Data and Code:Scale-dependent effects of herbivory on bryophyte communities in Arctic wetlands: A 25-year experiment
Bibliographic record
Abstract
Our study explored how snow goose foraging activity alters bryophytes community composition via direct effects on alpha and beta diversity and indirectly by changing the strength of competitive interactions between bryophyte species. To do so, we surveyed the abundance of individual bryophyte species inside exclosures where snow geese had been excluded for a long time (25 years) and outside at three spatial scales (cm to m scales) in an Arctic wetland (Bylot Island in Nunavut, Canada).We conducted this study in tundra wetlands, mostly fens created by polygon-patterned permafrost, located in the Qarlikturvik glacial valley on Bylot Island in Nunavut, Canada (73°N, 80°W). This region is the most important breeding site of the greater snow goose in the Arctic. Each summer, a breeding population of snow geese estimated at 20,000 pairs in one large colony covering approximately 65 km2 on the southern plain of Bylot Island. A total of 18 goose exclosures (4 m x 4 m) were randomly established in polygon fens of the study area in 1994 over a ca 3 km2 area. The exclosures were made of 2.5 cm mesh chicken wire, standing 50 cm tall and covered with a lightweight nylon netting. Exclosures were inspected and repaired annually to ensure that no snow geese could enter. The control plots were the polygon fens outside the exclosure. Initially, these polygon fens plots comprised vegetation similar to those inside the exclosures. In 2019, ten pairs of plots, each composed of one exclosure (only those that were still in good state and not influenced by major disturbances such as polar bears, landslides, or ice-wedge degradation) and one adjacent control were sampled.Additionally, we first positioned five quadrats (10 cm x 10 cm) inside each exclosure and five quadrats outside the exclosure over a similar-size area in the polygon fen. Quadrats were positioned according to the following criteria: (1) They were at a distance of at least 30 cm from the chicken-wire fence; (2) They were randomly thrown in areas with bryophytes, avoiding standing water or areas without bryophytes, which constituted <10 % of the area. We harvested the bryophytes of each quadrat at a height of 10 cm. Each quadrat was divided into 25 2 cm x 2 cm cells and dried in paper envelopes for 24 h at 50 °C or until a constant weight was reached and brought back to the lab for analysis. In the laboratory, we identified each bryophyte species and counted the total number of individual shoots of each species in each cell (total of 2500 cells), which was our measure of abundance. The design was thus hierarchically structured, with three nested levels: cell (4 cm2; N = 25/quadrat) within quadrat (100 cm2; N= 5/exclosure) within exclosure (16 m2; N = 10).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".