Estuary resilience and recovery following exclusion of a non-native invader, Canada Geese
Bibliographic record
Abstract
Estuaries are threatened by non-native resident Canada geese (Branta canadensis, “CAGO”). Excessive grazing and grubbing pressure by CAGO shifts herbaceous marsh habitat to denuded mudflat, leading to marsh recession. The degree to which vegetation and seed banks can recover after CAGO exclusion remains unknown. We compare the recovery trajectory exclosures that are 1 and 10 years old to undisturbed and denuded sites to ask the questions 1) How does CAGO grazing impact species and Plant Functional Groups (PFGs) over time? 2) How do seed banks respond over time since grazing exclusion? 3) Is seed bank composition similar to above-ground species composition? Using mixed effects models and NMDS, we show that 1) Recently grazed sites were dominated by annual species, and had higher proportion of unvegetated ground compared to ungrazed sites. Perennial graminoid cover increased between 1 and 10 years at exclosed sites, however sites exclosed for 10 years had higher cover of non-native graminoid species. 2) Seed abundance was highest in denuded sites dominated by annual species; however, seed species richness did not vary as expected between grazing conditions. 3) Seed bank species composition was most similar to above-ground vegetation in denuded sites dominated by annual species. Excluding geese is an effective method of protecting remnant vegetation; however, these sites may be prone to invasion during the recovery period. Understanding whether management actions facilitate resilience of above-ground vegetation and seed banks is important to inform conservation and restoration best practices.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".