Mexican conifers differ in their capacity to face climate change
Bibliographic record
Abstract
The recent massive dieback of forest trees due to drought stress makes assessment of the variability of physiological traits that might be critical for predicting forest response and adaptation to climate change even more urgent. We investigated xylem vulnerability to cavitation and xylem specific hydraulic conductivity in seven species of three principal conifer genera (Juniperus monticola, Juniperus deppeana, Juniperus flaccida, Pinus pseudostrobus, Pinus leiophylla, Pinus devoniana, and the endangered Picea chihuahuana) of the Mexican mountains in order to identify the species most vulnerable to future warmer and drier climates. Hydraulic traits were examined using the in situ flow centrifuge technique (Cavitron) on branches collected from adult trees of natural populations and seedlings growing in a common garden. We found evidence of significant differences in xylem safety between genera (P50: pressure inducing 50% loss of hydraulic conductance): the three juniper species exhibited low P50 values (ranging from -9.9 to -10.4 MPa), relative to the much more vulnerable pine and spruce species (P50 ranging between - 2.9 to - 3.3 MPa). Our findings also revealed no variation in P50 between adult trees assessed in the field and seedlings growing in a common garden. We therefore propose that if, as projected, climate change makes their natural habitats much warmer and drier, populations of Mexican pines and the studied spruce will be likely to decline severely as a result of drought-stress induced cavitation, while the juniper species will survive.
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".