Intensive grazing by Barnacle geese depletes High Arctic seed bank
Bibliographic record
Abstract
Studies in the Canadian Arctic show dramatic effects of increased goose grazing on vegetation structure and soil conditions, but little is known of the role of goose grazing in the European Arctic. We focused on how geese might affect plant recruitment via effects on seed production and soil seed bank in High Arctic Svalbard. Experimental grazing by captive Barnacle geese ( Branta leucopsis (Bechstein, 1803)) decreased flower densities both at normal and at high grazing pressure. Geese showed a clear preference for reproductive rather than vegetative shoots. Soil samples collected inside and outside 7-year-old exclosures in an intensively goose-grazed area revealed significant effects on the germinable soil seed bank. The density of viable seeds in the top soil layer inside exclosures was six times higher than in grazed plots. Lower densities of viable seeds occurred in the basal than in the top layer but there was no difference in basal layer seed density between exclosed and grazed plots. This study shows that geese have strong effects on floral abundance and consequently on the seed bank. We argue that goose grazing in these systems influences the potential for recovery after a disturbance event and thus the long-term plant species diversity and dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".