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Assessing migration patterns in Passerina ciris using the world's bird collections as an aggregated resource
Ethan Linck1, Eli S Bridge2, Jonah M Duckles3
1Department of Biology and Burke Museum of Natural History & Culture, University of Washington , Seattle, WA , United States.
Peerj
|April 15, 2016
Summary
Natural history museum collections reveal migratory bird movements. Abundance indices from specimens show a circular migration pattern and link bird density to vegetation productivity.
Area of Science:
- Ecology
- Ornithology
- Conservation Biology
Background:
- Natural history museum collections (NHCs) offer valuable data on species' demography and historical movements.
- Abundance indices, derived from corrected NHC specimen records, can estimate relative population densities.
- Understanding migratory bird behavior is crucial for effective conservation strategies.
Purpose of the Study:
- To investigate the migratory behavior of the New World migratory passerine, *Passerina ciris*.
- To demonstrate the utility of NHC data and abundance indices in inferring migratory patterns.
- To integrate NHC data with remote sensing to understand ecological associations.
Main Methods:
- Georeferenced NHC specimen data were used to calculate abundance indices over a 12-month period.
- Abundance index values were analyzed across regions and months to identify movement patterns.
- Enhanced Vegetation Index (EVI) from remote sensing data was correlated with specimen abundance.
Main Results:
- A statistically significant quasi-circular movement pattern was observed in *Passerina ciris* across its non-breeding range.
- Specimen record abundance showed a non-random association with areas of high primary productivity, indicated by EVI.
- NHC data successfully inferred previously unknown migratory behaviors.
Conclusions:
- Abundance indices from NHC specimens are a powerful tool for understanding migratory behavior and population dynamics.
- Integrating NHC data with remote sensing enhances ecological and demographic insights.
- This approach provides a deeper understanding of behavioral ecology across space and time.
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