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 C 3 ‐C 4 vegetation mixtures are often transformed to C 3 ‐ or C 4 ‐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 C 4 ‐derived component of SOC is more labile than C 3 ‐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 C 4 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 CO 2 , 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 C 4 ‐invaded sites. While we confirm that native savanna soils preferentially decomposed C 4 ‐derived SOC, we also show that transformed savanna soils preferentially decomposed the newly added pool of labile SOC, regardless of whether it was C 4 ‐derived (grass) or C 3 ‐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 C 4 ‐dominant sites was mainly derived from C 3 biomass, PyC in C3‐dominant sites and native savannas was mainly derived from C 4 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".