This project contains the R code used for data-processing and statistical analysis for the research paper Temporal changes of global soil respiration since 1987 (Lei et al.,2020). All datasets in support of the findings of this paper are also provided.
| File | Description |
|---|---|
| reference datasets | Directory of all input and output datasets and a list of studies containing the original data sources |
| 0_functions_plot.R | Load R packages and define functions |
| 1_data_cleaning.R | Preprocess srdb data: remove outliers, fill in part of the missing data, and redo classification of ecosystem and measurement method |
| 2_climatic_variable_extraction.R | Extract time-series temperature and precipitation data corresponding to RS sites. Retain only RS records with available matching climatic data. |
| 3_variables_generation.R | Match RS records with corresponding mean annual temperature (MAT) and precipitation (MAP), calculate T anomaly and P anomaly. Add "altitude" and "SOC stocks" fields, and deal with missing SOC stocks data. Detect and remove outliers by category of biomes. |
| 4_statistical_prep.R | Apply filtering criteria to obtain final working data. Divide data into multiple time periods and biomes. |
| 5_multivariate_model.R | Fit multivariate model |
| 6_moving_subset.R | Moving subset analysis for examining temporal RS trends across windows of time period, latitude and SOC stocks. Both simple linear regression and robust regression methods are used to calculate temporal trends. |
| 7_Linear_regressions_Rs.R | Breakpoint detection of RS temporal trend. Linear regressions of RS vs. time and RS vs. climatic factors. Both simple linear regression and robust regression are used. |
| 8_climate_trends.R | Examine temporal trends of temperature, precipitation and the anamolies across latitude gradients. |
| 9_RhRa_analysis.R | All-in-one analysis script for Rh&Ra data, including data screening, multivariate model fitting and linear regressions. |
| 10_temp_anomaly_moving_subset.R | Moving window analysis of temperature anomaly along latitude gradients. |
| 11_montecarlo.R | Estimate annual global RS with Monte Carlo simulation, including variable extraction, model construction, Monte Carlo simulation, and results summarization. |
| README.md | Generates this README |
| File | Description |
|---|---|
| 01_cleaneddata.csv | Output file from 1_data_cleaning.R |
| 02_sampledataset.csv | Output file from 2_climatic_variable_extraction.R |
| 03_processed_data_complete_final.csv | Output file from 3_variables_generation.R. Final output from RS data preparation. |
| 04_processed_data_rhra.csv | Final output from Rh/Ra data preparation. |
| NOLAI-BASE_ANALYSIS_gf_final_1987-2016_1000.csv | Output file from 10_montecarlo.R. Estimated global annual RS from 1987-2016. |
| Respiration-study-list.csv | A list of studies from which the records were collected |
| annual.precip.30y.csv | Intermediate output file from 3_variables_generation.R |
| annual.temp.30y.csv | Intermediateoutput file from 3_variables_generation.R |
| delta.precip.30y.csv | Intermediateoutput file from 3_variables_generation.R |
| delta.temp.30y.csv | Intermediateoutput file from 3_variables_generation.R |
| precip_extract_result.csv | Output file from 2_climatic_variable_extraction.R |
| srdb-data.csv | SRDB v20200220a |
| temp_extract_result.csv | Output file from 2_climatic_variable_extraction.R |
This project is covered under the “GNU Affero General Public License” version 3.
Jiesi Lei, Xue Guo, Yufei Zeng, Jizhong Zhou, Qun Gao, and Yunfeng Yang. Genuine thanks go to Dr. Ben Bond-Lamberty for providing access to the SRDB.