Movement patterns of foraging common terns (Sterna hirundo) breeding in an urban environment in coastal Virginia
Bibliographic record
Abstract
Nesting colonial seabirds are prime examples of central-place foragers, animals that must return to a central location (e.g., a breeding colony) after each bout of foraging. They must balance the costs and benefits of foraging with the need to return to their colonies frequently to form pair bonds during courtship, incubate, provision mates and offspring, and protect and rear young. For some populations, the loss and degradation of suitable breeding habitat due to human activities have necessitated the construction of new breeding sites and/or the restoration of previously occupied sites. South Island, which is part of the Hampton Roads Bridge-Tunnel (HRBT) complex in the Commonwealth of Virginia, U.S.A., is a human-created island that supported Virginia's largest mixed species seabird colony until 2020, when the expansion of the HRBT began and when all nesting seabirds were permanently excluded from the site. We studied the movement patterns of foraging common terns (Sterna hirundo) to determine how travel to and around foraging sites related to their colony location and to inform the siting and construction of a new breeding island. We tracked 18 individual common terns from 07 June to 29 June 2018, and we used a hidden Markov model to assign behavioral states and investigate common tern movements around the HRBT. Common terns spent more than half their time in the colony (58%), followed by time devoted to foraging (22%), and the remainder of their time was spent on outbound (15%) and inbound (5%) transit. Terns traveled as far as 98km from the colony, but on average foraged relatively close to South Island (13.6 ± 0.3km, mean ± 1 SD). Individuals tended to forage in the same locations, but there was variation among individuals. Flying to foraging sites uses energy during the already energetically costly breeding season, thus managers should prioritize placing a new colony site in a location that minimizes the distance traveled to the foraging locations frequented by the South Island birds while accounting for other life-history characteristics. These findings could help in the design and construction of new breeding sites or the restoration of current sites for other, related species, particularly for which these data do not exist.
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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 | 0.001 | 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) | 0.000 | 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".