First‐ever satellite tracking of Black Terns (<i>Chlidonias niger</i>): Insights into home range and habitat selection
Bibliographic record
Abstract
Abstract Understanding animal movement across the annual cycle is critical for developing appropriate conservation plans, but the large size and high cost of tracking devices can limit the spatial and temporal resolution at which movement data can be collected, especially for small avian species. Furthermore, for species with low breeding site fidelity, the ability to obtain tracking data from small, archival tags is hindered by low recapture rates. We deployed satellite tracking devices on four adult Black Terns (Chlidonias niger), a declining waterbird with low site fidelity, to examine space use and selection of resources within individual breeding home ranges. We also provide a preliminary assessment of habitat use during fall stopover. We found that home ranges were extensive (mean 283.7 km2) and distances travelled from the nest substantially larger (up to 35 km) than previously thought (~2.5 km). Terns showed selection for wetlands and open water on the breeding grounds, but also showed selection for developed areas. This may reflect humans selecting similar landscape features for recreation and development as terns, and suggests that terns can tolerate the light degree of development (e.g. cottages, boat launches, etc.) within our study area. Despite a small sample size, this is the first study to track individual Black Terns at a high resolution with implications for conservation and wetland management practices relevant to the spatial scales at which habitat is used by the species.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".