Data from: Interplay between rainfall and hillslope hydrology determines drought resistance of tropical vegetation
Bibliographic record
Abstract
Droughts are predicted to increase in both frequency and intensity by the end of the 21st century, but ecosystem response is not expected to be uniform. At the landscape scale, ecosystem response to drought is highly heterogeneous. Here we assess the importance of the hill-to-valley hydrologic gradient in shaping vegetation hydraulic properties related to drought resistance for three locations across a rainfall seasonality gradient in South America. For this, we use hydraulic traits related to xylem resistance to embolism and compare the functional composition and diversity of tree communities. We show that the hydrologic gradient systematically selects for community assemblages that are more vulnerable to embolism in valleys, regardless of rainfall. Under the same rainfall regime, diversity in resistance to embolism is higher on hills than valleys, suggesting that strategies to cope with drought are more important on hills. With increasing seasonality, diversity in embolism resistance increases on hills and decreases in valleys. Our results show that differential groundwater access from hilltops to valleys select for distinctive hydraulic properties, potentially explaining species turnover along topographical gradients. Incorporating this relationship might improve the representation of vegetation in climate models and the prediction of how different communities will respond to extreme droughts.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".