Land transformation in tropical savannas preferentially decomposes newly added biomass, whether C<sub>3</sub> or C<sub>4</sub> derived
Bibliographic record
Abstract
Abstract As tropical savannas are undergoing rapid conversion to other land uses, native C3‐C4 vegetation mixtures are often transformed to C3‐ or C4‐dominant systems, resulting in poorly understood changes to the soil carbon (C) cycle. Conventional models of the soil C cycle are based on assumptions that more labile components of the heterogenous soil organic C (SOC) pool decompose at faster rates. Meanwhile, previous work has suggested that the C4‐derived component of SOC is more labile than C3‐derived SOC. Here we report on long‐term (18 months) soil incubations from native and transformed tropical savannas of northern Australia. We test the hypothesis that, regardless of the type of land conversion, the C4 component of SOC will be preferentially decomposed. We measured changes in the SOC and pyrogenic carbon (PyC) pools, as well as the carbon isotope composition of SOC, PyC and respired CO2, from 63 soil cores collected intact from different land use change scenarios. Our results show that land use change had no consistent effect on the size of the SOC pool, but strong effects on SOC decomposition rates, with slower decomposition rates at C4‐invaded sites. While we confirm that native savanna soils preferentially decomposed C4‐derived SOC, we also show that transformed savanna soils preferentially decomposed the newly added pool of labile SOC, regardless of whether it was C4‐derived (grass) or C3‐derived (forestry) biomass. Furthermore, we provide evidence that in these fire‐prone landscapes, the nature of the PyC pool can shed light on past vegetation composition: while the PyC pool in C4‐dominant sites was mainly derived from C3 biomass, PyC in C3‐dominant sites and native savannas was mainly derived from C4 biomass. We develop a framework to systematically assess the effects of recent land use change vs. prior vegetation composition.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".