Spatial ecology of endangered roseate terns and foraging habitat suitability around a colony in the western North Atlantic
Bibliographic record
Abstract
Predicting habitat suitability and understanding habitat utilization are important to inform and orient conservation and management decisions for the recovery of endangered species. In North America, the roseate tern Sterna dougallii is listed as endangered in both the northeastern USA and Canada, where little is known about the foraging spatial ecology of the species. We equipped breeding roseate terns with miniature GPS tracking devices during incubation at North Brother Island, the main Canadian colony. Our aim was to characterize the spatial foraging ecology of the species, identify marine zones of importance, and develop a habitat suitability model around the colony. Our results provide novel, high resolution information on individual foraging trips, notably showing that individuals restricted their range around the colony (15.4 km) while performing multiple foraging trips: up to 11 daytime trips and a maximum total of 152.9 km travelled per day. Roseate terns concentrated their foraging effort around the colony and further south along the coast to the Cockerwit Passage. Using distance from colony, sea surface temperature, distance from land, bathymetry, and subtidal substrate type as covariates in a habitat suitability model, a high proportion of the deviance was explained (72.4%); the model also predicted high occurrence of foraging near the colony, in Cockerwit Passage, and at additional sites to which the birds were not tracked. Along with the description of important marine areas for roseate terns nesting on North Brother Island, this habitat suitability model provides a relevant and essential context for understanding roseate tern habitat use in a broad sense, but with a focus on habitat requirements during incubation.
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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 | 0.001 |
| 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".