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Differential response of non-adapted ammonia oxidising archaea and
bacteria to drying rewetting stress
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Cécile Thion and James I. Prosser
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Institute of Biological and Environmental Sciences, Cruickshank Building, St Machar
Drive, University of Aberdeen, Aberdeen, AB24 3UU, UK
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Correspondence: Cécile Thion, Institute of Biological and Environmental Sciences,
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University of Aberdeen, Cruickshank Building, St. Machar Drive, Aberdeen AB24 3UU, UK.
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Tel.: 0044 1224 272 700; fax: 0044 1224 272 703; e-mail: cecile.thion@abdn.ac.uk
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Short title:
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Response of soil ammonia oxidisers to drying-rewetting
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Keywords:
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Ammonia oxidising archaea, ammonia oxidising bacteria, nitrification, drought, rewetting,
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resistance, resilience
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Abstract:
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Climate change is expected to increase the frequency of severe drought events followed by
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heavy rainfall, which will influence growth and activity of soil microorganisms, through
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osmotic stress and changes in nutrient concentration. There is evidence of rapid recovery of
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processes and adaptation of communities in soils regularly experiencing drying/rewetting and
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lower resistance and resilience in non-adapted soils. A microcosm-based study of ammonia-
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oxidising archaea (AOA) and bacteria (AOB), employing a grassland soil that rarely
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experiences drought, was used to test this hypothesis and also whether AOB were more
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resistant and resilient, through greater tolerance of high ammonia concentrations produced
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during drought and rewetting. Treated soils were dried, incubated for three weeks, rewetted,
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incubated for a further three weeks and compared to untreated soils, maintained at a constant
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moisture content. Nitrate accumulation and AOA and AOB abundance (abundance of
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respective amoA genes) and community composition (DGGE analysis of AOA amoA and
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AOB 16S rRNA genes) were poorly adapted to drying-rewetting. AOA abundance and
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community composition were less resistant than AOB during drought and less resilient after
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rewetting, at times when ammonium concentration was higher. Data provide evidence for
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poor adaptation of microbial communities and processes to drying-rewetting in soils with no
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history of drought and indicate niche differentiation of AOA and AOB associated with high
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ammonia concentration.
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Introduction
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Climate change resulting from increased global warming is predicted to lead to more
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frequent and severe periods of drought followed by heavy rainfall in many areas including
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those previously considered to have temperate climate (IPCC, 2007; Gornall et al., 2010).
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Microbial response to a disturbance, such as drying-rewetting, can be quantified in terms of
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two components: resistance, the ability of a microbial property to remain unchanged during
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disturbance, and resilience, the ability of this property to recover following the end of
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disturbance (Orwin & Wardle, 2005, Griffiths & Philippot, 2013).
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Drying-rewetting stress can significantly affect microbial communities and the
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biogeochemical cycles that they control (DeVries et al., 2012; Placella et al., 2012, Placella
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& Firestone, 2013; Barnard et al., 2013; Kaisermann et al., 2013; Pohlon et al., 2013).
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Drying of soil introduces diffusional constraints, leading to resource limitation (Stark &
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Firestone, 1995), and increases concentrations of salts in the soil solution, requiring
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accumulation of compatible solutes to counteract high osmolarity (Roeßler & Müller, 2001,
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Schimel et al., 2007). As a physiological stress, that can lead to hypotonic shock and through
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rapid and massive increases in C and N mineralisation, rewetting also impacts on microbial
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communities (Birch, 1964, Fierer & Schimel, 2003). Community changes following drying-
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rewetting may therefore reflect differences in tolerance to stress and increased concentrations
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of nutrients that may be linked to phylogeny (Placella et al., 2012, Evans & Wallenstein,
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2012, Barnard et al., 2013).
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As key players in the soil nitrogen cycle, ammonia oxidisers (AO) drive the first, rate-
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limiting step of nitrification (Prosser & Nicol, 2008) and can be used as a model functional
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guild to assess microbial response to climate change. The impacts of agricultural practice,
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oxygen limitation and pH on AO are frequently studied (Francis et al., 2007; Schleper &
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Nicol, 2010; Norton, 2011; Prosser, 2011; Prosser & Nicol, 2012), but little is known of their
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resistance and resilience to water stress. Soil ammonia oxidation is performed by both
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ammonia oxidising archaea (AOA) and ammonia oxidising bacteria (AOB), and attempts
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have been made to identify niche differentiation between these groups (Nicol & Schleper,
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2006; Prosser & Nicol, 2008, 2012). AOA and AOB cohabit most soils (Norton, 2011;
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Prosser, 2011), but the ratio of abundances of AOA and AOB amoA genes (encoding
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ammonia-monooxygenase subunit A) range from <1 (Jia & Conrad, 2009; Höfferle et al.,
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2010) to >100 (Gubry-Rangin et al., 2010; Zhang et al., 2010). This ratio tends to be higher
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in acidic soils (Nicol et al., 2008; Gubry-Rangin et al., 2010; Yao et al., 2013) and lower in
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intensively N fertilized soils (Verhamme et al., 2011; Meyer et al., 2013; Strauss et al.,
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2014). This may be due to the higher ammonia affinity of AOA and/or their greater
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sensitivity to ammonia inhibition, but direct evidence for operation of these mechanisms in
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soil is lacking (Prosser & Nicol, 2012).
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Differential responses of AOA and AOB to drying-rewetting stress result from restrictions
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to ammonia mobilisation during drought and a flush in nitrogen mineralisation and
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mobilisation of ammonia following rewetting, potentially reflected in nitrification rate,
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although links between AO community composition and nitrification rate are unclear
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(Prosser, 2012). Archaea and bacteria also have distinct cell structure and physiology,
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including osmoadaptation strategies (Zillig, 1991; Roeßler & Müller, 2001), which may lead
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to different physiological responses to drying-rewetting stress in AOA and AOB.
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Nevertheless, the widespread distribution of both groups in soils subjected to drought stress
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gives no a priori indication for this as a mechanism for niche differentiation between soil
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AOA and AOB.
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Placella & Firestone (2013) reported maintenance of activity of both AOA and AOB
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during drought in two Californian soils and increases in transcriptional activity of both
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groups within hours of rewetting, with AOB activity recovering faster. This rapid response
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could result from acclimation to drying-rewetting cycles, as both soils annually experience
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several months of drought before heavy autumn rainfall (Placella & Firestone, 2013).
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Evidence exists for a legacy effect of water stress, with a history of altered rainfall
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modulating microbial response to new drying-rewetting cycles (DeVries et al., 2012; Evans
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& Wallenstein, 2012, 2014; Göranson et al., 2013).
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The aim of the present study was to assess and increase mechanistic understanding of the
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resistance and resilience to drought of AO in a non-acclimated soil, i.e. one that rarely
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experiences drought. We hypothesised that 1) non-adapted AO are poorly tolerant to drying-
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rewetting stress and 2) AOA and AOB differ in their resistance and resilience to this stress.
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Specifically, we hypothesised that greater NH4+-N concentration during drought and
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following rewetting would benefit AOB, due to their proposed greater tolerance to high
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ammonia concentration. To test these hypotheses, microcosms containing a grassland soil,
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experiencing a mild Atlantic climate, were dried for three weeks, rewetted and allowed to
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recover for three weeks. AOA and AOB resistance and resilience indices were calculated by
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comparing differences in abundance, activity and community composition between stressed
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and control microcosms.
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Material & Methods
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Study site and soil preparation
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Soil was a silt loam of the Brickfield 2 association (Avis & Harrop, 1983), collected from
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the 0 – 15-cm layer of a grassland located at Hazelrigg Weather Station, Lancaster University
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(England; 54º North 1’ 50”, 2º West 6’ 30”) in April 2013. This site experiences a mild
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Atlantic climate, with a narrow range of precipitation (average annual rainfall: 1,121 mm
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year-1 with 122 dry days and 159 days of rainfall ≥1 mm day-1) and temperature (average
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annual temperature: 9.6 ºC, min: 1.1 ºC, max: 19.4 ºC). After sampling, soil was dried in
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open air at 14ºC for 5 days, reaching a water content of ~20%, sieved (3.5 mm-diameter
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mesh), homogenised and stored at 4ºC until use. At the beginning of the experiment, water
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retention capacity (WRC) was 40.0 ± 6.6 g water g dry soil-1, pHwater was 5.13, total C and N
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were 22.9 and 1.9 g kg-1 dry soil, respectively, and combined ammonium-ammonia (NH4+ +
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NH3) and nitrate (NO3-) were 4.4 and 15.9 μg N g-1 dry soil, respectively.
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Experimental design
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Soil microcosms were established in sealed, 100-ml, sterile Duran glass bottles containing
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10 g equivalent dry soil, watered with sterile distilled water to reach an initial moisture
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content of 33% (g g dry soil-1). This corresponded to 82% WRC and a matric potential of -
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0.32 ± 0.04 MPa, as measured using a WP4C Dewpoint PotentiaMeter (Decagon, Pullman,
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UK). Microcosms were incubated in the dark at 30ºC and aerobic conditions were maintained
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by removing seals for 5 - 10 minutes every third day. After incubation for two weeks
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(‘acclimation period’), half of the microcosms (Stressed) were submitted to drought for three
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weeks by replacing the bottle plastic lids with sterile cotton wool plugs, allowing water to
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evaporate. Moisture rapidly decreased in these microcosms and reached a minimum of 11%
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after 7 days, corresponding to 27% WRC and -0.71 ± 0.06 MPa water potential. Throughout
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the experiment, moisture content was maintained in the remaining microcosms (Control) by
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weighing and readjusting to the initial mass with sterile distilled water. At the end of the
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‘drought period’, Stressed microcosms were rewetted to the initial moisture content using the
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same method and kept moist for 3 weeks after replacing the plastic lids (‘recovery period’).
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Triplicate microcosms were destructively sampled after 0, 7 and 14 days during the
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Acclimation period, then triplicate microcosms for each condition (Stressed and Control)
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were destructively sampled 7, 14 and 21 days after the Drought period started and finally 15
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min, 30 min, 1 h, 3 h, 9 h, and 1, 3, 7, 14 and 21 days after rewetting, i.e. during the Recovery
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period, leading to a total of 87 microcosms. For each microcosm, 2 g of sampled soil were
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stored at -80ºC for molecular analysis and the remaining soil was stored at -20ºC for chemical
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analysis.
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Chemical analysis
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Soil inorganic N, i.e. combined NH4+ + NH3 and NOx (NO2- + NO3-), concentrations were
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determined colorimetrically by flow injection analysis (FIA star 5010 Analyser, Foss Tecator
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AB, Höganäs, Sweden) (Allen, 1989) after extraction from 4 g of wet soil in 40 ml of 1 M
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KCl. As NO2- concentration was negligible, NOx is expressed as μg NO3--N g-1 dry soil. Net
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nitrification rate, i.e. rate of nitrate accumulation (Killham, 1990), was calculated by linear
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regression of NO3--N concentration vs. time and expressed as μg NO3--N g-1 dry soil day-1. To
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characterise soil and identify potential loss of N through denitrification, total N and C
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concentrations were determined using a Fisons NA-1500 NCS Analyser on 10 mg dry ground
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soil subsamples (Loughborough, UK).
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Molecular analysis
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Nucleic acids were extracted from 0.5 g soil as described by Griffiths et al. (2000) with
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modifications (Nicol et al., 2005). cDNA was prepared by DNAse treatment and RNA
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reverse-transcription immediately after extraction, using the methods described previously
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(Tourna et al., 2008; Gubry-Rangin et al., 2010) except that 3 μL of nucleic acid extract
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(approximately 500 ng nucleic acids) was used. The remaining nucleic acid extracts were
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considered as DNA templates and DNA concentration was estimated using a NanoDrop 1000
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Spectrophotometer (Thermo Scientific, Loughborough, UK) before dilution. All cDNA and
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DNA templates were stored at -20ºC until analysed.
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The composition of AOA and AOB communities was determined by PCR-DGGE analysis
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of amplified AOA amoA genes and AOB 16S rRNA genes, using methods previously
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described by Nicol & Prosser (2008). For AOB communities, it was chosen to target
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betaproteobacterial AOB-specific 16S rRNA genes using DGGE, providing better
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discrimination of closely related taxa than AOB amoA DGGE (Nicol et al., 2008, Tourna et
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al., 2010). Archaeal and bacterial amoA gene and transcript abundances were determined by
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real-time PCR in a RealPlex2 Mastercycler (Eppendorf, Stevenage, UK). Each reaction had a
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20 μL final volume with 0.2 mg mL-1 bovine serum albumin (BSA), 1.5 μM of both forward
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and reverse primers, 10 μL of QuantiFastTM qPCR master mix (Qiagen, Crawley, UK) and 2
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μL DNA template diluted to ~5 ng μL-1 or 2 μL cDNA. Primer pairs used for qPCR were
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CrenamoA23f/CrenamoA616r (Tourna et al., 2008) and amoA-1F/amoA-2R (Rotthauwe et
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al., 1997) for AOA and AOB amoA, respectively. For AOA amoA quantification, a standard
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dilution series of 101–108 amoA genes was constructed using a 2651-bp PCR fragment from
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Nitrosotalea devanaterra genomic DNA obtained with the primer pair Ndev-amo-f2/Ndev-
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amo-r
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GATTTAGTCCCACTTAGACC-3’, respectively). After purification of the PCR product
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using a Nucleospin Clean-Up Kit (Macherey-Nagel, Düren, Germany), the abundance the
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reference gene was calculated taking into account the concentration of DNA in the purified
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PCR product, amplicon length and assuming a molecular mass of 660 Da per base pair. The
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same method was used to generate an AOB amoA standard, with a 1750-bp PCR fragment
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obtained from Nitrosospira multiformis ATCC25196 using the primers AOB-amoC-
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305F/AOB-amoB-308r (Norton et al., 2002, 308r being slightly modified to 5’-
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TCCCAGCTCCGGTATGTTCATCC-3’). In both cases and for every qPCR run, two
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complete dilution series were assayed. Amplification efficiency ranged from 84.3% to
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100.1% and from 79.8 to 84.2% for AOA and AOB amoA, respectively, with r2 values ≥0.98.
(5’-
GAAAAGAGAGGGGGTGATGATTG-3’
and
5’-
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Cycling conditions were 15 min at 95ºC, followed by 40 cycles of 15 s at 94ºC, 1 min 30 s
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for AOA amoA and 1 min for AOB amoA at 60ºC, and plate fluorescence was measured after
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5 s at 80 ºC. Melting curves between 60 ºC and 95 ºC were analysed for each run. Detection
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limits were 8.103 and 104 genes or transcripts g-1 dry soil, for AOB and AOA, respectively.
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Attempts to amplify AOB amoA genes and transcripts were also made using the method
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described previously by (Gubry-Rangin et al., 2010), using the QuantiTect SYBR Green PCR
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Master Mix (Qiagen, Crawley, UK), but transcripts were below the detection limit.
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Calculation of resistance and resilience indices
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Resistance (RS) index was calculated for nitrification rate and AOA and AOB gene
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abundances (log10-transformed data), accounting for absolute difference between Control and
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Stressed treatments, according to Orwin & Wardle (2004) and using the following equation:
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RS = 1 – (2 x (|C0 - S0|) / (C0 + |C0 - S0|))
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Where C0 and S0 are the values of the relevant variable in Control and Stressed
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microcosms, respectively, at the end of the drought period. RS increases monotonically with
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resistance and values range between -1 and 1, where 1 represents no effect of drought.
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(Equation 1)
The resilience index (RL(n)), was calculated 3, 7, 14 and 21 days after rewetting for the
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same variables and according to the same authors as:
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RL(n) = [2 x (|C0 - S0|) / ((|Cn - Sn|) + (|C0 - S0|))] - 1
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Where C0 and S0 are the values of the relevant variable in Control and Stressed
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microcosms, respectively, at the end of the drought period and Cn and Sn are the values of the
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relevant variable in Control and Stressed microcosms, respectively, n days after rewetting. RL
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values can range between -1 and +1, where +1 represents complete recovery.
(Equation 2)
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The resistance and resilience of AOA and AOB community compositions were analysed
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using Bray-Curtis similarity indices to compare Control and Stressed microcosm DGGE
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profiles as a measure of resistance (at the end of the drought period) and resilience (7, 14 and
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21 days after rewetting). The similarity can range between 0 and 1, a value of 1 indicating
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maximum resistance or resilience, i.e. no effect of drought and complete recovery,
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respectively (DeVries & Shade, 2013).
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Statistical analyses
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All statistical analyses were performed using the program R 3.0.3 (R Development Core
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Team, 2013). Data for net nitrification rate, AOA and AOB abundance and AOA:AOB ratio,
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acclimation period, drought period and recovery period were analysed separately using two-
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way ANOVA, with time, treatment (either Control or Stressed) and their interaction as
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explanatory variables, and using log-transformed data for AOA and AOB abundance and
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AOA:AOB ratio. Fisher’s least significant difference tests were performed when ANOVA F
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values were significant (p≤0.05). All resilience indices were compared using the same
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approach, with time after rewetting and AO community composition (either AOA or AOB) as
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explanatory variables. Student’s t-tests were used to compare resistance indices of each
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variable between AOA and AOB.
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Results
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Resistance and resilience of nitrification rate to drying-rewetting stress
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Inorganic N data that were used to calculate resistance and resilience indices for
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nitrification rate are presented in Fig. S1. During the third week of drought, nitrification rate
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(expressed as g NO3--N accumulated μg-1 dry soil day-1) was lower in Stressed microcosms
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than in Control microcosms and exhibited a low resistance index (0.24 ± 0.14, Fig. 1). This is
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reflected in the significantly greater accumulation of NH4+ in Stressed microcosms during the
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drought period (Fig. S1, p=0.005). Rewetting was followed by a large flush of NH4+ in
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Stressed microcosms, reaching 116 ± 5 μg NH4+-N g-1 dry soil after three days. This NH4+
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flush was not associated with NO3- accumulation, and presumably resulted from
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mineralisation (Fig. S1). Indeed, NO3- concentration decreased during the first three days
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after rewetting, preventing calculation of net nitrification rate. There was no statistically
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significant decrease in total soil N in any of the microcosms, although small levels of
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denitrification would not have been detectable using this approach (Fig. S1). Net nitrification
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rate increased between three and seven days after rewetting in Stressed microcosms but was
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lower than in Control microcosms. The difference between Control and Stressed treatments
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increased during the drought period, leading to negative resilience indices until two weeks
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after rewetting (Fig. 2a). After three weeks of recovery, nitrification rate had still not reached
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control levels, showing a resilience index of 0.60 ± 0.27.
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Resistance and resilience of AO abundance and activity to drying-rewetting stress
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AOA and AOB abundance, as measured by amoA gene abundance, responded differently
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to drought stress. AOB amoA gene abundance was not significantly affected during the
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drought period (Fig. 3), leading to a high resistance index (0.96 ± 0.03, Fig. 1). In contrast,
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AOA amoA gene abundance was lower in Stressed than Control microcosms, giving a
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significantly lower resistance index of 0.82 ± 0.07 (Fig. 1). During the recovery phase, both
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AOA and AOB amoA abundance resilience indices remained negative (Fig. 2b) but, while the
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resilience of AOA amoA gene abundance tended to decrease, reaching a low negative value
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three weeks after rewetting (-0.55 ± 0.05), AOB amoA gene abundance resilience index
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varied during the recovery period but remained negative, with a final value of -0.09 ± 0.38.
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This difference in response is more apparent in the influence of drying-rewetting stress on
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the AOA:AOB amoA abundance ratio (Fig. 4). The ratio at the beginning of the experiment
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was 0.94 ± 0.42 and did not change significantly during acclimation. It then increased
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continuously during 3-week incubation of Control microcosms, up to 23.7 ± 13.1, while
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drought led to an initial reduction followed by an increase in Stressed microcosms, but with
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ratios always lower than Control values (4.3 ± 1.3 after three weeks drying). Accordingly,
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AOA:AOB amoA gene abundance ratio showed low resistance to drought, with an index of
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0.18 ± 0.06; (Fig. 1). The AOA:AOB ratio increased further during subsequent incubation of
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Control microcosms for 3 weeks, reaching 312 ± 170, but did not change significantly
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following rewetting in Stressed soil (2.09 ± 0.11 at the end of the recovery period). This led
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to low resilience values, which decreased significantly throughout the recovery period to -
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0.86 ± 0.03 (Fig. 2c).
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Attempts were made to link nitrification rate and AO activity by quantification of changes
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in AOA and AOB amoA transcripts. AOA amoA transcripts were detectable throughout
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incubation of Control microcosms (Fig. 3), and increased linearly with AOA amoA gene
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abundance (r2 = 0.36, p<0.0001). In Stressed microcosms, on the other hand, amoA
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transcripts could not be detected during the drought period except in one replicate microcosm
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after 21 days. Detection continued to be sporadic during the recovery period until two weeks
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after rewetting, when transcript abundance was significantly lower than in Control
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microcosms. The frequent inability to detect AOA amoA transcripts in Stressed microcosms
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prevented calculation of resistance and resilience indices for this parameter. Moreover,
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despite numerous attempts using a range of methods, AOB amoA transcripts could not be
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detected in any samples, including those from Control microcosms.
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Resistance and resilience of AO communities to drying-rewetting stress
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AOA and AOB community composition, assessed using DGGE, responded differently to
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drying-rewetting stress (Fig. S2). Bray-Curtis similarity indices (ranging from 0 to 1),
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calculated from abundances of 26 and 24 DGGE bands for AOA and AOB, respectively,
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were used to describe differences between communities in Control and Stressed microcosms.
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AOA community composition was more severely affected during the drought period and had
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a resistance index of 0.49 ± 0.05, compared to 0.73 ± 0.03 for AOB community composition
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(Fig. 1). In contrast, dissimilarity between Control and Stressed microcosms increased during
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the recovery period for both communities, with resilience indices decreasing to 0.30 ± 0.01
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and 0.36 ± 0.06 for AOA and AOB community composition, respectively, three weeks after
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rewetting of Stressed microcosms (Fig. 2).
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Discussion
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Resistance and resilience of nitrification to drying-rewetting
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Nitrification rate is inhibited by both very low moisture and water saturation, and is
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optimal 60-70% WHC (Rennenberg et al., 2009). Stark & Firestone (1995) showed that both
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cell dehydration and substrate limitation reduce nitrification at low water potential, the
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former being most detrimental at osmotic potential <-0.6 MPa, as in the present experiment.
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Acclimation to repeated extended drought periods followed by rewetting would require rapid
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recovery from drought stress to maximise competition for, and utilisation of ammonium
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formed during drought periods, mobilised by rewetting, and that arising from the flush of
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mineralisation. This is exemplified by significant nitrate accumulation within 24 h of
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rewetting in Mediterranean soils acclimated to long dry summers followed by heavy rainfall
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(Placella & Firestone, 2012). Similarly, potential nitrification rate was unaffected by drought
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in an acclimated upland soil but significantly reduced in an adjacent, non-acclimated wetland
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soil (Peralta et al., 2013). In the present experiment, we found no evidence for nitrifier
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adaptation to drying-rewetting stress in a non-acclimated grassland soil in a temperate climate
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with no history of drought-rewetting. Net nitrification rate was drastically reduced by
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drought, with both low resistance (Fig. 1) and low resilience (Fig. 2). Nitrate accumulation
13
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was not detectable within three days of rewetting and there was no evidence of complete
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recovery of nitrification three weeks later. Simultaneous nitrification and denitrification
305
would not be detectable after rewetting, but the water content was sufficiently low (~80%
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WRC) for negligible anaerobic denitrification in this soil.
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Ammonia oxidiser and drought: a historical legacy?
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A possible explanation for adaptation of nitrification to drought-rewetting is selection for
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communities that are themselves adapted to stress imposed by osmotic stress, low ammonia
310
and/or high ammonia concentration. In the ‘non- acclimated’ grassland soil investigated here
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there was little evidence for adaptation. AOA amoA transcripts were always detectable during
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drought in Control microcosms but rarely detected in Stressed microcosms during drought
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and even during the first two weeks of rewetting (Fig. S2). No AOB transcriptional activity
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could be detected in any sample, possibly through low physiological activity of AOB or
315
insufficient sensitivity of the qPCR method. Drying-rewetting also had major effects on AOA
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and AOB community composition and AOA abundance; recovery of abundance was
317
incomplete three weeks after rewetting; and differences between AO community composition
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in Stressed and Control microcosms increased during recovery (Fig. 1 and 2). This contrasts
319
with ‘drought-adapted’ Mediterranean soils (Placella & Firestone, 2012), where amoA
320
transcripts remained detectable during drought and increased rapidly after rewetting.
321
However, simulation of dry summers did not affect either potential nitrification rate or AOA
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and AOB amoA gene abundance in two Alpine soils, with no history of drought (Hartmann et
323
al., 2013), but these contrasting results could arise from very different experimental systems.
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Several studies provide evidence for adaptation of microbial communities to drought
325
stress. For example, De Vries et al. (2012) showed increased drought resistance of microbial
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communities in grassland soil previously subjected to drying-rewetting. Evans & Wallenstein
327
(2012) also found that longer drought periods and fewer but heavier rainfalls modified the
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328
response of soil respiration, fungal:bacterial ratio and microbial C:N ratio to new drying-
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rewetting cycles. This was associated with changes in relative abundance of several bacterial
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lineages, presumed to be more drought resistant (Evans & Wallenstein, 2014). In contrast,
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Fierer et al. (2003) found no or little effect of previous drying-rewetting cycles on bacterial
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community composition resistance, and soil type appeared to be the major driver.
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Differential response to drought of AOA and AOB
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AOA and AOB, and more generally of archaea and bacteria, have major differences in cell
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structure and physiology. Nonetheless, data on their respective response to disturbances,
336
other than agricultural practices, is lacking, including response to water stress.
337
Osmoprotection in both archaea and bacteria involves accumulation of compatible solutes,
338
although these solutes differ between the two kingdoms (Roeßler & Müller, 2001). Both
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archaea and bacteria are found in hypersaline environments (Dillon et al., 2013) and Barnard
340
et al. (2013) showed similar patterns of archaeal and bacterial 16S rRNA gene abundance
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during drought in three different grassland soils. In contrast, studies in pure cultures have
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shown inhibition of AOA at lower NH4+-N concentrations than commonly found for AOB
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(Park & Bae, 2009; Koper et al., 2010) and experiments with soils tend to support the
344
hypothesis of differential tolerance to high NH4+-N concentration (Verhamme et al., 2011;
345
Meyer et al., 2013; Strauss et al., 2014). These considerations suggest the possibility of niche
346
differentiation of AOA and AOB through differential responses to ammonia concentration,
347
rather than differences in adaptation to osmotic stress.
348
Our study provides three lines of evidence for differential responses of AOA and AOB.
349
Firstly, both AOA amoA gene abundance and community composition were significantly less
350
drought-resistant than those of AOB. Secondly, AOB community composition was more
351
resilient in the short-term, although its resilience decreased during incubation and was similar
352
to that of AOA three weeks after rewetting. Thirdly, drying-rewetting had a strong and
15
353
persistent negative effect on AOA:AOB amoA gene abundance ratio, whose resilience
354
decreased with time following rewetting. One should note that the chosen resilience index
355
takes into account the initial difference caused by the disturbance (in this case, the difference
356
between Control and Stressed microcosms at the end of the Drought period). A decrease in
357
resilience therefore indicates a greater difference between control and stressed conditions
358
than this initial difference (Orwin & Wardle, 2004). Placella & Firestone (2012) also found a
359
faster response in AOB transcriptional activity compared to AOA. In contrast, Hartmann et
360
al. (2013) did not find any differential response of AOA and AOB after two summers of
361
drought, but the drought itself showed no significant effect on either community. During the
362
drought period, NH4+-N concentration expressed per mass of dry soil was significantly higher
363
in Stressed than Control soil, suggesting that N mineralisation rate was greater than ammonia
364
consumption rate. Moisture content in drying soil decreased from ~33% to ~11% in ten days,
365
leading to an approximately 3-fold increase in solute concentration (including NH4+-N
366
concentration) in the soil solution. After 21 days of drought, NH4+-N concentration was
367
estimated to be >540 and ~160 μg ml-1 of soil solution in Stressed and Control microcosms,
368
respectively. Soil heterogeneity will lead to patchy distribution of N mineralisation at the
369
small scale, with dry zones enduring nutrient starvation and moist areas with even higher
370
NH4+-N contents (Richards & Kump, 2003). In addition, there was a large flush of NH4+-N
371
measured immediately after rewetting, which was expected as part of the Birch effect (Birch,
372
1964). NH4+-N concentration was > 350 μg ml-1 of soil solution in Stressed microcosms
373
within three days of rewetting. Altogether, these data support the hypothesis that the
374
differential response reflects distinct ecological niches for AOA and AOB, potentially
375
resulting from differences in tolerance to high NH4+-N concentration.
16
376
Concluding remarks
377
We have demonstrated the poor resistance and resilience to drought of nitrification and, to
378
a greater extent, AO in a non-adapted soil. We have also demonstrated for the first time a
379
differential response between AOA and AOB, suggesting niche specialisation. The link
380
between microbial community composition and ecosystem functioning is still debated and
381
our data indicate decoupling of AO functional guild community composition (assessed by
382
DGGE and AOA:AOB amoA ratio) and nitrification rate. Although no direct link with AOB
383
activity could be shown, similar dynamics of AOB abundance and nitrification rate could
384
indicate than AOB were the main driver of this process after drying-rewetting, whereas AOA
385
clearly outnumbered AOB in undisturbed control. This phenomenon could indicate functional
386
redundancy within each community. Maximum effort should be placed into a better
387
understanding of suggested AO functional redundancy, its extent and its possible
388
consequences for ecosystem stability.
389
Acknowledgements
390
We would like to acknowledge funding of the European Commission's FP7 programme, EU-
391
project ‘EcoFINDERS’ No. 264465. The authors also wish to thank Dr Franciska De Vries
392
and Professor Richard Bardgett, University of Manchester and Prof Paul Hallett, University
393
of Aberdeen, for their valuable comments and advice.
394
17
395
396
397
398
399
400
401
402
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Figure Legends:
539
Figure 1. Resistance indices of nitrification rate (Nitrif), AOA and AOB amoA gene
540
abundance and their ratio (Abundance) and AOA and AOB community composition
541
(Composition) after three weeks of drought, calculated according to Orwin & Wardle (2004).
542
Means ± standard errors of all combinations of triplicate Stressed microcosms vs. triplicate
543
Control microcosms are shown (n = 9). For AO abundance and community composition,
544
different letters indicate significant difference between AOA and AOB resistance indices as
545
determined by Student t-tests, with p≤0.05.
546
Figure 2. Resilience indices 1, 3, 7, 14 and 21 days after rewetting (recovery period) of a.
547
nitrification rate, b. AOA and AOB amoA gene abundance, c. AOA:AOB amoA gene
548
abundance ratio, calculated according to Orwin & Wardle (2004), and d. AOA and AOB
549
community composition, as represented by Bray-Curtis similarity indices. Means ± standard
550
errors of all combinations of triplicate Stressed microcosms vs. triplicate Control microcosms
551
are shown (n = 9). For AO abundance and community composition, p-values resulting from
552
two way (community, i.e. AOA vs. AOB, and time as independent factors) ANOVA are
553
given. Grey stars indicate significant difference between AOA and AOB. Different letters in
554
c indicate significant difference according to time as determined by ANOVA followed by
555
Fisher’s least significant difference test. Nd: Data not available through inability to calculate
556
net nitrification rate and where community composition similarity was not determined.
557
Figure 3. Changes in AOA amoA gene (top), AOB amoA gene (middle) and AOA amoA
558
transcript (bottom) abundance in Control (black) and Stressed (white) microcosms during
559
acclimation (left), drought (center) and recovery (right) periods. Means of triplicate
560
microcosms are shown, with error bars representing standard errors. p-values resulting from
561
one-way (acclimation period) or two-way (drought and recovery periods) ANOVA are given
562
and, when significant (significant effect of the interaction between time and treatment for
25
563
two-way ANOVA with p≤0.05), calculated Fisher’s least significant differences are
564
represented as black segments.
565
Figure 4. Changes in AOA:AOB amoA gene abundance ratio in Control (black) and
566
Stressed (white) microcosms during acclimation (left), drought (centre) and recovery (right)
567
periods. Means of triplicate microcosms are shown, with error bars representing standard
568
errors. p-values resulting from one-way (acclimation period) or two-way (drought and
569
recovery periods) ANOVA are given and, when significant (significant effect of the
570
interaction between time and treatment for two-way ANOVAs with p≤0.05), calculated
571
Fisher’s least significant differences are represented as black segments.
572
Figure S1. Changes in NH4+-N
(top) and NO3--N (middle) and total N (bottom)
573
concentration in Control (black) and Stressed (white) microcosms during acclimation (left),
574
drought (centre) and recovery (right) periods. Means of triplicate microcosms are shown,
575
with error bars representing standard errors. p-values resulting from one way (acclimation
576
period) or two way (drought and recovery periods) ANOVA are given and, when significant
577
(significant effect of the interaction between time and treatment for two-way ANOVA with
578
p≤0.05), calculated Fisher’s least significant differences are represented as black segments.
579
Figure S2: Changes in AOA (a) and AOB (b) community structure analysed by DGGE
580
targeting AOA amoA and AOB 16S rRNA genes, respectively, in triplicate Control (left) and
581
Stressed (right) microcosms, at the end of the Acclimation period, and during Drought and
582
Recovery periods.
26