Quantifying drylands' drought resistance and recovery: the importance of drought intensity, dominant life history and grazing regime
Bibliographic record
Abstract
Projected global change will increase the level of land-use and environmental stressors such as drought and grazing, particularly in drylands. Still, combined effects of drought and grazing on plant production are poorly understood, thus hampering adequate projections and development of mitigation strategies. We used a large, cross-continental database consisting of 174 long-term datasets from >30 dryland regions to quantify ecosystem responses to drought and grazing with the ultimate goal to increase functional understanding in these responses. Two key aspects of ecosystem stability, resistance to and recovery after a drought, were evaluated based on standardized and normalized aboveground net primary production (ANPP) data. Drought intensity was quantified using the standardized precipitation index. We tested effects of drought intensity, grazing regime (grazed, ungrazed), biome (grassland, shrubland, savanna) or dominant life history (annual, perennial) of the herbaceous layer to assess the relative importance of these factors for ecosystem stability, and to identify predictable relationships between drought intensity and ecosystem resistance and recovery. We found that both components of ecosystem stability were better explained by dominant herbaceous life history than by biome. Increasing drought intensity (quasi-) linearly reduced ecosystem resistance. Even though annual and perennial systems showed the same response rate to increasing drought intensity, they differed in their general magnitude of resistance, with annual systems being ca. 27% less resistant. In contrast, systems with an herbaceous layer dominated by annuals had substantially higher postdrought recovery, particularly when grazed. Combined effects of drought and grazing were not merely additive but modulated by dominant life history of the herbaceous layer. To the best of our knowledge, our study established the first predictive, cross-continental model between drought intensity and drought-related relative losses in ANPP, and suggests that systems with an herbaceous layer dominated by annuals are more prone to ecosystem degradation under future global change regimes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".