Migratory routes and wintering locations of declining inland North American Common Terns
Bibliographic record
Abstract
Common Terns (Sterna hirundo) breeding at inland lakes in North America have experienced significant population declines since the 1960s. Although management actions aimed at mitigating effects of habitat loss and predation have been largely effective, numbers continue to decline, which suggests that the population may be limited during the nonbreeding season. Between 2013 and 2015, we used light-level geolocators to track Common Terns nesting at 5 inland colonies—from Lake Winnipeg in Manitoba, Canada, to the eastern Great Lakes region of the United States and Canada—to identify migratory routes and stopover and wintering sites and to determine the strength of migratory connectivity among colonies. Within 46 recovered tracks, we found evidence of a longitudinal gradient in use of migration routes and stopover sites among colonies and identified major staging areas in the lower Great Lakes and at inland and coastal locations along the Atlantic coast, Florida, and the Gulf of Mexico. Low migratory connectivity across inland colonies illustrates high intermixing within wintering sites, with many birds spending the nonbreeding season in Peru (70%) and the remainder spread throughout the Gulf of Mexico, Central America, and northwestern South America. While the large spatial spread and intermixing of individuals during the nonbreeding season may buffer local effects of climate change and human disturbance, the aggregation of individuals along the coast of Peru could make them vulnerable to events or changes within this region, such as increased frequency and intensity of storms in the Pacific, that are predicted to negatively influence breeding productivity and survival of Common Terns. Identifying sources of mortality during the nonbreeding season, quantifying winter site fidelity, and reinforcing the importance of continued management of inland breeding colonies are vital priorities for effective conservation and management of this vulnerable population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".