Species-specific response to climate reconstruction in upper-elevation mixed-conifer forests of the western Sierra Nevada, California
Bibliographic record
Abstract
Dendrochronology climate reconstruction studies often sample dominant, open-grown trees to reduce competition effects and isolate annual climate influences on radial increment growth. However, there has been no examination of how species respond as stand densities increase or which species in mixed-conifer forests provide a better record of past climate. We sampled 579 trees representing five upper montane mixed-conifer species at the Teakettle Experimental Forest in California’s southern Sierra Nevada to determine species-specific responses to annual climatic fluctuations. Using the Kalman filter, we examined the affect of local stand density on growth response and whether the growth–climate relationship improved with a time lag. The Kalman filter iteratively calculates error for predicted versus actual radial growth and accounts for this variation in the corrector equation. Under current high-density conditions, shade-tolerant white fir ( Abies concolor (Gord. & Glend.) Lindl.) provided the best model for climate reconstruction. Shade-intolerant Jeffrey pine ( Pinus jeffreyi Grev. & Balf.) had a lagged response to annual climatic fluctuations, possibly because its roots may tap water reserves in granitic bedrock fissures. Open-grown trees provided more accurate records of climate. Changes in forest density in this forest may have resulted in changes in species-specific response to annual climatic fluctuations.
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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 | 0.000 | 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".