Classification of Avian Migration Patterns

Aparna Pal

Motivation Climate change and shifting migration patterns

Avian mortality and endangered species

Habitat disruption

How can migration prediction help? Allows us to offset damage done to endangered species with human

intervention

Gives us a good index for global climate change fluctuations

The Data Set Ninigret National Wildlife Refuge Banding Summary 2008-2012

Whooping Crane Eastern Partnership Annual Monitoring Report 2008-2012

Texas Observation Station Banding Summary

Evaluation of Shorebird use of Selected Refuge Habitats in the Lower Mississippi Valley

Based on data sets, the experiment became more of a classification problem

The Feature Set Data sets included:

Weights Season of Arrival* Flock size (or Total birds found) Gender Time Spotted

Shorebird Vs Land bird feature manually added by data set

K-Nearest Neighbor Classifier Weighted Euclidean Distance formula used

Best classification results found for testing sets with 10+ nearest neighbors used within calculations Classification rate jumps from ~76% to ~84% between 10 and 11

neighbors, but declines afterwards

Conclusions 11 Neighbors optimal for classification of this particular dataset

Caveats: The data set used was parsed together Missing information Unreliable data upkeep pre-2009 Classification could be more reliable with a more spanning data set

What’s next? Contact USGS

Find larger data sets

Possibility of prediction models?

References "IBP the MAPS Program." IBP the MAPS Program. Web. 20 Mar. 2016. "California Avian Data Center." California Avian Data Center. Web. 20

Mar. 2016. Cotton P.A, 2003 Avian migration phenology and global climate change.

Proc. Natl Acad. Sci. USA. 100,12219–12222. Richard Easterbrook, 2013, Ninigret National Wildlife Refuge Banding

Summary 2008-2012 U.S. Fish and Wildlife Service, 2012, Whooping Crane Eastern Partnership

Annual Monitoring Report 2008-2012 J. Wang and J.-D. Zucker, “Solving the multiple-instance problem: a lazy

learning approach,” in Proceedings of the 17th International Conference on Machine Learning, Stanford, CA, 2000, pp. 1119–1125.

Thank you!