Quantifying Vocal Activity and Detection Probability to Inform Survey Methods for Barred Owls (Strix varia)
Bibliographic record
Abstract
Owls can be difficult to detect due to their secretive behavior, typically low calling rate, and low density on the landscape. Low detection probability during surveys can result in an underestimation of the presence and abundance of a species. Thus, optimizing detection probability of surveys targeting owls is necessary to accurately address ecological questions. We used datasets collected in South Carolina, USA, and Alberta, Canada, to investigate how survey detection can be optimized for Barred Owls (Strix varia). We examined seasonal effects on the detection probability of Barred Owls as determined by playback surveys and autonomous recording unit (ARU) surveys, and whether daily patterns of Barred Owl vocal activity could be used to improve the efficiency of ARU surveys. For each survey method, we estimated the number of survey days needed to obtain a seasonal detection probability ≥ 90% of Barred Owls. We found detection probability with playbacks increased as the breeding season progressed. The effect of seasonality on detection probability with ARUs was dependent on the way encounter history was defined. Barred Owl vocal activity peaked twice per night, with one vocalization peak occurring immediately after sunset and another 7–9 hr after sunset. By targeting these vocalization peaks during surveys, we found that we could reduce ARU survey time by 50% and still retain .82% of the original site detections, thereby reducing survey processing time. Although playback surveys were more efficient than ARU surveys at detecting Barred Owls, ARUs have numerous advantages, such as reducing survey effort and disturbance to the target animal. Ultimately, survey designs are dictated by the budget, personnel capacity, study region, and research objectives, but our findings will help researchers plan studies that optimize detection probability and minimize survey cost and effort.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".