Ten years tracking the migrations of small landbirds: Lessons learned in the golden age of bio-logging
Bibliographic record
Abstract
In 2007, the first miniature light-level geolocators were deployed on small landbirds, revolutionizing the study of migration. In this paper, we review studies that have used geolocators to track small landbirds with the goal of summarizing research themes and identifying remaining important gaps in understanding. We also highlight research and opportunities using 2 recently developed tracking technologies: archival GPS tags and automated radio-telemetry systems. In our review, we found that most (54%) geolocator studies focused on quantifying natural history of migration, such as identifying migration routes, nonbreeding range, and migration timing. Studies of behavioral ecology (20%) uncovered proximate drivers of movements, including en route habitat quality; that migration routes, but not timing, may be flexible in some species; and different age and sex classes show significant differences in migration strategy. Studies of the evolution of migration (9%) have illustrated that migration is a potential barrier to hybridizing species or subspecies, and some work has correlated gene polymorphisms and methylation patterns with migration behavior. Studies of migratory connectivity (11%) have shown that a moderate level of connectivity is common, although variability across and within species exists. Studies of seasonal interactions (7%) have found mixed results: in some cases, carryover effects have been identified; in other cases, carryover effects are buffered during intervening stages of the annual cycle. Archival GPS tags provide unprecedented precision in locations of nonbreeding sites and migration routes, and will continue to improve understanding of migration across large spatial scales. Automated radio-telemetry systems are revolutionizing our knowledge of migratory stopover biology, and have led to discoveries of previously unknown stopover behaviors. Together, these tracking technologies will continue to provide insight into small migratory landbird movements and contribute important information for conservation of this rapidly declining group.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".