Loss of resource-conservative species affects plant phylogenetic and functional structure under long-term snow addition
Bibliographic record
Abstract
The ongoing biodiversity crisis is driven by global climate change, like extreme snowstorm and overgrazing, that alters community composition, necessitating a better understanding of community assembly. We investigated the effects of 15-year experimental grazing and snow addition on taxonomic, phylogenetic, and functional diversity on the Tibetan Plateau. Grazing did not alter community structure, but snow addition caused phylogenetic structure to go from randomness to over-dispersion, as lost species were phylogenetically more closely related to residents than to gained species. Functional community clustering remained unchanged due to opposing trends in individual traits. Moreover, functional traits served as a powerful tool underpinning diversity change. Particularly, species with higher leaf dry matter content and lower specific leaf area, which signify a conservative resource-use strategy, had an increased risk of loss and contributed to changes in community structure under snow addition. Finally, this research offers deeper insights into long-term plant dynamics under environmental changes.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".