Large herbivores trigger spatiotemporal changes in forest plant diversity
Bibliographic record
Abstract
Large herbivores can exert top-down control on terrestrial plant communities, but the magnitude, direction, and scale dependency of their impacts remain equivocal, especially in temperate and boreal forests, where multiple disturbances often interact. Using a unique, long-term, and replicated landscape experiment, we assessed the influence of a high density of white-tailed deer (Odocoileus virginianus) on the spatiotemporal dynamics of diversity, composition, and successional trajectories of understorey plant assemblages in recently logged boreal forests. This experiment provided a rare opportunity to test whether deer herbivory represents a direct filter on plant communities or if it mainly acts to suppress dominant plants, which, in turn, release other plant species from strong negative plant-plant interactions. These two hypotheses make different predictions about changes in community composition and alpha and beta diversity in different vegetation layers and at different spatial scales. Our results showed that deer had strong effects on plant community composition and successional trajectories, but the resulting impacts on plant alpha and beta diversity patterns were markedly scale dependent in both time and space. Responses of tree and non-tree vegetation layers were strongly asymmetric. Deer acted both as a direct filter and as a suppressor of dominant plant species during early forest succession, but the magnitude of both processes was specific to tree and non-tree vegetation layers. Although our data supported the ungulate-driven homogenization hypothesis, compositional shifts and changes of alpha diversity were poor predictors of beta diversity loss. Our findings underscore the importance of long-term studies in revealing nonlinear temporal community trends, and they challenge managers to prioritize particular community properties and scales of interest, given contrasting trends of composition and alpha and beta diversity across spatial scales.
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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".