Factors associated with the detectability of owls in South American temperate forests: Implications for nocturnal raptor monitoring
Bibliographic record
Abstract
ABSTRACT Owls occur at relatively low densities and are cryptic; thus, monitoring programs that estimate variation in detectability will improve inferences about their presence. We investigated temporal and abiotic sources of variation associated with detection probabilities of rufous‐legged owls ( Strix rufipes ), a threatened forest specialist, and austral pygmy‐owls ( Glaucidium nana ), a habitat generalist, in temperate forests of southern Chile. We also assessed whether detection of 1 species was related to the detection of the other species. During 2011–2013, we conducted 1,145 broadcast surveys at 101 sampling units established along an elevational gradient located inside and outside protected areas. We used a multi‐season occupancy framework for modeling occupancy (ψ) and detection ( p ), and ranked models using an information‐theoretic approach. We recorded 292 detections of rufous‐legged owls and 334 detections of austral pygmy‐owls. Occupancy was positively associated with elevation for rufous‐legged owls but constant (i.e., did not vary with covariates) for pygmy‐owls. Detectability for both owls increased with greater moonlight and decreased with environmental noise, and for pygmy‐owls greater wind speed decreased detectability. The probability of detecting pygmy‐owls increased nonlinearly with number of days since the start of surveys and peaked during the latest surveys of the season (23 Jan–7 Feb). Detection of both species was positively correlated with the detection of the other species. We suggest both species should be surveyed simultaneously for a minimum of 3–4 times during a season, survey stations should be located away from noise, and observers should record the moon phase and weather conditions for each survey. © 2014 The Wildlife Society.
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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".