Computational protocol: Soil Respiration and Bacterial Structure and Function after 17 Years of a Reciprocal Soil Transplant Experiment

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Protocol publication

[…] The 4–8 individual IRGA CO2 readings for each core and measurement were examined for obvious outliers and then a linear rate of change (δc/δt) for CO2 concentration was computed. Each core’s respiration flux (F) was then calculated following e.g. Steduto et al. [] as F=δcδtVMPaRT where V is the core-specific system volume, M the core dry mass as determined at the end of the incubation, Pa atmospheric pressure (101 kPa; the incubation chambers were ~120 m a.s.l.), R the universal gas constant (8.3 x 10−3 m3 kPa mol-1 K-1) and T the chamber air temperature (K) at time of measurement. The final respiration rate was expressed as mg C kg soil-1 day-1. All analyses were performed using the R language for statistical computing [] version 3.0.2.We used a linear model (lm in R) to test the fixed effects of core source (i.e., the site from which a core originally came), location (where it spent 1994–2012), and type (transplant versus native) on respiration rate (log F above; we transformed the dependent variable to allow for a nonlinear response). Core bulk density, water content, and incubation day were all tested for their effects on F. Air temperature for each observation was normalized relative to the chamber mean, resulting in adjusted T values of similar range for each chamber and thus facilitating their comparison []. Time-zero analyses were tested using multi-way analysis of variance in R, testing both individual treatments effects and their first-order interactions.The basal respiration and temperature sensitivity of the bulk respiration data were estimated with a Q10-style function [] using nonlinear least squares (nls) in R, i.e. F=F20Q10(T−20)/10 where F is as above, F20 is the flux (respiration rate) at 20°C, T the chamber air temperature (here°C), and Q10 the ‘apparent’ [] temperature sensitivity. The algorithm used initial-guess values of 5 mg C kg soil-1 day-1 for F20 and 2.0 for Q10. This equation is an empirical convenience [, ] but one that fit these data well with no trend or heteroscedasticity in its residuals (data not shown).DNA was extracted from 0.25 g of soil per sample using the PowerSoil® DNA Isolation Kit ( according to the manufacturer’s instructions. PCR amplification of the V4 region of the 16S rRNA gene was performed using the protocol developed by the Earth Microbiome Project (, and described in Caporaso et al. [], with the exception that the twelve base barcode sequence was included in the forward primer. Amplicons were sequenced on an Illumina MiSeq using the 500 cycle MiSeq Reagent Kit v2 ( according to the manufacturer’s instructions.We used the 16S sequence data to compare the bacterial community structures of all soil samples. The sequence data were demultiplexed and the paired ends joined, requiring an overlap of 100 bases with < 5% difference, using ea-utils (v.1.1.2–537; High quality joined sequences were converted from fastq to fasta using BioPerl ( and processed using mothur v.1.30.1 []. Briefly, sequences with ambiguous bases were excluded, as were sequences that: (1) did not align to the V4 region of the Silva 16S rDNA reference alignment ( [], or (2) were identified as chimeric by both UCHIME ( [] and ChimeraSlayer ( [], or (3) were classified as chloroplast, mitochondria, or unclassified by the RDP reference taxonomy ( []. After this processing, five samples yielded no sequences, and the remaining 193 samples (3 depth intervals x 3–4 replicates, depending on group) yielded 4,426,880 sequences (~23,000 per sample on average). Randomly subsampling 10,000 sequences from each sample eliminated five additional samples with insufficient data; the remaining 188 samples were retained for analysis. The 1,880,000 sequences were assigned to OTUs at ≥ 97% identity (with furthest neighbor linkage), and taxonomy assigned using the RDP reference taxonomy. Non-metric multidimensional scaling was performed in mothur (“nmds” command), using the Morisita-Horn index to describe the dissimilarity in community structure between samples, and the resulting ordination visualized in MATLAB® (MathWorks, Inc.). Analysis of molecular variance (AMOVA in mothur) was used to test transplant location and incubation effects on the community structure. […]

Pipeline specifications

Software tools ea-utils, BioPerl, mothur, UCHIME, ChimeraSlayer
Databases EMP
Application 16S rRNA-seq analysis
Organisms Bacteria
Chemicals Carbon, Carbon Dioxide