Mechanisms underlying the vulnerability of seasonally dry ecosystems to drought
Bibliographic record
Abstract
Root-zone water storage (RWS) dynamics regulate when plants experience drought-related water stress and mortality. However, because RWS capacity (Smax) is poorly known, it remains challenging to translate variability in precipitation to water stress. Here, we investigate the relationship between precipitation variability and Smax and implement a framework for identifying the vulnerability of seasonally dry woody ecosystems to projected climate change. Using novel estimates of Smax across California, we demonstrate that where dry-season RWS is routinely capped by Smax, plants are less vulnerable to precipitation variability relative to where dry-season RWS varies annually with precipitation. Using direct measurements of RWS and Smax at three field sites, we illustrate how these differences in vulnerability arise due to variations in bedrock properties. We calculate that up to 23% of California's total biomass is sensitive to year-to-year variations in precipitation and can experience carryover of moisture from one year to the next. Contrary to the notion that deep weathering and moisture carryover confer ecosystem resilience to moisture stress, the areas we identified where Smax commonly exceeds precipitation totals experienced disproportionately high rates of mortality during recent drought. In contrast, the 51-58% of California's total biomass that experiences annually reliable dry-season moisture supply showed lower drought-related mortality. This framework then allows us to use climate projections for the next century to determine that a transition from stable to unstable moisture supply is projected for 3% of the state's carbon stocks. Much of the area presently showing signs of vulnerability is expected to experience additional moisture stress in the coming century due to changes in precipitation amounts alone. An understanding of belowground conditions, including the deep root-zone in bedrock, contributes to prediction of conditions leading to ecosystem water stress.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".