Bioacoustically derived migration arrival times in boreal birds: implications for assessing habitat quality
Bibliographic record
Abstract
Long-distance migrant songbirds are declining globally. Reversing declines requires a good understanding of habitat quality. Local studies have shown that territory settlement date (arrival) is generally correlated with density and productivity. Despite widespread acceptance, large-scale multispecies demonstrations of arrival time being correlated with habitat quality are lacking. We investigated whether arrival date estimated from ecozonal scale bioacoustic monitoring could be predicted by an independent estimate of estimated density for Ovenbird (<em>Seiurus aurocapilla</em>), Tennessee Warbler (<em>Leiothlypis peregrina</em>), and Yellow-rumped Warbler (<em>Setophaga coronata</em>). We also examined local Ovenbird settlement patterns by comparing relative arrival and observed local density differences between nearby territories. Arrival date was estimated as the first focal species detection date on a breeding territory and the cure4insect R package was used to predict estimated average density. Using predicted average density as a habitat quality proxy, we found earlier arrivals in higher quality territories (Ovenbird 1.04 +/- 0.33 days earlier, Tennessee Warbler 1.96 +/- 0.36 days earlier, Yellow-rumped Warbler 1.23 +/- 0.54 days earlier). We also showed that arrival time was earlier in habitats preferred by each species. Spatial patterns of arrival varied among species although latitude was always an important predictor. Locations where predicted Ovenbird densities were estimated to be higher were filled before sites with lower predicted density but only 600 m away (2.9 +/- 1.4 days earlier). Correlating migrant arrival time and density suggests density is a reasonable measure of habitat quality. Combined, density and arrival data from bioacoustics provide a low-cost habitat-assessment tool that better informs the types of forest that need to be protected for species of concern, which will be particularly important as land-use and climate change impacts increase in the boreal forest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".