An ecological perspective on the temporal variation in Pileated Woodpecker ( Dryocopus pileatus ) drumming behavior in Alberta, Canada
Bibliographic record
Abstract
As old-growth forest ecosystems become increasingly scarce in North America, the need to accurately and efficiently survey old-growth specialists and keystone species, such as the Pileated Woodpecker (<em>Dryocopus pileatus</em>), becomes increasingly important. A common survey method for birds is to detect auditory cues to determine presence. Therefore, it is important to understand temporal patterns in audible cues, which are often linked to a species’ breeding phenology, to optimize survey timing. It is well known that the Pileated Woodpecker, as a non-migratory bird, has a breeding season that begins before most migratory passerines arrive at their breeding grounds. However, the timing of the peak Pileated Woodpecker breeding season, and therefore auditory activity, is relatively unknown at the northern extent of its range. We explored the temporal variation of Pileated Woodpecker drumming behavior using passive acoustic monitoring methods at the northern extent of its range in Alberta, Canada. Peaks in auditory cues were near sunrise (06:00) in early April (2 April). Mean daily temperature and day length were the most influential environmental variables that affected the drumming of Pileated Woodpeckers. Drums were more likely to be detected at daily mean temperatures close to zero degrees Celsius and on days where day length was approximately 13 hours long. Based on these findings, we calculated that a minimum of ten one-min long surveys should be conducted during the peak periods of Pileated Woodpecker activity (near sunrise in early April) to ensure accurate presence/absence data for this species in Alberta, Canada. These guidelines can be used for planning future surveys and methods to utilize existing non-optimized surveys to ensure the accuracy of Pileated Woodpecker site occupancy.
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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 | 0.000 | 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".