Flyway-scale GPS tracking reveals migratory routes, stopovers, and habitat associations of Lesser Yellowlegs
Bibliographic record
Abstract
Many populations of long-distance migrant shorebirds are declining rapidly. Since the 1970s, the Lesser Yellowlegs (Tringa flavipes) has experienced a pronounced reduction in abundance of ~63%. The potential causes of the species’ decline are complex and interrelated yet understanding the timing of migration and seasonal routes used by this species will aid in directing conservation planning to address potential threats. During 2018–2021, we tracked 118 adult Lesser Yellowlegs using GPS satellite tags deployed on birds from five breeding and two migratory stopover locations spanning the boreal forest of North America from Alaska to eastern Canada. Our objectives were to quantify migratory connectivity and identify key stopover and non-breeding locations. Individuals tagged in Alaska and central Canada followed similar southbound migratory routes through the Prairie Pothole Region of North America, whereas birds tagged in eastern Canada completed multi-day transoceanic flights covering distances of >4,000 km across the Atlantic between North and South America. Upon reaching their non-breeding locations, Lesser Yellowlegs populations overlapped, resulting in weak migratory connectivity. Lastly, freshwater and agricultural habitats of the Prairie Pothole region supported the highest proportion of Lesser Yellowlegs during southbound migration. Our findings suggest that while Lesser Yellowlegs travel long distances and traverse numerous political boundaries each year, the breeding population from which an individual originates likely has the greatest influence on which threats birds experience during migration. Further, the species’ dependence on wetlands in agricultural landscapes during migration may make them vulnerable to threats related to agricultural practices, such as pesticide exposure.
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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".