PHIL PATTON
PhD student
​
My dissertation research involves improving population assessments of non-migratory whales around the Main Hawaiian Islands. These populations have been challenging to assess because the data can be sparse, come from multiple sources, and tedious to process. To address these challenges, I will use state of the art machine learning tools that translate images into usable data. Ultimately, this improved data pipeline should produce demographic estimates that resource managers can confidently use to develop public policy.
​
email: pattonp@hawaii.edu
​
For publication pdfs, please visit the following links:
​
​
Biography
​
Phil earned a B.S. in Conservation Biology from the State University of New York College of Environmental Science and Forestry in 2013, followed by a M.S. in Fisheries, Wildlife, and Conservation Biology from North Carolina State University in 2016. His thesis research explored methods for incorporating species interactions and false positive sampling errors in site-occupancy models. After completing his master's degree, Phil worked as a data analyst in the public and private sectors. In this time, he developed his analytical and coding skills, particularly while working on a software development team in industry. His dissertation research is funded by the NOAA Quantitative Ecology and Socioeconomics Training (QUEST) program, which trains the next generation of management-focused researchers.
Publications
​
2023
​
Patton, P.T., Cheeseman, T., Abe, K., Yamaguchi, T., Reade, W., Southerland, K., Howard, A., Oleson, E.M., Allen, J.B., Ashe, E., Athayde, A., Baird, R.W., Basran, C., Cabrera, E., Calambokidis, J., Cardoso, J., Carroll, E.L., Cesario, A., Cheney, B.J., Corsi, E., Currie, J., Durban, J.W., Falcone, E.A., Fearnbach, H., Flynn, K., Franklin, T., Franklin, W., Vernazzani, B.G., Genov, T., Hill, M., Johnston, D.R., Keene, E.L., Mahaffy, S.D., McGuire, T.L., McPherson, L., Meyer, C., Michaud, R., Miliou, A., Orbach, D.N., Pearson, H.C., Rasmussen, M.H., Rayment, W.J., Rinaldi, C., Rinaldi, R., Siciliano, S., Stack, S., Tintore, B., Torres, L.G., Towers, J.R., Trotter, C., Moore, R.T., Weir, C.R., Wellard, R., Wells, R., Yano, K.M., Zaeschmar, J.R. & Bejder, L. (2023) A deep learning approach to photo–identification demonstrates high performance on two dozen cetacean species. Methods in Ecology and Evolution, 00, 1--1, https://besjournals.pericles-prod.literatumonline.com/doi/10.1111/2041-210X.14167.
2022
​
. Patton, P., Pacifici, K., Collazo, J., May 2022. Modeling and estimating co‑occurrence between the invasive Shiny Cowbird and its Puerto Rican hosts. Biological Invasions.