Vegetation associations along disturbance gradients on the sand dunes of Sable Island, Nova Scotia.
Bibliographic record
Abstract
Sable Island, Nova Scotia, is a dynamic dune ecosystem that is composed of plant communities exposed to varying levels of disturbance. The island is exposed to extreme weather events throughout the year, and this plays an important role in dune succession; however, the vegetation dynamics of this ecosystem are poorly understood. I investigated plant community responses to natural disturbance gradients using field measurements of community composition, abiotic variables, and grazing (and/or browsing) pressure from the island’s population of feral horses. Sampling plots were distributed across the entire island using a stratified random sampling design to capture the maximum range of environmental gradients and vegetation types. I measured species composition at each site in combination with predictor environmental variables: slope, organic layer presence, distance from shore, and evidence of grazing. I identified three different vegetation assemblages via hierarchical cluster analysis and non-metric multidimensional scaling ordination, and examined their associations with different environmental conditions and plant traits. Multivariate analyses indicated a strong relationship between community composition and distance from shore. Slope was the most important variable affecting whether a plot had vegetation and instances of grazing. Species with traits better suited to withstand sand burial and salt spray were present in areas closer to shore. Areas with less disturbance contained more shrub and heath communities. Evidence of grazing was present in all vegetation types with no observed relationship to plant species composition. Dune succession on Sable Island was not linear and is better described as the vegetative response to dynamic environmental stress rather than the result of gradual soil development and competitive displacement.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".