Survival and growth of the forage grass<i>Festuca rubra</i>in naturally and artificially devegetated sites in a sub-arctic coastal marsh
Bibliographic record
Abstract
:Festuca rubra is an abundant supratidal grass on sub-arctic James Bay (Canada) shorelines, forming extensive near-monocultures that are used as forage by nesting and migrating geese. Studies at other, more northern North American locations have shown grubbing by geese can have severe consequences for intertidal and supratidal marshes, but these studies have focussed on plant communities that differ substantially in species composition, physical environment, and extent from James Bay’s Festuca meadows. In this study, we examined the responses of this grass to natural and simulated goose grubbing in Festuca swards heavily used by lesser snow geese, Canada geese, and brant at Akimiski Island in James Bay. We transplanted Festuca into plots previously devegetated by geese, into plots where we removed vegetation to simulate goose grubbing, and into intact vegetation (controls). We found shoots transplanted into control and artificially grubbed plots survived well, but those transplanted into previously devegetated areas usually died. Growth initially was reduced in naturally devegetated sites, but the few survivors in the following year performed as well as plants transplanted into intact or artificially grubbed sites. Spot measurements suggested that naturally devegetated sites suffered from degraded soil conditions, such as hypersalinity and increased temperature. These results provide evidence that recovery of Festuca swards following loss of vegetation is likely to be difficult, probably as a result of deteriorating soil conditions. Models of goose–plant interactions developed at substantially more northern sites thus seem applicable to the significantly different plant communities of the James Bay shoreline.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".