Seeing the Savanna Through the Trees: Vegetation Structure, Composition and Function Along a Forest‐Savanna Boundary in Cambodia
Bibliographic record
Abstract
ABSTRACT In the seasonally dry landscapes of continental Southeast Asia, deciduous dipterocarp vegetation (DDF) and semi‐evergreen forests (SEF) form patchy landscape mosaics, with abrupt boundaries between them. DDF resembles savanna, with an open canopy and a continuous grassy ground layer, while SEF lacks grass and has high tree cover and a closed canopy. Alternative hypotheses suggest that these distinct vegetation types are alternative stable states maintained by fire‐vegetation feedbacks, that differences in edaphic conditions across landscapes explain their distributions, and/or that DDF are degraded or early successional forests whose distribution is determined by legacies of anthropogenic disturbance. Here, we compare structure, composition, and functional traits of woody vegetation across DDF‐SEF boundaries, and ask whether differences across vegetation types are associated with edaphic factors or fire history. We found major differences in vegetation structure and species composition across DDF and SEF, with few shared species across vegetation types. Dominant DDF tree species were not found in SEF, suggesting that DDF represents a distinct vegetation community, rather than early successional or degraded forest. Compared to SEF species, DDF species had lower specific leaf area and higher bark thickness, a key trait associated with fire tolerance. Soil texture and fertility did not differ across vegetation types. Together, these findings suggest that fire, not edaphic factors, likely is the key driver of vegetation at DDF‐SEF boundaries. Our results further support classifying and managing DDF as savanna. Conserving the unique biodiversity of DDF‐SEF mosaic landscapes will require research to support evidence‐based fire management.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".