Extreme migratory connectivity and apparent mirroring of non-breeding grounds conditions in a severely declining breeding population of an Afro-Palearctic migratory bird
Bibliographic record
Abstract
Understanding the distribution of breeding populations of migratory animals in the non-breeding period (migratory connectivity) is important for understanding their response to environmental change. High connectivity (low non-breeding population dispersion) may lower resilience to climate change and increase vulnerability to habitat loss within their range. Very high levels of connectivity are reportedly rare, but this conclusion may be limited by methodology. Using multiple tracking methods, we demonstrate extremely high connectivity in a strongly declining, peripheral breeding population of a long-distance migrant, the Common Nightingale in the UK. Non-breeding population dispersion is lower than for previously tracked populations of this and other species and likely lower than can usually be detected by light-level geolocation, the main tracking method for small bodied species. Extremely low levels of population mixing were also detected, so any impacts on this population on the non-breeding grounds are unlikely to be shared with more distant breeding populations, corresponding to the observed patterns of European population change. According to a species distribution model using independent field data, this population's non-breeding grounds had lower suitability than others and likely declined before the period we were able to assess. These results support hypotheses that climatic and habitat-related deterioration of non-breeding grounds contributes to population declines in peripheral and high-connectivity breeding populations of long-distance migrants, including the one studied here.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".