Geolocators reveal migratory connectivity between wintering and breeding areas of Golden-winged Warblers
Bibliographic record
Abstract
The conservation of migratory songbirds is often impeded by a lack of understanding of how populations in breeding and wintering areas are geographically linked (migratory connectivity). In recent years, light-level geolocators have improved our understanding of migratory connectivity. Such information is valuable for evaluating how conservation efforts align between the breeding and non-breeding areas of at-risk species, and help to more effectively prioritize the allocation of conservation funding. Golden-winged Warblers (Vermivora chrysoptera) are imperiled migratory songbirds, but the extent to which conservation efforts in their breeding and non-breeding areas coincide with patterns of migratory connectivity are not well known. We used light-level geolocators to evaluate the extent to which conservation actions targeting Golden-winged Warblers in Nicaragua and in their breeding range in North America align with patterns of migratory connectivity. We recovered six of 22 geolocators that had been deployed on male Golden-winged Warblers at the El Jaguar Reserve during the winter of 2015–2016. All six males migrated to breeding areas in the western Great Lakes region that includes eastern Minnesota, northern Wisconsin, southwestern Ontario, and Michigan's Upper Peninsula. All six males also had similar migration routes, with spring stopovers in southern Mexico, Guatemala, and Belize, a trans-Gulf flight, and a stopover in the region of Louisiana, Arkansas, eastern Oklahoma, and Texas. Our results, in combination with those of previous studies, demonstrate strong migratory connectivity between portions of the breeding and winter distributions of Golden-winged Warblers currently targeted for conservation. However, additional studies are needed to improve our understanding of the stopover ecology of Golden-winged Warblers, especially in areas where they remain for extended periods of time. Finally, patterns of migratory connectivity revealed in our study should be used in combination with existing demographic parameters for Golden-winged Warblers in the western Great Lakes and Nicaragua to help inform full life cycle population models for this imperiled songbird. La conservación de la migración de aves cantoras es normalmente obstruida por la falta de entendimiento sobre como las poblaciones de áreas de anidación e invierno están geográficamente unidas (conectividad migratoria). En los últimos años, los geolocalizadores de nivel de luz han mejorado nuestra comprensión sobre la conectividad migratoria. Esa información es valiosa para evaluar cómo los esfuerzos de conservación se alinean entre las áreas de reproducción y no reproducción de las especies en riesgo y ayudan a priorizar efectivamente la asignación de fondos para la conservación. Vermivora chrysoptera es un ave migratoria en peligro de extinción, pero en qué medida los esfuerzos de conservación en sus áreas de reproducción y no reproducción coinciden con los patrones de conectividad migratoria no es bien conocido. Utilizamos geolocalizadores de nivel de luz para evaluar hasta qué punto las acciones de conservación dirigidas a V. chrysoptera en Nicaragua y en su rango de reproducción en América del Norte se alinean con los patrones de conectividad migratoria. Recuperamos seis de los 22 geolocalizadores que habían sido puestos en machos de V. chrysoptera en la reserva el Jaguar durante el invierno de 2015–2016. Los seis machos emigraron a áreas de cría en la región occidental de los Grandes Lagos que incluye el este de Minnesota, el norte de Wisconsin, el suroeste de Ontario y la península superior de Michigan. Los seis machos también tenían rutas de migración similares, con escalas de primavera en el sur de México, Guatemala y Belice, un vuelo trans-Golfo, y una escala en la región de Louisiana, Arkansas, Oklahoma oriental y Texas. Nuestros resultados en combinación con los de estudios previos demuestran una fuerte conectividad migratoria entre las porciones de reproducción y la distribución de invierno de V. chrysoptera que actualmente es objetivo de conservación. Sin embargo, se necesitan estudios adicionales para mejorar nuestra comprensión sobre la ecología de escala de V. chrysoptera, especialmente en áreas donde permanecen por largos períodos de tiempo. Por último, los patrones de conectividad migratoria revelados en nuestro estudio se deben utilizar en combinación con los parámetros demográficos existentes para V. chrysoptera en el oeste de los grandes lagos y Nicaragua para ayudar a informar a los modelos completos del ciclo de vida de esta ave cantora amenazada.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".