Night-migratory songbird density is highest at stopover sites with intermediate forest cover and low proportion of forest in conifers in the surrounding landscape
Bibliographic record
Abstract
Some nocturnal migrant forest-breeding songbirds have suffered large population declines in recent decades. Declining availability of high-quality habitat where birds refuel during migration may be contributing to these declines. Our objective was to identify landscape attributes, including the relevant scales of effect, that make sites likely to be used as stopover sites during fall migration. We used autonomous recording units (ARUs) to sample birds between August and October 2018 at 37 fall migration potential stopover sites in southeastern Ontario, Canada. We placed ARUs in forest patches that varied in the amount and type of forest cover within the surrounding landscape. We interpreted recordings at intervals throughout the season to estimate the average numbers of calling birds per minute at each site. We found that bird density was highest at sites with an intermediate amount of forest within 2 km, while density decreased as the proportion of coniferous forest within 6 km increased. We infer that migrating birds avoid forest sites in landscapes with low amounts of forest cover and high proportions of conifer. The lower densities at high forest amounts may result from a dilution effect (birds spread across more forest), avoidance of conifers, which tended to be more abundant at the highest forest amounts, or reduced densities of edges at high forest amounts, if birds use forest edges for foraging. Our study highlights the importance of retaining landscapes with at least 50% forest cover, particularly deciduous forest, as stopover habitat for migrating songbirds.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".