Migration and wintering strategies of a Eurasian Stone-curlew ( Burhinus oedicnemus ) continental population, and their conservation implications
Bibliographic record
Abstract
The Eurasian Stone-curlew (Burhinus oedicnemus) is a declining and threatened species, yet its migration and wintering strategies are little documented. Here, we used GPS trackers to collect accurate data on this species’ migration routes, stopovers, flight altitude, and speed, and identified the wintering sites of 32 individuals from a western European population tracked between 2012 and 2020. In a comparison between individuals, we found that they used strikingly different migratory strategies, showing variability in wintering sites (Portugal, Spain, Morocco, or Algeria), stopovers (number, location, and duration), and migratory routes, despite the fact that they all belonged to the same breeding population. We also compared individual variability in migratory routes, migration timing, and wintering home range size, and found medium to high repeatability for most parameters. In contrast to many other migrant waders, spring migration in this species was not found to be shorter in duration than autumn migration. Our results provide insights into potential threats that may affect this species in the near future away from its breeding grounds, such as habitat quality or habitat loss in wintering areas because Iberian or Moroccan agriculture is changing very rapidly because of drought.
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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 | 0.000 |
| 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".