Moult migrant Tennessee Warblers undergo extensive stopover in peri-urban forests of southern Quebec
Bibliographic record
Abstract
This dataset contains the digitized treatments in Plazi based on the original journal article Poirier, Vanessa, Frei, Barbara, Lefvert, Mathilde, Morales, Ana, Elliott, Kyle H. (2024): Moult migrant Tennessee Warblers undergo extensive stopover in peri-urban forests of southern Quebec. Canadian Journal of Zoology 102 (3): 272-285, DOI: 10.1139/cjz-2023-0109, URL: https://doi.org/10.1139/cjz-2023-0109AbstractStopovers are the most energy- and time-consuming events during avian migration, yet individuals of certain species make long stopovers to moult (“moult migration”). Requiring abundant energy and a prolonged stay, moult migrants should occupy small stopover home ranges in resource-rich habitats. Understanding migrant behaviour at their stopovers is critical for implementing conservation efforts for declining Neotropical passerines. To examine the stopover timing and habitat use of one such moult migrating passerine, we radio-tagged 18 moulting and 4 post-moult Tennessee Warblers (Leiothlypis peregrina (A. Wilson, 1811)) at an autumn stopover site. Although our data were biased towards one sampling year, moult migrants generally arrived at the stopover site earlier (average = 2 August) than post-moult migrants (average = 12 September). Moult migrants also stayed longer (46 ± 5 days) than post-moult migrants (8 ± 6 days) and had large overlapping stopover home ranges (∼15 ha) that were dependent on high abundance of forest (%) and forest edge (m). We conclude that Tennessee Warblers occupied forested stopover sites within a peri-urban landscape where they successfully moulted before continuing migration. This study illustrates the importance of including stopover sites in conservation plans, particularly in cities where quality habitats are scarce.
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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 | 0.002 | 0.003 |
| 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) | 0.011 | 0.001 |
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".