Influence of boreal forest disturbance and conspecific attraction on the Canada Warbler ( Cardellina canadensis ) habitat choices during the breeding season
Bibliographic record
Abstract
Understanding how, when, and why species select habitats is essential to identify more accurate conservation strategies, particularly with increasing anthropogenic change. However, studies rarely disentangle the roles of environmental cues and social information when they examine habitat selection. We tested the influence of conspecific attraction and habitat disturbance on habitat choices of the Canada Warbler (Cardellina canadensis, CAWA) in forested landscapes that include managed and protected areas. We surveyed a gradient of disturbed areas (mainly due to forestry activity) during the 2021, 2022, and 2023 breeding seasons in Northwestern Ontario. We surveyed naturally occurring social aggregations of Canada Warbler, as well as simulated conspecific attraction by using playbacks of Canada Warbler songs and calls as an artificial cue during the pre-breeding season. We used generalized linear models to examine the influence of vegetation structure (shrub and canopy cover, canopy height, and forest type), level of post-harvest disturbance, and the song cues on the occurrence, social aggregation (abundance), and Canada Warbler settlement during pre-breeding season. Our results showed that vegetation structure plays an important role in Canada Warbler occurrence and social aggregation patterns, and that conspecific acoustic cues strongly influence pre-breeding settlement decisions. Disturbance at the local scale related to forest harvesting positively influenced social aggregation, whereas at the landscape scale, there was no effect of disturbance on Canada Warbler pre-breeding settlement periods. Conspecific songs during the pre-breeding season attracted males to settle in vacant sites in unharvested areas, thus an effective cue to males searching for breeding territories; however, likely because of these same cues, Canada Warbler males also settle in new sites (not previously occupied) in areas with a low level of disturbance due to forest harvesting.
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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 | 0.001 | 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".