Biological controls over the abundances of terrestrial ammonia oxidizers
Abstract
Aim
Ammonia-oxidizing archaea (AOA) and bacteria (AOB) are the primary agents for nitrification, converting ammonia (NH4+) into nitrate (NO3−) and modulating plant nitrogen (N) utilization and terrestrial N retention. However, there is still lack of a unifying framework describing the patterns of global AOA and AOB distribution. In particular, biotic interactions are rarely integrated into any of the conceptual models.
Location
World-wide.
Time period
2005–2016.
Major taxa studied
Ammonia-oxidizing archaea and ammonia-oxidizing bacteria.
Methods
A meta-analysis and synthesis were conducted to obtain a general picture of global AOA and AOB distribution and identify the primary driving factors. A microcosm experiment was then conducted to assess effects of relative carbon to nitrogen availability for heterotrophic microbes on AOA and AOB in two distinct soils. A mesocosm experiment was further carried out to characterize the effects of plant roots and their arbuscular mycorrhizal fungi (AMF) on AOA and AOB abundances using hyphae- or root-ingrowth techniques.
Results
Our meta-analysis showed that soil carbon to nitrogen (C/N) ratios explained the most variance in AOA and AOB abundances, although soil pH had a significant effect. Experimental results demonstrated that high cellulose and mineral N inputs increased total microbial biomass and microbial activities, but inhibited AOA and AOB, suggesting microbial inhibition of AOA and AOB. Also, AMF and roots suppressed AOA and AOB, respectively.
Main conclusions
Our study provides convincing evidence illustrating that relative carbon to nitrogen availability can predominantly affect the abundances of AOA and AOB. Our experimental results further validate that biotic competition among plants, heterotrophic microbes and ammonia oxidizers for substrate N is the predominant control upon AOA and AOB abundances. Together, these findings provide new insights into the role of abiotic and biotic factors in modulating terrestrial AOA and AOB abundances and their potential applications for management of nitrification in an increasing reactive N world.
1 INTRODUCTION
Soil nitrogen (N) is a major limiting factor of terrestrial ecosystem productivity (LeBauer & Treseder, 2008). The primary form of the reactive N entering the soil is ammonium, which predominantly originates from mineralization of organic matter, N deposition, or N fertilizers (Galloway, 2008). A suite of microbial processes then controls N transformations in soil, including microbial or plant immobilization (i.e., uptake), nitrification, and denitrification. Nitrification, the microbial oxidation of ammonia (NH3) to nitrate (NO3−) via nitrite (NO2−), is a key process in the N cycle (Chapin, Matson, & Vitousek, 2011; Galloway, 2008), and ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) are the primary agents controlling the first step of nitrification in most terrestrial ecosystems (Leininger et al., 2006; Prosser & Nicol, 2012). Although some Nitrospira species can perform complete nitrification (complete oxidation of ammonia to nitrate; Daims et al., 2015; Pinto et al., 2015; van Kessel et al., 2015), nitrification through the two-step process, through which ammonia is oxidized by AOA and AOB to nitrite and then by nitrite oxidizing bacteria to NO3−, likely dominates in most terrestrial ecosystems (Kuypers, Marchant, & Kartal, 2018; Vlaeminck, Hay, Maignien, & Verstraete, 2011).
An understanding of AOA and AOB distribution and their relative contribution to nitrification in soil is, therefore, critically important in predicting the fate of reactive N in the environment (Di et al., 2009; Jiang, Jin, & Sun, 2014; Leininger et al., 2006; Lu & Jia, 2013; Lu, Bottomley, & Myrold, 2015). Over the last decade, plenty of attention has been directed towards determining AOA and AOB abundances via quantifying archaeal and bacterial amoA genes and identifying the major factors that govern their distribution (Di et al., 2010; Gubry-Rangin et al., 2011; Hink, Gubry-Rangin, Nicol, & Prosser, 2018; Hu, Zhang, Dai, Di, & He, 2013; Nicol, Leininger, Schleper, & Prosser, 2008; Verhamme, Prosser, & Nicol, 2011; Yao et al., 2011), as amoA gene encodes ammonia monooxygenase (AMO) subunit A in AOA and AOB that converts NH3 to hydroxylamine (Nicol & Schleper, 2006). Various resource and environmental factors, for example, ammonium concentration (Belser, 1979; Di et al., 2010; Hink et al., 2018; Verhamme et al., 2011), soil pH (Gubry-Rangin et al., 2011; Hu et al., 2013; Nicol et al., 2008; Yao et al., 2011), temperature (Avrahami & Conrad, 2005; Belser, 1979; Daebeler et al., 2012; Fierer, Carney, Horner-Devine, & Megonigal, 2009), and fertilization practices (Daebeler et al., 2012; He et al., 2007), have been shown to affect AOA and AOB abundances. In particular, soil ammonia availability and soil pH have been proposed to be primary drivers controlling AOA and AOB abundances (Daebeler, Bodelier, Hefting, & Laanbroek, 2015; Verhamme et al., 2011). However, experimental results have been highly variable. Some studies found that ammonia availability only affected AOB but not AOA (Di et al., 2009, 2010; Pratscher, Dumont, & Conrad, 2011) or even influenced neither AOA nor AOB (Fierer, Strickland, Liptzin, Bradford, & Cleveland, 2009; Stopnišek et al., 2010; Yao et al., 2011). Similarly, the relationships between soil pH and AOA and/or AOB distribution were highly inconsistent, with either positive (Hu et al., 2013; Jia & Conrad, 2009; Stopnišek et al., 2010; Yao et al., 2011), negative (Nicol et al., 2008), or even neutral (Bru et al., 2011) relationships having been documented. These diverse effects may be due to the fact that soil pH can affect soil ammonia concentration (Frijlink, Abee, Laanbroek, de Boer, & Konings, 1992), permeability of cell membranes (Lehtovirta-Morley et al., 2016; Valentine, 2007), and nutrient and cationic metal solubility (Hu et al., 2013; Lauber, Hamady, Knight, & Fierer, 2009). More interestingly, emerging evidence has recently shown that soil ammonia oxidizers correlated positively with plant canopy N content but negatively with soil organic C (Ribbons et al., 2018; Soper et al., 2018) and that they were also suppressed by litter additions (Che et al., 2018) and arbuscular mycorrhizal fungi (AMF) (Bukovská et al., 2018). Together, accumulating evidence suggests that factors beyond soil ammonium and pH, biotic competition for N substrate in particular, need to be incorporated to better predict the abundances of soil ammonia oxidizers across larger spatial scales.
Almost all the existing models or frameworks, however, focus on the impact of one or two factors in a relatively static environment and have paid little attention towards the dynamic environment where ammonia-oxidizing microbes and other organisms co-exist (Daebeler et al., 2014; Prosser & Nicol, 2012). In this study, we first performed a meta-analysis to characterize global patterns of AOA and AOB abundances, and undertook structural equation modelling (SEM) to ascertain the relative impacts of diverse environmental and resource factors. Then, we conducted two independent, but complementary experiments to determine the effects of heterotrophic microbes, plant roots, and AMF on AOA and AOB. Finally, we proposed a new conceptual framework in which the dynamic interactions among resource availability, ammonia-oxidizing microbes and other organisms are integrated to explain the abundances and/or distribution of AOA and AOB. Our meta-analysis showed that the soil C/N ratio, an indicator of N limitation to both microbes and plants, was the primary driver of AOA and AOB distribution. As neither AOA nor AOB uses organic C as a primary energy or nutrient source (Belser, 1979; Jia & Conrad, 2009; Pratscher et al., 2011), the impacts of the C/N ratio likely stem from its indirect effects through affecting plant and microbial N utilization. This leads to our central hypothesis that biotic factors critically affect AOA and AOB abundances mainly through regulating the availability of N substrate for AOA and AOB. Two working hypotheses were then derived: (a) the relative availability of carbon to nitrogen for soil microbes significantly affects AOA and AOB abundances through modulating the competition for N between heterotrophic microbes and ammonia-oxidizing microbes (AOA and AOB), (b) plant roots and AMF likely compete for free ammonium ions against AOA and AOB, suppressing nitrification in consequence. A microcosm experiment with substrate additions of different C/N ratios and a mesocosm experiment with plants were therefore conducted to test these two hypotheses, respectively.
2 MATERIALS AND METHODS
2.1 A meta-analysis of major factors that control AOA and AOB abundances
2.1.1 Data collection
We conducted an extensive search for AOA- and AOB-related studies through Web of Science and Google Scholar to build a database. The following three sets of search terms were used to select the papers: (a) ammonia-oxidizing bacteria and ammonia-oxidizing archaea or ammonia oxidizers or nitrifiers, (b) amoA, (c) soil or terrestrial. In total, the search results produced c. 594 article hits on 26 April 2016, with no restriction on publication year. They were manually checked to ensure that each article on the topic related to field studies and to exclude studies in microcosms. The final dataset contained 467 sites reported by 57 publications, and 455 and 452 estimates of AOA and AOB abundances, respectively (see Supporting Information Appendix S1 for details).
The raw data were either obtained from tables or extracted by digitizing graphs using the GetData Graph Digitizer (version 2.22, Russian Federation http://getdata-graph-digitizer.com). For each paper, we used the following criteria to include papers in our analysis: abundances of AOA and AOB (amoA gene copy number per g soil), soil pH (H2O), concentrations of NH4+ and NO3− (standardized to mg N/kg soil), and depth of soil collection (0–25 cm). Measures of carbon (C) were recorded. Because different studies used diverse metrics [soil organic matter (SOM), total organic C, total C, dissolved organic C, water extractable organic C], all the C data were converted into total organic C, whenever possible. A few papers reported SOM rather than total organic C, and the total organic C was therefore obtained (estimated) by the conversion factor of 40% of SOM. Other variables, if provided, such as latitude, mean annual temperature (MAT) and mean annual precipitation (MAP) of the experimental sites were also extracted for further analysis. Each datum is the mean of analytical real-time quantitative polymerase chain reaction (qPCR) replicates of amoA genes and biological replicates from a particular sample type in each study. Data were log-transformed, if necessary, to fit data assumptions of statistical tests.
2.1.2 Meta-analysis
To examine the effect of ecosystem types on the abundances of AOA and AOB, the dataset was also divided into three subsets, representing three types of ecosystems, that is, forests (98 for AOA, 96 for AOB), grasslands (127 for AOA, 127 for AOB) and croplands (230 for AOA, 229 for AOB) (Supporting Information Figure S1). To analyse the effect of ecosystem types (forest, grassland and cropland) on the abundances of AOA and AOB, we used the Kruskal–Wallis H test followed by a nonparametric multiple comparison test, which compares the difference in the sum of ranks between the groups with the expected average difference. All other statistical analyses (single linear regression, multiple linear regression, and Pearson correlations) were conducted using spss 16.0 (SPSS, Inc., Chicago, IL, USA). For the multiple regression analysis, environmental and resource parameters such as soil pH, NH4+ concentration, total organic C and C/N ratio were taken as independent variables, and the gene abundance of AOA amoA or AOB amoA was taken as the dependent variable. Furthermore, we constructed a SEM model based on the known effects and potential relationships among the drivers of AOA and AOB, and estimated the strength of the direct, indirect and total effect of these variables (Supporting Information Table S1). SEM analyses were performed using amos 21.0 (IBM SPSS Inc, Chicago, IL, USA). On the basis of current ecological knowledge, we hypothesized a hierarchy of relationships in a path diagram (Supporting Information Figure S2). Each hierarchical model was simplified by step-wise exclusion of some pathways with low coefficients. The SEMs were implemented using the maximum likelihood estimation method and were fitted with the χ2 test, goodness-of-fit index (GFI), Akaike information criterion (AIC) and root mean squared error of approximation (RMSEA).
2.2 Experiment 1. A microcosm study examining the effects of different ratios of C and N inputs on AOA and AOB
This experiment was designed to determine the effect of resource C/N ratios on AOA and AOB through quantifying the responses of AOA and AOB to differences in the relative availability of organic C to mineral N. Soil samples were collected from two distinct soils: a pine plantation soil (PINE) in Nanjing (32°03′ N, 118°46′ E), Jiangsu Province, and a vegetable field soil (VF) in Nantong (32°01′ N, 120°51′ E), Jiangsu Province. The PINE and VF soils contained 0.9 and 1.1 g/kg of total N, 37.8 and 13.9 g/kg of total organic C and had pH values of 4.08 and 7.50, respectively. Field soil samples were sieved through a 2 mm sieve before being used for the incubation experiment.
Each soil was amended with organic C (cellulose) and inorganic N [(NH4)2SO4] at four C/N ratios as follows: 10, 25, 50 and 100. For each soil, two levels of N inputs were designed (low N at 75 and high N at 200 mg N/kg soil) and four levels of cellulose were added at each N level, leading to C/N ratios at 10, 25, 50 and 100 for each N level. These resulted in 16 treatment combinations with 18 replicates per treatment (2 soils × 2 N levels × 4 C/N ratios × 18 replicates = 288 microcosm jars), plus two controls (that is, soils with no C and no N amendments: 2 soils × 18 replicates = 36 microcosms).
The soils were pre-incubated for 1 week and the treatments were then applied. Cellulose (powder) were weighed and well mixed into 50.0 g soil (dry equivalent) and placed into a 250 ml jar with a surface area of 22 cm2. (NH4)2SO4 was applied in a liquid form. The soil moisture was adjusted to 65% of the water holding capacity for each soil. All the microcosm jars were randomly placed inside a room with a room temperature (air-conditioned) of 22–25 °C. The soil moisture was maintained by periodically weighing the microcosm jars and adding (spraying) distilled water with a syringe to compensate for any weight loss (every 3 days). The soil samples were incubated for about 3 months. To ensure sufficient O2, the cover for each jar was opened for 10 min each day for the first two months and every 4 days in the third month. The jar opening scheme with different frequencies at different times was designed to ensure sufficient O2 for AOA and AOB, because high soil respiration rate at the early stages consumed more O2 (as shown in Supporting Information Figure S7). A randomly selected subset of treatments (three replicates) was used for monitoring microbial respiration.
Subsamples of soils (each jar representing one subsample) were destructively taken at 3, 7, 14, 21, 35, 47, 60, 75 and 90 days after the application of the treatments to determine the amoA gene copies of the AOA and AOB populations, soil microbial biomass C (MBC), and extractable N (NH4+ and NO3−). Soil samples were taken at 7, 35, 60 and 90 days after the application of the treatments and soil DNA was extracted to determine the abundances of bacteria and fungi (more details below). Soil MBC was measured using the chloroform-fumigation extraction method (Vance, Brookes, & Jenkinson, 1987). NH4+ and NO3- were extracted with 2 M KCl and their concentrations were detected on a flow injection analyser (Tecator Inc., Hoganas, Sweden). Soil respiration was measured by an incubation-alkaline absorption method (Hu & van Bruggen, 1997).
Fresh soil (0.30 g) was extracted for DNA using MoBio Power soil TMDNA isolation kits (San Diego, CA) according to the manufacturer’s instructions. qPCR was performed to determine copy numbers of the amoA gene of AOA and AOB, and to quantify bacterial 16S ribosomal DNA and fungal 18S ribosomal DNA in the total DNA of the soil sample using an iCycler iQ 5 thermocycler (BioRad Laboratories, Hercules, CA). Primer sets amoA-1F/amoA-2R (Rotthauwe, Witzel, & Liesack, 1997), Arch-amoAF/Arch-amoAR (Francis, Roberts, Beman, Santoro, & Oakley, 2005), Eub338/Eub518 (Fierer, Jackson, Vilgalys, & Jackson, 2005) and ITS1f/5.8s (Fierer, Jackson, Vilgalys, & Jackson, 2005) were used for the amplification of the bacterial amoA gene, archaeal amoA gene, bacterial 16S ribosomal DNA, and fungal 18S ribosomal DNA fragments, respectively. qPCR was performed using the temperature profiles described in Supporting Information Table S2. Standard curves for real-time PCR assays were made as described by Di et al. (2009) for AOA and AOB and Fierer et al. (2005) for bacteria and fungi, respectively.
2.3 Experiment 2. A mesocosm experiment assessing the impact of plant roots and AMF on AOA and AOB
This experiment was conducted in a greenhouse at North Carolina State University (NCSU), Raleigh, North Carolina. Two sources of soils [organically managed soil (OM) and conventionally managed soil (conventional)] were employed to examine how plant roots and their associated AMF affected the abundances of AOA and AOB. These soils were collected from two farming systems at the Center for Environmental Farming Systems at NCSU (35°22′ N, 78°02′ W) that was established in 1999 in Goldsboro, North Carolina. While the conventional fields had been applied with mineral N, the organically managed fields had received organic manures and cover crops only since 1999 (Mueller et al., 2006; Tu, Louws, et al., 2006). Both fields were planted with corn (Zea mays L.) prior to soil collection in 2014. Soil samples were partially air-dried and sieved through a 4 mm sieve. The OM and conventional soils contained 24 and 21 μg/g of inorganic N, 178 and 42 μg/g of labile C, and had pH values of 6.50 and 5.50, respectively.
We employed hyphae- or root-ingrowth techniques to examine how the presence of AMF only or roots with their associated AMF affects AOA and AOB abundances in the two soils described above. Plexi-glass mesocosms were used to manipulate roots and/or mycorrhizae, and each microcosm was divided into six compartments with each compartment measuring 13 × 14 × 15 cm (width × depth × height; Tu, Booker, et al., 2006). Three compartments in a row were designated as HOST compartments (containing host plants inoculated with AM fungi) and the three adjacent compartments were designated TEST compartments to assess the effects of AMF and/or roots on AOA and AOB. The HOST and TEST compartments were separated by a replaceable mesh fabric panel (Tetko/Sefar mesh, Sefar America, Buffalo, NY) that prevented plant roots or both roots and AMF hyphae from growing into the TEST compartments, respectively. Consequently, this leads to three treatments of AMF and/or roots based on whether the TEST soil is accessible by roots and/AMF from plants in the HOST compartment: (a) the control with no penetration of plant roots and AMF to the TEST soil (CK), (b) penetration of AMF hyphae to the TEST soil (AMF) and (c) penetration of both AMF hyphae and plant roots to the TEST soil (Root). Three different mesh screens, that is, 0.45 µm, 20 µm and 1.6 mm, were used for the CK, the AMF treatment and the Root treatment, respectively.
The AMF inoculum was a mixture of multiple AM fungal species that were trap-cultured from an agricultural soil, collected from the Center for Environmental Farming Systems at NCSU, and was then pot-cultured to increase fungal biomass. Twelve AM fungal species were identified and characterized according to the International Culture Collection of (Vesicular) Arbuscular Mycorrhizal Fungi (INVAM) (Tu, Booker, et al., 2006). The AMF inoculum consisted of culture media containing spores, hyphae, and colonized root pieces.
Each compartment was filled with 3.5 kg soil mixed with 100 g AMF inoculum. Organically managed soil was applied with chicken manure at a rate of 12.5 t/ha, while conventional soil was supplied with urea at a rate of 180 kg N/ha. The chicken manure (Microstart60, Perdue AgriRecycle LLC, Kings Mountain, NC) was in the form of pellets with a ratio of 3:2:3 for N, P2O5 and K2O, respectively, and was finely ground and mixed with soil before the corn seeds were sown. For the conventional soil, half of the urea fertilizer (dissolved in deionized water) was applied to the soil prior to seed sowing and the remaining half was applied 4 weeks later. Each time, urea was dissolved in water and then applied to the soil. All sides of all mesocosm units were covered in aluminium foil to block the lights throughout the experimental period.
Four corn seeds (Zea mays L., variety F1 Incredible) were sown in each of the HOST compartments on 4 March 2014 (days after sowing 0 day: DAS0). The mesocosm units were placed in a temperature-controlled glasshouse in a randomized block design. All treatments were replicated three times. The mean daily temperature was 28 ± 2°C. To ensure 16-hr daylight for corns, overhead lights (400 W, Son-T Agro, Amsterdam, Netherlands) were used during the early evening hours. The photosynthetically active radiation flux was 505 mmol/m2/s with a day length of 16 hr. The plants were watered with deionized water as needed.
The corn plants were allowed to grow for 66 days. All plants were then harvested, dried and weighed, and subsamples were analysed for C and N contents. Soil samples from the TEST compartments were collected for analyses of AOA and AOB. Fresh soil (0.50 g) was extracted for nucleic acid using a FastDNA SPIN kit (MP Bio, Solon, OH) according to the manufacturer's instructions. Quantitative real-time PCR was performed on each soil sample (CFX96 Real-Time PCR Detection System, Bio-Rad, Hercules, CA) to determine the amoA gene copy numbers of AOA and AOB with the primer sets CrenamoA23f and CrenamoA616r (Tourna, Freitag, Nicol, & Prosser, 2008) and amoA-1F/amoA-2R (Rotthauwe et al., 1997), respectively. Standard curves for qPCR assays were made following serial dilutions of the plasmid DNA method (Qiu et al., 2018).
2.4 Statistical analyses
We analysed the data on the abundances of AOA and AOB, and soil properties from experiments 1 and 2 by one-way analysis of variance (ANOVA). Differences between treatments were analysed by using the least significance different test. All statistical analyses were conducted using spss v. 17.0. Data were log-transformed for normality when needed. Differences between treatments were considered significant at p < .05.
3 RESULTS
3.1 Meta-analysis: AOA and AOB abundances in global terrestrial ecosystems and potential drivers
Abundances of AOA and AOB varied markedly among different types of ecosystems, with the mean AOA being almost one order of magnitude higher than AOB across all ecosystems (Figure 1a,b). Averaged AOA abundances were 2.02 ± 0.32 × 108, 3.34 ± 0.46 × 108 and 2.43 ± 0.38 × 108 copies/g soil in forests, grasslands and croplands, respectively (Figure 1a). In contrast, mean AOB densities were 1.57 ± 0.64 × 107, 5.86 ± 0.12 × 107 and 4.45 ± 0.10 × 107 copies/g soil in forests, grasslands and croplands, respectively (Figure 1b). The difference was significant between ecosystems (Kruskal–Wallis H test, AOA: H(2) = 20.23, p < .001, forests versus grasslands, Q = 4.45, p < .001; forests versus croplands, Q = 2.19, p < .05; croplands versus grasslands, Q = −2.98, p < .05; AOB: H(2) = 57.18, p < .001; forests versus grasslands, Q = 7.31, p < .001; forests versus croplands, Q = 6.01, p < .001; croplands versus grasslands, Q = −2.39, p = .05) (Figure 1a,b).
Ammonium concentrations were negatively related to AOA (R2 = .072, p < .01) and AOB (R2 = .043, p < .01) abundances across all the ecosystems (Supporting Information Table S3 and Figure S3a,b), but these negative correlations mainly stemmed from cropland soils with high ammonium concentrations [log (NH4+) > 2] (Supporting Information Figure S3). In contrast, nitrate concentration was not related to AOB abundance (p > .05), but had a weak relationship with AOA (R2 = .013, p < .001) (Supporting Information Table S3). Positive correlations were observed between soil pH and both AOA (R2 = .045, p < .001) and AOB (R2 = .123, p < .001) abundances (Supporting Information Figure S4). However, AOB was more sensitive to low pH than AOA, as shown by the steeper slope between soil pH and AOB abundance than that between soil pH and AOA (Supporting Information Figure S4). AOA abundance nonlinearly increased with increasing total organic C (R2 = .071, p < .001), whereas no relationship between AOB and total organic C was detected (Supporting Information Figure S5). Surprisingly, soil C/N ratios were negatively correlated with both AOA (R2 = .123, p < .001) and AOB (R2 = .214, p < .001) abundances (Figure 1c,d, and Supporting Information Table S3). These significant negative relationships between soil C/N ratios and AOA or AOB were also observed in all three types of ecosystems (Figure 1c,d).
We subsequently conducted a SEM analysis to examine relationships between the resource/environmental factors and AOA or AOB abundances (Figure 2). The best SEM model (χ2 = 0.527, df = 1, p = .468; RMSEA = 0.000, GFI = .999, AIC = 40.527) explained 56.0 and 51.7% of the variations in AOA and AOB, respectively. In particular, soil C/N ratio alone explained 43.5% and 33.3% variations in AOA and AOB, respectively. Soil C/N ratios also indirectly influenced AOA and AOB abundances through soil pH (Figure 2). In addition, there was no significant correlation between soil ammonium concentrations and AOB abundance. Finally, total organic C was significantly positively correlated with AOA (R2 = .49.6; p < .001) but not AOB abundance (p > .05) (Figure 2).
3.2 Experiment 1: impact of cellulose and mineral N amendments on AOA and AOB
The AOA abundance in PINE and VF soils was 6.71 ± 0.25 × 107 and 3.83 ± 1.05 × 108 copies/g soil, respectively, while the AOB abundance was 3.26 ± 0.80 × 105 and 7.70 ± 1.08 × 107 copies/g soil, respectively. Despite the marked differences in soil pH and initial AOA and AOB abundances in these two soils, the C/N ratio of the C and N amendments significantly affected their AOA and AOB abundances with a similar pattern: the highest AOA and AOB occurred at the lowest C/N ratio of 10 and the lowest ones occurred at the highest C/N ratio of 100 (Figures 3 and 4). Over the 90-day incubation period, AOA and AOB remained largely unchanged in all the controls and the treatments with the lowest C/N ratio (10) amendment, but decreased by one to three orders of magnitude in soils with the highest C/N ratio (100) amendment (Figures 3 and 4). In particular, with both the highest cellulose and N inputs (C/N = 100), AOA decreased by 99.9 and 99.6%, and AOB decreased by 48.3 and 96.1%, respectively, as compared to their respective controls in PINE and VF soils.
Cellulose and N inputs stimulated fast growth of most microbes, increasing MBC and activity (respiration) (Supporting Information Figures S6 and S7). Fungal abundance significantly increased with increasing C/N ratio of the amendments (Supporting Information Figures S8 and S9). The AOA abundance was positively correlated with soil NO3− concentration, but negatively correlated with MBC and fungal abundance in both soils. Similarly, the AOB abundance positively correlated with soil NO3− concentration, but negatively correlated with soil microbial respiration, fungal abundance and MBC (only in the VF soil; Table 1).
| MBC | SR | Fungi | Bacteria | NH4+ | NO3− | AOA | |
|---|---|---|---|---|---|---|---|
| PINE | |||||||
| SR | .47** | ||||||
| Fungi | .61** | .47** | |||||
| Bacteria | .51** | .56** | .46** | ||||
| NH4+ | −.12 | .21 | −.13 | .14 | |||
| NO3− | −.51** | −.39** | −.43** | −.67** | −.08 | ||
| AOA | −.54** | −.23 | −.69** | −.29* | .18 | .41** | |
| AOB | −.10 | −.41** | −.26* | −.25* | −.24* | .40** | .26* |
| VF | |||||||
| SR | .50** | ||||||
| Fungi | −.28* | −.09 | |||||
| Bacteria | .47** | .58** | .01 | ||||
| NH4+ | .58** | .32** | −.20 | .55** | |||
| NO3− | .35** | .04 | −.25* | −.06 | −.06 | ||
| AOA | −.37** | .07 | −.44** | −.07 | .06 | .49** | |
| AOB | −.48** | −.37** | −.47** | −.18 | −.32** | .24* | .62** |
- Abbreviations: MBC = microbial biomass carbon; PINE = a pine plantation; SR = soil microbial respiration; VF = a vegetable field soil.
- * p < .05;
- ** p < .01.
3.3 Experiment 2: impacts of plant roots and AMF on AOA and AOB
The AOA abundance (as quantified by amoA gene copies) was 9.91 ± 1.14 × 108 and 9.61 ± 0.52 × 108 copies/g soil (Figure 5a,b) in conventional and OM soils, respectively. In comparison, the AOB abundance (again by amoA gene copies) was significantly higher in the conventional (3.26 ± 0.73 × 108 copies/g soil) than the OM soil (2.76 ± 0.13 × 108 copies/g soil) (Figure 5c,d). In the OM soil, the presence of AMF alone or AMF with roots significantly reduced AOA but did not have significant effects on AOB (Figure 5a,c), suggesting that AMF might compete against AOA for small organic N compounds or low concentration of mineral N. In the conventional soil, in contrast, AMF had no effect but the presence of roots significantly reduced both AOA and AOB (Figure 5b,d), implying that root competition dominated.
4 DISCUSSION
Results from our meta-analysis showed that soil C/N ratios, rather than soil NH4+ and pH, explained the most of variance in AOA and AOB abundances (Figures 1 and 2), which contrasts with predictions from the existing models (Prosser & Nicol, 2012). Because organic C is not the primary energy or nutrient source of AOB and AOA (Belser, 1979; Jia & Conrad, 2009; Pratscher et al., 2011), the impact of substrate C/N ratio may largely occur through altering biotic interactions that control N (ammonia) availability. Effects of biotic interactions on the abundances of AOA and AOB have, however, so far been largely overlooked (Chen et al., 2013; Daebeler et al., 2014; Jiang et al., 2014). Soil microbes and plant roots may affect AOA and AOB through multiple mechanisms, including direct competition for N substrates (Chen et al., 2013; Veresoglou, Sen, Mamolos, & Veresoglou, 2011), suppression of AOA and AOB by plant root exudates (Gopalakrishnan et al., 2009; Subbarao et al., 2015; Zakir et al., 2008) and/or microbial secondary compounds (Bahram et al., 2018), and alteration of soil environmental conditions (Stopnišek et al., 2010).
The concurrence of high soil microbial biomass (Supporting Information Figure S6) and heterotrophic respiration (Supporting Information Figure S7) with low AOA and AOB (Figures 3 and 4) in our high C/N ratio treatments indicated that heterotrophs may directly compete against AOA and AOB for available N. Most soil microbes (both bacteria and fungi) are heterotrophs that rely on organic C as their energy source, while requiring N and other elements to balance their growth (Bardgett, Bowman, Kaufmann, & Schmidt, 2005; Fanin, Hättenschwiler, Schimann, & Fromin, 2015; Xu et al., 2014). The relative availability of C to N is an important parameter that regulates the growth and functioning of heterotrophs (Bardgett et al., 2005). Although there is little empirical evidence to directly support the correlation between the soil C/N ratio and ammonia oxidizers, a substantial body of research has specifically examined the responses of soil nitrification and ammonia oxidizers to NH4+ addition (Giguere, Taylor, Myrold, & Bottomley, 2015; Ouyang, Norton, Stark, Reeve, & Habteselassie, 2016) or organic C amendment (Bernhardt & Likens, 2002; Strauss & Lamberti, 2000; Zeng, DeGuardia, Ziebal, DeMacedo, & Dabert, 2013). Ammonium fertilizer additions generally increased nitrification and stimulated and/or maintained AOA and AOB growth and activity (Giguere et al., 2015; Ouyang et al., 2016; Taylor, Zeglin, Dooley, Myrold, & Bottomley, 2011). In contrast, organic C amendments often inhibited nitrification rates, and the inhibition was more significant under high than low quality of organic C (Bernhardt & Likens, 2002; Che et al., 2018; Soper et al., 2018; Strauss & Lamberti, 2000; Zeng et al., 2013). These contrasting effects of NH4+ addition and organic C amendment on nitrification and/or nitrifiers indirectly support our results observed in this study, suggesting that competition from heterotrophs for available N can be a major factor regulating AOA and AOB abundances and activities.
We also observed that plant roots and their associated AMF significantly affected AOA and AOB (Figure 5). While AMF or AMF with roots suppressed AOA only in the organic soil (Figure 5a,b), roots suppressed both AOA and AOB in the conventional soil (Figure 5c,d). Several previous studies have shown that AMF can impact AOA and/or AOB, but AOA and AOB appeared with different sensitivities to AMF in different experiments. For example, Veresoglou et al. (2011) reported that AMF associated with Plantago lanceolata likely competed against AOB and led to a shift in the AOB community. In contrast, Chen et al. (2013) reported that inoculation of alfalfa plants (Medicago sativa L.) with the AM fungus Glomus intraradices affected AOA but not AOB. Our results of significantly lower AOA in the presence of AMF suggest that AMF may compete against AOA for low concentrations of N in the organic system where organic N was the primary N source. Many experiments have shown that plant roots can have significant effects on ammonia oxidizers and their activities (Coskun, Britto, Shi, & Kronzucker, 2017a,2017b; Gopalakrishnan et al., 2009; Subbarao et al., 2015; Zakir et al., 2008). While root uptake of NH4+ directly reduces N substrates for ammonia oxidizers, root exudates of some plant species are inhibitive to ammonia oxidizers (Coskun et al., 2017a,2017b; Gopalakrishnan et al., 2009), suppressing nitrification in soil (Subbarao et al., 2015; Zakir et al., 2008). Our results of root suppression of both AOA and AOB in the conventional system with pulse mineral N inputs imply that roots may exert a dominant role on AOM in N-rich agroecosystems. Together, these findings suggest that plant roots and AMF may play a significant role in shaping the community and activities of AOA and AOB in the rhizosphere or mycorhizosphere.
In addition, the negative correlation between soil C/N and soil pH in soils (mainly forests systems; Supporting Information Figure S11) suggests that plants and heterotrophic microbes may interact to affect the soil environment and thus constrain ammonia oxidizing microbes through altering pH (Stopnišek et al., 2010). Organic residues in many forest ecosystems, particularly under coniferous plants, have a high C/N ratio. Microbial decomposition of high C/N materials and humification produce organic acids and the products of the humification process such as fulvic acid and humic acid have strong carboxylic acidity (Sánchez-Monedero, Cegarra, García, & Roig, 2002; Ussiri & Johnson, 2003). Fungi dominantly control the decomposition of organic materials with high C/N (Henriksen & Breland, 1999; Thiet, Frey, & Six, 2006), acidifying their microenvironment by excretion of organic acids or by excretion of protons via the plasma membrane proton translocating ATPase (Jellison et al., 1997). Yet, biotic modulation of soil pH has rarely been explicitly integrated into any models to explain AOA and AOB abundances or distributions.
We thus propose a new hypothetical framework in which biotic interactions among plants, heterotrophic microbes and nitrifying microbes are the dominant control upon the population density of AOA and AOB (Figure 6b) and soil C/N ratios function as a critical mediator (Figure 6a). Plant roots, heterotrophic microbes, and nitrifiers all compete for ammonium in soil (Verhagen, Laanbroek, & Woldendorp, 1995). The soil C/N ratio, to a large degree, reflects the degree of N limitation in an ecosystem (Chapin et al., 2011). In soils with low C/N ratios, low C availability is the primary limiting factor to heterotrophic microbes, and these microbes likely release a fraction of organic N as ammonium (Kuzyakov & Xu, 2013; Mooshammer et al., 2014). In soils with high doses of reactive N pulse inputs such as N fertilization, available N is much higher than the needs of plants and microbes, allowing AOB and likely AOA to flourish (Figure 6a,b). In soils where organic C is in relative excess over available N, in contrast, intensive competition from heterotrophic microbes and plant roots for N may constrain AOA and AOB (Chen et al., 2013; Horz, Barbrook, Field, & Bohannan, 2004; Veresoglou et al., 2011). In particular, high C/N environments promote fungal dominance in the soil microbial community (as shown in Supporting Information Figure S10; Soper et al., 2018). This is because many fungi have higher biomass C/N ratios than bacteria (de Vries, Hoffland, Van Eekeren, Brussaard, & Bloem, 2006; Fierer, Strickland, et al., 2009; Holland & Coleman, 1987) and can recycle N within the mycelium via cytoplasm translocation or self-lysis (Frey, Elliott, Paustian, & Peterson, 2000; Hu & van Bruggen, 1997). Also, fungi possess a high physiological capacity to decompose recalcitrant organic matter (de Boer, Folman, Summerbell, & Boddy, 2005). Moreover, mycorrhizal fungi, which form symbioses with over 80% of terrestrial plants, likely reach decomposing hotspots and utilize small molecular organic N (Bukovská et al., 2018; Cheng et al., 2012) and bypass the last step of N mineralization, thereby limiting NH4+ production (Figure 6b) and further intensifying the competition for N between heterotrophs and AOA and/or AOB (Che et al., 2018).
5 CONCLUSIONS
Our synthesis, for the first time, provides a framework highlighting the dynamic, interactive controls of soil abiotic and biotic factors over AOA and AOB. In this framework, the soil C/N ratio is an integrated indicator of the relative availability of organic C to mineral N that regulates the intensity of competition among plants, heterotrophic microbes and ammonia-oxidizing microbes. In N-limiting systems, plants and their symbiotic mycorrhizal fungi may critically affect AOA and AOB in the rhizosphere or mycorrhizosphere. In N-rich systems, enhancement of heterotrophic microbes through increasing soil C availability can exert major impacts on AOA and AOB. Rapid AOA and AOB decreases in response to high cellulose and mineral N inputs observed in our study illustrate that altering the relative availability of C to N for heterotrophic microbes may result in changes in AOA and AOB in a time frame relevant to field N management, particularly in the face of increasing N applications and/or deposition under future climate change and agricultural production scenarios.
ACKNOWLEDGMENTS
The authors are very grateful to Dr. F. Stuart Chapin III and Dr. Mary K. Firestone for their constructive inputs to the conceptual model. The authors thank Chunhui Zhang and Hongquan Song for statistical analysis support. We are also grateful to many people who were involved in sampling and maintaining the long-term field experiments but are not listed as coauthors. This work was partially supported by National Natural Science Foundation of China (grant no. 31600383), National Key R&D Program of China (grant no. 2017YFC0503902), a China Scholarship Council scholarship to Yunpeng Qiu (CSC no. 201306320137), and a grant from NIFA, USDA (USDA-2012-02978-230561) to Shuijin Hu.
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Open Research
DATA AVAILABILITY STATEMENT
The database compiled for this study is available in Supporting Information Appendix S1.
REFERENCES
Biosketches
Rui Xiao is a PhD student in microbial ecology and her dissertation research focuses on microbial nitrogen transformations in terrestrial ecosystems.
Shuijin Hu is an ecosystem ecologist and is interested in global change ecology, mycorrhizae, plant–microbial interactions and their responses to climate change.
