Towards integrated population monitoring based on the fieldwork of volunteer ringers: productivity, survival and population change of Tawny Owls<i>Strix aluco</i>and Ural Owls<i>Strix uralensis</i>in Finland
Bibliographic record
Abstract
ABSTRACT Capsule: Monitoring of demographic parameters by volunteer ringers provides insight into the factors driving population changes in owls. Aims: To assess the value of national ringing, recapture and recovery data from volunteers to understand population dynamics. Methods: We analysed 49 years of ringing, recapture and recovery data from throughout Finland for Tawny Owls Strix aluco and Ural Owls Strix uralensis and compared them with annual population and productivity indices from other volunteer-based surveys. Results: Volunteer-based ringing data show that all aspects of the demography of Ural and Tawny Owls fluctuate dramatically in relation to an approximately three-year cycle of voles. When voles are abundant, a high proportion of owls breed and many young are produced; however, few of those young survive because vole populations crash the following winter. Survival of adults fluctuates less than that of young, suggesting that adults are better able to survive on alternative prey. In 2005, when vole populations remained high two years in row, many young were produced and survived, leading to a peak in owl breeding populations four years later at the top of the next vole cycle. This was immediately followed by a crash in populations suggesting a densitydependent interaction with vole abundance. Changing climate could affect owls both directly, by influencing winter survival, as well as indirectly through impacting prey availability. Conclusion: Encouraging similar, volunteer-based national-scale ringing efforts for owls elsewhere in Europe, especially for Tawny Owls which occur in most countries, would be a cost-effective way to understand how factors such as changing prey availability, climate and habitat availability are influencing the population levels of this and other raptors.
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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".