Long-distance dispersal patterns in the Cerulean Warbler: a case study from Indiana
Bibliographic record
Abstract
Dispersal, defined as movement an individual makes from one breeding population to another, is a process that strongly influences the population dynamics of many animal species. Although dispersal across longer distances is believed to be a relatively uncommon phenomenon for most bird species, movements between populations drive numerous ecological processes, and understanding rates and directions of dispersal are especially important when considering species of conservation concern. The Cerulean Warbler (Setophaga cerulea) is a Nearctic-Neotropical songbird that breeds in mature forests of eastern and central North America and has experienced significant declines in recent decades largely due to habitat loss on the breeding grounds. Previous research suggests that Cerulean Warblers exhibit high rates of long-distance dispersal and that populations may be shifting away from the peripheral edges of its breeding range. The potential impacts of dispersal on reproductive success, however, remain unknown for this species. In this study, we used a long-term dataset (2013–2021) to investigate dispersal rates, age-related differences in dispersal, and effects of immigration on nest success in a population of Cerulean Warblers in south-central Indiana. To categorize birds as either immigrants or residents, we analyzed naturally occurring stable-hydrogen isotopes in tail feathers grown on the breeding grounds. We found an overall high rate (26.5%) of long-distance dispersal in this population, and the majority of these immigrants appeared to have originated from latitudes south of our study site. Additionally, our findings suggest that dispersal rates of juveniles and adults are very similar in this population, and that immigration appears to have no effect on reproductive success. This study contributes to our limited knowledge of the Cerulean Warblers' full annual cycle ecology, and our reported high dispersal rate and lack of effect of immigration on nest success have encouraging implications for the conservation of this declining species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".