 
           Active 35 649 ± 11 111a 64 276 ± 34 446a 67 047 ± 5591a 41 874 ± 12 922a 
          Fungi 
           Total 34 051 ± 17 222a 34 047 ± 6896a 7799 ± 3426b 5900 ± 1627b 
           Active 13 719 ± 885a 10 987 ± 623a 19 304 ± 12 705a 9128 ± 3300a 
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          Table 2.

          Alpha diversity Chao 1 richness for bacterial and fungal total and active communities across sites. Mean ± standard deviation values are presented with post-hoc analysis for site differences (P < 0.05 via ANOVA) denoted via lowercase letters.

           Site A Site B Site C Site D 
          Bacteria 
           Total 44 395 ± 21 017a 135 943 ± 42 170b 67 797 ± 20 669a 45 872 ± 5332a 
           Active 35 649 ± 11 111a 64 276 ± 34 446a 67 047 ± 5591a 41 874 ± 12 922a 
          Fungi 
           Total 34 051 ± 17 222a 34 047 ± 6896a 7799 ± 3426b 5900 ± 1627b 
           Active 13 719 ± 885a 10 987 ± 623a 19 304 ± 12 705a 9128 ± 3300a 
           Site A Site B Site C Site D 
          Bacteria 
           Total 44 395 ± 21 017a 135 943 ± 42 170b 67 797 ± 20 669a 45 872 ± 5332a 
           Active 35 649 ± 11 111a 64 276 ± 34 446a 67 047 ± 5591a 41 874 ± 12 922a 
          Fungi 
           Total 34 051 ± 17 222a 34 047 ± 6896a 7799 ± 3426b 5900 ± 1627b 
           Active 13 719 ± 885a 10 987 ± 623a 19 304 ± 12 705a 9128 ± 3300a 
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          Similarly, richness in both the total and active fungal communities had a site by community interaction (P = 0.05), in which total community richness in sites A and B were greater than sites C and D (P <0.05; Table  2 ). There were no differences in richness between sites for the active fungal community (P >0.05; Table  2 ). Estimated active richness declined compared to the total community in site B (–23 060; P = 0.05).

          Diversity of total and active microbial communities

          Bacterial community composition differed between total and active communities (Pseudo-F = 1.2; P = 0.01) and between sites across the regional gradient (Pseudo-F = 1.1; P <0.01; no site by community interaction, Pseudo-F = 1.0; P = 0.29). NMDS ordination from the Bray–Curtis dissimilarity of OTU abundances revealed that site differences drove the separation across NMDS axis 1 (Fig.  1a ), whereas total and active bacterial communities separated across NMDS axis 2. The direction of change between total and active bacterial communities was consistent across all study sites. Bacterial community composition within both the total and active communities differed by site across the regional gradient (total bacteria Pseudo-F = 1.1; P <0.01; active bacteria Pseudo-F = 1.1; P <0.01).

          Figure 1.
          NMDS ordinations based on the Bray–Curtis dissimilarity of OTU abundance within (a) bacterial or (b) fungal communities reveal spatial variation by site and between total and active communities. Each marker represents an individual plot in the NMDS charts.

          View large Download slide

          NMDS ordinations based on the Bray–Curtis dissimilarity of OTU abundance within (a) bacterial or (b) fungal communities reveal spatial variation by site and between total and active communities. Each marker represents an individual plot in the NMDS charts.

          Figure 1.
          NMDS ordinations based on the Bray–Curtis dissimilarity of OTU abundance within (a) bacterial or (b) fungal communities reveal spatial variation by site and between total and active communities. Each marker represents an individual plot in the NMDS charts.

          View large Download slide

          NMDS ordinations based on the Bray–Curtis dissimilarity of OTU abundance within (a) bacterial or (b) fungal communities reveal spatial variation by site and between total and active communities. Each marker represents an individual plot in the NMDS charts.

          Fungal community composition also differed between total and active communities (Pseudo-F = 1.4; P <0.01) and between sites across the regional gradient (Pseudo-F = 2.0; P <0.01; no site by community interactions, Pseudo-F = 1.0; P = 0.63). NMDS ordination revealed that site differences separated across NMDS axis 1 (Fig 1b ), while total and active fungal communities separated across NMDS axis 2. The direction of change between total and active fungal communities was consistent across sites. Fungal community composition within both the total and active communities differed by site across the regional gradient (total fungi Pseudo-F = 1.5; P <0.01; active fungi Pseudo-F = 1.5; P <0.01).

          Taxonomic differences between total and active microbial communities

          We further investigated the relative abundance of microbial taxa to determine how they differed between total and active communities (Table  3 ). The dominant microbial taxa (≥ 1% relative abundance) were consistent between the total and active communities. Although, we found that, within the bacterial communities, there was an overrepresentation in the relative abundance of active Sphingobacteriales (+2%; P = 0.03; Table  3 ), Planctomycetales (+2%; P = 0.05) and Pseudomonadales (+3%; P = 0.04) as compared to the total community, with a simultaneous underrepresentation in active Rhizobiales (−3%; P = 0.03) and Caulobacterales (−2%; P = 0.04).

          Table 3.

          Relative abundance of total and active bacterial and fungal taxa (mean ± standard deviation) with mean percent difference from total community (± mean). Significant differences from total community (P <0.05 via ANOVA) displayed.a

           Relative abundance (%)  
           % Total % Active Difference from total 
          Bacteria 
          Actinomycetales 22 ± 5.5 20 ± 2.8 ns 
          Rhizobiales 14 ± 3.6 11 ± 1.5 –3% 
           Acidobacteria Gp1 14 ± 8.8 11 ± 6.5 ns 
          Sphingobacteriales 8 ± 1.2 10 ± 2.4 +2% 
          Burkholderiales 5 ± 1.6 7 ± 2.7 ns 
          Caulobacterales 5 ± 2.4 3 ± 1.3 –2% 
          Planctomycetales 4 ± 2.5 6 ± 2.1 +2% 
          Rhodospirillales 4 ± 2.1 6 ± 2.5 ns 
          Sphingomonadales 4 ± 1.2 4 ± 1.3 ns 
          Flavobacteriales 1 ± 0.9 1 ± 1.1 ns 
          Pseudomonadales 1 ± 1.0 4 ± 3.8 +3% 
           Unclassified 13 ± 5.1 12 ± 4.4 ns 
           Otherb 5 ± 4.0 6 ± 4.0 ns 
          Fungi 
          Leotiomycetes 45 ± 12.8 32 ± 13.8 –13% 
          Sordariomycetes 20 ± 11 15 ± 7.1 ns 
          Dothideomycetes 8 ± 2.4 12 ± 3.6 +4% 
          Agaricomycetes 5 ± 3.4 12 ± 6.5 +7% 
          Tremellomycetes 3 ± 1.1 6 ± 2.1 +3% 
          Microbotryomycetes 2 ± 1.1 5 ± 2.4 +3% 
          Eurotiomycetes 1 ± 0.9 2 ± 1.1 ns 
           Unclassified 15 ± 4.8 15 ± 4.4 ns 
           Otherc 1 ± 1.3 2 ± 2.1 +1% 
           Relative abundance (%)  
           % Total % Active Difference from total 
          Bacteria 
          Actinomycetales 22 ± 5.5 20 ± 2.8 ns 
          Rhizobiales 14 ± 3.6 11 ± 1.5 –3% 
           Acidobacteria Gp1 14 ± 8.8 11 ± 6.5 ns 
          Sphingobacteriales 8 ± 1.2 10 ± 2.4 +2% 
          Burkholderiales 5 ± 1.6 7 ± 2.7 ns 
          Caulobacterales 5 ± 2.4 3 ± 1.3 –2% 
          Planctomycetales 4 ± 2.5 6 ± 2.1 +2% 
          Rhodospirillales 4 ± 2.1 6 ± 2.5 ns 
          Sphingomonadales 4 ± 1.2 4 ± 1.3 ns 
          Flavobacteriales 1 ± 0.9 1 ± 1.1 ns 
          Pseudomonadales 1 ± 1.0 4 ± 3.8 +3% 
           Unclassified 13 ± 5.1 12 ± 4.4 ns 
           Otherb 5 ± 4.0 6 ± 4.0 ns 
          Fungi 
          Leotiomycetes 45 ± 12.8 32 ± 13.8 –13% 
          Sordariomycetes 20 ± 11 15 ± 7.1 ns 
          Dothideomycetes 8 ± 2.4 12 ± 3.6 +4% 
          Agaricomycetes 5 ± 3.4 12 ± 6.5 +7% 
          Tremellomycetes 3 ± 1.1 6 ± 2.1 +3% 
          Microbotryomycetes 2 ± 1.1 5 ± 2.4 +3% 
          Eurotiomycetes 1 ± 0.9 2 ± 1.1 ns 
           Unclassified 15 ± 4.8 15 ± 4.4 ns 
           Otherc 1 ± 1.3 2 ± 2.1 +1% 
          a

          Percent difference from total community by site displayed in Fig. S2 (Supporting Information).

          b

          ‘Other’ bacterial orders listed in Table S3 (Supporting Information).

          c

          ‘Other’ fungal classes listed in Table S4 (Supporting Information).

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          Table 3.

          Relative abundance of total and active bacterial and fungal taxa (mean ± standard deviation) with mean percent difference from total community (± mean). Significant differences from total community (P <0.05 via ANOVA) displayed.a

           Relative abundance (%)  
           % Total % Active Difference from total 
          Bacteria 
          Actinomycetales 22 ± 5.5 20 ± 2.8 ns 
          Rhizobiales 14 ± 3.6 11 ± 1.5 –3% 
           Acidobacteria Gp1 14 ± 8.8 11 ± 6.5 ns 
          Sphingobacteriales 8 ± 1.2 10 ± 2.4 +2% 
          Burkholderiales 5 ± 1.6 7 ± 2.7 ns 
          Caulobacterales 5 ± 2.4 3 ± 1.3 –2% 
          Planctomycetales 4 ± 2.5 6 ± 2.1 +2% 
          Rhodospirillales 4 ± 2.1 6 ± 2.5 ns 
          Sphingomonadales 4 ± 1.2 4 ± 1.3 ns 
          Flavobacteriales 1 ± 0.9 1 ± 1.1 ns 
          Pseudomonadales 1 ± 1.0 4 ± 3.8 +3% 
           Unclassified 13 ± 5.1 12 ± 4.4 ns 
           Otherb 5 ± 4.0 6 ± 4.0 ns 
          Fungi 
          Leotiomycetes 45 ± 12.8 32 ± 13.8 –13% 
          Sordariomycetes 20 ± 11 15 ± 7.1 ns 
          Dothideomycetes 8 ± 2.4 12 ± 3.6 +4% 
          Agaricomycetes 5 ± 3.4 12 ± 6.5 +7% 
          Tremellomycetes 3 ± 1.1 6 ± 2.1 +3% 
          Microbotryomycetes 2 ± 1.1 5 ± 2.4 +3% 
          Eurotiomycetes 1 ± 0.9 2 ± 1.1 ns 
           Unclassified 15 ± 4.8 15 ± 4.4 ns 
           Otherc 1 ± 1.3 2 ± 2.1 +1% 
           Relative abundance (%)  
           % Total % Active Difference from total 
          Bacteria 
          Actinomycetales 22 ± 5.5 20 ± 2.8 ns 
          Rhizobiales 14 ± 3.6 11 ± 1.5 –3% 
           Acidobacteria Gp1 14 ± 8.8 11 ± 6.5 ns 
          Sphingobacteriales 8 ± 1.2 10 ± 2.4 +2% 
          Burkholderiales 5 ± 1.6 7 ± 2.7 ns 
          Caulobacterales 5 ± 2.4 3 ± 1.3 –2% 
          Planctomycetales 4 ± 2.5 6 ± 2.1 +2% 
          Rhodospirillales 4 ± 2.1 6 ± 2.5 ns 
          Sphingomonadales 4 ± 1.2 4 ± 1.3 ns 
          Flavobacteriales 1 ± 0.9 1 ± 1.1 ns 
          Pseudomonadales 1 ± 1.0 4 ± 3.8 +3% 
           Unclassified 13 ± 5.1 12 ± 4.4 ns 
           Otherb 5 ± 4.0 6 ± 4.0 ns 
          Fungi 
          Leotiomycetes 45 ± 12.8 32 ± 13.8 –13% 
          Sordariomycetes 20 ± 11 15 ± 7.1 ns 
          Dothideomycetes 8 ± 2.4 12 ± 3.6 +4% 
          Agaricomycetes 5 ± 3.4 12 ± 6.5 +7% 
          Tremellomycetes 3 ± 1.1 6 ± 2.1 +3% 
          Microbotryomycetes 2 ± 1.1 5 ± 2.4 +3% 
          Eurotiomycetes 1 ± 0.9 2 ± 1.1 ns 
           Unclassified 15 ± 4.8 15 ± 4.4 ns 
           Otherc 1 ± 1.3 2 ± 2.1 +1% 
          a

          Percent difference from total community by site displayed in Fig. S2 (Supporting Information).

          b

          ‘Other’ bacterial orders listed in Table S3 (Supporting Information).

          c

          ‘Other’ fungal classes listed in Table S4 (Supporting Information).

          View Large

          Between the fungal communities, the relative abundance of Leotiomycetes was lower in the active community relative to the total community (−13%; P = 0.02; Table  3 ). There was also an overrepresentation of active Dothideomycetes (+4%; P = 0.01; Table  3 ), Agaricomycetes (+7%; P <0.01), Tremellomycetes (+3%; P <0.01) and Microbotryomycetes (+3%; P <0.01) as compared to the total community.

          Environmental factors related to total and active microbial communities

          Site differences in soil moisture, pH, carboxyl and N concentration each individually explained a portion of dissimilarity within the total bacterial and fungal communities (P <0.05; Table  4 ). Similarly, these same environmental factors, with the addition of C concentration explained a significant portion of dissimilarity within the active bacterial and fungal communities (P <0.05; Table  4 ). Soil moisture explained the greatest portion of dissimilarity in the active bacterial as well as total and active fungal communities (active bacteria R2 = 0.100; total fungi R2 = 0.142; active fungi R2 = 0.138; Table  4 ). However, pH was the most influential individual environmental factor explaining dissimilarity in the total bacterial community across the regional gradient (R2 = 0.105; Table  4 ). The concentrations of chemical constituents’ alkyl and O/N-alkyl did not explain a significant portion of dissimilarity within total or active bacterial or fungal communities (P >0.05; Table  4 ). Aryl content and C:N were excluded from DistLM marginal tests due to evidence of collinearity with other environmental factors (|r| ≥ 0.80; see Table S2, Supporting Information).

          Table 4.

          Influence of individual environmental factors on total and active bacterial and fungal composition. Significant variations in microbial composition attributed to an environmental factor (P <0.05 via marginal DistLM) displayed.a

           Bacteria Fungi 
           Total R2 Active R2 Total R2 Active R2 
          Physical 
           Moistureb 0.104 0.100 0.142 0.138 
           pH 0.105 0.096 0.135 0.130 
          Chemical 
           Alkyl ns ns ns ns 
           O/N-alkyl ns ns ns ns 
           Carboxylc 0.099 0.098 0.121 0.120 
          Biogeochemical 
           C ns 0.095 ns 0.110 
           Nd 0.099 0.096 0.122 0.124 
           Bacteria Fungi 
           Total R2 Active R2 Total R2 Active R2 
          Physical 
           Moistureb 0.104 0.100 0.142 0.138 
           pH 0.105 0.096 0.135 0.130 
          Chemical 
           Alkyl ns ns ns ns 
           O/N-alkyl ns ns ns ns 
           Carboxylc 0.099 0.098 0.121 0.120 
          Biogeochemical 
           C ns 0.095 ns 0.110 
           Nd 0.099 0.096 0.122 0.124 
          a

          All non-significant (ns) R2 values displayed in Table S5 (Supporting Information).

          b

          Moisture collinear with factor C:N (r = –0.80; Table S2, Supporting Information); C:N excluded from marginal DistLM.

          c

          Carboxyl collinear with factor Aryl (r = 0.91; Table S2, Supporting Information); Aryl excluded from marginal DistLM.

          d

          N content collinear with factor C:N (r = –0.95; Table S2, Supporting Information); C:N excluded from marginal DistLM.

          View Large
          Table 4.

          Influence of individual environmental factors on total and active bacterial and fungal composition. Significant variations in microbial composition attributed to an environmental factor (P <0.05 via marginal DistLM) displayed.a

           Bacteria Fungi 
           Total R2 Active R2 Total R2 Active R2 
          Physical 
           Moistureb 0.104 0.100 0.142 0.138 
           pH 0.105 0.096 0.135 0.130 
          Chemical 
           Alkyl ns ns ns ns 
           O/N-alkyl ns ns ns ns 
           Carboxylc 0.099 0.098 0.121 0.120 
          Biogeochemical 
           C ns 0.095 ns 0.110 
           Nd 0.099 0.096 0.122 0.124 
           Bacteria Fungi 
           Total R2 Active R2 Total R2 Active R2 
          Physical 
           Moistureb 0.104 0.100 0.142 0.138 
           pH 0.105 0.096 0.135 0.130 
          Chemical 
           Alkyl ns ns ns ns 
           O/N-alkyl ns ns ns ns 
           Carboxylc 0.099 0.098 0.121 0.120 
          Biogeochemical 
           C ns 0.095 ns 0.110 
           Nd 0.099 0.096 0.122 0.124 
          a

          All non-significant (ns) R2 values displayed in Table S5 (Supporting Information).

          b

          Moisture collinear with factor C:N (r = –0.80; Table S2, Supporting Information); C:N excluded from marginal DistLM.

          c

          Carboxyl collinear with factor Aryl (r = 0.91; Table S2, Supporting Information); Aryl excluded from marginal DistLM.

          d

          N content collinear with factor C:N (r = –0.95; Table S2, Supporting Information); C:N excluded from marginal DistLM.

          View Large

          Relationships between environmental factors and total and active microbial taxa

          Correlations between significant environmental factors (see Table  4 ) and significant microbial taxa (see Table  3 ) within total or active communities provide insight into the relationships between these taxa and their environment (Table  5 ). Active Caulobacterales and Rhizobiales were negatively correlated with soil moisture, pH and N (rs <–0.59; Table  5 ) and positively correlated with C (rs = 0.59). Total Pseudomonadales were positively correlated with carboxyl content (rs = 0.61; Table  5 ), whereas active Sphingobacteriales were negatively correlated with carboxyl content (rs = –0.71). Planctomycetales was not correlated with any environmental factor within either the total or active community (|rs| <0.59; Table  5 ).

          Table 5.

          Spearman’s rank correlations (rs) between bacterial and fungal taxa representing significant community effects and environmental factors representing significant site effects. Significant correlations (|rs| >0.59) displayed.a

           Moisture pH Carboxyl Cb 
           Total Active Total Active Total Active Total Active Total Active 
          Bacteria 
          Caulobacterales ns –0.82 –0.71 –0.66 ns ns  0.59 ns –0.92 
          Planctomycetales ns ns ns ns ns ns  ns ns ns 
          Pseudomonadales ns ns ns ns 0.61 ns  ns ns ns 
          Rhizobiales ns –0.70 ns –0.66 ns ns  ns ns –0.73 
          Sphingobacteriales ns ns ns ns ns –0.71  ns ns ns 
          Fungi 
          Agaricomycetes ns 0.71 0.71 0.64 ns ns  ns 0.69 0.83 
          Dothideomycetes ns ns ns ns ns ns  ns ns ns 
          Leotiomycetes ns ns ns ns 0.71 ns  ns ns ns 
          Microbotryomycetes ns ns ns ns ns ns  ns ns ns 
          Tremellomycetes ns ns ns ns ns ns  ns ns ns 
           Moisture pH Carboxyl Cb 
           Total Active Total Active Total Active Total Active Total Active 
          Bacteria 
          Caulobacterales ns –0.82 –0.71 –0.66 ns ns  0.59 ns –0.92 
          Planctomycetales ns ns ns ns ns ns  ns ns ns 
          Pseudomonadales ns ns ns ns 0.61 ns  ns ns ns 
          Rhizobiales ns –0.70 ns –0.66 ns ns  ns ns –0.73 
          Sphingobacteriales ns ns ns ns ns –0.71  ns ns ns 
          Fungi 
          Agaricomycetes ns 0.71 0.71 0.64 ns ns  ns 0.69 0.83 
          Dothideomycetes ns ns ns ns ns ns  ns ns ns 
          Leotiomycetes ns ns ns ns 0.71 ns  ns ns ns 
          Microbotryomycetes ns ns ns ns ns ns  ns ns ns 
          Tremellomycetes ns ns ns ns ns ns  ns ns ns 
          a

          All non-significant (ns) correlation values displayed in Table S6 (Supporting Information).

          b

          C content correlations excluded from total communities as factor was not significant in marginal DistLM.

          View Large
          Table 5.

          Spearman’s rank correlations (rs) between bacterial and fungal taxa representing significant community effects and environmental factors representing significant site effects. Significant correlations (|rs| >0.59) displayed.a

           Moisture pH Carboxyl Cb 
           Total Active Total Active Total Active Total Active Total Active 
          Bacteria 
          Caulobacterales ns –0.82 –0.71 –0.66 ns ns  0.59 ns –0.92 
          Planctomycetales ns ns ns ns ns ns  ns ns ns 
          Pseudomonadales ns ns ns ns 0.61 ns  ns ns ns 
          Rhizobiales ns –0.70 ns –0.66 ns ns  ns ns –0.73 
          Sphingobacteriales ns ns ns ns ns –0.71  ns ns ns 
          Fungi 
          Agaricomycetes ns 0.71 0.71 0.64 ns ns  ns 0.69 0.83 
          Dothideomycetes ns ns ns ns ns ns  ns ns ns 
          Leotiomycetes ns ns ns ns 0.71 ns  ns ns ns 
          Microbotryomycetes ns ns ns ns ns ns  ns ns ns 
          Tremellomycetes ns ns ns ns ns ns  ns ns ns 
           Moisture pH Carboxyl Cb 
           Total Active Total Active Total Active Total Active Total Active 
          Bacteria 
          Caulobacterales ns –0.82 –0.71 –0.66 ns ns  0.59 ns –0.92 
          Planctomycetales ns ns ns ns ns ns  ns ns ns 
          Pseudomonadales ns ns ns ns 0.61 ns  ns ns ns 
          Rhizobiales ns –0.70 ns –0.66 ns ns  ns ns –0.73 
          Sphingobacteriales ns ns ns ns ns –0.71  ns ns ns 
          Fungi 
          Agaricomycetes ns 0.71 0.71 0.64 ns ns  ns 0.69 0.83 
          Dothideomycetes ns ns ns ns ns ns  ns ns ns 
          Leotiomycetes ns ns ns ns 0.71 ns  ns ns ns 
          Microbotryomycetes ns ns ns ns ns ns  ns ns ns 
          Tremellomycetes ns ns ns ns ns ns  ns ns ns 
          a

          All non-significant (ns) correlation values displayed in Table S6 (Supporting Information).

          b

          C content correlations excluded from total communities as factor was not significant in marginal DistLM.

          View Large

          Agaricomycete fungi were positively correlated with soil pH and N in both the total and active fungal communities (rs >0.59; Table  5 ), but positively correlated with soil moisture only in the active community (rs = 0.71). Leotiomycetes in the total community were positively correlated with carboxyl content (rs = 0.71; Table  5 ). Dothideomycetes, Microbotryomycetes and Tremellomycetes were not correlated with any measured environmental factor within total or active communities (|rs| <0.59; Table  5 ).

          DISCUSSION

          Here, we provide empirical evidence that soil moisture, pH, carboxyl content, as well as soil C and N shape the composition of total and active bacterial and fungal communities across the geographical range of northern hardwood forests. Although our results are consistent with previous evidence that soil moisture and pH shape the total communities of bacteria and fungi across this regional gradient (Cline and Zak 2014 ; Freedman and Zak 2015 ), along with chemical constituents (i.e. carboxyl), and C and N concentration in multiple other ecosystems (Morris and Boerner 1999 ; Fierer and Jackson 2006 ; Zak et al. 2008 ; Baumann et al. 2009 ; Garbeva and de Boer 2009 ; Brockett, Prescott and Grayston 2012 ; Ramirez, Craine and Fierer 2012 ; Ng et al. 2014 ), we expand upon this knowledge by demonstrating that the same environmental factors shape the composition of both the total and active communities of bacteria and fungi. More specifically, soil moisture and pH were the environmental factors most strongly related to bacterial and fungal community composition. Soil moisture was the most important factor related to the composition of active bacteria and fungi, and the total fungal community. The influence of soil moisture on active communities is consistent with previous studies that found soil moisture was a major influence regulating functional expression of soil microbial communities (Hackl et al. 2005 ). Soil pH was the environmental factor most strongly related to the composition of the total bacterial community, which supports previous studies relating pH to microbial community composition (Fierer and Jackson 2006 ; Rousk, Brookes and Bååth 2009 ; Rousk et al. 2010 ).

          Differences between total and active microbial communities at the time of sampling primarily involved five ecologically important bacterial orders and five fungal classes. Distinguishing differences between the total microbial community and the community that is metabolically active provides insight into the relationships between communities. Such relationships between total and active communities can be interpreted as potential shifts in soil functions as soil functions are more likely to be connected to the active communities (Nannipieri et al. 2003 ). For example, between total and active fungal communities, the 13% underrepresentation in active Leotiomycetes may suggest a decreased potential for cellulolytic decay of plant litter (Boberg, Ihrmark and Lindahl 2011 ), which can be further supported by the overrepresentation of active Dothideomycetes, whose members appear to lack explicit cellulolytic and lignolytic physiology (Boberg, Ihrmark and Lindahl 2011 ). Also, the potential for wood rot activity may increase as we observed an overrepresentation in active Agaricomycetes and Tremellomycetes, whose members are closely associated with wood rot (Hibbett 2006 ; Middelhoven 2006 ; Yurkov et al. 2012 ; Floudas et al. 2015 ). Between bacterial communities, active Rhizobiales were underrepresented compared to the total community. Studies have demonstrated that members of Rhizobiales interact with, and compete against, white-rot fungi during the initial decay stages of down trees, where fungi outcompete these opportunistic bacteria both on the wood surface and in the surrounding soil (Folman et al. 2008 ). This underrepresentation in active Rhizobiales could be associated with the potential overrepresentation of active wood rot fungi in the Agaricomycetes and Tremellomycetes classes.

          Although experimental validation is necessary to directly associate any individual environmental factor with changes in the relative abundance of certain taxa between total and active communities, the relationships we observed highlight how environmental factors may shape communities across the regional gradient. For example, negative correlations between active Caulobacterales and Rhizobiales with soil moisture, pH and N could potentially account for the underrepresentation of these active taxa compared to the total community. Additionally, active Sphingobacteriales had a negative relationship with SOM carboxyl content, which often represents relatively labile C forms that can strongly influence microbial activity (Ng et al. 2014 ). Sphingobacteriales, who are members of copiotrophic Bacteroidetes, thrive in conditions of elevated C availability (Fierer, Bradford and Jackson 2007 ; Eilers et al. 2010 ); thus, an overrepresentation in active Sphingobacteriales abundance could be linked with an increase in SOM carboxyl content. However, other potential relationships between communities for bacterial taxa are difficult to distinguish from site differences of individual environmental factors. Planctomycetales composition was not correlated with any identified environmental factor in either total or active communities. Likewise, relationships between fungal taxa and environmental factors provide little insight into how changes in environmental factors potentially influence differences in composition between total and active communities. An exception may be Agaricomycetes, whose relative abundance was positively correlated with soil moisture only in the active community, suggesting that soil moisture may be influencing the overrepresentation in their active abundance. Similarly, Leotiomycetes in the total community were positively correlated with carboxyl content, where the 13% underrepresentation in active Leotiomycetes could possibly be attributed, in part, to declines in carboxyl content north to south over our regional gradient.

          It is plausible that a portion of dissimilarity between total and active communities of any microbial taxa may be attributed to environmental factors that we did not measure. Likewise, observed shifts in relative taxon abundances could be driven by intertaxa interactions within the total and active communities based on competition or cooperation for nutrients and other resources (Deng et al. 2012 ). Our previous studies have revealed that environmental factors such as plant biomass, overstory composition or seasonal changes in soil temperature or precipitation shape microbial community structure (Cline and Zak 2014 ; Freedman and Zak 2015 ). However, we were unable to discern how variation in these environmental factors across sites directly contributes to compositional differences between total and active microbial communities at our specific time of sampling. Future studies should focus on discerning how change in any individual environmental factor affects microbial community composition by controlling for changes in the environmental factor being tested. Recent examples of such studies include Matulich and Martiny ( 2015 ) that utilize microcosms to discern if the magnitude of a microbial community’s response to a certain environmental change was explained, in part, by changes in composition over the course of the experiment. Such experiments could contribute to our understanding of how microbial composition can be useful for predicting functional responses to environmental change.

          CONCLUSIONS

          Here, we demonstrate that metabolically active microorganisms differ from the total community yet are still a subset of the taxa present in the total community. Further, we reveal that both the total and active communities of bacteria and fungi are shaped by the same subset of measured environmental factors. In addition to these novel findings, understanding the impact of environmental factors on differences in relative abundance between total and active communities provides insights into environmental controls on microbial activity. Further research should focus on experiments that manipulate the environmental factors identified in this study (i.e. soil moisture, pH, carboxyl, C and N) to determine their effects on microbial composition both within and between total and active communities to best understand how climate-induced environmental change may influence future microbial functions across northern hardwood forests.

          SUPPLEMENTARY DATA

          Supplementary Data .

          The authors would like to thank Lauren Cline and Elizabeth Entwistle for their thoughtful feedback on this project, as well as Genevieve Romanowicz and two anonymous reviewers for their comments on this manuscript. The authors would also like to thank Christine McHenry, Joseph Washburn and Robert Lyons for their assistance in DNA sequencing and analysis, as well as Dr Ingrid Kögel-Knabner and Dr Markus Steffens at Technische Universität Müchen for collaboration with solid-state 13C-NMR analysis.

          FUNDING

          This work was supported by the Department of (Grant number ), and the (Grant number ).

          Conflict of Interest. None declared.

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          • soil
          • microorganisms
          • community

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