Atmospheric drying and soil drying: Differential effects on grass community composition
Bibliographic record
Abstract
Global surface temperatures are projected to increase in the future; this will modify regional precipitation regimes and increase global atmospheric drying. Despite many drought studies examining the consequences of reduced precipitation, there are few experimental studies exploring plant responses to atmospheric drying via relative humidity and vapor pressure deficit (VPD). We examined eight native California perennial grass species grown in pots in a greenhouse in Los Angeles, California for 34 weeks. All pots were well-watered for 21 weeks, at which point we reduced watering to zero and recorded daily growth and dormancy for 3 weeks. We used this information to better understand the drought tolerance of our species in a larger soil drying × atmospheric drying experiment. In this larger experiment, we grew all eight species together in outdoor mesocosms and measured changes in community composition after 4 years of growth. Soil drying in our small pot experiment mirrored compositional shifts in the larger experiment. Namely, our most drought-tolerant species in our pot experiment was Poa secunda, due to a summer dormancy strategy. Similarly, the grass community shifted toward P. secunda in the driest soils as P. secunda was mostly unaffected by either soil drying or atmospheric drying. We found that some species responded strongly to soil drying (Elymus glaucus, Festuca idahoensis, and Hordeum b. californicum), while others responded strongly to atmospheric drying (Bromus carinatus and Stipa cernua). As result, community composition shifted in different and interacting ways in response to soil drying, atmospheric drying, and their combination. Further study of community responses to increasing atmospheric aridity is an essential next step to predicting the future consequences of climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".