Gene-informed decomposition model predicts lower soil carbon loss due to persistent microbial adaptation to warming

Soil microbial respiration is an important source of uncertainty in projecting future climate and carbon (C) cycle feedbacks. However, its feedbacks to climate warming and underlying microbial mechanisms are still poorly understood. Here we show that the temperature sensitivity of soil microbial respiration (Q10) in a temperate grassland ecosystem persistently decreases by 12.0 ± 3.7% across 7 years of warming. Also, the shifts of microbial communities play critical roles in regulating thermal adaptation of soil respiration. Incorporating microbial functional gene abundance data into a microbially-enabled ecosystem model significantly improves the modeling performance of soil microbial respiration by 5–19%, and reduces model parametric uncertainty by 55–71%. In addition, modeling analyses show that the microbial thermal adaptation can lead to considerably less heterotrophic respiration (11.6 ± 7.5%), and hence less soil C loss. If such microbially mediated dampening effects occur generally across different spatial and temporal scales, the potential positive feedback of soil microbial respiration in response to climate warming may be less than previously predicted.


Statistics
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For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection All the software used in current study for data collection are commercial. Daily GPP values were obtained from a corrected 8-day GPP product based on the MODIS GPP (MOD17A2/MOD17A2H).

Data analysis
Raw amplicon sequences were analyzed in the Galaxy sequence analysis pipeline (http://zhoulab5.rccc.ou.edu:8080). OTUs were clustered by UPARSE (2013). Geochip data were analyzed in the Microarray Data Manager on our website (http://ieg.ou.edu/microarray). Shotgun data were analyzed in our EcoFUN-MAP pipeline (http://www.ou.edu/ieg/tools/data-analysis-pipeline.html). Statistical analyses were performed in R version 3.1.1 (www.R-project.org) or available pipelines. Detailed information is provided in the text. The code for modeling analysis are performed in FORTRAN and accessible at https://github.com/wanggangsheng/MENDokw.git. We used the Shuffled Complex Evolution (SCE) algorithm to determine model parameters. We also applied the probabilistic inversion (Markov Chain Monte Carlo) to quantity parameter uncertainties.
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Data
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Study description
The study is a long-term manipulated, climate change, field experiment with 4 warmed (+3 °C) and 4 control plots in a block design. The experiment site was established in July 2009.

Research sample
In this study, we primarily focus on the responses of microbial respiration and community functions to warming. To model microbial respiratory responses to climate warming, long-term experiments under more realistic field-settings with time-series microbial data are needed. Therefore, the study includes a total of 56 annual soil surface (0-15cm) samples (4 warmed plots and 4 control plots in each year) from 2010 to 216. The differences of warmed and control treatments represent the effects of long-term experimental warming on ecosystem functions and soil microbial communities.

Sampling strategy
In this study, 8 surface (0-15 cm) soil samples, four from the warmed and four from the control plots, were collected annually at approximately the date of peak plant biomass (September or October) from 2010 to 2016. Three soil cores (2.5 cm diameter x 15 cm deep) were taken using a soil sampler tube in each plot and composited to have enough samples for soil chemistry, microbiology and molecular biology analyses. A total of 56 soil samples were analyzed in this study.

Data collection
All sample collection from the experiment site was performed by authors XG, MY, LYW and the lab technician. Three soil cores (2.5 cm diameter x 15 cm deep) were collected in each field plots using a soil sampler tube and composited to have enough samples for soil chemistry, microbiology and molecular biology analyses. Plant survey, and measurements of ecosystem C fluxes and soil respirations were performed by the lab technician following standard protocols. Soil DNA extraction and PCR were performed by XG, LC and XZ in the University of Oklahoma. GeoChip hybridization and MiSeq sequencing were performed by X.G., X.Z., and R.T. Soil chemical and substrate analyses were performed by the Soil, Water, and Forage Analytical Laboratory at Oklahoma State University. Soil decomposition rate was measured by JF and XG, and BIOLOG analysis was performed by XG. Shotgun sequencing was performed at the Oklahoma Medical Research Foundation's Genomics Core using the Illumina HiSeq 3000 platform Timing and spatial scale In this study, We collected soil samples for seven consecutive years. Specifically, a total of 56 annual soil samples was collected annually from 4 warmed and 4 control plots (September or October) from 2010 to 2016 in this long-term warming experiment site (34̊ 59ʹ N, 97̊ 31ʹW). The data of ecosystem C fluxes, soil respirations and plant biomass were also collected annually.

Data exclusions
There were no data exclusions.

Reproducibility
16S rRNA gene and ITS amplicons were sequenced by MiSeq platform(Illumina, SanDiego, CA, USA) using a 500-cycle v2 MiSeq reagent cartridge (Illumina). Shotgun sequencing was performed at the Oklahoma Medical Research Foundation's Genomics Core using the Illumina HiSeq 3000 platform with a 2 x 150 bp paired-end kit. GeoChip 5.0M was used for all 56 samples to analyze functional structure of soil microbial community from 2010 to 2016. Statistical analyses of amplicon sequencing data, shotgun sequencing data and GeoChip data showed consistent results.

Randomization
Treatments were set up in a randomized block design.

Blinding
All samples taken were labeled with a single number to track samples during lab processing, but included no information as to the treatment from which it originated.
Did the study involve field work?

Yes No
Field work, collection and transport

Field conditions
This experimental site was conducted in an old-field tallgrass prairie abandoned from cropping 40 years ago with light grazing until 2008. Ambrosia trifida, Solanum carolinense and Euphorbia dentate belonging to C3 forbs, and Tridens flavus, Sporobolus compositus and Sorghum halapense belonging to C4 grasses are dominant in the site. Annual mean temperature is 16.3 °C and annual precipitation is 914 mm. The soil type of this site is Port-Pulaski-Keokuk complex with 51% of sand, 35% of silt and 13% of clay, which is a well-drained soil that is formed in loamy sediment on flood plains. The soil has a high available water holding capacity (37%), neutral pH and 1.2 g cm-3 bulk density with 1.9% total organic matter and 0.1% total nitrogen (N).

Location
The experimental site is located at the Kessler Atmospheric and Ecological Field Station (KAEFS) in the US Great Plains in McClain County, Oklahoma (34̊ 59ʹ N, 97̊ 31ʹW).
Project and class site use requests were completed for our study. Liability waivers were completed hard copies provided to KAEFS.

Disturbance
Infrared heaters may disturb the grassland ecosystem. To minimize these disturbances, 'dummy' heaters were used in this study.

Reporting for specific materials, systems and methods
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