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Carbon Cycle Dynamics in Soil Warming Experiments at Harvard Forest 2019

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  • Lead: Kristen DeAngelis
  • Investigators: Grace Pold, Serita Frey, Jerry Melillo
  • Contact: Information Manager
  • Start date: 2019
  • End date: 2019
  • Status: complete
  • Location: Prospect Hill Tract (Harvard Forest)
  • Latitude: +42.54 degrees
  • Longitude: -72.18 degrees
  • Elevation: 365 meter
  • Datum: WGS84
  • Taxa:
  • Release date: 2023
  • Language: English
  • EML file: knb-lter-hfr.431.2
  • DOI: digital object identifier
  • EDI: data package
  • DataONE: data package
  • Related links:
  • Study type: long-term measurement, short-term measurement
  • Research topic: forest-atmosphere exchange; large experiments and permanent plot studies;
  • LTER core area: population studies
  • Keywords: climate change, metabolism, microbes, nutrient cycling, soil carbon, soil warming
  • Abstract:

    Microbes are responsible for cycling carbon (C) through soils, and predicted changes in soil C stocks under climate change are highly sensitive to shifts in the mechanisms assumed to control the microbial physiological response to warming. Two mechanisms have been suggested to explain the long-term warming impact on microbial physiology: microbial thermal acclimation and changes in the quantity and quality of substrates available for microbial metabolism. Yet studies disentangling these two mechanisms are lacking. To resolve the drivers of changes in microbial physiology in response to long-term warming, we sampled soils from 13- and 28-year-old soil warming experiments in different seasons. We performed short-term laboratory incubations across a range of temperatures to measure the relationships between temperature sensitivity of physiology (growth, respiration, carbon use efficiency, and extracellular enzyme activity) and the chemical composition of soil organic matter. We observed apparent thermal acclimation of microbial respiration, but only in summer, when warming had exacerbated the seasonally-induced, already small dissolved organic matter pools. Irrespective of warming, greater quantity and quality of soil carbon increased the extracellular enzymatic pool and its temperature sensitivity. We propose that fresh litter input into the system seasonally cancels apparent thermal acclimation of C-cycling processes to decadal warming. Our findings reveal that long-term warming has indirectly affected microbial physiology via reduced C availability in this system, implying that earth system models including these negative feedbacks may be best suited to describe long-term warming effects on these soils.

  • Methods:

    Soil collection

    Soils were collected from two long-term warming experiments at the Harvard Forest Long-Term Ecological Research (LTER) sites in Petersham MA USA (42°30′30″N, 72°12′28″W). The two experiments are located immediately adjacent to each other and had been warmed +5°C for 13 (Soil Warming and Nitrogen Addition Study [SWaN]; Contosta et al., 2011) or 28 years (Prospect Hill [PH]; Melillo et al., 2002). We sampled control and heated plots in SWaN, and disturbance control and heated plots in PH; these disturbance controls had heating cables buried as in the heated plots, but the power was never turned on. Experimental plots are 6 × 6 m at Prospect Hill, and 3 × 3 m at SWaN, and follow a randomized block design in the former and a completely randomized design in the latter.

    The sites of the two warming experiments are co-located in the same forest stand, characterized by coarse-loamy inceptisols and the same dominant tree species: paper and black birch (Betula papyrifera and lenta), red maple (Acer rubrum), black and red oak (Quercus velutina and rubra), and American beech (Fagus grandifolia). SWaN plots are situated under a canopy gap relative to PH, resulting in slightly different understory vegetation between the two sites (Muth and Bazzaz, 2002). We pooled the control plot data from both sites for analysis since the control plots do not differ in the parameters measured here, with the exception of SOM lability index (I index). However, warmed treatment plots at SWaN and PH have not followed the same respiration trajectory over the course of the experiment, so we do not consider the field experiments as two stages in a chronosequence. Additional site details can be found in Table S1.

    Soils were sampled on 15 July and 19 October 2019, timings which were chosen to reflect soil processes before and after autumn litter deposition (Munger and Wofsy, 2021). We observed that between July and October of 2019, most of the Acer trees had lost their leaves, resulting in an increase in forest floor litter material between the two time points.

    Two 10 cm depth soil cores were collected from each of five plots per site, treatment, and time point using a 5.7 cm diameter tulip bulb planter. After removing and excluding the undecomposed forest floor litter material, these cores were separated into organic and mineral soil by color and texture. Soils were then sieved to 2 mm on site and transported back to the laboratory at ambient temperature, within 4 h of collection. We ultimately processed and analyzed a total of 79 samples because the organic soil exceeded the length of the corer and prevented sampling of the mineral soil in one control SWaN plot in July. Immediately upon arrival at the laboratory, different subsamples of soil were dried to constant mass at 65°C overnight to determine soil moisture content or placed in the −80°C for metabolomic analysis. The remaining soil was left in plastic containers at room temperature (20°C) overnight. Samples which had greater than the target of 48% water holding capacity were left with the lids of the container ajar to allow them to dry slightly overnight (n = 2 samples). Soil was then weighed for the CUE, respiration, enzyme assays, microbiological assays, and SOM analyses described below.

    Microbial community catabolic potential

    We used BioLog Ecoplates to evaluate the intrinsic potential growth rate and substrate preferences of microbes in our soil samples. Within 24 h of collecting soils, we placed 0.25 g (organic) or 0.5 g (mineral) soil in a 125 ml Erlenmeyer flask with 50 ml of 0.9% sodium chloride and mixed on a shaker table at 180 rpm for 30 min. The flasks were then left for 5 min to allow the particles to settle, and then 100 μl of supernatant was transferred to each well of the EcoPlate. The initial absorbance of each plate was measured at 590 and 750 nm and after every 4–12 h afterwards to capture the full growth curves. The measurements at 750 nm were subtracted from the measurements at 590 nm to differentiate the change in dye reduction through time from the change in cell growth. Then the amount of dye color development in the water only well was subtracted out to quantify the metabolic activity due to the substrate itself. Negative control plates containing just buffer were used to verify the assays were not contaminated. We subsequently fit growth curves to both individual substrates and to the average color across the plate (mean well color development) using the growthcurver package (Sprouffske and Wagner, 2016). Growth rates not significantly different from zero (p > .05) were set to zero before all substrate-specific growth rates were used for calculating substrate class-specific growth rates and for principal coordinates analysis. Principal coordinates analysis was completed using the vegan package (Oksanen et al., 2019) with a Hellinger-transformed relative growth rate matrix rather than the raw growth rate data so that relative substrate preference could be separated from the overall differences in growth rate under the assay conditions. We refer to the first axis of this ordination as the “ecoplate substrate diversity.”

    Extracellular enzyme activity

    Between the collection and completion of extracellular enzyme assays (within 4 days), we stored soils at 15°C, which was near the middle of field temperature range. We assayed the cellulose-degrading enzyme beta-glucosidase (BG), the chitin- and peptidoglycan-degrading enzyme N-acetylglucosaminidase (NAG), and the oxidative enzyme pool (phenol oxidase + peroxidase; OX) at 4, 10, 15, 20, 25, and 30°C, covering most of the range of growing season temperatures experienced by heated and control plot soil communities at these sites. These enzymes were selected for their propensity to target chemically similar (BG) or chemically diverse (OX) bonds, as well as microbial necromass (NAG) (Burns and Dick, 2002).

    Slurries were prepared with 1.25 g fresh wet weight soil in 175 ml 50 mm pH 4.7 sodium acetate buffer, using a Waring blender set to high for 1 min. The slurry was stirred at 300 rpm during pipetting to ensure an even distribution of soil. Fourteen technical replicates of 200 μl slurry were pipetted into black plates for BG and NAG, and 500 μl into deep well 2 ml plates for total oxidative enzyme pool. Plates were placed at the assay temperature for at least 25 min before substrate addition to allow them to reach the specific target temperature. Fifty microliter of 4000 μm 4-methylumbelliferyl β-d-glucopyranoside or 2000 μm 4-methylumbelliferyl N-acetyl-β-d-glucosaminide, or 500 μl 25 mm L-DOPA +0.03% H2O2 were added to each well, which we previously deemed to be sufficient to attain the maximum activity rate. Each hydrolytic plate contained a slurry-only control (200 μl slurry, 50 μl buffer) and a standard curve (50 μl 0–500 μm in twofold dilution, in 200 μl buffer). A separate plate for mineral and organic soil quenches (200 μl slurry, 50 μl standard) and substrate-only controls (200 μl buffer, 50 μl substrate) was prepared for each temperature.

    Hydrolytic assay plates were measured at an excitation/emission wavelength pair of 360/450 nm immediately following substrate addition and after an additional 2, 4, and 8 h. Greatest activity was observed between 0 and 2 h, so we used data from this period in our analyses. A 100 μl aliquot of the supernatant from oxidative assay plates was removed to a clear polystyrene plate after 4 h of incubation and read at 460 nm. We used a SpectraMax M2 plate reader controlled by the SoftMaxPro v. 5.4 software for all measurements. Extracellular enzyme rates were calculated as previously (German et al., 2011) and normalized by the sample-specific microbial biomass carbon (MBC) measurement (described below).

    Respiration, growth, and CUE

    We used the substrate-independent (H218O-CUE) method (Spohn et al., 2016) to evaluate soil microbial CUE. Weighing for growth and respiration measurements began the day following soil collection. Three 0.3 g (mineral) or 0.15 g (organic) replicate aliquots of soil were weighed into small vials, placed in a larger tube, and sealed with parafilm to prevent additional moisture loss. Once all samples were weighed (2 days after soil collection), water was added to bring them to 60% water holding capacity. Two replicates received water so that 20% of the total water was present as 18O-water, while the remaining replicate received all water as 16O-water to account for natural abundance 18O. Tubes were stoppered with a neoprene bung and immediately placed in the appropriate incubator (4, 10, 15, 20, 25, or 30°C). Empty tubes were also sealed after every 5–10 tubes in order to measure the starting CO2 levels in tubes. After 24 (15–30°C) or 48 (4 and 10°C) hours, the CO2 was measured in the tube using a 30 ml headspace sample injected into a Quantek instruments infrared gas analyzer with 10 ppm sensitivity. We used a longer time for the lower temperatures because preliminary incubations indicated that it was necessary to reliably detect respiration. The soil samples were placed at −80°C until DNA extraction. We also incubated larger quantities of soil in order to validate respiration measurements from the CUE incubations. One gram (organic) or 2 g (mineral) soil were brought to 60% water holding capacity with 16O-water, and the tubes were incubated alongside the CUE samples.

    CUE measurements using the (H218O-CUE) method estimate the new microbial biomass produced during the incubation period based on 18O-DNA at the end of the incubation period. DNA was extracted from all soils incubated with 18O-water and a subset of soils incubated with 16Owater using the Qiagen Powersoil kit. Technical duplicates were pooled before quantification using Qubit. The 18O enrichment of the DNA was measured using TC/EA-IRMS (Delta V Advantage, Thermo Fisher) at the UC Davis Stable Isotope Facility. CUE was calculated as per Spohn et al. (2016) but using a sample-specific conversion factor rather than the overall average because large differences in MBC:DNA ratios across community types can bias CUE measurements (Pold, Domeignoz-Horta and DeAngelis, 2020).

    Temperature response curve fitting

    We anticipated that respiration and extracellular enzyme activity would increase non-linearly with incubation temperature. Therefore, we compared fits of the Taylor exponential model (Lloyd and Taylor, 1994; ln[rate] = ln[a] + b × Temperature), and the Ratkowsky square root model (Ratkowsky et al., 1982; sqrt[rate] = b[T−T0]) to these data. Models for respiration were fit to individual soil samples using the lm() function in R where the response variable (i.e., respiration or extracellular enzyme activity) was either natural log- or square root transformed. We extracted the slope (i.e., temperature sensitivity; b in equations above) estimates from the model for each sample to compare among experimental factors.

    Visually, the Ratkowsky model consistently underestimated the respiration at higher temperatures compared to the Taylor model, and the R2 statistic was also consistently lower (median R2 = .91 [range 0.65–0.98] versus 0.96 [range 0.66–0.99], median difference = 0.052; Table S3). Therefore, we proceeded to extract the slope parameter from the Taylor model and used this as a metric of temperature responsiveness for downstream statistical analyses for both respiration and extracellular enzyme activity. For BG, the R2 for the Taylor model varied from 0.42 to 0.99, with a median of 0.85. For NAG, R2 varied from 0.04 to 0.99 with a median of 0.85 ( Table S4). Since all R2 below .4 were restricted to October, we excluded NAG activity from this time for our analysis. The Taylor model similarly showed a poor fit to the oxidative enzyme activity (median R2 = .88, range .00–.99) with instances of poor fit seen in data from both time points. Therefore, we did not further analyze oxidative enzyme activity temperature sensitivity (Table S4). The growth and CUE data were very variable compared to the respiration data and individual samples did not follow a consistent pattern. Therefore, we could not fit a curve to the data and instead used ranked CUE and growth rates at different temperatures for each soil sample. We considered the CUE temperature optimum to be the temperature where we measured its highest value (Bölscher et al., 2020).

    Quantitative PCR

    The abundance of total fungi and total bacteria was assessed using quantitative PCR (qPCR) with ITS primers (ITS1: 5′-TCCGTAGGTGAACCTGCGG-3′ and 5.8 S: 5′-CGCTGCGTTCTTCATCG-3′)and 16 S ribosomal RNA primers (Eub338: 5′-ACTCCTACGGGAGGCAGCAG-3′ and Eub518: 5′ATTACCGCGGCTGCTGG-3′) (Fierer et al., 2005), respectively. The abundance in each soil sample was based on increasing fluorescence intensity of the SYBR Green dye during amplification. The qPCR assay was carried out in a 15 μl reaction volume containing 2 ng of DNA, 7.5 μl of SYBR Green PCR master mix (Qiagen quantifast SYBR kit), and each primer at a concentration of 1 μm. Inhibition tests were performed by running serial dilutions of DNA extractions and did not indicate inhibition of amplification. For each sample at least two independent qPCR assays were performed for each gene with technical duplicates within each assay. The qPCR efficiencies for both genes ranged between 78% and 110%. Values are reported as gene copy number g−1 dry soil.

    Microbial biomass carbon

    Quadruplicate 0.5 g (organic) or 1 g (mineral) soil samples for MBC were weighed out with the CUE samples. We kept the weighed out soil samples at 15°C (the middle of the temperature range used for CUE and enzyme measurements) until the end of CUE incubations so growth and biomass measurements could be taken on similarly treated soils. Two replicates were exposed to chloroform vapor fumigation under vacuum for 24 h, while the other two replicates were placed at 4°C for the duration. Soils with and without chloroform were subsequently extracted in 15 ml of 0.05 m K2SO4, which a preliminary trial indicated led to similar levels of extractable organic carbon and MBC detected as the more standard 0.5 m K2SO4 for our soils. DOC was measured on a Shimadzu TOC analyzer.

    SOM quality

    We used ramped thermal rock-eval® pyrolysis (RE) to evaluate SOM quality (Soucémarianadin et al., 2018). During RE, carbon oxides are quantified as they come off a soil sample subject to increasing temperatures, thereby providing a metric of SOM intrinsic thermal stability. Compounds with high thermal stability include aromatic and phenolic non-lignin compounds, while lipids and polysaccharides tend to have lower thermal stability (Sanderman and Grandy, 2020). Mineral and organic soils were dried at 65°C and crushed to a fine powder in a mortar and pestle. Between 50and 70 mg soil was pyrolyzed over a temperature ramp from 200 to 650°C, followed by combustion to 850°C using a rock-eval 6 pyrolyzer (Vinci technologies) at the Institute of Earth Sciences of the University of Lausanne (Switzerland). Hydrocarbons released during this process were measured by a flame ionization detector. The resultant thermogram was used to calculate the I index (“labile carbon fraction”) and R index (“recalcitrant carbon fraction”) as previously (Sebag et al., 2016). We also used the thermogram of each sample to calculate a Bray–Curtis distance matrix of all samples, taking the peak height at each 1°C increment as an input value to create the distance matrix between all samples (Domeignoz-Horta et al., 2021). This approach allowed us to evaluate SOM composition in addition to C quantities in the different soils. The first axis of the NMDS was used as a proxy for SOM quality as it was strongly correlated with the I index (R2 = .89, p  less than  .0001).


    Water-extractable polar metabolites (here, “water-extractable organic matter,” or WEOM) were obtained by shaking 1 g dry soil equivalent with 5 ml (mineral soil) or 10 ml (organic soil) of LC/MS-grade water at 200 rpm and 4°C for 1 h. Every extraction was accompanied with a water-only control. The soil extracts and the water controls were filtered through a 45 μm PTFE filter. The filtrate was lyophilized and sent for analysis at the DOE Joint Genome User Facility at Lawrence Berkeley National Laboratory. Polar metabolites were resuspended in 170 μl of 100% methanol containing 13C-15N labeled amino acids (30 m, 767964, Sigma). UHPLC normal phase chromatography was performed using an Agilent 1290 LC stack, with MS and MS/MS data collected using a Q Exactive HF Orbitrap MS (Thermo Scientific). Full MS spectra were collected from m/z 70–1050 at 60 k resolution in both positive and negative ionization mode, with MS/MS fragmentation data acquired using stepped 10, 20, and 40 eV collision energies at 17,500 resolution. Mass spectrometer source settings included a sheath gas flow rate of 55 (au), auxiliary gas flow of 20 (au), sweep gas flow of 2 (au), spray voltage of 3 kV, and capillary temperature of 400°C. Normal phase chromatography was performed using a HILIC column (InfinityLab Poroshell 120 HILIC-Z, 2.1150 mm, 2.7 m, Agilent, 683775-924) at a flow rate of 0.45 ml min−1 with a 2 uL injection volume. To detect metabolites, samples were run on the column at 40°C equilibrated with 100% buffer B (99.8% 95:5 v/v ACN:H2O and 0.2% acetic acid, w/ 5 mm ammonium acetate) for 1 min, diluting buffer B down to 89% with buffer A (99.8% H2O and 0.2% acetic acid, w/ 5 mm ammonium acetate and 5 mmethylene-di-phosphonic acid) over 10 min, down to 70% over 4.75 min, down to 20% over 0.5 min, and isocratic elution for 2.25 min, followed by column re-equilibration by returning to 100% B over 0.1 min and isocratic elution for 3.9 min. The injection order of the 79 experimental samples and 5 extraction controls was randomized and an injection blank (2 uL of 100% MeOH) run between each sample. Metabolomics data were analyzed using Metabolite Atlas (Yao et al., 2015) and with in-house Python scripts to obtain extracted ion chromatograms and peak heights for each metabolite.

    Metabolite identifications were verified with authentic chemical standards and validated based on three metrics comparing (1) detected versus theoretical m/z (less than 5 ppm), (2) retention time (≤0.5 min), and (3) fragmentation spectra similarity to a chemical standard run using the same chromatography and LC–MS/MS method. Data from internal standards and quality control samples (included throughout the run) were analyzed to ensure consistent peak heights and retention times. Peak height of different samples was calculated out of four replicates and we excluded metabolites that showed a quality score under 1 and were not measured in every sample. Where multiple ions existed for the same compound within a sample analyses we summed the peak heights. Where compounds appeared in both the positive and the negatively charged compound summary table, we used the greatest value. After quality control we had 227 compounds. The WEOM matrix was normalized by sum and pareto scaled before calculating Bray–Curtis distance and completing principal coordinates analysis using the MetabolAnalyze, vegan and ape packages, respectively (Nyamundanda et al., 2010; Oksanen et al., 2019; Paradis and Schliep, 2019). Pareto scaling divides each compound by the square root of the variance. We selected this scaling approach to moderately reduce the importance of the extremely dominant compounds and remove the large differences in total peak height between mineral and organic soil samples without completely abolishing all relative abundance information.

    Compound complexity was determined based on the Bertz/Hendrickson/Ihlenfeldt complexity reported in PubChem. The complexity score is higher in polymers compared to monomers of the same compound, and in asymmetric and/or heteropolymeric compared to symmetric and/or homopolymeric compounds. Therefore, the complexity score can be considered an imprecise proxy for how difficult a compound is to break down. These values were downloaded from PubChem. Mean nominal oxidation state was calculated as per (LaRowe and Van Cappellen, 2011) (NOSC = -((−Z + 4C + H−3 N−2O + 5P -2 S)/ C) + 4). NOSC was selected as a proxy for the quality of DOC because oxidation of compounds with higher NOSCs is associated with greater electron transfer (LaRowe and Van Cappellen, 2011), while compounds with lower NOSCs have greater potential energy but require a larger investment to acquire it. As such, the NOSC of SOM generally decreases as decomposition proceeds (Gunina and Kuzyakov, 2022). The NOSC and complexity indices for each compound are reported in Table S5.

    Metagenome sequencing and annotation

    Metagenomes were obtained from organic soil collected in July from both sites, totaling 20 samples. DNA extracted from soils incubated at 20°C was sequenced at the Joint Genome Institute (Methods S1 and Table S2). Reads were assembled and PFAMs were annotated in the assembled fraction of reads using the DOE-JGI Metagenome Annotation Pipeline (Huntemann et al., 2016). This led to an average of 1.72 Gbp assembled and 2,807,085 genes predicted per metagenome. We then mapped PFAMs to CAZys (Pold et al., 2016) and used mean mapping depth for CAZys relative to the mapping depth to the single copy rpoB gene (PF04563, PF04561, PF04565, PF10385, PF00562, PF04560) as our proxy of CAZy abundance across samples. We elected to focus on CAZymes as they are responsible for the polymerization and depolymerization of many of the dominant compounds found in leaf litter and microbial cell walls, including cellulose, lignin, and chitin. We subsequently categorized CAZymes based on the primary substrates they decompose (Berlemont et al., 2015).

  • Organization: Harvard Forest. 324 North Main Street, Petersham, MA 01366, USA. Phone (978) 724-3302. Fax (978) 724-3595.

  • Project: The Harvard Forest Long-Term Ecological Research (LTER) program examines ecological dynamics in the New England region resulting from natural disturbances, environmental change, and human impacts. (ROR).

  • Funding: National Science Foundation LTER grants: DEB-8811764, DEB-9411975, DEB-0080592, DEB-0620443, DEB-1237491, DEB-1832210. Other funding: NSF Macrosystems Biology and NEON-Enabled Science grant DEB-1926341.

  • Use: This dataset is released to the public under Creative Commons CC0 1.0 (No Rights Reserved). Please keep the dataset creators informed of any plans to use the dataset. Consultation with the original investigators is strongly encouraged. Publications and data products that make use of the dataset should include proper acknowledgement.

  • License: Creative Commons Zero v1.0 Universal (CC0-1.0)

  • Citation: DeAngelis K. 2023. Carbon Cycle Dynamics in Soil Warming Experiments at Harvard Forest 2019. Harvard Forest Data Archive: HF431 (v.2). Environmental Data Initiative:

Detailed Metadata

hf431-01: temperature acclimation data 2019

  1. Sample: sample number
  2. SampleID: sample ID
  3. Site: site
    • PH: Prospect Hill
    • SWaN: Soil Warming and Nitrogen Addition Study
  4. ExperimentDuration: experiment duration
    • 13 years: 13 years
    • 28 years: 28 years
  5. Plot: experimental plot
  6. Treatment: treatment
    • control: control
    • heated: heated
  7. Horizon: soil horizon
    • Mineral: Mineral
    • Organic: Organic
  8. WarmingDuration: warming duration of soil, number of years (unit: dimensionless / missing value: NA)
  9. Timepoint: month
    • July: July
    • October: October
  10. TOC: total organic carbon measured by rock-eval pyrolysis in percent per g dry soil (unit: dimensionless / missing value: NA)
  11. I_index: thermal lability index (unit: dimensionless / missing value: NA)
  12. R_index: thermal stability index (unit: dimensionless / missing value: NA)
  13. pct_moisture_content: percent moisture content (unit: dimensionless / missing value: NA)
  14. pct_water_content: percent water content (unit: dimensionless / missing value: NA)
  15. LOI_pct: loss on ignition in % (unit: dimensionless / missing value: NA)
  16. MBC: microbial biomass carbon - ug C g soil (unit: microgramPerGram / missing value: NA)
  17. DOC_ug_g_soil: dissolved organic carbon - ug C g soil (unit: microgramPerGram / missing value: NA)
  18. DOC_MBC: ratio of DOC to MBC (unit: dimensionless / missing value: NA)
  19. Respiration_20C: respiration at 20C - ug C g soil hour (unit: microgramPerGramPerHour / missing value: NA)
  20. CUERespiration: respiration when measuring CUE - ug C g soil hour (unit: microgramPerGramPerHour / missing value: NA)
  21. MassSpecRespiration: respiration divided by MBC (unit: dimensionless / missing value: NA)
  22. BG_intercept: betaglucosidase intercept calculated from temperature sensitivity curve (unit: dimensionless / missing value: NA)
  23. BG_a_SE: betaglucosidase "a component" standard deviation calculated from temperature sensitivity curve (unit: dimensionless / missing value: NA)
  24. BG_TS: betaglucosidase temperature sensitivity (unit: dimensionless / missing value: NA)
  25. BG_b_SE: betaglucosidase "b component" standard deviation calculated from temperature sensitivity curve (unit: dimensionless / missing value: NA)
  26. NAG_intercept: N-acetylglucosaminidase intercept calculated from temperature sensitivity curve (unit: dimensionless / missing value: NA)
  27. NAG_a_SE: N-acetylglucosaminidase "a component" standard deviation calculated from temperature sensitivity curve (unit: dimensionless / missing value: NA)
  28. NAG_TS: N-acetylglucosaminidase temperature sensitivity (unit: dimensionless / missing value: NA)
  29. NAG_b_SE: N-acetylglucosaminidase "b component" standard deviation calculated from temperature sensitivity curve (unit: dimensionless / missing value: NA)
  30. Ecoplate: ecoplate number
  31. Ecoplate_k: growth parameter (unit: dimensionless / missing value: NA)
  32. Ecoplate_k_se: standard deviation of the of maximum carrying capacity measured using Biolog Ecoplate and calculated with the growthcurver package (unit: dimensionless / missing value: NA)
  33. Ecoplate_k_p: p value of maximum carrying capacity when significant growth is measured using Biolog Ecoplate and calculated with the growthcurver package (unit: dimensionless / missing value: NA)
  34. Ecoplate_n0: estimated growth rate at time zero (the intercept) calculated with the growthcurver package measured with Biolog Ecoplate (unit: dimensionless / missing value: NA)
  35. Ecoplate_r: growth rate (unit: dimensionless / missing value: NA)
  36. Ecoplate_r_se: standard deviation of the growth rate calculated with the growthcurver package measured with Biolog Ecoplate (unit: dimensionless / missing value: NA)
  37. Ecoplate_r_p: p value of growth rate when significant growth is measured using Biolog Ecoplate and calculated with the growthcurver package (unit: dimensionless / missing value: NA)
  38. Ecoplate_auc_l: area under the curve calculated with the growthcurver package measured with Biolog Ecoplate (unit: dimensionless / missing value: NA)
  39. Ecoplate_t_mid: time needed to reach half-max of optical density calculated with the growthcurver package with Biolog Ecoplate (unit: dimensionless / missing value: NA)
  40. X16S_rRNA_nbc_g_dry_soil: real time qPCR from 16S rRNA gene - number of copies per g dry soil (unit: dimensionless / missing value: NA)
  41. ITS_nbc_g_dry_soil: real time qPCR from ITS gene - number of copies per g dry soil (unit: dimensionless / missing value: NA)
  42. F_B_ratio: fungal to bacterial ratio (unit: dimensionless / missing value: NA)
  43. RespirationSum: sum of respiration measurements performed under each temperature - ug C g dry soil (unit: microgramPerGramPerHour / missing value: NA)
  44. Respiration_TS: respiration temperature sensitivity (unit: dimensionless / missing value: NA)
  45. resp_slope_SEexp: standard deviation calculated from temperature sensitivity curve of respiration (unit: dimensionless / missing value: NA)
  46. resp_Exp_intercept: respiration intercept calculated from temperature sensitivity curve (unit: dimensionless / missing value: NA)
  47. resp_Exp_intercept_SE: standard deviation of the respiration intercept calculated from temperature sensitivity curve of respiration (unit: dimensionless / missing value: NA)
  48. Ecoplate_Bray_axis1: axis 1 from ecoplate growth data (unit: dimensionless / missing value: NA)
  49. Ecoplate_Bray_axis2: axis 2 from ecoplate growth data (unit: dimensionless / missing value: NA)
  50. BG: betaglucosidase enzyme activity at 20Celsius (unit: dimensionless / missing value: NA)
  51. NAG: N-acetylglucosaminidase enzyme activity at 20Celsius (unit: dimensionless / missing value: NA)
  52. OX: oxidative enzyme activity at 20Celsius (unit: dimensionless / missing value: NA)
  53. Growth: growth measured at 20Celsius - ug C g dry soil hour (unit: microgramPerGramPerHour / missing value: NA)
  54. CUE: carbon use efficiency - % (unit: dimensionless / missing value: NA)
  55. NMDS_RockEval_Axis1: axis 1 from SOM thermal analysis (unit: dimensionless / missing value: NA)
  56. NMDS_RockEval_Axis2: axis 2 from SOM thermal analysis (unit: dimensionless / missing value: NA)
  57. PC1_metabol: axis 1 from principal coordinate analysis from metabolomics (unit: dimensionless / missing value: NA)
  58. PC2_metabol: axis 2 from principal coordinate analysis from metabolomics (unit: dimensionless / missing value: NA)
  59. PC3_metabol: axis 3 from principal coordinate analysis from metabolomics (unit: dimensionless / missing value: NA)
  60. Axis1_metabol: axis 1 from Bray curtis analysis from metabolomics (unit: dimensionless / missing value: NA)
  61. Axis2_metabol: axis 2 from Bray curtis analysis from metabolomics (unit: dimensionless / missing value: NA)
  62. PCOA_Axis1: axis 1 from SOM thermal analysis from principal coordinate analysis (unit: dimensionless / missing value: NA)
  63. PCOA_Axis2: axis 2 from SOM thermal analysis from principal coordinate analysis (unit: dimensionless / missing value: NA)
  64. Metabolomics_complexity: metabolomics complexity index (unit: dimensionless / missing value: NA)
  65. CAZY_perRpoB: cazies per RpolB (unit: dimensionless / missing value: NA)
  66. NOSC: mean nominal oxidation state (unit: dimensionless / missing value: NA)