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Article

The Response of Soil CO2 Efflux to Water Limitation Is Not Merely a Climatic Issue: The Role of Substrate Availability

by
Giovanbattista De Dato
1,*,
Alessandra Lagomarsino
2,
Eszter Lellei-Kovacs
3,
Dario Liberati
4,
Renée Abou Jaoudé
4,
Rosita Marabottini
4,
Silvia Rita Stazi
4,
Gabriele Guidolotti
5,
Edit Kovacs-Lang
3,
György Kroel-Dulay
3 and
Paolo De Angelis
4
1
Council for Agriculture Research and Economics, Centre for Forestry and Wood (CREA-FL), Viale Santa Margherita 80, 52100 Arezzo, Italy
2
Council for Agriculture Research and Economics, Research Centre for Agriculture and Environment (CREA-AA), Via di Lanciola 12, 50125 Firenze, Italy
3
Hungarian Academy of Sciences, Centre for Ecological Research Institute of Ecology and Botany, H-2163 Vácrátót, Hungary
4
University of Tuscia, Department for Innovation in Biological, Agro-food and Forest systems (DIBAF), Via S. Camillo de Lellis snc, 01100 Viterbo, Italy
5
National Research Council of Italy (CNR) Institute for Agro-Environment and Forest Biology (IBAF), Via Salaria km 29,300, 00015 Monterotondo Scalo, Italy
*
Author to whom correspondence should be addressed.
Forests 2017, 8(7), 241; https://doi.org/10.3390/f8070241
Submission received: 22 April 2017 / Revised: 25 June 2017 / Accepted: 4 July 2017 / Published: 7 July 2017
(This article belongs to the Special Issue Forest Soil Microbe and Biogeochemical Cycling: Biogeochemical Processes in Forest Soils)
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Versions Notes

Abstract

:
Water availability, together with temperature, represents the most limiting abiotic factor regulating soil CO2 efflux (SR). Besides the direct effect of water limitation, drought also influences plant activity, determining changes in the quality and quantity of root exudates, thus indirectly affecting soil microbial activity. To determine how the seasonal changes of plant activity and soil microbial metabolism and structure affect SR response to drought, we investigated the correlation between leaf gas exchange, soil carbon pools and soil respiration sources and the role of soil carbon pools on microbial populations and soil respiration, in a summer deciduous Mediterranean (SDS) and a winter deciduous temperate (WDS) shrublands, experiencing a dry summer period. In both sites, drought reduced photosynthesis, but affected SR differently: in SDS, SR decreased, although microbial heterotrophic respiration (SRh) remained unchanged; in WDS, SR did not vary but SRh was reduced. While in SDS the microbial community was able to respire more complex substrates, in WDS it was strongly dependent on easily decomposable molecules, thus on plant activity. Therefore, the response of soil CO2 efflux to water limitation is not exclusively influenced by climate as it is modulated by the degree of adaptation of the microbial community to drought.

1. Introduction

Water availability is a key driver of biogeochemical processes in all terrestrial ecosystems, especially in arid and semi-arid ones, which are very sensitive to climatic variation [1]. In these ecosystems, both carbon assimilation (photosynthesis) and soil CO2 efflux (i.e., soil respiration, SR) are driven by the seasonal changes in the precipitation regime [2,3,4,5]. It is supposed that the impact of water limitation can be longer lasting on assimilation than on soil CO2 efflux [6], due to the likely different capacity of leaves (and consequently canopy) to reverse to pre-drought conditions compared to microorganisms’ ability [7,8,9]. However, as soil respiration represents one of the largest fluxes of CO2 [10], it is essential to determine how water availability directly and indirectly affects carbon release from the perspective of climate change.
Soil CO2 efflux (SR) results from the respiratory activity of the autotrophic and heterotrophic components of the pedosphere, deriving from both plant (root respiration) and microorganisms (microbial respiration; [11]). The contribution of microbial respiration in different ecosystems can account for 10–90% [12,13] of the total CO2 efflux, depending on biotic (plant community, phenological stage, relative contribution of coarse and fine roots, microbial biomass and community; [14,15,16]) and abiotic factors (climatic drivers, soil conditions, such as pH and soil structure, topography, sampling period, management type; [17,18,19]).
Several studies show that the microbial community structure and activity can vary according to plant activity [20], as long as there are labile soil carbon inputs and available substrates [21,22]. In fact, plants sustain soil CO2 efflux by “pumping” fresh carbon assimilated via photosynthesis to roots, therefore feeding roots and the microorganisms inhabiting the narrow region of soil that is directly influenced by root secretions (plant assimilates and rhizodepositions), the so-called rhizosphere [23,24,25]. Thus, the contribution of this root-derived CO2 is strongly dependent on plant activity, decreasing during plant dormant periods or under stress conditions and increasing during the non-moisture limited growing period.
Microbial activity is strongly controlled by temperature but other constraints can modify the temperature response, by altering the microbial structure and function [26].
Especially when water availability is the most limiting environmental factor, due to a combination of high temperatures and low rainfall, the physiological performance of soil fauna can be directly and indirectly reduced, subjecting the microbial community to strong adaptive adjustments [27].
Microbial communities and functionality show elevated plasticity in terms of composition, diversity and physiological modifications. According to Allison and Martiny [28], microbial communities can be resistant to environmental changes, remaining unaltered, but they can also exhibit resilience and quickly return to their original state after a disturbance; further, there are cases showing redundancy, consisting in microbial composition changes but unaffected functionality.
These observations become particularly evident under drought conditions, when bacterial community assortments show higher diversity compared to wet conditions, highlighting the role of rainfall regime in determining soil microbial properties [29,30]. According to frequency and intensity of drought events, microbial structure may be selected towards groups that are more tolerant to desiccation. Moreover, different drought patterns may result in different community assemblages of functional groups, able to differently respond to moisture stress [31], and to regulate heterotrophic respiration [32,33,34,35]. This microbial behaviour appears particularly striking in a Mediterranean climate, where the intense seasonality of soil water availability and temperature, in the long run, may have selected microbial communities able to tolerate this strong seasonal shift [36].
In order to determine how the response of SR to drought can be modulated by plant activity and soil microbial characteristics, we analysed the seasonal change of: (1) plant performances (assimilation, stomatal conductance) and water status (pre-dawn water potential); (2) soil CO2 efflux and its components; and (3) substrate availability (soil carbon pools, water-extractable organic carbon); (4) microbial activity (by induced respiration) and composition (by ester-linked fatty acid methyl ester-EL-FAME), analysing relationship between leaf gas exchange, soil carbon pools and soil respiration sources and the role of soil carbon pools on microbial populations and soil respiration. We monitored two different sites: a summer deciduous Mediterranean and a winter deciduous temperate shrubland experiencing a dry summer period.

2. Materials and Methods

2.1. Study Site

The Italian site (Table 1; Figure A1 and Figure A2) is located in the Regional Park Porto Conte-Capo Caccia in northwest Sardinia, Italy. The climate of the area is Dry Thermo-Mediterranean [37] with the dry period extending from May to September. The soil is rocky and shallow; the texture is sandy loam, with an ABC profile. Vegetation cover is a typical Mediterranean shrubland made up of plants with 1 m maximum height, constituted of Cistus monspeliensis L., Helichrysum italicum G. Don. and Dorycnium pentaphyllum L. As with many summer semi-deciduous plants, Cistus is characterised by a drought-avoiding phenology, with summer leaf shedding. Hereafter, this site will be referred to as summer deciduous shrubland (SDS).
The Hungarian site (Table 1; Figure A1 and Figure A2) is located in the Kiskunság National Park. The climate is Pannonian—a temperate climate with continental and sub-Mediterranean influence [38], with cold winters and warm, dry summers. Peak precipitation usually occurs in June, often followed by a midsummer drought. Soil of the Kiskunság region is Pleistocene sand from the River Danube. The coarse sandy soil, very poor in organic matter (<1%), is mainly in the upper 0–10 cm horizon and has an extreme heat and water regime. The vegetation is characterized by the presence of a forest–steppe transitional biome. The dominant species are the winter deciduous clonal Populus alba L. and the perennial C3 bunchgrass Festuca vaginata W. et K. In the warm and dry summers the activity of C3 grasses decreases, while the activity of poplar clones is continuous during the vegetation period. Hereafter, this site will be referred to as winter deciduous shrubland (WDS).
We sampled at three occasions during 2010, under different conditions of water availability, soil temperature and plant phenological status: in Italy, the three campaigns were in March, August and October; in the Hungarian site the campaigns were in May, July and November. The different timings will be named Spring, Summer and Autumn in the following text. These campaigns were chosen in order to find comparable climatic conditions but different phenological stages (Table 2).
All the measurements described below were taken, during the different campaigns, on the same ten dominant individuals of C. monspeliensis in SDS and P. alba in WDS and on the soil around the canopy of these plants.

2.2. Leaf Gas Exchange and Pre-Dawn Water Potential

Plant activity and water status were determined by measuring photosynthesis (Anet), stomatal conductance (gs) and pre-dawn leaf water potential (PWP).
Due to differences in leaf shape of the two analysed species, Anet was measured by Li-6400 (Licor Biosciences, Inc., Lincoln, NE, USA), equipped with 6400-02B LED light source during the Hungarian campaigns, and with the 6400-22L Package (which combines the 6400-22 Opaque Conifer Chamber and the 6400-18 RGB Light Source) during the Italian ones. Photosynthesis was measured on attached leaves of 10 dominant plants at midday. The cuvette was conditioned according to the season by setting the internal values of the photon flux density (PAR), the relative humidity and the air temperature equal to the external values (Table A1), while [CO2] was always set to 400 ppm. Leaf area clipped during the Hungarian campaigns was equal to the chamber area (6 cm2), being the cuvette totally filled in with the poplar leaves; in the Italian site, Cistus shoots were cut after each measurement and leaves were scanned.
At each sampling date, pre-dawn leaf water potential was measured by the pressure bomb SKPM 1400 (Skye Instruments Ltd, Llandrindod Wells, UK) on C. monspeliensis shoots in SDS, because of the lack of an evident petioles, while in WDS it was measured on poplar leaves. The measurements were done on one sample per plant. The shoots were cut and put in plastic bags in an insulated box and measured within some minutes.

2.3. Soil CO2 Efflux

Soil CO2 efflux (SR) was measured on fixed PVC collars (one collar next to each of the 10 dominant individuals, inserted into the soil for 5 cm at a distance from the plant base of 30–50 cm under the projection of the plant canopy) by Licor 8100 (Licor Biosciences, Inc., Lincoln, NE, USA) equipped with the 8100–102 (10 cm) chamber. The collars were installed a couple of days prior the start of each measurement campaign and kept free of vegetation.
The Soil Temperature Probe Type E (Licor Biosciences, Inc., Lincoln, NE, USA) was used to measure soil temperature (Ts) at each sampling point. Soil water content at the time of measurements was determined on a soil sub-sample deriving from the soil cores collected inside each collar and used for the determination of the heterotrophic component of SR (SRh, see below) in the laboratory. These sub-samples were dried in an oven at 110 °C until they reached constant weight.

2.4. Soil Heterotrophic Respiration, Heterotrophic Basal Respiration and Temperature Sensitivity

After SR measurements, one soil core (mineral layer 0–5 cm) inside each collar was collected. The cores were sampled by an undisturbed soil corer (07.53.SC, Eijkelkamp Soil & Water, Giesbeek, The Netherlands), having an area of 19.63 cm2 (ø 5 cm) and height of 5 cm. After sampling, cores were stored at 5 °C for 2–3 days until performing the incubation in the laboratory, in order to exclude from the measurement the contribution of the autotrophic component of CO2 efflux and to minimize the decomposition of dead roots [39].
The incubation was performed by a water bath (Haake, type Q with F3 digital controller) equipped with a tank. Soil cylinders were inserted into plastic glasses (ø 6 cm), which were attached to the bottom part of the tank lid and dipped into the water flowing in the tank. Attached on the top of the lid, concentric to the holes for soil cylinders, plastic rings (ø 10 cm) were placed, in order to fit the Licor 8100–102 chamber connected to a Licor 8100 (Licor Biosciences, Inc., Lincoln, NE, USA), used for CO2 efflux measurements. Soil temperature was monitored by thermocouples (Tecno.el, s.r.l., Formello, Roma, Italy) connected to a datalogger (CR10X, Campbell Scientific, Inc., Logan, UT, USA). After the soil had reached the desired temperature (Ts of 5-10-15-18-20-25-30 °C, and midday field soil temperature, therefore varying with the seasons and defined as actual soil temperature), we started CO2 efflux measurements in 20–30 min. CO2 efflux measurement at field temperature was used to determine the heterotrophic respiration (SRh). Heterotrophic respirations at the different temperature steps were fitted by the exponential equation SRh = a*exp(k*Ts) (Equation (1)), where SRh is the heterotrophic soil CO2 efflux, a and k are estimated parameters, and Ts is the soil temperature. From these curve, we derived the heterotrophic basal respiration SRb = a for Ts = 0 °C, and the temperature coefficient of the heterotrophic respiration Q10 = exp(10*k).

2.5. Soil C Pools

Total soil Organic (TOC) and total N (TN) were determined at the beginning of the campaigns (in spring) in the bulk (non-fractionated) soil and in particle and aggregate-size fractions. Between 20 and 40 mg oven-dried material of each unit was weighed into AGram−foil capsules. The method was based on dry combustion using an elemental analyser (FlashEA 1112, Thermo Fisher Scientific Inc., Waltham, MA, USA).
Water-extractable organic carbon (WEOC) was determined after extraction of 15 g of soil in 30 mL of deionised cold water. Total C in the aqueous extracts was measured by dry combustion on a C and N soil analyser Flash 2000 Series (Thermo Fisher Scientific Inc.).

2.6. Community Level Physiological Profiles (CLPP) Based on Substrate-Induced Respiration (SIR)

Microbial activity was determined by analysis of CLPP using the MicroResp soil respiration system (MicroResp™ Macaulay Scientific Consulting Ltd., Aberdeen, UK), as described in Campbell et al. [40] and Lagomarsino et al. [39]. Mineralization of 15 carbon substrates was tested for CLPP (Table A2): five carbohydrates (glucose, galactose, fructose, arabinose, trehalose); five amino acids (lysine, alanine, ɣ-aminobutyric acid, arginine); four carboxylic acids (citric acid, oxalic acid, ascorbic acid, malic acid); and one amide (N-acetyl-glucosamine). Carbon substrates were selected depending on their ecological relevance to soil and their similarity to substrates used for enzyme activities. In particular, rhizospheric sources (carboxylic acids and carbohydrates) were chosen considering the importance of root inputs for microbial metabolism.
The sources were dissolved in deionised water and added before the soil to ensure that the soil contacted the source at the same time for all wells. For each soil a water control was present in the plate. To obtain the substrate induced respiration (SIR), the soil respiration values for the various carbon sources were adjusted by subtracting the average value of the water control. Three laboratory replicates were used for each field replicate. To estimate the evolved CO2, a colorimetric method relying on the change in the pH of a gel-based solution of bicarbonate was used. The absorbance at 590 nm was read with a Biolog MicroStation (Microlog™ Release 4.20.04, Biolog, Inc., Hayward, CA, USA) immediately before and after 6 h of incubation at 28 °C. The absorbance after 6 h was normalized for any differences recorded at time zero and then converted to % CO2 by using the following calibration curve: y = A + B/(1 + D x Ai), where A = −0.3409, B = −1.4604, D = −7.882 and Ai is the absorbance at 590 nm. The % CO2 was then converted to µg CO2 g−1 h−1 production rate using gas constant, T (°C), soil d.w. (g) and the incubation time (hour) (Table A1).

2.7. Microbial Community Composition by EL-FAME Characterization

Microbial community structure was characterized by ester-linked fatty acid methyl ester (EL-FAME) analysis, which employed a mild alkaline methanolysis method to extract ester-linked (EL) fatty acids but not free fatty acids [41]. In brief, 16.6 mL of 0.2 M potassium hydroxide (KOH) in methanol and 5 μL of internal standard solution (0.023 mg mL−1 methylnonadecanoate 19:0) were added to a 50-mL centrifuge tube containing 5 g of freeze-dried soil. The tubes were incubated at 37 °C for 1 h in an orbital shaker. Then 6.6 mL of 1.0 M acetic acid were added to neutralize the pH of the tube contents. Extracted FAMEs were partitioned into an organic phase by adding 10 mL of hexane followed by centrifugation at 4500 rpm for 15 min. The hexane layer was transferred into a clean glass tube, dehydrated with Na2SO4, filtered and evaporated under a steam of N2. Finally, FAMEs were dissolved in 200 μL of dichloremethane and transferred to an amber vial for GCMS analysis for quantification. Mass spectra were recorded by the use of a QP-5050 (Shimadzu, Kyoto, Japan) spectrometer equipped with an AT 20 capillary column (0.25 mm i.d., 25 m) (Alltech, Nicholasville, KY, USA) at 80–280 °C with a splitless injection and an isothermal program at 89 °C for 2 min, then 6 °C min−1 up to 280 °C and finally isothermal at 280 °C for 5 min. Methylated fatty acids were identified according to their mass spectra and using BAME 24 (47080 U) and 37 FAME Mix (47885-U, Sigma–Aldrich) as chemical standards.
Standard nomenclature is used to describe FAs (Table A3), which are designated by the total number of carbon atoms/number of double bonds, followed by the position of the double bond from the methyl (aliphatic) end (ω) of the molecule. The prefixes “a” and “i” refer to anteiso- and iso-branched FAs. The prefix “10Me” indicates a methyl group on the tenth carbon atom from the carboxyl end of the molecule and “cy” indicates cyclopropane FAs. Biomarkers of specific functional groups were assigned according to Table A2 reporting the classification used. Total bacteria were calculated as the sum of Gram+ (G+), Gram− (GRAM−), General and Actinomycetes.

2.8. Statistical Analysis and Functional Diversity Calculation

Temporal variation of CO2 assimilation, pre-dawn water potential, soil CO2 efflux, water soluble carbohydrates, EL-FAMEs and induced respiration was detected by analysis of variance (ANOVA) separately for the two sites with season as the independent variable.
Correlations among the fluxes with biotic and abiotic variables were performed.
The responses of microbial population to substrates (CLPPs data) and population structures (EL-FAME data) to sampling time and abiotic factors were analysed by Principal Component Analysis (PCA). Dependent data are expressed as relative amounts. Prior to the analyses, data were transformed for normalization.
Statistical analyses were run by Systat 13 (Systat Software, Inc., Chicago, IL, USA) using the GLM model for ANOVA and the multivariate analysis for the PCA.

3. Results

3.1. Gas Exchanges and Pre-Dawn Water Potential

In the WDS, photosynthesis (Anet) of poplar was higher in spring (17.7 µmol CO2 m−2 s−1), with a reduction in summer (6.3 µmol CO2 m−2 s−1, p < 0.01) and no activity in autumn because of the leaf fall (Figure 1A). In the SDS, Anet was similar in spring and autumn (on average 7.4 µmol CO2 m−2 s−1), while a lower value was observed in summer (3.8 µmol CO2 m−2 s−1, p < 0.05; Figure 1B).
In the WDS, significantly higher stomatal conductance (gs) was observed in spring (0.32 mol H2O m−2 s−1, p < 0.01) compared to summer (0.06 mol H2O m−2 s−1; Figure 1C). In the SDS, gs was significantly lower in summer (0.03 mol H2O m−2 s−1) than in the other two seasons (p < 0.01), while the maximum value was observed in autumn (0.2 mol H2O m−2 s−1; Figure 1D).
In the WDS, the pre-dawn water potential (PWP) remained constant (on average −0.32 MPa) between the spring and the summer campaigns (Figure 1E). In the SDS, the lowest PWP value among the three seasons was measured in summer (−4.2 MPa, p < 0.001), while in spring and autumn the PWP values were comparable (−0.9 MPa; Figure 1F).

3.2. Soil CO2 Efflux

In the WDS site, soil CO2 efflux (SR) was on average 1.8 µmol CO2 m−2 s−1 in spring and summer, followed by a significant reduction in autumn (0.8 µmol CO2 m−2 s−1, p < 0.01; Figure 2A). The soil temperatures (Ts) was 20, 37 and 10 °C, respectively in spring, summer and autumn, coupled with soil water content (SWC) values of 7, 3, and 7% (Figure 2C).
In the SDS, SR was similar in spring and autumn (on average 3.0 µmol CO2 m−2 s−1), while a lower value was observed in summer (2.1 µmol CO2 m−2 s−1, p < 0.05; Figure 2B). The Ts was 15, 34 and 16 °C respectively in spring, summer and autumn, coupled with SWC values of 18, 5 and 18% (Figure 2D).
In the WDS, heterotrophic respiration (SRh; Figure 2A) was at a maximum in spring (1.5 µmol CO2 m−2 s−1), then significantly decreased to 0.8 µmol CO2 m−2 s−1 (p < 0.05) by the following two sampling dates. In relative terms, autumn SRh represented 98% of SR, while in spring and summer this contribution was 77% and 42%, respectively.
In the SDS, SRh did not change among seasons being on average equal to 2.0 µmol CO2 m−2 s−1. In relative terms, summer SRh represented 98% of SR, while in spring and autumn this contribution was 59% and 67%, respectively (Figure 2B).

3.3. Heterotrophic Soil Basal Respiration and Temperature Sensitivity

In the WDS, a higher heterotrophic basal respiration (SRb; Figure 3A) was measured in spring (0.78 µmol CO2 m−2 s−1, p < 0.05), while similar values were observed in summer and autumn (0.5 µmol CO2 m−2 s−1). The temperature sensitivity of the heterotrophic respiration (Q10; Figure 3C) was significantly higher in autumn (1.6, p < 0.05) compared to summer (1.3), while in spring it was 1.4.
In the SDS, SRb (Figure 3B) was 1.3 µmol CO2 m−2 s−1 in spring. SRb significantly dropped to about 1 µmol CO2 m−2 s−1 (p < 0.05) and then to 0.7 µmol CO2 m−2 s−1 (p < 0.05), respectively in summer and autumn. The Q10 (Figure 3D) was on average 1.4, spanning from the highest value in summer (1.6, p < 0.05) to the minimum (1.3, p < 0.05) in autumn.

3.4. C Pools

The C content in the topsoil (0–10 cm depth) was on average 0.74% and 3.8% respectively in the WDS and the SDS (data not shown).
In the WDS, the concentration of water-extractable organic carbon (WEOC) was higher in spring (66 µg g−1, p < 0.05) and equally reduced in summer and autumn (40 µg g−1; Figure 4A). In the SDS, higher WEOC was measured in summer (88 µg g−1, p < 0.05) compared to spring and autumn (38 µg g−1; Figure 4B). A positive correlation (not shown) between WEOC and the respiration measured in laboratory (SRh) was observed in both sites (WDS: SRh = 0.02*WEOC − 0.30, R2 = 0.94, p < 0.001; SDS: SRh = 0.017*WEOC + 1.25, R2 = 0.93, p < 0.001).

3.5. Community Level Physiological Profiles (CLPP) Based on Substrate-Induced Respiration (SIR)

In the WDS, the induced respiration (SIR) by easily decomposable substrates (carbohydrates) did not change over time, being on average 9.6 µmol CO2 g−1 h−1 (Figure 5A). More complex and slowly decomposable substrates’ respiration (carboxylic acids, amino acids and N-acetyl glucosamine) was higher in spring (10.3 µmol CO2 g−1 h−1) than in summer and autumn (2.2 µmol CO2 g−1 h−1, p < 0.01). In summer, SIR by carbohydrates was higher than by other more complex substrates.
In the SDS, SIR by carbohydrates showed a significant temporal dynamic (Figure 5B), with a higher flux in summer (15.8 µmol CO2 g−1 h−1, p < 0.05) and lower in spring and autumn (5.1 µmol CO2 g−1 h−1). SIR by complex substrates was constant at the three sampling dates (14.8 µmol CO2 g−1 h−1). Moreover, in spring and autumn SIR by complex substrates was higher compared to SIR by carbohydrates (p < 0.001 and p < 0.05 respectively).
The Principal Component Analysis (PCA) of the community level physiological profiles (CLPP; Figure 6A) showed a separation of microbial functional diversity in the WDS along Root 1 (mainly discriminating carbohydrates from the other substrates; Figure 6B), which accounted for 52% of the entire variability, with the spring samples grouping apart from the summer and autumn ones. In the SDS a separation was observed in three different groups.

3.6. Microbial Community Composition by EL-FAME Characterization

In the WDS, the total amount of ester-linked fatty acid methyl ester (tot FAME) was on average 0.16 nmol g−1, with a significant negative trend from spring to autumn (Table 3). Most of the EL-FAME (85% of the total) derived from bacteria. G+ showed a higher value in spring compared to both summer and autumn, differently from GRAM− dynamic which remained constant. Fungi were similar in spring and summer and then significantly decreased in autumn.
In the SDS, the tot FAME was significantly higher in summer and lower in autumn. The FAME from bacteria was about 82% of the total, with a significant lower amount in autumn compared to spring and summer (Table 3). G+ bacteria were equal in spring and summer and decreased in autumn; GRAM− showed an insignificant decreasing trend from spring to autumn. Fungi EL-FAME was on average 0.09 nmol g−1, independently of the season (Table 3).
The PCA (Figure 7) showed a similar EL-FAME structure in all seasons in the WDS. In the SDS, the three seasons formed three different groups (Figure 7A). EL-FAME from the WDS and the SDS were different along Root 1. Root 1 grouped most of the different EL-FAMEs, while two of them, characterizing Fungi, where distributed along Root 2 (Figure 7B).
SIR and total El-FAME were both significantly mostly correlated with SRh, with the exception of carbohydrates and amino acids, and fungi and protozoa respectively (Table 4). SR showed significant correlation with the microbial degradation of carboxylic acids and N-acetyl glucosamine substrate and the G+, GRAM− and protozoa (Table 4). SIR and total FAME showed a high correlation (Table 4).

4. Discussion

In ecosystems facing severe spells of drought, plant assimilation is strictly dependent on water availability [42,43,44,45], whose duration controls the length of the growing season [46,47,48]. In this study, photosynthesis showed a strong seasonal variation, as confirmed by the significant correlation of Anet with the environmental variables (soil and air temperature and soil water content; Table A4) in both species. In both WDS and SDS, the highest assimilation rates were coupled with the highest stomatal conductance (gs), while summer Anet was reduced due to a drop of gs. The summer drop was consistently different for the two species, with a more evident shrink in poplar compared to Cistus, underlined by the higher correlation of Anet with SWC. This can be attributed to the different water use efficiency of the two species. In fact, poplar is a winter deciduous plant; it boosts its assimilation rates, at the expense of water control, during the more favourable period [49,50]. Differently, C. monspeliensis, a summer deciduous Mediterranean species, taking advantage of the autumn period to extend the growing season after the summer drought, adopts more constant and lower assimilation rates in order to minimize both water loss throughout the year and the unit cost of leaf production [51,52].
Soil water shortage also affected the relation between total belowground fluxes (SR) and soil temperature. In fact, the response of SR to summer drought was different in the two sites: in SDS, SR was reduced, while in WDS it did not change and was similar to spring values. This was evidenced by the SR-Ts correlation; (Table A5), that was significant in both sites, but with opposite signs. This difference was attributable to the concurrent reversed sign of the correlation SR-SWC, confirming the stronger role of SWC than the solely temperature in regulating SR in dry ecosystems subjected to drought. These results are in line with the most recent outcomes on SR modelling in ecosystems facing drought, pointing to include soil moisture, inversely related to soil temperature [53,54,55].
SR partitioning demonstrated a different response of the two components of SR (autotrophic and heterotrophic-SRh) to summer drought. In WDS, although SR did not change, SRh contribution halved spring values and compensated with higher autotrophic respiration, probably due to an increase in root activity to maintain constant leaf water potential under water shortage [56]. In SDS, SRh did not change in summer and was comparable to spring values. This different dynamic of CO2 efflux partitioning under water shortage can be explained by factors other than aridity. These factors can be different in the two sites. In fact, Kuzyakov and Gavrichkova [24] reported that in plants with a high root allocation of photosynthates and during a period that is not water-limited, together with the climatic drivers, the direct effect of plant activity on soil respiration also has to be taken into account because of the close link between photosynthesis and rhizosphere processes [23,57]: photosynthesis supplies carbohydrates to roots, modulating substrates’ availability to the rhizosphere. During summer in SDS, the Cistus plant community, unable to access deep water, moved to a dormant status in order to maintain turgor. This caused a reduction in photosynthates and substrate availability; however, the observed summer depression of SR was not due to a modification of SRh but to the suppression of autotrophic respiration, as already similarly observed in other ecosystem types under drought [13,58,59], and SRh accounted for almost 100% of SR. Concurrently, a significant increase in WEOC was observed, likely because of the high root mortality occurring in summer in Mediterranean ecosystems [60,61] and the simultaneous photodegradation of surface litter at high irradiance [62,63], which both release high amounts of C into the soil. At the same time, a change in the microbial community assemblage could be detected. In WDS, during summer the reduced assimilation was related to a decrease of SRh and also coupled with limited substrate availability. In this case, WEOC, which is strictly connected to the release of root exudates [64] changed accordingly. As reflected by SIR analysis, summer microbial community in WDS was able to respire carbohydrates rather than more complex substrates. Thus, a reduction of fresh C from aboveground, because of low stomatal conductance and reduced phloem transport, negatively affected SRh. In this case, substrate quality and quantity more than water availability can modulate CO2 release in this ecosystem. This result is further confirmed by autumn fluxes, showing low SRh fluxes because of the leaf shedding and the absence of photosynthate production.
The drought season differently affected the temperature coefficient of the heterotrophic respiration (Q10). In WDS, the dry period corresponded with the lower Q10, reflecting the low bacterial activity in this site reported by [65]. For SDS, the dry period corresponded to the higher Q10, as was already observed by de Dato et al. [53]. Davidson and Janssens [66] reported that the decomposition of more complex substrates could likely lead to higher Q10. In summer, when the microbial community is potentially able to feed on substrates more easily, the presence of root residues from root mortality might have acted as a priming effect, increasing the decomposition of more complex substrates (high Q10), thus compensating for the climatic constraint.
The composition of the microbial community may differ among plant communities and season, likely translating into functional shifts in microbial metabolic capacity. In the present study, CLPP and EL-FAME revealed a dynamic assemblage of microbial communities, as could be expected under seasonal drought cycles [30,67]. However, SDS revealed a more plastic and opportunistic behaviour of microbial community than the WDS. In fact, in the WDS there was a constant use of carbohydrates no matter the season. Differently, in the SDS the complex substrates’ use was constant all through the year and higher than carbohydrate use in both spring and autumn. Since substrates like carboxylic acids, amino acids and glucosamine are considered to mainly derive from roots (exudates and tissue degradation) [64,68], higher belowground C allocation in dry Mediterranean ecosystems [69] determines microbial survival even with a low presence of photosynthates. This was further confirmed by a significant relationship between SR and the degradation of these substrates, which was shown to be mostly used by the microbial communities. In addition, the generally more complex and physiologically diverse microbial community at the Mediterranean site, evidenced by the higher FAME, determined a higher resilience to unfavourable climatic and edaphic conditions. Unexpectedly f [70], fungi were shown to be less involved in CO2 production in these two ecosystems, suggesting a larger sensitivity to drought with respect to other microbial groups. However, microbial communities in SDS appeared to be more driven by fungi than in WDS, which points to the importance of these groups in Mediterranean ecosystems [71].

5. Conclusions

The microbial community is known to be able to adapt easily to varying environmental conditions. In this study, this plasticity appeared different in two sites with dissimilar plant community facing an evident dry season. More plastic and opportunistic behaviour of the soil microbial community was observed in a semi-deciduous Mediterranean ecosystem compared to a winter deciduous temperate shrubland. In the first case, the microbial community was less connected to plant activity and thus photosynthates, but more prone to take advantage of substrates, whatever their origin, in any season. In the second case, as the microbial composition and metabolism were driven by easily decomposable substrates, a reduction in plant photosynthetic activity (due to reduced stomatal conductance or leaf shedding) caused a decrease in heterotrophic respiration.
We can conclude that the response of soil CO2 efflux to water limitation is not exclusively dependent on the climate and that this response is modulated by the degree of adaptation of both plants and the microbial community to drought.

Acknowledgments

This research was founded by the EU-funded infrastructure INCREASE (Grant agreement no. 227628).

Author Contributions

G.d.D., A.L. and P.D.A. conceived and designed the experiment; G.d.D., A.L., S.R.S., R.M., D.L., G.G. performed experiment and analyses; S.R.S. contributed reagents/materials; G.d.D. and A.L. analysed the data; G.d.D. wrote the paper. All authors commented on the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Forests 08 00241 g008 550
Figure A1. (A) Location of the study sites showed by the red and black dots for the Hungarian and Italian sites, respectively; the map is taken from Metzger et al. 2005; (B) Walther–Lieth diagrams of the long-term temperature and precipitation data (1901–2015, data from monthly CRU TS v.4.00, KNMI Climate Explorer http://climexp.knmi.nl, resolution 0.5 °C) for the Hungarian site (upper panel) and Italian site (lower panel).
Figure A1. (A) Location of the study sites showed by the red and black dots for the Hungarian and Italian sites, respectively; the map is taken from Metzger et al. 2005; (B) Walther–Lieth diagrams of the long-term temperature and precipitation data (1901–2015, data from monthly CRU TS v.4.00, KNMI Climate Explorer http://climexp.knmi.nl, resolution 0.5 °C) for the Hungarian site (upper panel) and Italian site (lower panel).
Forests 08 00241 g008
Forests 08 00241 g009a 550Forests 08 00241 g009b 550
Figure A2. Details of the ecosystem types under investigation: (A) the Hungarian forest–steppe transitional biome (WDS); and (B) the Mediterranean shrubland (SDS).
Figure A2. Details of the ecosystem types under investigation: (A) the Hungarian forest–steppe transitional biome (WDS); and (B) the Mediterranean shrubland (SDS).
Forests 08 00241 g009aForests 08 00241 g009b
Table
Table A1. Microclimate conditions inside the cuvette during gas exchange measurements in the two study sites. T: leaf temperature; VPD: vapour pressure deficit; PAR: photosynthetic active radiation.
Table A1. Microclimate conditions inside the cuvette during gas exchange measurements in the two study sites. T: leaf temperature; VPD: vapour pressure deficit; PAR: photosynthetic active radiation.
TVPDPAR
°CkPaµmol m−2 s−1
WDSspring21.41.31300
summer36.53.72000
autumn---
SDSspring20.00.71200
summer33.03.32000
autumn19.10.81000
Table
Table A2. Substrates used for CLPP analysis. Different amounts of delivered C depend on substrates’ solubility in water.
Table A2. Substrates used for CLPP analysis. Different amounts of delivered C depend on substrates’ solubility in water.
SubstratesTypeDelivered (mg mL−1)Label
d-glucoseCarb30G
d-galactoseCarb30GAL
d-fructoseCarb30FRU
l-arabinoseCarb30ARA
TrehaloseCarb0.3TRE
Citric acidCarbAc30CIT
Oxalic acidCarbAc7.5OX
Ascorbic acidCarbAc30ASC
Malic acidCarbAc0.3MAL
Aspartic acidAmAc7.5ASP
LysineAmAc3LIS
AlanineAmAc7.5ALA
ɣ-aminobutyric acidAmAc30BUT
ArginineAmAc30ARG
N-acetyl-glucosamineamide7.5NAG
Carb: carbohydrates; CarbAc: Carboxylic acid; AmAc: Amino acid.
Table
Table A3. Ester linked fatty acids (EL-FAMEs) signature assignment used in this study.
Table A3. Ester linked fatty acids (EL-FAMEs) signature assignment used in this study.
BiomarkersReferences
Gram+
i 14:0[72,73,74,75,76,77]
i 15:0
a 15:0
i 16:0
i 17:0
a 17:0
General
14:0[73,74,75,76,77]
15:0
16:0
Gram−
16:1 ω7c[73,74,75,76,77]
Cy 17:0
18:1 ω9t
Actinomycetes
10Me 16:0[72,73,78]
10Me18:0
Fungi
18:1 ω 9c[73,74,79]
18:2 ω 6,9
Protozoa
20:0[80]
Table
Table A4. Correlations table between the fluxes (SR and Anet) with the climatic variables and the plant water status in the two sites. Bold values are significant at p < 0.05.
Table A4. Correlations table between the fluxes (SR and Anet) with the climatic variables and the plant water status in the two sites. Bold values are significant at p < 0.05.
WDS
SRAnetTsSWCTagsPWP
SR1
Anet0.331
Ts0.50−0.911
SWC−0.420.91−0.931
Ta−0.36−0.900.99−0.991
gs0.330.93−0.920.93−0.931
PWP−0.250.06−0.150.14−0.190.181
SDS
SRAnetTsSWCTagsPWP
SR1
Anet0.221
Ts−0.49−0.671
SWC0.560.49−0.871
Ta−0.54−0.650.960.911
gs0.160.83−0.720.64−0.741
PWP0.530.59−0.930.83−0.910.661
A = Assimilation; SR = soil CO2 efflux; Ts = soil temperature; SWC = soil water content; Ta = air temperature; gs = stomatal conductance; PWP = pre-dawn leaf water potential.
Table
Table A5. Correlations table between the soil CO2 emission, heterotrophic soil CO2 emission, Q10 and soil microclimatic variables in the two sites. Bold values are significant at p < 0.05.
Table A5. Correlations table between the soil CO2 emission, heterotrophic soil CO2 emission, Q10 and soil microclimatic variables in the two sites. Bold values are significant at p < 0.05.
WDS
SRSRhQ10TsSWC
SR1
SRh0.151
Q100.300.181
Ts0.50−0.27−0.441
SWC−0.420.510.36−0.931
SDS
SRSRhq10TsSWC
SR1
SRh−0.241
Q10−0.280.321
Ts−0.490.200.571
SWC0.56−0.23−0.63−0.961
SR = soil CO2 efflux; SRh = heterotrophic soil CO2 efflux; Q10 = temperature sensitivity of SRh; Ts = soil temperature; SWC = soil water content.

References

  1. Knapp, A.K.; Beier, C.; Briske, D.D.; Classen, A.T.; Luo, Y.; Reichstein, M.; Smith, M.D.; Smith, S.D.; Bell, J.E.; Philip, A.; et al. Consequences of more extreme precipitation regimes for terrestrial ecosystems. Bioscience 2008, 58, 811–821. [Google Scholar] [CrossRef]
  2. Rambal, S.; Ourcival, J.M.; Joffre, R.; Mouillot, F.; Nouvellon, Y.; Reichstein, M.; Rocheteau, A. Drought controls over conductance and assimilation of a Mediterranean evergreen ecosystem: Scaling from leaf to canopy. Glob. Chang. Biol. 2003, 9, 1813–1824. [Google Scholar] [CrossRef]
  3. Granier, A.; Reichstein, M.; Bréda, N.; Janssens, I.A.; Falge, E.; Ciais, P.; Grunwald, T.; Aubinet, M.; Berbigier, P.; Bernhofer, C.; et al. Evidence for soil water control on carbon and water dynamics in European forests during the extremely dry year: 2003. Agric. For. Meteorol. 2007, 143, 123–145. [Google Scholar] [CrossRef]
  4. Mouillot, F.; Rambal, S.; Limousin, J.-M.; Longepierre, D.; Kheder-Chiraz, B.; Ouelhazi, B. Mediterranean ecosystems facing global change: Resilient or close to tipping point? In The mediterranean Region under Climate Change—A Scientific Update; Sabrie, M.-L., Gibert-Brunet, E., Mourier, T., Eds.; IRD Editions Institut De Recherche Pour Le Développement: Marseille, France, 2016. [Google Scholar]
  5. Sperlich, D.; Barbeta, A.; Ogaya, R.; Sabaté, S.; Peñuelas, J. Balance between carbon gain and loss under long-term drought: Impacts on foliar respiration and photosynthesis in Quercus ilex L. J. Exp. Bot. 2016, 67, 821–833. [Google Scholar] [CrossRef] [PubMed]
  6. Guidolotti, G.; De Dato, G.; Liberati, D.; De Angelis, P. Canopy Chamber: A useful tool to monitor the CO2 exchange dynamics of shrubland. iForest 2017, 10, 597–604. [Google Scholar] [CrossRef]
  7. Reichstein, M.; Tenhunen, J.D.; Roupsard, O.; Ourcival, J.M.; Rambal, S.; Miglietta, F.; Peressotti, M.; Pecchiari, M.; Tirone, G.; Valentini, R. Severe drought effects on ecosystem CO2 and H2O fluxes at three Mediterranean evergreen sites: Revision of current hypotheses? Glob. Chang. Biol. 2002, 8, 999–1017. [Google Scholar] [CrossRef]
  8. Reichstein, M.; Tenhunen, J.D.; Roupsard, O.; Ourcival, J.-M.; Rambal, S.; Dore, S.; Valentini, R. Ecosystem respiration in two Mediterranean evergreen holm Oak forest: Drought effects and decomposition dynamics. Funct. Ecol. 2002, 16, 27–39. [Google Scholar] [CrossRef]
  9. Chen, S.; Lin, G.; Huang, J.; Jenerette, D.G. Dependence of carbon sequestration on the differential responses of ecosystem photosynthesis and respiration to rain pulses in a semiarid steppe. Glob. Chang. Biol. 2009, 15, 2450–2461. [Google Scholar] [CrossRef]
  10. Bond-Lamberty, B.; Thomson, A. A global database of soil respiration data. Biogeosciences 2010, 7, 1915–1926. [Google Scholar] [CrossRef]
  11. Kuzyakov, Y. Sources of CO2 efflux from soil and review of partitioning methods. Soil Biol. Biochem. 2006, 38, 425–448. [Google Scholar] [CrossRef]
  12. Hanson, P.J.; Edwards, N.T.; Garten, C.T.; Andrews, J.A. Separating root and soil microbical contributions to soil respiration: A review of methods and observations. Biogeochemistry 2000, 48, 115–146. [Google Scholar] [CrossRef]
  13. Tang, J.; Baldocchi, D.D. Spatial–temporal variation in soil respiration in an oak–grass savanna ecosystem in California and its partitioning into autotrophic and heterotrophic components. Biogeochemistry 2005, 73, 183–207. [Google Scholar] [CrossRef]
  14. Curiel Yuste, J.; Janssens, I.A.; Carrara, A.; Ceulemans, R. Annual Q10 of soil respiration reflects plant phenological patterns as well as temperature sensitivity. Glob. Chang. Biol. 2004, 10, 161–169. [Google Scholar] [CrossRef]
  15. Churchland, C.; Grayston, S.J.; Bengtson, P. Spatial variability of soil fungal and bacterial abundance: Consequences for carbon turnover along a transition from a forested to clear-cut site. Soil Biol. Biochem. 2013, 63, 5–13. [Google Scholar] [CrossRef]
  16. Savage, K.; Davidson, E.A.; Tang, J. Diel patterns of autotrophic and heterotrophic respiration among phenological stages. Glob. Chang. Biol. 2013, 19, 1151–1159. [Google Scholar] [CrossRef] [PubMed]
  17. Reth, S.; Reichstein, M.; Falge, E. The effect of soil water content, soil temperature, soil pH-value and the root mass on soil CO2 efflux: A modified model. Plant. Soil 2005, 268, 21–33. [Google Scholar] [CrossRef]
  18. Vargas, R.; Allen, M.F. Environmental controls and the influence of vegetation type, fine roots and rhizomorphs on diel and seasonal variation in soil respiration. New Phytol. 2008, 179, 460–471. [Google Scholar] [CrossRef] [PubMed]
  19. Song, Q.-H.; Tan, Z.-H.; Zhang, Y.-P.; Cao, M.; Sha, L.-Q.; Tang, Y.; Liang, N.-S.; Schaefer, D.; Zhao, J.-F.; Zhao, J.-B.; et al. Spatial heterogeneity of soil respiration in a seasonal rainforest with complex terrain. iForest Biogeosc. For. iForest 2013, 6, 65–72. [Google Scholar] [CrossRef]
  20. de Vries, F.T.; Manning, P.; Tallowin, J.R.B.; Mortimer, S.R.; Pilgrim, E.S.; Harrison, K.A.; Hobbs, P.J.; Quirk, H.; Shipley, B.; Cornelissen, J.H.C.; et al. Abiotic drivers and plant traits explain landscape-scale patterns in soil microbial communities. Ecol. Lett. 2012, 15, 1230–1239. [Google Scholar] [CrossRef] [PubMed]
  21. Scott-Denton, L.E.; Rosenstiel, T.N.; Monson, R.K. Differential controls by climate and substrate over the heterotrophic and rhizospheric components of soil respiration. Glob. Chang. Biol. 2006, 12, 205–216. [Google Scholar] [CrossRef]
  22. De Graaff, M.-A.; Classen, A.T.; Castro, H.F.; Schadt, C.W. Labile soil carbon inputs mediate the soil microbial community composition and plant residue decomposition rates. New Phytol. 2010, 188, 1055–1064. [Google Scholar] [CrossRef] [PubMed]
  23. Högberg, P.; Nordgren, A.; Buchmann, N.; Taylor, A.F.S.; Ekblad, A.; Högberg, M.N.; Nyberg, G.; Ottosson-Löfvenius, M.; Read, D.J. Large-scale forest girdling shows that current photosynthesis drives soil respiration. Nature 2001, 411, 789–792. [Google Scholar] [CrossRef] [PubMed]
  24. Kuzyakov, Y.; Gavrichkova, O. Time lag between photosynthesis and carbon dioxide efflux from soil: A review of mechanisms and controls. Glob. Chang. Biol. 2010, 16, 3386–3406. [Google Scholar] [CrossRef]
  25. Subke, J.-A.; Voke, N.R.; Leronni, V.; Garnett, M.H.; Ineson, P. Dynamics and pathways of autotrophic and heterotrophic soil CO2 efflux revealed by forest girdling. J. Ecol. 2010, 99, 186–193. [Google Scholar] [CrossRef]
  26. Collins, S.L.; Sinsabaugh, R.L.; Crenshaw, C.; Green, L.; Porras-Alfaro, A.; Stursova, M.; Zeglin, L.H. Pulse dynamics and microbial processes in aridland ecosystems. J. Ecol. 2008, 96, 413–420. [Google Scholar] [CrossRef]
  27. Fuchslueger, L.; Bahn, M.; Fritz, K.; Hasibeder, R.; Richter, A. Experimental drought reduces the transfer of recently fixed plant carbon to soil microbes and alters the bacterial community composition in a mountain meadow. New Phytol. 2014, 201, 916–927. [Google Scholar] [CrossRef] [PubMed]
  28. Allison, S.D.; Martiny, J.B.H. Resistance, resilience, and redundancy in microbial communities. Proc. Natl. Acad. Sci. USA 2008, 105, 11512–11519. [Google Scholar] [CrossRef] [PubMed]
  29. Bell, C.W.; Tissue, D.T.; Loik, M.E.; Wallenstein, M.D.; Acosta-Martinez, V.; Erickson, R.A.; Zak, J.C. Soil microbial and nutrient responses to 7 years of seasonally altered precipitation in a Chihuahuan Desert grassland. Glob. Chang. Biol. 2014, 20, 1657–1673. [Google Scholar] [CrossRef] [PubMed]
  30. Wasserstrom, H.; Kublik, S.; Wasserstrom, R.; Schulz, S.; Schloter, M.; Steinberger, Y. Bacterial community composition in costal dunes of the Mediterranean along a gradient from the sea shore to the inland. Sci. Rep. 2017, 7, 40266. [Google Scholar] [CrossRef] [PubMed]
  31. Evans, S.E.; Wallenstein, M.D. Soil microbial community response to drying and rewetting stress: Does historical precipitation regime matter? Biogeochemistry 2012, 109, 101–116. [Google Scholar] [CrossRef]
  32. Cleveland, C.C.; Nemergut, D.R.; Schmidt, S.K.; Townsend, A.R. Increases in soil respiration following labile carbon additions linked to rapid shifts in soil microbial community composition. Biogeochemistry 2007, 82, 229–240. [Google Scholar] [CrossRef]
  33. Garcia-Pausas, J.; Paterson, E. Microbial community abundance and structure are determinants of soil organic matter mineralisation in the presence of labile carbon. Soil Biol. Biochem. 2011, 43, 1705–1713. [Google Scholar] [CrossRef]
  34. Whitaker, J.; Ostle, N.; Nottingham, A.T.; Ccahuana, A.; Salinas, N.; Bardgett, R.D.; Meir, P.; McNamara, N.P. Microbial community composition explains soil respiration responses to changing carbon inputs along an Andes-to-Amazon elevation gradient. J. Ecol. 2014, 102, 1058–1071. [Google Scholar] [CrossRef] [PubMed]
  35. Li, Y.; Liu, Y.; Wu, S.; Niu, L.; Tian, Y. Microbial properties explain temporal variation in soil respiration in a grassland subjected to nitrogen addition. Sci. Rep. 2015, 5, 18496. [Google Scholar] [CrossRef] [PubMed]
  36. Curiel Yuste, J.; Fernandez-Gonzalez, A.J.; Fernandez-Lopez, M.; Ogaya, R.; Penuelas, J.; Sardans, J.; Lloret, F. Strong functional stability of soil microbial communities under semiarid Mediterranean conditions and subjected to long-term shifts in baseline precipitation. Soil Biol. Biochem. 2014, 69, 223–233. [Google Scholar] [CrossRef]
  37. SardegnaARPA. Available online: http://gis.sar.sardegna.it/gfmaplet/?map=carta_bioclimatica (accessed on 7 July 2017).
  38. Metzger, M.J.; Bunce, R.G.H.; Jongman, R.H.G.; Mücher, C.A.; Watkins, J.W. A climatic stratification of the environment of Europe: A climatic stratification of the European environment. Glob. Ecol. Biogeogr. 2005, 14, 549–563. [Google Scholar] [CrossRef]
  39. Lagomarsino, A.; De Angelis, P.; Moscatelli, M.C.; Grego, S. The influence of temperature and labile C substrates on heterotrophic respiration in response to elevated CO2 and nitrogen fertilization. Plant. Soil 2009, 317, 223–234. [Google Scholar] [CrossRef]
  40. Campbell, C.D.; Chapman, S.J.; Cameron, C.M.; Davidson, M.S.; Potts, J.M. A rapid microtiter plate method to measure carbon dioxide evolved from carbon substrate amendments so as to determine the physiological profiles of soil microbial communities by using whole soil. Appl. Environ. Microbiol. 2003, 69, 3593–3599. [Google Scholar] [CrossRef] [PubMed]
  41. Schutter, M.E.; Dick, R.P. Comparison of Fatty Acid Methyl Ester (FAME) Methods for Characterizing Microbial Communities. Soil Sci. Soc. Am. J. 2000, 64, 1659–1668. [Google Scholar] [CrossRef]
  42. Chaves, M.M.; Pereira, J.S.; Maroco, J.; Rodrigues, M.L.; Ricardo, C.P.P.; Osório, M.L.; Carvalho, I.; Faria, T.; Pinheiro, C. How plants cope with water stress in the field? Photosynthesis and growth. Ann. Bot. 2002, 89, 907–916. [Google Scholar] [CrossRef] [PubMed]
  43. Flexas, J.; Diaz-Espejo, A.; Gago, J.; Gallé, A.; Galmés, J.; Gulías, J.; Medrano, H. Photosynthetic limitations in Mediterranean plants: A review. Environ. Exp. Bot. 2014, 103, 12–23. [Google Scholar] [CrossRef]
  44. Limousin, J.M.; Bickford, C.P.; Dickman, L.T.; Pangle, R.E.; Hudson, P.J.; Boutz, A.L.; Gehres, N.; Osuna, J.L.; Pockman, W.T.; McDowell, N.G. Regulation and acclimation of leaf gas exchange in a pinon-juniper woodland exposed to three different precipitation regimes. Plant Cell Environ. 2013, 36, 1812–1825. [Google Scholar] [CrossRef] [PubMed]
  45. Grassi, G.; Magnani, F. Stomatal, mesophyll conductance and biochemical limitations to photosynthesis as affected by drought and leaf ontogeny in ash and oak trees. Plant Cell Environ. 2005, 28, 834–849. [Google Scholar] [CrossRef]
  46. Barbeta, A.; Ogaya, R.; Penuelas, J. Dampening effects of long-term experimental drought on growth and mortality rates of a Holm oak forest. Glob. Chang. Biol. 2013, 19, 3133–3144. [Google Scholar] [CrossRef] [PubMed]
  47. Hoover, D.L.; Rogers, B.M. Not all droughts are created equal: The impacts of interannual drought pattern and magnitude on grassland carbon cycling. Glob. Chang. Biol. 2015, 22. [Google Scholar] [CrossRef] [PubMed]
  48. Swidrak, I.; Schuster, R.; Oberhuber, W. Comparing growth phenology of co-occurring deciduous and evergreen conifers exposed to drought. Flora 2013, 208, 609–617. [Google Scholar] [CrossRef]
  49. Ripullone, F.; Lauteri, M.; Grassi, G.; Amato, M.; Borghetti, M. Variation in nitrogen supply changes water-use efficiency of Pseudotsuga menziesii and Populus x euroamericana; a comparison of three approaches to determine water-use efficiency. Tree Physiol. 2004, 24, 671–679. [Google Scholar] [CrossRef] [PubMed]
  50. Fichot, R.; Laurans, F.; Monclus, R.; Moreau, A.; Pilate, G.; Brignolas, F. Xylem anatomy correlates with gas exchange, water-use efficiency and growth performance under contrasting water regimes: Evidence from Populus deltoides x Populus nigra hybrids. Tree Physiol. 2009, 29, 1537–1549. [Google Scholar] [CrossRef] [PubMed]
  51. Marty, C.; Lamaze, T.; Pornon, A. Leaf life span optimizes annual biomass production rather than plant photosynthetic capacity in an evergreen shrub. New Phytol. 2010, 187, 407–416. [Google Scholar] [CrossRef] [PubMed]
  52. De Dato, G.D.; Micali, M.; Abou Jaoudé, R.; Liberati, D.; De Angelis, P. Earlier summer drought affects leaf functioning of the Mediterranean species Cistus monspeliensis L. Environ. Exp. Bot. 2013, 93, 13–19. [Google Scholar] [CrossRef]
  53. de Dato, G.; De Angelis, P.; Sirca, C.; Beier, C. Impact of drought and increasing temperatures on soil CO2 emissions in a Mediterranean shrubland (gariga). Plant. Soil 2010, 327, 153–166. [Google Scholar] [CrossRef]
  54. Lellei-Kovács, E.; Botta-Dukát, Z.; de Dato, G.; Estiarte, M.; Guidolotti, G.; Kopittke, G.R.; Kovács-Láng, E.; Kröel-Dulay, G.; Larsen, K.S.; Peñelas, J.; et al. Temperature Dependence of Soil Respiration Modulated by Thresholds in Soil Water Availability Across European Shrubland Ecosystems. Ecosystems 2016, 19, 1460–1477. [Google Scholar] [CrossRef]
  55. Carey, J.C.; Tang, J.; Templer, P.H.; Kroeger, K.D.; Crowther, T.W.; Burton, A.J.; Dukes, J.S.; Emmett, B.; Frey, S.D.; Heskel, M.A.; et al. Temperature response of soil respiration largely unaltered with experimental warming. Proc. Natl. Acad. Sci. USA 2016, 113, 13797–13802. [Google Scholar] [CrossRef] [PubMed]
  56. Steudle, E. Water uptake by roots: Effects of water deficit. J. Exp. Bot. 2000, 51, 1531–1542. [Google Scholar] [CrossRef] [PubMed]
  57. Högberg, P.; Högberg, M.N.; Göttlicher, S.G.; Betson, N.R.; Keel, S.G.; Metcalfe, D.B.; Campbell, C.; Schindlbacher, A.; Hurry, V.; Lundmark, T.; et al. High temporal resolution tracing of photosynthate carbon from the tree canopy to forest soil microorganisms. New Phytol. 2008, 177, 220–228. [Google Scholar] [CrossRef] [PubMed]
  58. Balogh, J.; Papp, M.; Pintér, K.; Fóti, S.; Posta, K.; Eugster, W.; Nagy, Z. Autotrophic component of soil respiration is repressed by drought more than the heterotrophic one in dry grasslands. Biogeosciences 2016, 13, 5171–5182. [Google Scholar] [CrossRef]
  59. Risk, D.; Nickerson, N.; Phillips, C.L.; Kellman, L.; Moroni, M. Drought alters respired δ13 CO2 from autotrophic, but not heterotrophic soil respiration. Soil Biol. Biochem. 2012, 50, 26–32. [Google Scholar] [CrossRef]
  60. López, B.; Sabaté, S.; Gracia, C. Fine roots dynamics in a Mediterranean forest: Effects of drought and stem density. Tree Physiol. 1998, 8, 601–606. [Google Scholar] [CrossRef]
  61. López, B.; Sabaté, S.; Gracia, C.A. Annual and seasonal changes in fine root biomass of a Quercus. ilex L. forest. Plant. Soil 2001, 230, 125–134. [Google Scholar] [CrossRef]
  62. Rey, A. Mind the gap: Non-biological processes contributing to soil CO2 efflux. Glob. Chang. Biol. 2015, 21, 1752–1761. [Google Scholar] [CrossRef] [PubMed]
  63. Gliksman, D.; Rey, A.; Seligmann, R.; Dumbur, R.; Sperling, O.; Navon, Y.; Haenel, S.; De Angelis, P.; Arnone, J.A.; Grünzweig, J.M. Biotic degradation at night, abiotic degradation at day: Positive feedbacks on litter decomposition in drylands. Glob. Chang. Biol. 2017, 23, 1564–1574. [Google Scholar] [CrossRef] [PubMed]
  64. Jones, D.; Nguyen, C.; Finlay, R. Carbon flow in the rhizosphere: Carbon trading at the soil–root interface. Plant. Soil 2009, 321, 5–33. [Google Scholar] [CrossRef]
  65. Lellei-Kovács, E.; Kovács-Láng, E.; Botta-Dukát, Z.; Kalapos, T.; Emmett, B.; Beier, C. Thresholds and interactive effects of soil moisture on the temperature response of soil respiration. Eur. J. Soil Biol. 2011, 47, 247–255. [Google Scholar] [CrossRef]
  66. Davidson, E.A.; Janssens, I.A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 2006, 440, 165–173. [Google Scholar] [CrossRef] [PubMed]
  67. Waldrop, M.; Firestone, M. Seasonal dynamics of microbial community composition and function in oak canopy and open grassland soils. Microb. Ecol. 2006, 52, 470–479. [Google Scholar] [CrossRef] [PubMed]
  68. Baudoin, E.; Benizri, E.; Guckert, A. Impact of artificial root exudates on the bacterial community structure in bulk soil and maize rhizosphere. Soil Biol. Biochem. 2003, 35, 1183–1192. [Google Scholar] [CrossRef]
  69. Almagro, M.; LÛpez, J.; Boix-Fayos, C.; Albaladejo, J.; MartÌnez-Mena, M. Belowground carbon allocation patterns in a dry Mediterranean ecosystem: A comparison of two models. Soil Biol. Biochem. 2010, 42, 1549–1557. [Google Scholar] [CrossRef]
  70. Manzoni, S.; Schimel, J.P.; Porporato, A. Responses of soil microbial communities to water stress: Results from a meta-analysis. Ecology 2012, 93, 930–938. [Google Scholar] [CrossRef] [PubMed]
  71. Yuste, J.C.; Peñuelas, J.; Estiarte, M.; Garcia-Mas, J.; Mattana, S.; Ogaya, R.; Pujol, M.; Sardans, J. Drought-resistant fungi control soil organic matter decomposition and its response to temperature. Glob. Chang. Biol. 2011, 17, 1475–1486. [Google Scholar] [CrossRef]
  72. Bååth, E.; Frostegård, Å.; Fritze, H. Soil bacterial biomass, activity, phospholipid fatty acid pattern, and pH tolerance in an area polluted with alkaline dust deposition. Appl. Environ. Microbiol. 1992, 58, 4026–4031. [Google Scholar] [PubMed]
  73. Frostegård, A.; Tunlid, A.; Baath, E. Phospholipid fatty acid composition, biomass, and activity of microbial communities from two soil types experimentally exposed to different heavy metals. Appl. Environ. Microbiol. 1993, 59, 3605–3617. [Google Scholar] [PubMed]
  74. Zelles, L. Phospholipid fatty acid profiles in selected members of soil microbial communities. Chemosphere 1997, 35, 275–294. [Google Scholar] [CrossRef]
  75. Zogg, G.P.; Zak, D.R.; Ringelberg, D.B.; White, D.C.; Macdonald, N.W.; Pregitzer, K.S. Compositional and functional shifts in microbial communities due to soil warming. Soil. Sci. Soc. Am. J. 1997, 61, 475–481. [Google Scholar] [CrossRef]
  76. Waldrop, M.P.; Balser, T.C.; Firestone, M.K. Linking microbial community composition to function in a tropical soil. Soil Biol. Biochem. 2000, 32, 1837–1846. [Google Scholar] [CrossRef]
  77. Fierer, N.; Schimel, J.P.; Holden, P.A. Variations in microbial community composition through two soil depth profiles. Soil Biol. Biochem. 2003, 35, 167–176. [Google Scholar] [CrossRef]
  78. Allison, V.; Yermakov, Z.; Miller, R.; Jastrow, J.; Matamala, R. Using landscape and depth gradients to decouple the impact of correlated environmental variables on soil microbial community composition. Soil Biol. Biochem. 2007, 39, 505–516. [Google Scholar] [CrossRef]
  79. Federle, T.W. Microbial distribution in soil-new techniques. In Perspectives in Microbial Ecology; Megusar, F., Gantar, M., Eds.; Slovene Society for Microbiology: Ljubljana, Slovenia, 1986; pp. 493–498. [Google Scholar]
  80. White, D.C.; Stair, J.O.; Ringelberg, D.B. Quantitative comparisons of in situ microbial biodiversity by signature biomarker analysis. J. Ind. Microbiol. 1996, 17, 185–196. [Google Scholar] [CrossRef]
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Figure 1. (A,B) assimilation rate (A ± s.e.; n = 10); (C,D) stomatal conductance (gs ± s.e.; n = 10,); (E,F) pre-dawn water potential (PWP ± s.e.; n = 10) in the winter deciduous (WDS, left panels, A,C,E) and summer deciduous (SDS, right panels, B,D,F) sites. Gas exchange was not measured in autumn in the WDS site because of the leaf fall. Lowercase letters show significant differences among the seasons at p < 0.05.
Figure 1. (A,B) assimilation rate (A ± s.e.; n = 10); (C,D) stomatal conductance (gs ± s.e.; n = 10,); (E,F) pre-dawn water potential (PWP ± s.e.; n = 10) in the winter deciduous (WDS, left panels, A,C,E) and summer deciduous (SDS, right panels, B,D,F) sites. Gas exchange was not measured in autumn in the WDS site because of the leaf fall. Lowercase letters show significant differences among the seasons at p < 0.05.
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Figure 2. CO2 efflux (±s.e., n = 10; bars, upper panels), soil water content (SWC ± s.e.; n = 10; lower panels; right y-axis) and soil temperature (Ts ± s.e., n = 10; lower panels; left y-axis; temperature is full circle with continuous line, soil water content is white circle with dashed line) in winter deciduous (WDS, left panels, A,C) and summer deciduous (SDS, right panels, B,D) sites. In panels (A) and (B), total (field measurements; white bars) and heterotrophic (laboratory measurements; strikethrough bars) CO2 efflux are shown; percentage numbers are the contribution of the heterotrophic respiration to the total soil CO2 efflux; ***, **, * indicate differences between total and heterotrophic respiration at p < 0.001, p < 0.01, p < 0.05, respectively. Capital and lowercase letters in panels (A,B) show significant seasonal variations (p < 0.05) of total and heterotrophic CO2 efflux, respectively. In panels (C,D) normal and italic lowercase letters indicate seasonal differences of soil temperature and soil water content, respectively.
Figure 2. CO2 efflux (±s.e., n = 10; bars, upper panels), soil water content (SWC ± s.e.; n = 10; lower panels; right y-axis) and soil temperature (Ts ± s.e., n = 10; lower panels; left y-axis; temperature is full circle with continuous line, soil water content is white circle with dashed line) in winter deciduous (WDS, left panels, A,C) and summer deciduous (SDS, right panels, B,D) sites. In panels (A) and (B), total (field measurements; white bars) and heterotrophic (laboratory measurements; strikethrough bars) CO2 efflux are shown; percentage numbers are the contribution of the heterotrophic respiration to the total soil CO2 efflux; ***, **, * indicate differences between total and heterotrophic respiration at p < 0.001, p < 0.01, p < 0.05, respectively. Capital and lowercase letters in panels (A,B) show significant seasonal variations (p < 0.05) of total and heterotrophic CO2 efflux, respectively. In panels (C,D) normal and italic lowercase letters indicate seasonal differences of soil temperature and soil water content, respectively.
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Figure 3. Heterotrophic soil basal respiration SRb and temperature sensitivity Q10 calculated by fitting the exponential equation SRh = a*exp(k*Ts). SRb = a for Ts = 0 °C, Q10 = exp(10*k) in winter deciduous (WDS, left panels, A,C) and summer deciduous (SDS, right panels, B,D) sites. Lowercase letters show significant seasonal differences (p < 0.05) in each site for each parameter (±s.e., n = 10).
Figure 3. Heterotrophic soil basal respiration SRb and temperature sensitivity Q10 calculated by fitting the exponential equation SRh = a*exp(k*Ts). SRb = a for Ts = 0 °C, Q10 = exp(10*k) in winter deciduous (WDS, left panels, A,C) and summer deciduous (SDS, right panels, B,D) sites. Lowercase letters show significant seasonal differences (p < 0.05) in each site for each parameter (±s.e., n = 10).
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Figure 4. Water-extractable organic carbon (WEOC ± s.e.) in winter deciduous (WDS) (A) and summer deciduous (SDS) (B) sites, during the three seasons. Different letters show significant differences (p < 0.05) among the seasons. Spring = grey; Summer = dashed; Autumn = white.
Figure 4. Water-extractable organic carbon (WEOC ± s.e.) in winter deciduous (WDS) (A) and summer deciduous (SDS) (B) sites, during the three seasons. Different letters show significant differences (p < 0.05) among the seasons. Spring = grey; Summer = dashed; Autumn = white.
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Figure 5. Substrate-induced respiration (SIR ± s.e.) by easily decomposable substrates (Carbohydrates; grey bars) and more complex and slowly decomposable substrates (average of carboxylic acids, amino acids and N-acetyl glucosamine; dashed bars) in winter deciduous (WDS) (A) and summer deciduous (SDS) (B) sites. Capital letters show significant differences (p < 0.05) among the seasons for SIR easily decomposable substrates; lowercase letters show significant differences (p < 0.05) among the seasons for slowly decomposable substrates. Asterisks (***, * for p < 0.001, and p < 0.05, respectively) show significant differences between SIR by the easily decomposable and slowly decomposable substrates in each season.
Figure 5. Substrate-induced respiration (SIR ± s.e.) by easily decomposable substrates (Carbohydrates; grey bars) and more complex and slowly decomposable substrates (average of carboxylic acids, amino acids and N-acetyl glucosamine; dashed bars) in winter deciduous (WDS) (A) and summer deciduous (SDS) (B) sites. Capital letters show significant differences (p < 0.05) among the seasons for SIR easily decomposable substrates; lowercase letters show significant differences (p < 0.05) among the seasons for slowly decomposable substrates. Asterisks (***, * for p < 0.001, and p < 0.05, respectively) show significant differences between SIR by the easily decomposable and slowly decomposable substrates in each season.
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Figure 6. Principal Component Analysis (PCA) of the community level physiological profile (CLPP). (A) Factor coordinates of cases: empty triangles = WDS, full circles = SDS; Spr = spring, sum = Summer, aut = Autumn (B) factor loadings of variables; for labels see Table A2.
Figure 6. Principal Component Analysis (PCA) of the community level physiological profile (CLPP). (A) Factor coordinates of cases: empty triangles = WDS, full circles = SDS; Spr = spring, sum = Summer, aut = Autumn (B) factor loadings of variables; for labels see Table A2.
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Figure 7. Principal Component Analysis (PCA) of microbial population structure based on the EL-FAME profiles. (A) Factor coordinates of cases: empty triangles = WDS, full circles = SDS; Spr = Spring; sum = Summer; aut = Autumn; (B) factor loadings of variables; for labels see Table A3.
Figure 7. Principal Component Analysis (PCA) of microbial population structure based on the EL-FAME profiles. (A) Factor coordinates of cases: empty triangles = WDS, full circles = SDS; Spr = Spring; sum = Summer; aut = Autumn; (B) factor loadings of variables; for labels see Table A3.
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Table
Table 1. Main characteristics of the study areas. The two values of air temperature and precipitation refer to the 30-year average measured in a proximal meteorological station and to the 2010 values measured at the study site.
Table 1. Main characteristics of the study areas. The two values of air temperature and precipitation refer to the 30-year average measured in a proximal meteorological station and to the 2010 values measured at the study site.
Hungary (WDS)Italy (SDS)
Location46°53′ N, 19°23′ E40°36′ N, 8°9′ E
Altitude (m)10835
Air temperature (°C)30-year/201030-year/2010
Annual10.6/10.316.8/16.3
July21.1/22.229.9/24.5
January−1.9/−2.26.7/10.0
Precipitation (mm year−1)30-year/201030-year/2010
505/1026643/824
Main growing season(s) > 5° more than 5 daysApril–SeptemberMarch–June October–December
PET potential evapotranspiration (mm)9141026
AET actual evapotranspiration (mm)203246
GDD (°C) (5 < T < 25) growing degree days23863180
Soil Characteristics
Soil type (FAO standard)Calcaric ArenosolsLuvi and Leptosoils
Top soil
Depth (cm)0–100–20
pH7.97.7
C/N14.334
SOM (%)0.743.8
Bulk density (g cm−3)1.41.1
Deeper soil
Depth (cm)10–2020–40
pH8.17.8
C/N6.752
SOM (%)0.324.7
Bulk density (g cm−3)1.451.1
Plants
Dominant Plant speciesPopulus alba Festuca vaginata Cynodon dactylon Stipa borysthenicaCistus monspeliensis Helichrysum italicum Dorycnium pentaphyllum
Plant cover (%)3582
Main rooting depth0–200–10
Table
Table 2. General characteristics of climatic conditions (soil water content (SWC) and temperature), phenological status (vegetative period) and related source of C for soil CO2 efflux (Pl = CO2 from plant activity, i.e., sustained by plant photosynthesis; SOM = CO2 from soil organic matter).
Table 2. General characteristics of climatic conditions (soil water content (SWC) and temperature), phenological status (vegetative period) and related source of C for soil CO2 efflux (Pl = CO2 from plant activity, i.e., sustained by plant photosynthesis; SOM = CO2 from soil organic matter).
SpringSummerAutumn
WDSSDSWDSSDSWDSSDS
SWCnot limitingnot limitinglimitinglimitingnot limitingnot limiting
Temperaturenot limitingnot limitingnot limitingnot limitinglimitingnot limiting
Vegetative periodactiveactiveactivenot activenot active 1active
Source of CPl + SOMPl + SOMPl + SOMSOMPl + SOMPl + SOM
1 Autumn is not an active period in Hungary (WDS) for poplars and C4 plants in dormant status, while the C3 plants (Festuca vaginata) are inactive only in frosty or snow-covered winter periods.
Table
Table 3. Content of EL-FAME (nmol g−1 soil) along the seasons in winter deciduous (WDS) and summer deciduous (SDS) sites. Different lowercase letters mean significant (p < 0.05) temporal variation in each group.
Table 3. Content of EL-FAME (nmol g−1 soil) along the seasons in winter deciduous (WDS) and summer deciduous (SDS) sites. Different lowercase letters mean significant (p < 0.05) temporal variation in each group.
WDSSDS
SpringSummerAutumnSpringSummerAutumn
total FAME0.204 a0.158 a0.116 b0.621 b0.722 a0.466 c
Total Bacteria0.174 a0.132 ab0.102 b0.516 b0.613 a0.367 c
G+0.051 a0.033 b0.025 b0.163 a0.166 a0.090 b
G−0.046 a0.034 a0.033 a0.138 a0.114 ab0.080 b
Actinomycetes0.012 a0.005 b0.011 ab0.033 a0.031 a0.021 b
Fungi0.027 b0.023 b0.008 a0.082 a0.096 a0.085 a
Protozoa0.003 b0.003 b0.006 a0.022 a0.013 b0.014 b
Table
Table 4. Pearson coefficients of correlation between SR and SRh with SIR and EL-FAME parameters. * p < 0.05; ** p < 0.01; *** p < 0.001; n.s. = not significant.
Table 4. Pearson coefficients of correlation between SR and SRh with SIR and EL-FAME parameters. * p < 0.05; ** p < 0.01; *** p < 0.001; n.s. = not significant.
SRSRhSIR
SIR0.66n.s.0.92**
carbohydrates−0.65n.s.−0.05n.s.−0.03n.s.
carboxylic acids0.91**0.79*0.83*
amino acids0.64n.s.0.88n.s.0.99***
NAG0.93*0.77*0.74*
total FAME0.68n.s.0.92**0.91**
Total bacterial0.70n.s.0.90*0.94***
G+0.72*0.87*0.96***
G−0.79*0.82*0.93***
Actinomycetes0.70n.s.0.88***0.96***
Fungi0.26n.s.0.65n.s.0.39n.s.
Protozoa0.78*0.70n.s.0.77*

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De Dato, G.; Lagomarsino, A.; Lellei-Kovacs, E.; Liberati, D.; Abou Jaoudé, R.; Marabottini, R.; Stazi, S.R.; Guidolotti, G.; Kovacs-Lang, E.; Kroel-Dulay, G.; et al. The Response of Soil CO2 Efflux to Water Limitation Is Not Merely a Climatic Issue: The Role of Substrate Availability. Forests 2017, 8, 241. https://doi.org/10.3390/f8070241

AMA Style

De Dato G, Lagomarsino A, Lellei-Kovacs E, Liberati D, Abou Jaoudé R, Marabottini R, Stazi SR, Guidolotti G, Kovacs-Lang E, Kroel-Dulay G, et al. The Response of Soil CO2 Efflux to Water Limitation Is Not Merely a Climatic Issue: The Role of Substrate Availability. Forests. 2017; 8(7):241. https://doi.org/10.3390/f8070241

Chicago/Turabian Style

De Dato, Giovanbattista, Alessandra Lagomarsino, Eszter Lellei-Kovacs, Dario Liberati, Renée Abou Jaoudé, Rosita Marabottini, Silvia Rita Stazi, Gabriele Guidolotti, Edit Kovacs-Lang, György Kroel-Dulay, and et al. 2017. "The Response of Soil CO2 Efflux to Water Limitation Is Not Merely a Climatic Issue: The Role of Substrate Availability" Forests 8, no. 7: 241. https://doi.org/10.3390/f8070241

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