Large-scale citizen science survey of a common nocturnal raptor: urbanization and weather conditions influence the occupancy and detectability of the Tawny Owl<i>Strix aluco</i>
Bibliographic record
Abstract
Capsule Tawny Owl Strix aluco site occupancy and detectability are influenced by habitat and environmental variables.Aims: To determine factors influencing Tawny Owl occupancy and detectability around British homes and gardens using a large-scale citizen science survey across two main survey periods.Methods Surveys of 20 min duration were undertaken one evening a week from the homes and gardens of volunteers, for up to 26 weeks between October and March of 2005/2006 and 2018/2019, and analysed primarily using multi-season occupancy modelling.Results During two survey periods, more than 9000 sites were surveyed across the breeding range of the Tawny Owl within Britain. The main drivers of occupancy were found to be the extent of broadleaf woodland cover and the degree of urbanization. Detection probability was influenced by date, time, weather, and moon phase. Using the current method, a minimum of five to six survey visits per site would be required to have 95% confidence over the presence or absence of Tawny Owls at a given site, but it may be possible to optimize the survey method further to increase efficiency by surveying in the autumn or early spring, early after dusk, and on cloudless dry evenings close to the full moon.Conclusion The findings indicate that survey methodologies for surveying Tawny Owls can be optimized to increase the efficiency of detection, if present at a site. We highlight the need for further research on the effects of urbanization on Tawny Owls, particularly with regards to artificial light pollution and its effects on behaviour and settlement, along with the need for greater understanding of Tawny Owl activity budgets, which would aid the interpretation of survey results.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".