Efficacy of decoys and familiar versus unfamiliar playback calls in attracting Common Terns to a rehabilitated wetland on Lake Ontario
Bibliographic record
Abstract
Common Terns (Sterna hirundo) are colonial-nesting waterbirds that have experienced long-term population declines on the Great Lakes. Lack of nesting habitat, especially due to competition with Ring-billed Gulls (Larus delwarensis), is thought to be an important cause of this decline. Therefore, it is important to create and maintain good nesting habitats for Common Terns, as was done in Windermere Basin, a historically polluted wetland in Hamilton Harbour. It was extensively restored from 2010–2013, including the construction of three new habitat islands designed for Common Tern nesting. We used playback systems and decoys to attract Common Terns to the new islands in 2013 and assessed the effectiveness of three different call types: local colony sounds recorded in Hamilton Harbour, foreign calls recorded elsewhere in the Great Lakes, and on the Atlantic Coast of North America. These playback types were rotated through sound systems on the three new islands, and a control island had no equipment. Two-hundred and seventy-three pairs of Common Terns successfully nested in the new habitat, and 244 chicks were hatched. However, we did not find evidence that social attractants were effective. Furthermore, there was no significant difference in the effect of the Hamilton Harbour, Great Lakes and Atlantic Coast of North America playbacks on Common Tern numbers or nests, but there was a significant negative effect of Gull numbers on Tern numbers. Colonizing individual Terns likely had prior experience nesting in Windermere Basin, and took advantage of the newly available habitat. These early colonizers would have been more salient to other Terns than the social attractants used in our experiment. Gull numbers declined after we carried out several nest removals (under permit). Therefore, availability of good quality habitat, with Gull numbers controlled, appeared important in re-establishing colonies of Common Terns in the Great Lakes region. Further research is required on the efficacy of social attractants.
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 | 0.000 | 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".