Factors Affecting Detection of Burrowing Owl Nests During Standardized Surveys
Bibliographic record
Abstract
Abstract: Identifying causes of declines and evaluating effects of management practices on persistence of local populations of burrowing owls ( Athene cunicularia ) requires accurate estimates of abundance and population trends. Moreover, regulatory agencies in the United States and Canada typically require surveys to detect nest burrows prior to approving developments or other activities in areas that are potentially suitable for nesting burrowing owls. In general, guidelines on timing of surveys have been lacking and surveys have been conducted at different times of day and in different stages of the nesting cycle. We used logistic regression to evaluate 7 factors that could potentially affect probability of a surveyor detecting a burrowing owl nest. We conducted 1,444 detection trials at 323 burrowing owl nests within 3 study areas in Washington and Wyoming, USA, between February and August 2000–2002. Detection probability was highest during the nestling period and increased with ambient temperature. The other 5 factors that we examined (i.e., study area, time of day, timing within the breeding season, wind speed, % cloud cover) interacted with another factor to influence detection probability. Use of call‐broadcast surveys increased detection probability, even during daylight hours when we detected >95% of owls visually. Optimal timing of surveys will vary due to differences in breeding phenology and differences in nesting behavior across populations. Nevertheless, we recommend ≥3 surveys per year: one that coincides with the laying and incubation period, another that coincides with the early nestling period, and a third that coincides with the late nestling period. In northern latitudes, surveys can be conducted throughout the day. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):688–696; 2008)
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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 | 0.001 | 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".