Short- and long-term growth characteristics associated with tree mortality in southwestern mixed-conifer forests
Bibliographic record
Abstract
Continued increases in global temperatures and incidences of drought have been implicated in elevated tree mortality in many regions, prompting interest in better understanding tree mortality processes. A recent extreme drought in the southwestern U.S. (1996–2003) contributed to elevated tree mortality throughout the region. We used this event to investigate the relationship of short- and long-term tree growth characteristics to recent (1996–2008) tree mortality in the mixed-conifer forests in northern Arizona. We compared radial growth characteristics over a 50-year period between paired live and recently dead white fir (Abies concolor (Gordon & Glend.) Lindl. ex Hildebr.), limber pine (Pinus flexilis E. James), trembling aspen (Populus tremuloides Michx.), and Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco). We found that (i) dead trees of all species typically had lower mean growth rates than live trees, (ii) dead trees of most species had a greater coefficient of variation in growth over long time periods (>20 years) than live trees, (iii) dead aspen and dead Douglas-fir trees had larger negative growth trends than live trees for some time periods, (iv) dead trees of most species had larger numbers of abrupt growth declines than live trees, and (v) a combination of short- and long-term growth characteristics distinguished live and dead trees, with greater importance of short-term growth for aspen, long-term growth for limber pine, and a mix of short- and long-term growth for white fir and Douglas-fir. These results strongly suggest that recent tree mortality in southwestern mixed-conifer forests is caused by a mixture of short- and long-term processes.
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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 | 0.001 |
| 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 | 0.000 |
| 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".