1. Classification of Avian Migration Patterns Aparna Pal

2. Motivation  Climate change and shifting migration patterns  Avian mortality and endangered species  Habitat disruption

3. 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

4. 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

5. 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

6. 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

7. 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

8. What’s next?  Contact USGS  Find larger data sets  Possibility of prediction models?

9. 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.

10. Thank you!