Grazer exclusion alters plant spatial organization at multiple scales, increasing diversity
Bibliographic record
Abstract
Grazing is one of the most important factors influencing community structure and productivity in natural grasslands. Understanding why and how grazing pressure changes species diversity is essential for the preservation and restoration of biodiversity in grasslands. We use heavily grazed subalpine meadows in the Qinghai-Tibetan Plateau to test the hypothesis that grazer exclusion alters plant diversity by changing inter- and intraspecific species distributions. Using recently developed spatial analyses combined with detailed ramet mapping of entire plant communities (91 species), we show striking differences between grazed and fenced areas that emerged at scales of just one meter. Species richness was similar at very small scales (0.0625 m(2)), but at larger scales diversity in grazed areas fell below 75% of corresponding fenced areas. These differences were explained by differences in spatial distributions; intra- and interspecific associations changed from aggregated at small scales to overdispersed in the fenced plots, but were consistently aggregated in the grazed ones. We conclude that grazing enhanced inter- and intraspecific aggregations and maintained high diversity at small scales, but caused decreased turnover in species at larger scales, resulting in lower species richness. Our study provides strong support to the theoretical prediction that inter- and intraspecific aggregation produces local spatial patterns that scale-up to affect species diversity in a community. It also demonstrates that the impacts of grazing can manifest through this mechanism, lowering diversity by reducing spatial turnover in species. Finally, it highlights the ecological and physiological plant processes that are likely responding to grazing and thereby altering aggregation patterns, providing new insights for monitoring, and mediating the impacts of grazing.
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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 | 0.000 | 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".