GPS tracking of non-breeding ravens reveals the importance of anthropogenic food sources during their dispersal in the Eastern Alps
Bibliographic record
Abstract
In many songbirds, the space use of breeders is well studied but poorly understood for non-breeders. In common ravens, some studies of non-breeders indicate high vagrancy with large individual differences in home range size, whereas others show that up to 40% of marked non-breeders can be regularly observed at the same anthropogenic food source over months to years. The aim of this study was to provide new insights on ravens' behavior during dispersal in the Eastern Alps. We deployed Global Positioning System (GPS) loggers on 10 individuals to gather accurate spatial and temporal information on their movements to quantify: 1) the dimension of the birds' space use (home range size with seasonal effects and daily/long-term travel distances), 2) how long they stayed in a dispersal stage of wandering as opposed to settling temporarily, and 3) their destination of movements. We recorded movements of up to 40 km per hour, more than 160 km within 1 day and more than 11,000 km within 20 months, indicating high vagrancy. Switching frequently between temporarily settling and travelling large distances in short time intervals leads to extensive home ranges, which also explains and combines the different findings in the literature. The destinations are rich anthropogenic food sources, where the birds spent on average 75% of their time. We discuss how ravens may find these "feeding hot spots" and which factors may influence their decision to stay/leave a site. The strong dependence on anthropogenic resources found in this population may have implications for site management and conservation issues.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".