Assessing Black Tern (Chlidonias niger) Habitat Associations in Saskatchewan, Canada, Using Aerial Imagery
Bibliographic record
Abstract
Wetland degradation throughout the interior of North America has resulted in a loss of breeding habitat for many waterbird species. The Black Tern (Chlidonias niger) is an obligate marsh-breeding colonial waterbird that has experienced widespread, long-term population declines. Habitat loss and degradation through agricultural conversion, wetland drainage, and agrochemical runoff have been identified as key threats, and studies have suggested that a decline in breeding habitat may be a contributing factor to population declines. Habitat association studies have noted relationships between Black Terns and wetland characteristics, including both local-scale factors such as vegetation type, and landscape-scale factors such as wetland density. However, similar studies have not been conducted in Saskatchewan, the core of the species range in North America. We used high-resolution remotely-sensed imagery to relate habitat, land use, and geographic covariates at wetlands in Saskatchewan to the occurrence of breeding Black Terns and numbers at their colonies. We found that colony occurrence was positively associated with the extent of emergent aquatic vegetation present at a wetland. There was a strong non-linear effect of latitude, whereby colony occurrence and abundance were highest at mid-latitudes in Saskatchewan, corresponding to the boreal transition zone between the prairies to the south and boreal forest to the north. Our results suggest that Black Terns may be first selecting habitat at the landscape scale, perhaps in relation to wetland density, then occupying specific breeding colonies based on wetland characteristics.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".