A rapid transition from spruce-fir to pine-broadleaf forests in response to disturbances and climate warming on the southeastern Qinghai-Tibet Plateau
Bibliographic record
Abstract
A better understanding of the structure and dynamics of disturbed forests is key for forecasting their future successional trajectories. Despite vulnerability of subalpine forests to warming climate, little is known as to how their community composition has responded to disturbances and climate warming over decades. Before the 1970s, subalpine forests on the southeastern Qinghai-Tibet Plateau mainly experienced logging and fire, but afterwards they were more impacted by climate warming. Thus, they provide an excellent setting to test whether disturbances and climate warming led to changes in forest structure. Based on the analysis of 3145 forest inventory plots at 4- to 5-year resolution, we found that spruce-fir forests shifted to pine and broadleaved forests since the early 1970s. Such a turnover in species composition mainly occurred in the 1994-1998 period. By strongly altering site conditions, disturbances in concert with climate warming reshuffle community composition to warm-adapted broadleaf-pine species. Thus, moderate disturbances shifted forest composition through a gradual loss of resilience of spruce-fir forests. Shifts in these foundation species will have profound impacts on ecosystem functions and services. In the future, broadleaved forests could expand more rapidly than evergreen needle-leaved forests under moderate warming scenarios. In addition to climate, the effects of anthropogenic disturbances on subalpine forests should be considered in adaptive forest management and in projections of future forest changes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".