Snowmelt variation contributes to topoclimatic refugia under montane Mediterranean climate change
Bibliographic record
Abstract
Improved knowledge of the influence of climate parameters on the distribution of plant species is needed to identify potential refugia under climate change. Species abundance of trees, mainly conifers, as measured by species relative cover, was evaluated on 132 sites in the southern Sierra Nevada mountain range of California, USA. These mountains experience a montane Mediterranean climate characterized by a deep winter snowpack and an extended summer drought. The cover data were analyzed in terms of the average snowpack water content at its maximum and the average date when snow on each site has finally melted. These snow-related parameters were calculated from a semi-empirical snow model, taking into account site slope and aspect. For the pine, juniper, and oak species studied, these parameters were found to have a much stronger effect on species abundance at a site than does elevation. For the conifer species, this allows the identification of topographic refugia from climate change. This result appears to be related to growth phenology. Elevation was found to be more important for the fir species studied. The results on the importance of growth phenology should be useful in identifying topographic refugia in mountains experiencing a Mediterranean climate worldwide.
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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) | 0.001 | 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".