Plant trait responses to grazing – a global synthesis
Bibliographic record
Abstract
Abstract Herbivory by domestic and wild ungulates is a major driver of global vegetation dynamics. However, grazing is not considered in dynamic global vegetation models, or more generally in studies of the effects of environmental change on ecosystems at regional to global scale. An obstacle to this is a lack of empirical tests of several hypotheses linking plant traits with grazing. We, therefore, set out to test whether some widely recognized trait responses to grazing are consistent at the global level. We conducted a meta‐analysis of plant trait responses to grazing, based on 197 studies from all major regions of the world, and using six major conceptual models of trait response to grazing as a framework. Data were available for seven plant traits: life history, canopy height, habit, architecture, growth form (forb, graminoid, herbaceous legume, woody), palatability, and geographic origin. Covariates were precipitation and evolutionary history of herbivory. Overall, grazing favoured annual over perennial plants, short plants over tall plants, prostrate over erect plants, and stoloniferous and rosette architecture over tussock architecture. There was no consistent effect of grazing on growth form. Some response patterns were modified by particular combinations of precipitation and history of herbivory. Climatic and historical contexts are therefore essential for understanding plant trait responses to grazing. Our study identifies some key traits to be incorporated into plant functional classifications for the explicit consideration of grazing into global vegetation models used in global change research. Importantly, our results suggest that plant functional type classifications and response rules need to be specific to regions with different climate and herbivory history.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".