Scale‐dependent effects of herbivory on moss communities in Arctic wetlands: A 25‐year experiment
Bibliographic record
Abstract
Abstract Arctic ecosystems are undergoing rapid changes, including increasing disturbance by herbivore populations, which can affect plant species coexistence and community assemblages. Although the significance of mosses in Arctic wetlands is well recognized, the long‐term influence of medium‐sized herbivores on the composition of moss communities has received limited attention. We used data from a long‐term (25 years) Greater Snow Goose (Anser caerulescens atlanticus) exclusion experiment in Arctic tundra wetlands to assess changes in the composition of moss communities at multiple spatial scales (cell, 4 cm2; quadrat, 100 cm2; exclosure, 16 m2). We investigated how snow goose grazing and grubbing can alter the composition of the moss community by measuring changes in alpha and beta diversity, as well as in the strength of plant interspecific interactions between moss species. Our results indicate that goose foraging significantly increased species diversity (richness, evenness, and inverse Simpson index) of moss communities at the cell and quadrat scales but not the exclosure scale. Goose foraging reduced the dissimilarity (beta diversity) of moss communities at all three scales, mainly due to decreased species turnover. Furthermore, goose foraging increased positive interaction between moss species pairs. These findings emphasize the critical role of geese in promoting moss species coexistence and increasing homogeneity in Arctic wetlands. This study illustrates how top‐down regulation by herbivores can alter plant communities in Arctic wetlands and highlights the importance of considering herbivores when examining the response of Arctic plant biodiversity to future climate 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".