Effect of Revisitation Surveys on Detection of Wetland Birds with Different Diel Vocalization Patterns
Bibliographic record
Abstract
Abstract Reliable species distribution data are important for valid scientific conclusions and effective conservation planning. Mismatch between survey timing and animal behaviors that influence detection may result in false absences that can lead to poorly informed management decisions. Birds exhibit a diversity of diel vocalization patterns, but many large-scale multispecies surveys are based on the songbird dawn chorus, indicating the potential for bias to detect birds with other diel vocalization patterns. In this study, we quantified bias in point counts and morning acoustic recordings to measure the number of occupied sites detected for a set of dawn chorusing birds (songbirds) and irregularly vocalizing wetland birds (waterfowl) relative to estimates obtained from 10-min acoustic recordings conducted hourly throughout 24-h periods for three consecutive days. Furthermore, we investigated which revisitation schedule—same day or different day sampling, as well as increased sampling effort—best minimized false-negative detections for songbirds and waterfowl. Morning surveys significantly underestimated the number of occupied sites for 10 of 13 species. No differences were found between same-day and between-day revisitation schedules with identical sampling effort, regardless of whether birds exhibited a dawn chorus or irregular vocalization patterns. Detection improved with increased sampling effort. Subsampled recordings captured the majority of occupied sites for songbirds (up to 87% of occupied sites detected), but less so for waterfowl (up to 60% of occupied sites detected). Accurate detection for irregularly vocalizing species such as waterfowl will require more intensive sampling effort (likely throughout 24-h periods) when using acoustic recordings.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".