Harvard Forest Data Archive

HF254

Warming Effects on Microbial Structure and Decomposition at Harvard Forest 2011

Related Publications

Data

• hf254-01: microbial community structure and function (preview)
• hf254-02: soil gravimetric water content (preview)
• hf254-03: microbial community composition

Overview

• Lead: Melissa Cregger, Aimee Classen, Robert Dunn, Nathan Sanders
• Investigators:
• Contact: Information Manager
• Start date: 2011
• End date: 2011
• Status: complete
• Location: Prospect Hill Tract (Harvard Forest)
• Latitude: +42.53 degrees
• Longitude: -72.19 degrees
• Elevation: 340 meter
• Datum: WGS84
• Taxa:
• Release date: 2023
• Language: English
• EML file: knb-lter-hfr.254.5
• DOI: digital object identifier
• EDI: data package
• DataONE: data package
• Related links:
• Ants Under Climate Change at Harvard Forest and Duke Forest since 2009
• Study type: short-term measurement
• Research topic: large experiments and permanent plot studies
• LTER core area: organic matter movement
• Keywords: climate change, community structure, decomposition, microbes, soil
• Abstract:

  Because microorganisms are sensitive to temperature, ongoing global warming is predicted to influence microbial community structure and function. We used large-scale warming experiments established at two sites near the northern and southern boundaries of US eastern deciduous forests to explore how microbial communities and their function respond to warming at sites with differing climatic regimes. Soil microbial community structure and function responded to warming at the southern but not the northern site. However, changes in microbial community structure and function at the southern site did not result in changes in cellulose decomposition rates. While most global change models rest on the assumption that taxa will respond similarly to warming across sites and their ranges, these results suggest that the responses of microorganisms to warming may be mediated by differences across the geographic boundaries of ecosystems.

• Methods:

  This experiment is described in Pelini et al. (2011). Briefly, 12 octagonal (5 m diameter) OTCs were established in 2009 and activated in January 2010 at a southern site (Duke Forest, 35°52′0″N, 79°59′45″W) and a northern site (Harvard Forest, 42°31′48″N, 72°11′24″W). Each chamber has a ±20 cm oak tree in the center of the chamber to serve as a thermal storage mass in order to avoid a cold core in the middle of the chamber. Three chambers serve as unheated controls, and the remaining nine chambers manipulate air temperature in 0.5 °C increments using hydronic heating and forced air from 1.5 °C to 5.5 °C above ambient. Target and observed temperatures are strongly correlated (r2 = 0.99). Mean annual air temperature was 15.5 °C and mean annual precipitation was 1140 mm at the southern site and 7.1 °C and 1066 mm, respectively at the northern site (Pelini et al., 2011). The southern site was established in a mixed deciduous, 80 year-old oak-hickory (Quercus alba-Carya sp.) forest, with an understory that was dominated by oak (Quercus alba), red maple (Acer rubrum), and hickory (Carya sp.). The soils are mainly Ultic Alfisols with a soil pH, as measured in calcium chloride (Carter and Gregorich, 1993), of 3.5 ± 0.03. The northern site was established in a mixed deciduous, 70 year-old oak-maple (Quercus rubra-Acer rubrum) forest with an understory that was dominated by blueberry (Vaccinium sp.), pine (Pinus strobus), and maple (Acer pensylvanicum). The soils are mainly of the Canton series (coarse-loamy over sandy or sandy skeletal, mixed, semi-active, mesic Typic Dystrudepts) (Melillo et al., 2011) with a soil pH, as measured in calcium chloride (Carter and Gregorich, 1993), of 3.6 ± 0.08. Previous research on ants at these sites showed that ant forager abundance and richness correlated to experimental temperature increases at the southern site, but not the northern site. Further, individual ant species responded differently to temperature increases at the southern site (Stuble et al., 2013). Another approach to examining whether the responses of microbial communities differ among regions would consist of installations of this warming experiment at multiple sites from Duke Forest to Harvard Forest rather than at only two sites. However, such a design is currently cost prohibitive (both in terms of setting up the experiment and in processing samples). But, importantly, our design allows us to explore the dynamics of communities at range boundaries, where it is predicted that the strongest responses to ongoing warming will be Parmesan and Yohe (2003) and Walther, Berger and Sykes (2005).

  We monitored air temperature as well as organic and mineral soil temperature continuously in each chamber with Apogee data loggers (model SQ110; Apogee Instruments Inc., Logan, UT, USA). Relative humidity (HS-2000V capacitive polymer sensors; Precon, Memphis, TN, USA) and soil moisture (Model CS616 TDR probes; Campbell Scientific Inc.) were also continuously monitored in each chamber at both sites. Monitored air temperatures within the chambers matched the target temperatures (Burt et al., in press). Soil temperature in the organic and inorganic layers was positively correlated with air temperature whereas soil moisture was never correlated with air temperature (Burt et al., in press).

  Five soil cores (2-cm diameter, 5-cm depth) were collected from within each of the 12 warming chambers on April 23rd, 2011 at Duke Forest and on May 17th, 2011 at Harvard Forest (5 cores/chamber × 12 chambers × 2 sites = 120 soil cores). We were unable to sample multiple times across the year because we needed to limit disturbance to the plots, thus we selected a time when we knew the microbial community would be actively degrading soil carbon. Soil from each chamber was homogenized (24 total samples); 15 g of soil were immediately removed from the homogenized sample, stored on dry ice in the field, and kept frozen at −80  °C until analyzed. The remaining soil was sieved (2 mm) and assayed for potential extracellular enzymatic activity and soil gravimetric water content within 48 h of collection.

  To explore how warming altered microbial community structure and function we assessed microbial abundance using quantitative PCR (Castro et al., 2010), microbial community composition using terminal restriction fragment length polymorphism (TRFLP) (Cregger et al., 2012; Singh et al., 2006), the potential activity of nine extracellular enzymes, and a microbially mediated ecosystem function—cellulose decomposition.

  To assess bacterial and fungal gene copy number, a commonly used proxy for abundance (Allison and Treseder, 2008), we ran quantitative polymerase chain reaction (qPCR) on each individual sample in conjunction with primers Eub 338 and Eub 518 for 16S ribosomal DNA and nuSSU1196F and nuSSU1536R for 18S ribosomal DNA (Castro et al., 2010). PCR mixtures for both 16S rRNA and 18S rRNA gene amplification contained 15 µl of SYBR green master mix (Invitrogen, Life Technologies, Grand Island, NY), 5 µmol of each primer (Eurofins MWG Operon, Huntsville, AL), and 1 µl of sample DNA diluted 1:10 in sterile water. Reactions were brought up to 30 µl with sterile water. Amplification protocol for the 16S rRNA gene consisted of an initial denaturing cycle of 95 °C for three minutes. This cycle was followed by 39 cycles of 95 °C for 15 s, 53 °C for 15 s, and 72 °C for 1 min. Amplification of the 18S rRNA gene consisted of an initial denaturing cycle of 95 °C for three minutes. This cycle was followed by 39 cycles of 95 °C for 15 s, 53 °C for 15 s, and 70 °C for 30 s. Abundance was quantified by comparing unknown samples to serial dilutions of 16S and 18S rDNA from Escherichia coli and Saccharomyces cerevisiae, respectively in each PCR run. After completion, for both ribosomal genes, a melting curve analysis was conducted to ensure purity of the amplification product. PCR amplification was performed on a 96-well Chromo4 thermocycler (Bio-Rad Laboratories, Hercules, CA).

  We assessed microbial community composition using terminal-restriction fragment length polymorphism (TRFLP), which provided fingerprints of the bacterial and fungal communities (Singh et al., 2006). Due to decreases in fluorescence when samples were multiplexed, we performed bacterial and fungal TRFLPs in separate reactions. PCR was performed to amplify the 16S rRNA gene from bacteria using primers 63f (Marchesi et al., 1998) and 1087r (Hauben et al., 1997) and the fungal ITS region using primers ITS1f (Gardes and Bruns, 1993) and ITS4r (Singh et al., 2006). PCR mixtures contained 5 µl 10× KCL reaction buffer, 2 µl 50 mM MgCl2, 5 µl 10 mM dNTPs (Bioline, Tauton, MA), 1 µl 20 mg/ml BSA (Roche, location), 0.5 µl (2.5 Units) Taq DNA polymerase (Bioline, Tauton, MA), either 1 µl of each bacterial primer or 2 µl of each fungal primer (Labeled primers—Invitrogen, Life Technologies, Grand Island, NY; unlabeled primers—Integrated DNA Technologies, Coralville, IA), and 1 µl sample DNA diluted 1:10 in sterile water. All PCR reactions were performed using a 96-well Tgradient thermocycler (Biometra, Germany). DNA was amplified with an initial step of 95 °C for 5 min, followed by 30 cycles at 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 1 min. This was followed by extension at 72 °C for 10 min. PCR product quality was assessed with 1% agarose gel electrophoresis. PCR products were cleaned using the QIAquick PCR purification kit (Qiagen, Valencia, CA), quantified using a Synergy HT microplate reader (Biotek, Winooski, Vermont, USA), and digested with MspI. After digestion, a cocktail was made containing 0.5 µl LIZ labeled GeneScan 1200 internal size standard (Applied Biosystems, Grand Island, NY), 12.5 µl Hi–Di formamide (Applied Biosystems, Grand Island, NY), and 1 µl of digested product which was centrifuged, then incubated at 94 °C for 4 min followed by incubation at 4 °C for 5 min. Fragments were analyzed on an ABI Prism 3100 genetic analyzer (Applied Biosystems, Grand Island, NY).

  TRFLP profiles were measured using the GeneMapper software (Applied Biosystems, NY) with a cutoff of 50 bp. The relative abundance of a TRF in a TRFLP profile was calculated by dividing the peak height of the TRF by the total peak height of all TRFs in the profile (Singh et al., 2006). Community analyses of fragments were conducted using Primer 6 with site specified as a factor and soil temperature and soil moisture specified as covariates (Primer-E Ltd., United Kingdom). Soil temperature and soil moisture were significantly different between the southern and northern site (soil temperature, F = 187.8, p less than 0.01; soil moisture, F = 17.6, p less than 0.01). Thus, we followed up the community analyses and separated the data by site using a distance based linear model (DISTLM) that assessed the effect of soil temperature and soil moisture on total microbial, fungal, and bacterial community composition at each site (Anderson, 2004; Langlois, Anderson and Babcock, 2006). Additionally, bacterial, fungal, and total microbial richness for all chambers at each site was calculated by summing the unique number of TRFs in each sample.

  We assayed microbial activity by measuring potential extracellular enzyme activity using methylumbelliferone (MUB) linked substrates and 3,4 Dihydroxyphenylalanine (L-DOPA) (Saiya-Cork, Sinsabaugh and Zak, 2002). Soils were assayed for nine ecologically relevant enzymes in order to assess the functional diversity of the soil community: sulfatase (hydrolysis of sulfate esters), nitrogen acetylglucosaminidase (nagase; mineralization of nitrogen from chitin), xylosidase (hemicellulose degradation), phosphatase (hydrolysis of phosphomonoesters and phosphodiesters releasing phosphate), α-glucosidase (degradation of storage carbohydrates), β-glucosidase (degradation of cellulose and other–1,4 glucans), cellobiohydrolase (cellulose degradation), phenol oxidase (lignin degradation), and peroxidase (lignin degradation). Soils were prepared by adding 125 ml of 0.5 M sodium acetate buffer (buffer, pH 5) to approximately 1 g of soil and homogenized for 2 min by immersion blending. Sulfatase, nagase, xylosidase, phosphatase, α-glucosidase, β-glucosidase, and cellobiohydrolase were measured using MUB linked substrates. We prepared 96 well plates with blanks, experimental controls, and samples, which were replicated 8 times each. All plates were incubated at room temperature in the dark. The nagase and phosphatase reactions were incubated for 0.5 h, while sulfatase, xylosidase, α-glucosidase, β-glucosidase, and cellobiohydrolase were incubated for 2 h. Fluorescence was read at an excitation of 365 nm and an emission of 450 nm (Biotek, Winooski, Vermont, US). Phenol oxidase and peroxidase activity were measured using L-DOPA. Assays were replicated 16 times and reactions were incubated in the dark for 24 h. Absorbance was read at 460 nm on a Synergy HT microplate reader (Biotek, Winooski, Vermont, US). Potential enzymatic activity is presented as nmol h−1g−1 (Saiya-Cork, Sinsabaugh and Zak, 2002; Sinsabaugh, 1994).

  The decomposition rate of a standard cellulose substrate was measured in each chamber to determine how warming might alter the rate of carbon degradation, a microbially mediated process. Twelve mesh decomposition bags (10 cm × 10 cm; 3 mm mesh double layered on top and 1.3 mm mesh on bottom) containing 10 g of Whatman # 1 filter paper were deployed in each of the chambers and collected after 3, 6, 9, and 12 months. All data are shown on an ash-free oven dry mass basis. K-constants were calculated for each chamber at each site following collection (Olson, 1963).

  Because microbial communities experience changes in soil temperature and moisture as a result of changing air temperature, we used an analysis of covariance (ANCOVA) to examine the effect of site, average organic layer soil temperature on the day of sampling, average volumetric soil moisture on the day of sampling, and the interactions of these factors on microbial community composition, abundance, enzymatic activity, and decomposition rates. When three way interactions among site, soil temperature, and soil moisture were detected, we separated the data by site and ran regressions using soil temperature and soil moisture as factors. We assessed the effect of minimum, maximum, and variation in soil temperature and moisture over one year on microbial structure and function, but found no significant effects, so results including those factors are not presented.

• 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.

• 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: Cregger M, Classen A, Dunn R, Sanders N. 2023. Warming Effects on Microbial Structure and Decomposition at Harvard Forest 2011. Harvard Forest Data Archive: HF254 (v.5). Environmental Data Initiative: https://doi.org/10.6073/pasta/6d91d6cb964735e8dd11bc55175db06a.

Detailed Metadata

hf254-01: microbial community structure and function

1. plot: plot code
2. chamber: chamber number
3. site: site code
   • HF: Harvard Forest
4. treatment: three chambers serve as unheated controls, and the remaining nine chambers manipulate air temperature in 0.5 °C increments using hydronic heating and forced air from 1.5 °C to 5.5 °C above ambient
5. avg.org.soilt: daily average organic soil temperature (unit: celsius / missing value: NA)
6. avg.soil.moisture: daily average soil moisture (%) (unit: dimensionless / missing value: NA)
7. fungi: quantitative PCR data, fungi per gram of dry soil. Quantitative PCR can be used as a proxy for fungal abundance. (unit: numberPerGram / missing value: NA)
8. bacteria: quantitative PCR data, bacteria per gram of dry soil. Quantitative PCR can be used as a proxy for bacterial abundance. (unit: numberPerGram / missing value: NA)
9. fungi.bacteria: ratio of fungi to bacteria (unit: dimensionless / missing value: NA)
10. simpsons.div: Simpson’s diversity (unit: dimensionless / missing value: NA)
11. fungal.otu: number of unique trflp peaks for fungi (unit: number / missing value: NA)
12. bacterial.otu: number of unique trflp peaks for bacteria (unit: number / missing value: NA)
13. total.otu: number of unique trflp peaks for fungi and bacteria (unit: number / missing value: NA)
14. xylosidase: soil extracellular enzymatic activity. All enzymes are measured in nmol/h/g dry soil. (unit: nanomolePerGramPerHour / missing value: NA)
15. sulfatase: soil extracellular enzymatic activity. All enzymes are measured in nmol/h/g dry soil. (unit: nanomolePerGramPerHour / missing value: NA)
16. cellobiohydrolase: soil extracellular enzymatic activity. All enzymes are measured in nmol/h/g dry soil. (unit: nanomolePerGramPerHour / missing value: NA)
17. b.gluc: soil extracellular enzymatic activity. All enzymes are measured in nmol/h/g dry soil. (unit: nanomolePerGramPerHour / missing value: NA)
18. a.gluc: soil extracellular enzymatic activity. All enzymes are measured in nmol/h/g dry soil. (unit: nanomolePerGramPerHour / missing value: NA)
19. nagase: soil extracellular enzymatic activity. All enzymes are measured in nmol/h/g dry soil. (unit: nanomolePerGramPerHour / missing value: NA)
20. phosphatase: soil extracellular enzymatic activity. All enzymes are measured in nmol/h/g dry soil. (unit: nanomolePerGramPerHour / missing value: NA)
21. phenol.ox: soil extracellular enzymatic activity. All enzymes are measured in nmol/h/g dry soil. (unit: nanomolePerGramPerHour / missing value: NA)
22. perox: soil extracellular enzymatic activity. All enzymes are measured in nmol/h/g dry soil. (unit: nanomolePerGramPerHour / missing value: NA)
23. decomp.0: percent cellulose substrate mass remaining on day 0 (unit: dimensionless / missing value: NA)
24. decomp.90: percent cellulose substrate mass remaining on day 90 (unit: dimensionless / missing value: NA)
25. decomp.180: percent cellulose substrate mass remaining on day 180 (unit: dimensionless / missing value: NA)
26. decomp.270: percent cellulose substrate mass remaining on day 270 (unit: dimensionless / missing value: NA)
27. k.constant: K constant (unit: dimensionless / missing value: NA)

hf254-02: soil gravimetric water content

1. sample: sample id
2. tin: tin id
3. tin.wt: weight of tin (unit: gram / missing value: NA)
4. sample.tin.wt: weight of sample plus tin (unit: gram / missing value: NA)
5. drywt.tin: dry weight of sample plus tin (unit: gram / missing value: NA)
6. gwc: gravimetric water content (%) (unit: dimensionless / missing value: NA)

hf254-03: microbial community composition

• Compression: zip
• Format: csv
• Type: document