Data and code from: Loss of resource-conservative species affects plant phylogenetic and functional structure under long-term snow addition
Bibliographic record
Abstract
Climate change and human activities are increasingly influencing ecological communities. Within this context, increasing extreme snow events and persistent livestock grazing are known to pose significant challenges to alpine ecosystems on the Tibetan Plateau. However, the mechanisms driving long-term community assembly and structural changes under these concurrent pressures remain unclear. Here, we used a 16-year field experiment in a Tibetan alpine grassland to investigate the effects of spring snow addition and yak grazing on taxonomic, phylogenetic, and functional community diversity and structure. We found that snow addition was the primary driver of community structure, while the effects of grazing were less pronounced. Specifically, snow addition shifted the phylogenetic structure from being random to overdispersed. This shift was driven by the selective loss of species with conservative resource-use strategies (i.e., those with high leaf dry matter content and low specific leaf area), which were phylogenetically more closely related to the residents than were the gained species. In contrast, communities remained functionally clustered under all treatments. This resulted from opposing structural shifts in individual traits, where leaf dry matter content became more overdispersed, while plant height and leaf nitrogen content (LNC) became more clustered, driven by the loss of taller species and the gain of species with low LNC. This decoupling between phylogenetic and functional responses suggests that environmental filtering selects for convergent functional adaptations among phylogenetically distant species. Our findings highlight the importance of considering multi-faceted diversity metrics when exploring community assembly, and provide the first experimental evidence that long-term snow addition reshapes plant phylogenetic community structure on the Tibetan Plateau. Importantly, the loss of conservative species suggests that altered snow regimes may potentially shift key ecosystem functions in alpine grasslands. Our findings also demonstrate that integrating species gain and loss is essential for a predictive understanding of long-term community dynamics under global change.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.330 | 0.148 |
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".