Deforestation for agriculture increases microbial carbon use efficiency in subarctic soils
Bibliographic record
Abstract
This repository contains all necessary raw data as well as the R code used to conduct statistical analysis and create figures of the publication Deforestation for agriculture increases microbial carbon use efficiency in subarctic soils Julia Schroeder1, Tino Peplau1, Frank Pennekamp2, Edward Gregorich3, Christoph C. Tebbe4, Christopher Poeplau1 1 Thünen Institute of Climate-Smart Agriculture, Bundesallee 68, 38116 Braunschweig, Germany 2 Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland 3 Research and Development Centre, Central Experimental Farm, Agriculture and Agri-Food Canada, 960 Carling Ave, Ottawa, Ontario K1A 0C6, Canada 4 Thünen Institute of Biodiversity, Bundesallee 65, 38116 Braunschweig, Germany DOI: https://doi.org/10.1007/s00374-022-01669-2 This study investigated how and through which pathways deforestation and conversion to agricultural land (i.e. grassland, cropland) alters the microbial carbon use efficiency (CUE) in subarctic soils to allow the development of mitigation strategies to alleviate C losses. We assessed CUE using 18O-labelled water in a paired-plot approach on soils collected from 19 farms across the subarctic region of Yukon, Canada, comprising 14 pairs of forest-to-grassland conversion and 15 pairs of forest-to-cropland conversion. Microbial CUE significantly increased following conversion to grassland and cropland. Land-use conversion resulted in a lower estimated abundance of fungi, while the archaeal abundance increased, as assessed by qPCR. Interestingly, structural equation modelling revealed that increases in CUE were mediated by a rise in soil pH and a decrease in soil C:N ratio rather than by shifts in microbial community composition, i.e. the ratio of fungi, bacteria and archaea. Our findings indicate a direct control of abiotic factors on microbial CUE via improved nutrient availability and facilitated conditions for microbial growth. The R code was developed under R v3.6.3 and adapted to work under version R v.4.1.2. The repository includes the following files: general_soil_parameters_per_site.csv - general soil data assessed on pooled reference forest plot (n=19) general_soil_parameters_per_plot.csv - general soil data assessed on pooled replicated field samples (n=48) sample_data.csv - data measured for each laboratory sample (n=147) Land-use change effects on 18O-CUE.Rproj - Rproject (load project to work on provided scripts and data) load_data_script.R - loads required data Multivariate_normality_script.R - tests for multivariate normaility in dataset PCA_script.R - calculates PC1 and 2 of clay mineralogy data to reduce dimensions map_Yukon_script.R - create Figure 1 plot_density_script.R - create Figure 2 linear_mixed-effects_models_script.R - calculates response ratios plot_boxplots_script.R - plot boxplots per land use including compact letter display indicating significant differences, create Figure 3 + 4 correlogram_script.R - correlation analysis to identify drivers of CUE, create Figure 6 plot_correlations_script.R - plot drivers of CUE, create Figure 5 + 7 SEM_script.R - development of structural equation model, create Figure 8
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".