Soil properties affect pinyon pine – juniper response to drought
Bibliographic record
Abstract
ABSTRACT Since the late 1990s, drought‐driven dieback has affected more than a million hectares of pinyon pine‐juniper woodlands in the southwestern USA. Analysis of annual aerial surveys by the US Forest Service and soil survey data shows that most of the mortality occurred between 2003 and 2004 and that 70% was restricted to soils mapped as having available water storage capacities (Ac) <100 mm. We conducted a more refined analysis and found that asAcincreased in increments of 50 mm up to 300 mm, the distribution of areas with observed mortality decreased exponentially from 42% to 3% (n = 6 classes,r2 = 0.93). We used this information in a process‐based stand growth model, physiological principles predicting growth, to assess year to year variation in gross photosynthesis between 1985 and 2005 with climatic data at monthly intervals from four weather stations where pinyon‐juniper woodlands were confirmed by satellite imagery. A sensitivity analysis identified sustained periods of drought and supported field observations that once canopy leaf area approaches a maximum value, the majority of mortality should be restricted to soils withAcvalues <100 mm. Additional analyses indicated that differences in soil texture played a small part (<10%) in the variation of gross photosynthesis and that consecutive years of drought may have a cumulative effect on pinyon pine vulnerability to bark beetle attack. Disturbances reducing canopy leaf area index should result in less pine mortality in the future, although conversion to shrub and grassland may occur if climate conditions continue to become less favorable. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.000 | 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".