High and variable hydraulic resistance to embolism among three Asian juniper species
Bibliographic record
Abstract
Over a background of a warming planet, with increasing occurrence of extreme climate events, junipers stand out as tree species thriving in inhospitable environments, enduring the cold, hot, and dry. Being part of the drought-adapted Cupressaceae Gray, some juniper species have been identified having among the most negative P50 values (vulnerability to xylem embolism) in the plant kingdom. Still, previous analyses skipped many Asian juniper species, some of which growing in arid conditions. Here we measured xylem vulnerability to embolism in three, yet unexplored, Asian juniper species: Juniperus drupacea growing in Israel, and J. pseudosabina and J. seravschanica growing in Uzbekistan. Branches were sampled from five trees per species and vulnerability curves were constructed using the Cavitron method, from which P50 was calculated. Xylem anatomy measurements were used to define torus overlap and maximum xylem specific hydraulic conductivity. These hydraulic traits were tested against species and site climate and in the context of species phylogeny. P50 of the Eastern Mediterranean J. drupacea was -11.8±0.3 MPa, much more negative than that of the two Asian junipers measured here, but within the same range of values measured in European and North American junipers. P50 was more negative with mean annual temperature and with aridity index of the driest quarter at both species (climatic conditions at the entire distribution range) and site levels. We did not find the relationships generally found in conifers between the properties of xylem pit membrane and vulnerability to embolism, nor with the overall aridity of the environment. To the best of our knowledge, our observations identify J. drupacea as the most drought-tolerant tree species in Asia.
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".