Optimizing survey timing for detecting a declining aerial insectivore, the Black Swift ( Cypseloides niger borealis )
Bibliographic record
Abstract
Much of the biology of Black Swifts (Cypseloides niger) is poorly known due to the species’ mostly aerial existence and cryptic nesting habitat. Important aspects of the biology of the northern subspecies of Black Swift (C. n. borealis) have been described in the past two decades, but the difficulty in locating and accessing Black Swift breeding sites constrains a range-wide understanding of the species’ biology and abundance, and ultimately, the cause(s) of its population decline. The most widely used method to determine Black Swift breeding site occupancy is to monitor suitable nesting habitat during the last hours of daylight (evening surveys) to detect the presence of adults attending nests (i.e., flying to or from a cliff face). Potentially, dawn surveys could result in more detections if adults reliably leave their nests or roost sites at first light, after overnight attendance. Because the effectiveness of occupancy surveys has not been quantified, we compared detection rates between evening surveys and surveys in the first hours of daylight (dawn surveys) at nine known breeding sites in southern British Columbia in 2018. Next, we measured how detections varied over the dawn period at eight active breeding sites surveyed between 2018–2020 and seven breeding sites surveyed in August 2020 in southern British Columbia and western Alberta. At low light levels, 5.38 times the number of birds/minute were detected, on average, during dawn surveys, compared with evening surveys, with dawn to evening detection ratios as high as 8.93 at some sites. During dawn surveys, detections peaked 8 to 9 min before sunrise and declined rapidly as light levels increased after sunrise. We suggest using dawn surveys to determine breeding site occupancy and prioritize areas for nest searches, monitoring, and habitat protection to ultimately aid in conserving Black Swifts.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".