Growth–climate relationships for six subalpine tree species in a Mediterranean climate
Bibliographic record
Abstract
A better understanding of the growth–climate relationship for subalpine trees is key to improving predictions about their future distributions under climate change. In subalpine regions of Mediterranean mountains, drought is an annual event, yet many sites can have long-lasting snowpack. We analyzed the growth–climate relationship from 1896 to 2006 for the six most abundant subalpine tree species (red fir (Abies magnifica A. Murray bis), whitebark pine (Pinus albicaulis Engelm.), Sierra/Cascade lodgepole pine (Pinus contorta var. murrayana (Balf.) Engelm.), Jeffrey pine (Pinus jeffreyi Balf.), western white pine (Pinus monticola Douglas ex D. Don), and mountain hemlock (Tsuga mertensiana (Bong.) Carrière)) of the central Sierra Nevada, California, USA, a region with deep spring snowpack followed by strong summer drought. Chronologies for the six species exhibited a high degree of synchrony in their response to annual fluctuations in temperature and precipitation. For all six species, cool, wet conditions in the year prior to growth are conducive to good radial growth, as well as warm springs with sufficient moisture during the year of growth. For species more common on protected slopes, such as mountain hemlock, deep spring snowpack can limit growth. Although predictions of future precipitation trends in the region are uncertain, drought stress appears to already be increasing. If this trend continues, radial growth is likely to be inhibited for most or all species in our study. Trees growing where snowpack is deep may be least likely to suffer reduced growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".