Long‐term effects of changes in goose grazing intensity on arrowgrass populations: a spatially explicit model
Bibliographic record
Abstract
Summary Field studies on effects of geese on arrowgrass ( Triglochin palustris ) on the Yukon‐Kuskokwim Delta (SW Alaska) have demonstrated that Pacific black brant geese ( Brant bernicla nigricans ) can have both positive and negative effects on arrowgrass populations, but cannot predict unambiguously the effects of increased goose numbers on arrowgrass demography. A cellular automata model was used to predict effects of changes in goose grazing intensity on small‐scale (within‐patch) arrowgrass dynamics. We examined effects of making some of the plant competitors edible to geese, of goose faeces increasing arrowgrass reproduction but reducing size of ungrazed arrowgrass, and of the presence of other species protecting arrowgrass from grazing. We also compared the effects of a random vs. patchy distribution of geese, and of incorporating threshold numbers of arrowgrass below which grazing ceased. The results indicate that arrowgrass populations are likely to be highest at medium to high levels of grazing. Inclusion of edible competitors and positive effects of faecal deposition resulted in greater changes in arrowgrass population dynamics than did inclusion of associative refuges. For a given grazing intensity, models generally resulted in lower arrowgrass populations with increased aggregation if distributions of geese were patchy, suggesting that decreased colonization may result from lower dispersal. Inclusion of a feedback effect (grazing only above a certain plant population) caused arrowgrass populations to persist for much longer. Temporal variability in whether plots were grazed (unrelated to arrowgrass numbers) could not account for this result. The model results suggest that knowledge of both small‐scale and large‐scale foraging behaviour is needed to predict the long‐term effects of goose grazing on arrowgrass. Small‐scale effects on the population may be particularly important where dispersal distances are short. The ability of plant populations to persist locally may be increased if grazing is suspended when herbivory reduces forage plants below a threshold level.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".