Greater biomass from Arctic greening absorbs increased grazing pressure from a large herbivore
Bibliographic record
Abstract
Arctic warming is causing widespread “greening” of tundra ecosystems. What this means for plant–herbivore relations, including the grazing pressure herbivores exert on increasingly productive tundra ecosystems, is poorly understood. Svalbard is one of the fastest warming places on Earth, with concomitant increases in both forage biomass and reindeer numbers. In 11 years between 1998 and 2023, we measured grass biomass and the proportion of shoots grazed in mesic grass-dominated tundra to evaluate whether increased forage biomass of grass absorbed the grazing pressure of more reindeer. Also, we used GPS data from adult female reindeer (2009–2023) to identify if grazing pressure was relieved by spillover into other habitats. During the study period, reindeer abundance, estimated by annual capture-mark-recapture, tripled, while grass biomass only doubled. Grazing pressure increased from 4% to 8%, which was lower than expected from the increased reindeer density. This discrepancy was not caused by spillover into other habitats, but rather by increased grazing in higher-biomass patches that have emerged with summer warming. Our findings support the notion that increased summer forage has contributed to Svalbard reindeer population growth, notably by making available higher biomass grass swards that allow for greater food offtake.
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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) | 0.002 | 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".