Download Plant nitrogen-use strategy as a driver of

FEMS Microbiology Ecology, 92, 2016, fiw091
doi: 10.1093/femsec/fiw091
Advance Access Publication Date: 28 April 2016
Research Article
RESEARCH ARTICLE
Plant nitrogen-use strategy as a driver of rhizosphere
archaeal and bacterial ammonia oxidiser abundance
Cécile E. Thion1,2,∗ , Jessica D. Poirel1 , Thomas Cornulier1 ,
Franciska T. De Vries3 , Richard D. Bardgett3 and James I. Prosser1
1
Institute of Biological and Environmental Sciences, University of Aberdeen, Cruickshank Building, St. Machar
Drive, Aberdeen, AB24 5UU, UK, 2 Laboratoire Ampère, CNRS UMR 5005, École Centrale de Lyon, University of
Lyon, 36 avenue Guy de Collongue, 69134 Ecully, France and 3 Faculty of Life Sciences, Michael Smith Building,
The University of Manchester, Oxford Road, Manchester, M13 9PT, UK
∗
Corresponding author: Laboratoire Ampère, CNRS UMR 5005, École Centrale de Lyon, Université de Lyon, 36 avenue Guy de Collongue, 69134 Ecully,
France. Tel: +0033-4-72-18-60-99; Fax: +0044-1224-272-703; E-mail: [email protected]
One sentence summary: The effect of plants on relative abundances of AOA and AOB was linked to plant N concentration, suggesting greater success of
AOA than AOB when competing with plants for ammonia.
Editor: Riks Laanbroek
ABSTRACT
The influence of plants on archaeal (AOA) and bacterial (AOB) ammonia oxidisers (AO) is poorly understood. Higher
microbial activity in the rhizosphere, including organic nitrogen (N) mineralisation, may stimulate both groups, while
ammonia uptake by plants may favour AOA, considered to prefer lower ammonia concentration. We therefore hypothesised
(i) higher AOA and AOB abundances in the rhizosphere than bulk soil and (ii) that AOA are favoured over AOB in the
rhizosphere of plants with an exploitative strategy and high N demand, especially (iii) during early growth, when plant N
uptake is higher. These hypotheses were tested by growing 20 grassland plants, covering a spectrum of resource-use
strategies, and determining AOA and AOB amoA gene abundances, rhizosphere and bulk soil characteristics and plant
functional traits. Joint Bayesian mixed models indicated no increase in AO in the rhizosphere, but revealed that AOA were
more abundant in the rhizosphere of exploitative plants, mostly grasses, and less abundant under conservative plants. In
contrast, AOB abundance in the rhizosphere and bulk soil depended on pH, rather than plant traits. These findings provide
a mechanistic basis for plant-ammonia oxidiser interactions and for links between plant functional traits and ammonia
oxidiser ecology.
Keywords: ammonia-oxidising archaea; ammonia-oxidising bacteria; grasslands; plant traits; resource-use strategy;
rhizosphere effect
INTRODUCTION
Archaeal (AOA) and bacterial (AOB) ammonia oxidisers (AO) oxidise ammonia (NH3 ) to nitrite (NO2 − ), which is further oxidised
to nitrate (NO3 − ) in the final stage of nitrification. This process causes worldwide losses of nitrogen fertiliser, eutrophication of groundwater and emission of the greenhouse gas nitrous
oxide (Schlesinger 2009). AOA and AOB cohabit the vast majority of terrestrial ecosystems (Norton 2011; Prosser 2011), but
their relative abundance and contribution to nitrification in soil
vary widely, suggesting that they may occupy different ecological niches (Prosser and Nicol 2012). The most important factors determining this niche specialisation are believed to be (i)
soil pH, with AOA generally predominant in acidic soils (Nicol
Received: 3 February 2016; Accepted: 20 April 2016
C FEMS 2016. All rights reserved. For permissions, please e-mail: [email protected]
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 7
et al. 2008; Gubry-Rangin, Nicol and Prosser 2010; Yao et al. 2013;
Baxendale et al. 2014) and (ii) ammonia concentration, with limited evidence that AOA have a greater affinity for ammonia and
greater sensitivity to inhibition by high ammonia concentration (Prosser and Nicol 2012). As a consequence, AOB are often
found to dominate in soil with greater inorganic N availability
(Di et al. 2009; Verhamme, Prosser and Nicol 2011; Meyer et al.
2013; Strauss, Reardon and Mazzola 2014).
Plants significantly impact soils through litter deposition, but
also through their roots, which influence microbial community
structure, nutrient availability, pH and soil structure (Bardgett,
Mommer and De Vries 2014). Nevertheless, little attempt has
been made to identify the mechanisms underpinning plant effects on AOA and AOB, and the scarce literature on plant-AO
interactions has generated contradictory results. Some studies
suggest that AOA, rather than AOB, could be favoured in the rhizosphere (Chen et al. 2008; Herrmann, Saunders and Schramm
2008; Hussain et al. 2011; Kleineidam et al. 2011), while others
indicate the reverse (Glaser et al. 2010; Wei et al. 2011; Trias
et al. 2012). While interactions between plants and pathogenic,
symbiotic or heterotrophic microbes are easier to conceptualise
(Hartmann et al. 2009), direct interactions between plants and
autotrophic ammonia oxidisers are less obvious. If the rhizosphere does affect the AO community, it may be linked to partitioning of soil inorganic N between them. To fulfil their N requirements, plants uptake the protonated form of ammonia,
ammonium (NH4 + ), which dominates in acidic soils, free amino
acids and, to a lesser extent, NO3 − (Näsholm, Kielland and Ganetaeg 2009). Uptake of NH4 + and amino acids may theoretically
reduce substrate availability for ammonia oxidation directly or
indirectly through reduction of N mineralisation. The magnitude
of plant-nitrifier competition for NH4 + relies partly on the availability and diffusion of the different forms of N in soil and may
be significant in low-N systems, such as low productivity, unfertilised grassland soils (Schimel and Bennett 2004), but there
is very little evidence for competition between plants and nitrifiers. Importantly, plant functional traits, in particular traits
linked to N-use strategy, could influence among AO.
Plant resource-use strategies are typically considered to be
‘conservative’ in slow-growing plants, with traits like low specific leaf area, low N-content, high dry matter content (DMC) and
low shoot:root biomass ratio, or ‘exploitative’ in fast-growing
plants, with the opposite traits including higher N requirements (Gitay and Noble 1997; Lavorel and Garnier 2002). There
is increasing evidence that plant resource-capture strategies
influence soil microbial communities and their functioning at
various spatial scales (Orwin et al. 2010; De Vries et al. 2012;
Baxendale et al. 2014; Legay et al. 2014; Moreau et al. 2015), and
modify chemical and physical characteristics of the rhizosphere
(Bardgett, Mommer and De Vries 2014). In the long term, plants
exhibiting more exploitative traits have been linked to higher
soil inorganic N availability (Orwin et al. 2010; Grigulis et al.
2013; Baxendale et al. 2014), potentially because they may release
more N through root exudation and litter deposition (Wedin and
Tilman 1990). Moreover, high photosynthetic activity in young
plants requires N accumulation in aerial parts, i.e. greater N uptake than adult plants (Caloin and Yu 1984; Agren and Franklin
2003). This suggests that young fast-growing plants, with greater
N uptake rates and N concentration (Greenwood et al. 1990;
Poorter et al. 1991; Masclaux-Daubresse et al. 2009), will compete
more strongly with soil microbes for soil N than more conservative strategists.
The effect of plants has mainly been studied in complex ecosystems and/or mixed plant communities, preventing
mechanistic insights into the role of plant traits on AO ecology. The aim of this study was to consider how plant resourceuse strategies may influence AOA and AOB abundances in the
rhizosphere. Specifically, we first hypothesised that, regardless of plant resource-use strategy or plant species, both AOA
and AOB abundances will be greater in the rhizosphere than
in the bulk soil, due to greater flux of ammonia through increased N mineralisation. Second, we hypothesised that AOA
preference for lower ammonium concentration will make them
more competitive than AOB in the rhizosphere of plants exhibiting more exploitative traits, including high N requirements. Finally, we propose that this advantage to AOA will
be greater during early vegetative growth, when plant uptake of N is greater, but will decrease during later growth,
when uptake is lower and release of N to the rhizosphere is
greater.
MATERIALS AND METHODS
Experimental set-up
The soil used in this study, a pH 5.11 (±0.25) silt loam of the
Brickfield 2 association (Avis and Harrop 1983), was sampled,
prepared and stored as described previously (Thion and Prosser
2014). Briefly, the top layer (0–20 cm) of a grassland soil, located
at Hazelrigg Weather Station, Lancaster University (UK), was
sampled in April 2013, air-dried, sieved, homogenised and stored
until use for planting. A total of 20 commonly occurring British
mesotrophic grassland C3 forbs and grasses were chosen, providing a wide range of functional traits related to plant resourceuse strategy, including C:N ratio and N concentration, as suggested by the TRY Plant Trait Database (http://www.try-db.org).
They comprised 12 grasses (Agrostis capillaris, Anthoxanthum odoratum, Cynosurus cristatus, Dactylis glomerata, Deschampsia cespitosa, Festuca rubra, Holcus lanatus, Lolium perenne, Phleum pratense,
Poa pratensis, Poa trivialis and Trisetum flavescens) and 8 forbs (Centaurea nigra, Filipendula ulmaria, Hypochaeris radicata, Leontodon
hispidus, Leucanthemum vulgare, Plantago lanceolata, Prunella vulgaris and Rumex acetosa). Legumes, which can influence AO community structure, abundance and activity (Malchair et al. 2010;
Le Roux et al. 2013), and C4 species, differing from C3 plants in
their photosynthetic N-use efficiency (Greenwood et al. 1990),
were excluded. None of the species used are known to produce
biological nitrification inhibitors (Subbarao et al. 2007), although
not all have been tested. Seeds (supplied by Emorsgate Seeds,
King’s Lynn, UK) were broadcast sown in large germination trays
(40 × 25 × 5 cm) in May and June 2013. Plants were incubated
in a growth chamber with simulated day/night cycles of 16/8
h and 24o C /15o C, respectively, and maintained at 80% water
holding capacity. Plantlets were transplanted to individual plastic cups (6 × 6 × 10 cm) after 4–6 weeks and grown under the
same conditions until final transplantation. In September 2013,
nine individuals per species were transplanted to individual pots
and nine pots were left unplanted (NP treatment), a total of 189
individual pots, arranged as detailed in Fig. S1, Supporting Information. Rhizosphere and bulk soil, ∼300 and 500 g, respectively, were separated by containing roots within a ‘rhizosphere
bag’ (12 × 12 cm) made of 35-μm nylon mesh (Normesh Limited, Oldham, UK) sealed by heating. NP pots were also equipped
with rhizosphere bags, to test the effect of the bags themselves.
Pots were incubated until destructive sampling of soil and
plant material, with three replicate pots for each plant species
and NP treatment, 6, 12 and 18 weeks after the start of the
experiment.
Thion et al.
3
Measurement of plant traits
Statistical analyses
Aerial and root biomass were collected separately for each plant.
Roots were thoroughly rinsed to remove residual soil and gently
dried by blotting with tissue. Fresh biomass was determined and
material was dried for 48 h at 60o C and reweighed for calculation
of DMC and aerial (FA ) and root (FR ) fractions. Aerial and root N
and C concentrations were determined using a Fisons NA-1500
NCS Analyser (Loughborough, UK) on 5 mg ground dry biomass
subsamples (3 min grinding with a Retsch Ball Mill MM200,
Hope Valley, UK). Total plant C and N concentrations were calculated using aerial and root C and N concentrations determined
by FA and FR in total plant biomass. Plant relative growth rate
(RGR) at harvest n was calculated according to the following
equation:
All analyses were performed using the program R 3.1.2 (Sarkar
2008; R Development Core Team 2013). Two complementary statistical analyses were performed. First, effects of plant type
(forbs or grasses) and time on plant traits and soil chemical parameters were tested by two ANOVAs followed by post hoc Tukey
tests for plants traits and Dunnett’s test with unplanted pots
as a reference for soil chemical characteristics. An exploratory
partial least square regression (PLS, R Package ‘plsdepot’, Tenenhaus 1998) was performed to explore correlations between plant
traits, soil pH, time and rhizosphere abundances of AOA and
AOB amoA.
Second, hypotheses were tested using Bayesian analysis.
Two multivariate generalised linear mixed models (GLMM), detailed in Supplementary Material, were specified and fitted using Markov chain Monte Carlo (R Package ‘MCMCglmm’, Hadfield 2010). To consider both intra- and interspecific variation,
variables were measured at the individual pot rather than the
plant species level. Both models considered the log abundance of
AOA and AOB amoA genes as distinct response variables, assuming a bivariate-normal distribution with kingdom-specific (i.e.
archaeal versus bacterial) variances and one residual (withinreplicate pot) covariance parameter to be estimated. PLS results
were used as a basis for selection of predictors for both models,
which were simplified by stepwise variable selection as detailed
in Supplementary Material and Results section. The multivariate GLMM approach allows estimation of the absolute response
of each AO domain to treatments while enabling a straightforward comparison of their relative responses. Residual covariances within pots account for non-independence between AOA
and AOB not explained by fixed or random effects in the model,
for instance resulting from direct competition or stimulation.
Model 1 was used to test the first hypothesis (a positive rhizosphere effect) and was fitted to both rhizosphere and bulk soil
data, to assess the difference between them. Model 2 was fitted
to rhizosphere data only, assessing both the second and third
hypotheses (i.e. AOA are favoured in the rhizosphere of plants
exhibiting exploitative resource-use strategy, particularly during early plant growth), by comparing effects on AOB to those
on AOA. To minimise bias due to non-controlled variations, detailed records of non-controlled events were taken during plant
growth and at harvest. These included the presence of earthworms in either rhizosphere or bulk soil (scored 0–5 worms per
soil type, 6 pots concerned) and colonisation of aerial parts by
aphids (scored 0–3, based on visual estimation, 12 pots concerned). Importantly, as phenological status greatly influences
plant traits, we recorded flowering (scored 0–4 flowers, 9 pots
concerned) and visible leaf withering or senescence (scored 0–1,
6 pots concerned) as other potentially confounding factors. Finally, the presence of roots in the bulk soil compartment, following growth over or through rhizosphere bags, was also recorded
as a confounding factor, and scored 0 (no root in the bulk soil) to
5 (large colonisation of the bulk soil). This occurred in 54 of 280
planted pots, in which bulk soil was well colonised (scored 4 or
5) in only 20 pots. These uncontrolled factors were included in
the initial models to assess their significance.
All effects were considered significant when the 95% highest posterior density interval of the parameter value did not include zero. Markov chains were run for 1500 000 iterations, with
thinning intervals of 100 iterations, and discarding the first 10
000 iterations. For all analyses, values of plant biomass, nitrogen
concentration, aerial fraction, DMC, RGR and soil pH were scaled
and centred. R-squared values for fixed and random effects on
RGRn = (ln Biomassn − ln Biomassn−1 ) / (Timen − Timen−1 ) .
(1)
As initial biomass values were unavailable, RGR at the first
harvest could not be directly calculated. In order to minimise
the number of missing values for modelling and use the best estimate available, RGR was calculated at first and third harvests,
according to differences in dry aerial biomass between weeks
12 and 6 and between weeks 18 and 12, respectively. A mean of
these two values was then used as the closest estimate of RGR
at the second harvest.
Soil chemistry
All of the soil in a rhizosphere bag was considered to be rhizosphere soil, as plant roots had colonised the whole bag, and was
analysed separately from the bulk soil. Each soil sample was
homogenised and subsamples were stored at –80o C and –20o C
for molecular and chemical analyses, respectively. Inorganic N
(combined NH4 + + NH3 and NOx (NO2 − + NO3 − )) concentration
was determined using flow injection analysis, as described in
Thion and Prosser (2014). Total soil N and C concentrations were
measured with an elemental analyser as described above for
plant biomass, except that 10 mg ground soil was used. The pH
of rhizosphere and bulk soil was also measured separately, in 1:5
soil:distilled water solutions.
Abundance of soil ammonia oxidisers
Rhizosphere and bulk soil DNA were extracted from 0.5 g
soil as described by Griffiths et al. (2000) with modifications
(Nicol et al. 2005), and raw extracts were stored at –80o C. Archaeal amoA gene abundance was determined by real-time PCR
in a RealPlex2 Mastercycler (Eppendorf, Stevenage, UK) using
R
R
QuantiFast
SYBR
Green PCR Master Mix (Qiagen, Crawley,
UK), as described in Thion and Prosser (2014). The abundance of
R
R
bacterial amoA genes was determined using QuantiTect
SYBR
Green PCR Master Mix (Qiagen, Crawley, UK) as described by
Gubry-Rangin, Nicol and Prosser (2010). DNA concentration was
estimated in raw extracts using a NanoDrop 1000 Spectrophotometer (Thermo Scientific, Loughborough, UK). Raw nucleic
acid extracts were diluted 20–50 times to reach <20 ng DNA
real-time PCR reaction−1 with DNase-free water, these two levels of dilution showing similar levels of real-time PCR inhibition (see Supplementary Methods). Two complete standard series, prepared as in Thion and Prosser (2014), were assayed for
both genes and for every real-time PCR reaction. Amplification
efficiency ranged from 81% to 89% and from 87% to 102% for AOB
and AOA amoA, respectively, with r2 values ≥0.98.
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 7
AOA and AOB abundances in each model were calculated according to the method developed by Nagakawa and Schielzeth
2013).
RESULTS
Plant traits and soil abiotic characteristics
Average N concentration varied considerably among species,
ranging from 5–25 to 4–12 mg g−1 aerial and root biomass, respectively (Fig. S2A and S2E, Supporting Information). High interspecific variability was also found for C:N ratio (Fig. S2C and
S2G, Supporting Information). Total plant N concentration and
aerial:root N ratio, i.e. the proportion of N allocated to aerial
parts, decreased significantly with time (data not shown). In
general, root N concentration was higher (P = 0.001; Fig. S2F, Supporting Information) and C:N ratio was lower (P < 10−14 ; Fig. S2H,
Supporting Information) in grasses than in forbs. As expected,
plant N decreased and plant biomass increased over time (Fig.
S3, Supporting Information), with strong negative correlation
between them (r = –0.75). DMC increased with time as expected
(P < 10−15 ) and was generally greater in grasses (P < 10–10 ; Fig.
S4A and S4B, Supporting Information). Aerial fraction (FA ) did
not vary significantly with time or plant type (Fig. S4C and S4D,
Supporting Information), and RGR was initially higher for forbs
than grasses, but this difference was no longer apparent after 18
weeks (Fig. S4E and S4F, Supporting Information).
Concentrations of NH4 + remained low (<8 μg N g−1 ) in all
treatments (Fig. S5A, Supporting Information). NH4 + concentration was significantly lower in the rhizosphere than in the
bulk soil after 6 weeks (P = 0.006), but not after 12 weeks. Since
NO2 − concentration was negligible (data not shown), NOx concentration was considered equivalent to NO3 − concentration.
NO3 − concentration was significantly lower in the rhizosphere
soil of all plants throughout the experiment (P = 0.012; Fig. S5B,
Supporting Information). Soil pH was 0.3–0.4 units higher under plants after 6 weeks (P < 10−15 ; Fig. S5C, Supporting Information), but not after 12 weeks, as pH increased in nonplanted soil. There was no significant effect of plant species,
plant type or time on total soil N or C concentration or soil C:N
ratio.
Relationships among plant traits and AO abundances
Rhizosphere AOA abundance varied with plant species: when
compared to bare soil, AOA amoA gene abundance was significantly greater under L. perenne and F. rubra, where both have
relatively high N (plants with the first and fourth highest total
N concentration, respectively, on average throughout the experiment) and lower abundance under H. lanatus, one of the low N
concentration plants (Fig. 1A). Plant species per se did not affect
AOB amoA abundance (Fig. 1B). Correlations between plant type,
plant traits, soil pH, time and AO abundance are illustrated in
the PLS correlation circle (Fig. 2). AOA and AOB amoA gene abundances in the rhizosphere were not correlated, indicating no direct facilitation or antagonism between them. Root and aerial
N concentrations, aerial fraction and RGR were positively correlated with AOA amoA abundance, in order of decreasing amplitude. Plant biomass, root and aerial C:N ratios, time and, to a
lesser extent, DMC were negatively correlated with AOA amoA
abundance. In contrast, only plant DMC and soil pH correlated,
positively, with AOB amoA abundance. Inclusion of plant species
in the PLS analysis did not change these relationships (Fig. S6,
Supporting Information).
Hypothesis testing
As plant N concentrations, total mass and C:N ratios were intercorrelated (positively or negatively, Fig. 2), only the strongest and
most biologically meaningful variable of this group, plant root
N, was selected as a predictor in Models 1 and 2, to avoid covariate co-linearity and inflation of standard error coefficients. Notably, plant biomass was almost equally strongly correlated with
AOA amoA abundance in the PLS analysis but, according to our
hypotheses, this correlation was assumed to arise from a root
N effect, as plant N concentration would decrease during plant
growth. Additionally, plant DMC, soil pH and harvest (equivalent
to time) were identified as independent predictors of both AOA
and AOB abundances (Fig. 2). All of these covariates and their interactions were initially included as predictors of AOA and AOB
amoA abundances. None of the measured uncontrolled factors,
even colonisation of bulk soil by roots in several pots, had significant effect on AO abundances and the inclusion of these factors in the models did not change the amplitude or direction of
the effects of the other variables, allowing their removal from
final models. Stepwise variable selection allowed simplification
of both models by removal of non-significant effects and interactions (see Supplementary Material for details).
Rhizosphere effect on AOA and AOB abundances
Model 1 tested the first hypothesis, i.e. rhizosphere AO > bulk
soil AO, including plant DMC, root N concentration, time, and
soil pH as explanatory variables (see Supplementary Material
for details of the modelling approach and outputs). r2 values for
Model 1 were 0.17 and 0.22 for fixed effects (root N, DMC, harvest
and soil pH) and 0.55 and 0.47 when including random effects
(replicate pot and plant species) for AOA and AOB, respectively.
There was no significant ‘rhizosphere effect’ on non-planted
soil, indicating that rhizosphere bags did not influence amoA
abundance and enabling removal of non-planted pots from the
analysis. Considering both bulk and rhizosphere soils, the model
indicated that AOA amoA abundance decreased with increasing
DMC, root N concentration and soil pH (P = 0.010, 0.006 and
0.026, respectively), and increased with time (P = 0.042). AOB
abundance was similar in rhizosphere and bulk soil (P = 0.09),
regardless of plant N-use strategy (no significant interaction between root N and rhizosphere effect on AOB abundance). In contrast, rhizosphere had no significant overall effect on AOA abundance (P = 0.40), but when taking into account interactions, root
N concentration strongly affected the amplitude and direction of
the rhizosphere effect on AOA (P < 10−5 ), decreasing (≤2.5 orders
of magnitude) and increasing (≤2 orders of magnitude) abundance under N poor and rich plants, respectively (Fig. 3). Indeed,
AOA abundance was strongly dependent on plant N, decreasing in the rhizosphere and increasing in bulk soil when root N
decreased (Figs 4A and S7A, Supporting Information). Moreover,
AOA abundance increased significantly over time (P = 0.042), particularly at low pH (P = 0.016). In contrast, AOB abundance was
independent of time (P = 0.611), and N concentration (Figs 3B
and S7B, Supporting Information), and was positively correlated
to soil pH (P = 0.006), although this effect decreased with time
(P = 0.028). Overall, Model 1 results disprove our first hypothesis.
Effect of plant resource-use strategy and time on AO
abundance in the rhizosphere
Model 2 tested the second hypothesis (i.e. AOA growth > AOB
growth in the rhizosphere of exploitative plants) and third
Thion et al.
5
Figure 1. Rhizosphere AOA (A) and AOB (B) amoA gene abundances in pots planted with 20 different species of grasses (yellow) and forbs (green) harvested 6, 12 and 18
weeks (darkening shades) after the experiment started. Intraspecific means of log10 values ±SE (N = 3) are presented. Plants are ordered on the x-axis in decreasing order
of total N concentration, on average throughout the experiment. Lol: Lolium perenne, Tri: Trisetum flavescens, PoaT: Poa trivialis, Fes: Festuca rubra, Phle: Phleum pratense,
PoaP: Poa pratensis, Fil: Filipendula ulmaria, Agro: Agrostis capillaris, Des: Deschampsia cespitosa, Cyn: Cynosurus cristatus, Dac: Dactylis glomerata, Leu: Leucanthemum vulgare,
Leo: Leontodon hispidus, Pru: Prunella vulgaris, Hol: Holcus lanatus, Hyp: Hypochaeris radicata, Plan: Plantago lanceolata, Cen: Centaurea nigra, Anth: Anthoxanthum odoratum
and Rum: Rumex acetosa.
hypothesis (i.e. this advantage will decrease as plants grow
older), fitting only rhizosphere data and comparing effects on
AOB and AOA amoA abundances (Supplementary Material). r2
values for Model 1 were 0.19 and 0.08 for fixed effects (root N,
DMC, harvest and soil pH) and 0.91 and 0.89 when including random effects (replicate pot and plant species) for AOA and AOB,
respectively. In accordance with Model 1, root N concentration
increased AOA but not AOB abundance (P < 10−5 ), suggesting
that exploitative plants favour AOA growth as predicted by our
second hypothesis. Importantly, for both AOA and AOB abundances, variance between plant species (0.32 and 0.02, respectively) was lower than variance within species (i.e. residual variance between replicate pots: 0.71 and 0.73, respectively), suggesting a greater effect of individual plant traits over species.
Besides, rhizosphere AOA abundance decreased (P = 0.006) and
AOB abundance increased (P < 10−5 ) as soil pH increased and
both communities exhibited opposite responses to plant DMC (P
= 0.036). Again, no direct competition with AOB was observed,
as AOA and AOB within a pot varied independently of each other
after accounting for fixed and random effects in the model. Finally, in contrast to the predictions of the third hypothesis, stimulation of AOA growth by root N did not depend on time.
DISCUSSION
Our major objective was to assess whether differential uptake of
inorganic N by plants with different functional traits influenced
the relative abundance of AOA and AOB in the rhizosphere. The
rhizosphere can be seen as an overcrowded megalopolis and
a hotspot of activity in soils. Plant roots stimulate rhizosphere
heterotrophs, activate mineralisation of soil organic N and rootderived N and increase ammonium flux; this is the basis of our
first hypothesis, that plants stimulate AO growth and there is
a greater AOA and AOB abundance in rhizosphere than bulk
soil. This was not supported by analysis of data using Model 1.
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 7
Rhizo
Bulk
6 weeks
12 weeks
6.5
7.0
7.5
18 weeks
6.0
Log10 AOA amoA gene
abundance g -1 soil
8.0
(A)
4
6
8
10
12
Root N content (mg g-1)
Rhizo
Bulk
6 weeks
12 weeks
6.5
7.0
7.5
18 weeks
6.0
Figure 2. Circle of correlations obtained from exploratory multivariate partial
least square (PLS) regression analysis. AOA and AOB amoA gene abundances
are response variables (blue arrows), and predictor variables used (red arrows)
were time, soil pH, plant type, plant DMC, plant RGR, aerial fraction, plant total
biomass, aerial and root C:N ratio and N concentration.
Log10 AOB amoA gene
abundance g -1 soil
8.0
(B)
4
8
10
12
2
1
0
1
Figure 4. AOA (A) and AOB (B) amoA gene abundances as a function of plant
root N concentration, in rhizosphere (pink shades for AOA and blue shades for
AOB) and bulk soil (grey shades for both AOA and AOB). Each dot represents the
intraspecific mean across three replicate pots and regression lines derived from
the first model are fitted for each harvest, assuming a value of pH fixed at the
mean for the data set. Circles, triangles and squares correspond to first, second
and third harvests, respectively. (NB: similar plot presenting all individual pot
data used for Model 1 is displayed as Fig. S7, Supporting Information.).
AOB
AOA
6 weeks
2
Rhizosphere effect
6
Root N content (mg g-1)
12 weeks
18 weeks
4
6
8
10
12
Root N content (mg g-1)
Figure 3. Rhizosphere effect on AOA (pink shades) and AOB (turquoise shades)
amoA gene abundances as a function of plant root N concentration. Circles, triangles and squares correspond to first, second and third harvests, respectively.
Rhizosphere effect was calculated as the difference between log10 -transformed
amoA gene abundance in rhizosphere and bulk soils: for instance, a positive effect of 1 on the chart indicates an order of magnitude higher abundance in the
rhizosphere than in the bulk soil. Regression lines derived from Model 1.
There was no rhizosphere effect on AOB abundance, and no
temporal changes, suggesting that AOB did not benefit from increased N supply from roots, and/or that plants and other microorganisms compete better for ammonia than AOB. Importantly, AOB abundance did depend on pH (Model 1, Fig. 5), which
may have limited AOB growth in this acidic soil (pH 5.11). In fact,
pH is an important factor controlling AO growth and niche differentiation between AOA and AOB, with acidic soils favouring
AOA over AOB (Prosser and Nicol 2012), and AOB may have behaved differently in less acidic conditions.
In contrast, there was a significant rhizosphere effect
on AOA, which was strongly dependent on root N, i.e. it
was generally positive for exploitative plants and negative
for more conservative strategists (Model 1, Fig. 5). While
this finding does not support our first hypothesis, which predicts
an unconditional positive rhizosphere effect, it does supports
our second hypothesis, that AOA perform well, and better than
AOB, in the rhizosphere of plants with high N demand. Competition for nutrients, especially N, between and within plants,
AO and other microorganisms may be stronger in the crowded
rhizosphere than in the bulk ‘countryside’ (Kaye and Hart 1997;
Hodge, Robinson and Fitter 2000; Hodge et al. 2000). Although
plants can use NO3 − as an inorganic N source, use of NH4 + is
more efficient. This potentially reduces substrate availability for
AO, especially in relatively low N environments such as those
used in this study (Schimel and Bennett 2004; Näsholm, Kielland and Ganetaeg 2009). The ability of plants to take up amino
acids at relatively high rates, even in the presence of inorganic
N (Falkengren-Grerup, Månsson and Olsson 2000; Thornton and
Robinson 2005; Harrison, Bol and Bardgett 2007), could also result in the reduction of NH4 + available for AO, through limitation
of amino acid N mineralisation. The classical view that plants
Thion et al.
7
Figure 5. Summarised effect of soil pH, plant N and soil compartment (rhizosphere versus bulk) on AOA (purple) and AOB (blue) abundances. The radius of each
semicircle corresponds to the predicted relative amoA gene abundance under the stated conditions, as a percentage of the average abundance shown at the bottom
for reference, i.e. of the average AOA or AOB amoA gene abundance in all the conditions (i.e. 4.78 × 106 and 1.01 × 107 genes g−1 soil for AOA and AOB, respectively).
Defined root N concentration values, corresponding to the 10th and 90th percent quantiles of root N concentrations in our experiment, were used as proxy for low and
high N plants, respectively. Defined soil pH values, corresponding to the 10th and 90th percent quantiles of soil pH in our experiment, were used as proxy for low and
elevated pH, respectively.
are inferior to microbes in competing for N, irrespective of its
form (Näsholm, Kielland and Ganetaeg 2009), generally focuses
on heterotrophic microbes and is challenged by studies showing significant effects of plant N uptake and plant functional
traits on leaching of inorganic N (De Vries et al. 2015; De Vries
and Bardgett 2016), which consists mainly of water-soluble NO3 −
produced by nitrifiers.
The relative abundance of AOA, within AO, generally increases in low NH4 + environments, while AOB are more abundant in fertilised soils (Verhamme, Prosser and Nicol 2011; Meyer
et al. 2013; Strauss, Reardon and Mazzola 2014). There is evidence
that AOA may have higher affinity (Km < 0.02 μM NH3 +NH4 + ) for
NH4 + than AOB (Km = 6–11 μM NH3 ; Prosser and Nicol 2012). In
contrast to AOB, AOA possess high-affinity NH4 + channel transporters (Offre et al. 2014; Lehtovirta-Morley et al. 2016a), with
affinity constants similar to those of plants (Km down to 0.5 μM;
Courty et al. 2015). Moreover, AOA may be more sensitive to high
ammonium concentration than AOB (Prosser and Nicol 2012),
although this assumption is challenged by the recent isolation
of an ammonia-tolerant, neutrophilic AOA (Lehtovirta-Morley
et al. 2016b). In this study, there was no evidence of a direct
link between AO abundance and total N (data not shown) or
NH4 + concentration in the rhizosphere. There was also no evidence for direct competition (i.e. antagonistic interaction) between AOA and AOB, as their respective abundances did not covary, but AOA growth was greater in the rhizosphere of exploitative plants, while AOB did not grow at all in the rhizosphere
(Model 2, Fig. 5). This illustrates how the suggested differential NH4 + concentration preferences between AOA and AOB may
impact their respective survival in real-life situations, here in
planted soils.
Our third hypothesis was based on observations that N uptake by plants is initially high, but decreases during a subsequent storage phase (Caloin and Yu 1984; Greenwood et al.
1990; Agren and Franklin 2003), predicting that any competitive advantage of AOA over AOB under exploitative plants will
decrease with time. However, there was no significant interaction between time and root N effects on AO abundance. N uptake was greater during early growth, as expected, with higher
aerial and root N concentration and lower soil NH4 + and NO3 −
for young plants, but stimulation of AOA growth under high N
plants did not decrease with time. The persistence of this positive effect on AOA growth may result from stimulation by exudation of organic N by roots, which increases with plant age
(Badri and Vivanco 2009). Indeed, AOA were shown to be stimulated by organic N in laboratory culture (Di et al. 2010; Tourna
et al. 2011; Levičnik-Höfferle et al. 2012) and soils (Di et al. 2010),
suggesting that some AOA are capable of mixotrophic growth
and/or depend on NH4 + production by soil heterotrophic partners with which they may be co-located. Stimulation by root N
exudation could explain the unexpected decrease in AOA abundance in bulk soil, while it increased in the rhizosphere under fast-growing, high-N plants, suggesting migration of AOA
between compartments. Similarly, the lower AOA abundance
under slow-growing low-N plants such as P. lanceolata, R. acetosa and C. nigra, compared to the bulk soil, could be linked to
lower input and/or higher uptake of organic N, such as amino
acids, by these plants. Indeed, amino acid turnover rate in the
rhizosphere depends on a number of biotic and abiotic factors, including plant species and potentially plant N-use strategy (Näsholm, Kielland and Ganetaeg 2009). Alternatively, the
lowest N plants also have highest root biomass and we cannot
8
FEMS Microbiology Ecology, 2016, Vol. 92, No. 7
exclude the possibility of inhibition of AOA growth in denser root
systems.
Most variability in rhizosphere AOB abundance between
plant species was explained by Model 2 (variance of 0.02) but
variance in AOA abundance between plant species was not negligible (0.32). For both models, r2 for fixed effects were moderate
(0.19 and 0.17 for AOA and 0.22 and 0.08 for AOB, for Models 1 and
2, respectively). r2 increased considerably when the random effects of individual replicates and plant species were taken into
account (0.55 and 0.91 for AOA and 0.47 and 0.89 for AOB, for
Models 1 and 2, respectively), showing an important variability in AO abundance resulting from differences between plant
species and individuals, and interactions with other abiotic and
biotic factors.
Our hypotheses considered AOA and AOB as two distinct
communities, with all taxa within each community sharing
common patterns of niche preferences, but plant functional
traits may also affect AOA and AOB community composition. Importantly, AO community structure may be influenced, by phytochemicals exuded by roots, through: (i) selection of different
competing heterotrophs (Hartmann et al. 2009) and (ii) selection of mixotrophic phenotypes in AO (Prosser and Nicol 2012).
The quantity and composition of organic compounds exuded by
plants also vary between species (Hertenberger, Zampach and
Bachmann 2002; Moe 2013) and cultivars (Lucas Garcı́a et al.
2001) and with nutrient availability (Carvalhais et al. 2011), plant
growth stage (Aulakh et al. 2001) and root colonisation by arbuscular mycorrhizal (AM) fungi (Timonen and Marschner 2006). AM
symbiosis is plausible here, as it is known to be stimulated in
low N soils (Veresoglou, Chen and Rillig 2012) and endogenous
AM spores were not killed before establishment of mesocosms.
Root AM colonisation has been shown to reduce the emission
of N2 O (Bender et al. 2014) and nitrification potential rate (Veresoglou et al. 2011). Although there is no direct evidence that
AM fungi increase plant N content (Veresoglou, Chen and Rillig 2012), we cannot exclude a possible impact of these important rhizosphere inhabitants. Finally, the existence of organisms
capable of oxidising ammonia to nitrate (Daims et al. 2015; van
Kessel et al. 2015) introduces the possibility of additional competitors for NH4 + in soil.
Plant functional traits are increasingly used as predictors of
plant effects on belowground communities and processes, especially in relation to transformation of C and N, but also including subsequent plant performance (Bardgett, Mommer and
De Vries 2014; De Deyn, Cornelissen and Bardgett 2008). For
instance, growth of a broad range of grassland plant species
was enhanced in soil conditioned by fast-growing, exploitative
grasslands species due to increased N mineralisation and inorganic N availability (Baxendale et al. 2014). Greater immobilisation of N by soil microbial biomass, and lower inorganic N
leaching suggest that more N is available for heterotrophs and
AO microbes under conservative than exploitative plants (Harrison, Bol and Bardgett 2008; Grigulis et al. 2013; De Vries and
Bardgett 2016). The strongest effects on almost all tested variables in our experiment were attributed to the N-richest plant,
L. perenne, which was previously associated with significant reduction of nitrification and N2 O emission (Abalos et al. 2014).
This was thought to be due to its high inorganic N uptake ability, reducing N availability for AO and denitrifiers, with greater
plant N uptake being associated with a reduction in abundance of nitrate-reducing bacteria (Moreau et al. 2015). Similarly,
the study above found variation in AOA, but not AOB, abundance
with plant species; even though this did not depend on plant N
concentration. In aerobic conditions, N2 O emission is mainly at-
tributable to ammonia oxidisers, among which AOB have much
higher N2 O production yield than AOA in N-amended soil (Hink,
Nicol and Prosser 2016). Our results provide the first evidence
for stimulation of AOA by exploitative plants, with potential impacts on ecosystem functioning, particularly N loss due to nitrification and N2 O emission.
Functional plant ecology aims to classify plant species with
respect to their ecological niche and functional role in the
ecosystem (Smith et al. 1993). In practice, all intermediate strategies exist and, at the individual level, plants can adapt their
resource-use strategy to environmental conditions, such as
changing climate, soil nutrient status or intra- and interspecific
competition with other plant individuals (Grassein, Till-Bottraud
and Lavorel 2010; Mitchell and Bakker 2014). In addition, N uptake and sequestration strategy of individual plants varies with
time, according to their phenological status (Neto et al. 2011;
Metay et al. 2015). Interaction with numerous other biotic factors
can also shape plant resource use. This individual variability in
N-use strategy is exaggerated at the ecosystem scale, with natural plant community composition being highly dynamic, both
spatially and temporally. This could account for the unexplained
variability of AO abundance and activity in natural grasslands
and other vegetated soils. For instance, our findings may explain the relative insensitivity of AOA growth to soil fertilisation
in the rhizosphere of barley, where AOB growth was dependent
on ammonium-based fertilisation and higher in bulk soil (Glaser
et al. 2010).
CONCLUSIONS
This study investigated, for the first time, the differential growth
of AOA and AOB in the rhizosphere, where flux of, and competition for ammonium are strong, within the context of plant
resource-use strategy. This was achieved through a controlled
experimental set-up and appropriate statistical modelling, limiting the impact of confounding factors and allowing a mechanistic view of plant-AO interaction. Our study provides a basis for plant-AO interactions and may increase understanding
of previous results obtained in less controlled conditions. Here,
we found that the effect of plants on the relative abundance
of AOA and AOB was linked to plant N-use strategy, suggesting greater success of AOA than AOB under exploitative plants
through competition for ammonia and niche specialisation between the two AO communities.
SUPPLEMENTARY DATA
Supplementary data are available at FEMSEC online.
FUNDING
We would like to acknowledge funding of the European Commission’s FP7 programme, EU-project Ecological Function and Biodiversity Indicators in European Soils ‘EcoFINDERS’, no. 264465.
Conflict of interest. None declared.
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