Philip T. Patton

Graduate Research Assistant & NOAA QUEST Fellow

Education

  • Ph.D., Marine Biology, Hawaiʻi Institute of Marine Biology, 2025 (anticipated)
  • M.S., Fisheries, Wildlife, and Conservation Biology, North Carolina State University, 2016
  • B.S., Conservation Biology, SUNY College of Environmental Science and Forestry, 2013

Research Experience

  • NOAA QUEST Fellow, Pacific Islands Fisheries Science Center, NOAA Fisheries, 2021 - Present
  • Graduate Research Assistant, Hawaiʻi Institute of Marine Biology, University of Hawaiʻi at Mānoa, 2021 - Present
  • Graduate Research Assistant, Quantitative Ecology & Resource Management, University of Washington, 2016 - 2017
  • Graduate Research Assistant, Applied Ecology, North Carolina State University, 2014 - 2016

Professional Experience

  • Data Analyst, Health Services, Deschutes County, 2020 - 2021
  • Data Analyst, Supply Chain AI & Machine Learning, Starbucks Coffee Company, 2019
  • Quantitative Analyst, Seattle City Light, City of Seattle, 2017 - 2019

Grants, Awards, and Fellowships

  • Peter Castro HIMB Graduate Student Support Fund - Travel, Hawaiʻi Institute of Marine Biology, 2023, $500
  • Linda and Jim Collister Scholarship, Hawaiʻi Institute of Marine Biology, 2023, $1,000
  • Quantitative Ecology and Socioeconomic Training Fellowship (QUEST), NOAA Fisheries, 2021 to present, $180,000
  • Achievement Scholarship, University of Hawaiʻi at Mānoa, 2023, $500
  • Colonel Willys E. & Sandina L. Lord Endowed Scholarship, Hawaiʻi Institute of Marine Biology, 2022, $2,000
  • Student Travel Award, University of Washington, 2017, $500
  • Student and Postdoc Travel Award, University of Washington, 2017, $750
  • Travel Award, University of Washington, 2017, $500
  • Global Change Fellowship, USGS, 2015 to 2016, $12,000

Papers

  1. Patton, P.T., Pacifici, K., Allen, J.B., Ashe, E., Athayde, A., Baird, R.W., Basran, C., Cabrera, E., Calambokidis, J., Cardoso, J., Carroll, E.L., Cesario, A., Cheeseman, T., 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., Oleson, E.M., 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. (TBD) Evaluating trade–offs between automation and bias in population assessments relying on photo-identification. (TBD) Evaluating trade-offs between automation and bias in population assessments relying on photo-identification. In prep
  2. Patton, P.T. Pacifici, K., Miller, D.A.W., & Collazo, J. (TBD) Partial pooling of data among species improves performance of occupancy models subject to two types of sampling error. In prep
  3. 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, 14, 2611–2625. featured on cover
  4. Vivier, F., Wells, R.S., Hill, M.C., Yano, K.M., Bradford, A.L., Leunissen, E.M., Pacini, A., Booth, C.G., Rocho-Levine, J., Currie J.J., Patton, P.T., & Bejder, L. (2023) Quantifying the age-structure of free-ranging delphinid populations: testing the accuracy of Unoccupied Aerial System-photogrammetry. Ecology and Evolution, 13, e10082.
  5. Patton, P. T., Pacifici, K., & Collazo, J. A. (2022) Modeling and estimating co-occurrence between the invasive Shiny Cowbird and its Puerto Rican hosts. Biological Invasions, 24, 2951–2960

Presentations

  1. Patton, P.T. Some hierarchical and machine learning models for wildlife science. Invited talk at University of Natural Resources and Life Sciences, Vienna (BOKU). July 2023.
  2. Patton, P.T. et al. The effect of fully automated photo–identification on mark-recapture estimates. Paper presented at the EURING Analytical Meeting. Montpellier, France. April 2023
  3. Patton, P.T. Assessing populations of resident cetaceans. HIMB Scholarship Symposium. Kāneʻohe, Hawaiʻi. April 2022.
  4. Patton, P. T. & Gardner, B. Misspecifying movement models in spatial capture recapture studies. Paper presented at The Ecological Society of America Conference. Portland, OR, USA. August 2017
  5. Patton, P. T. et al. Modeling and estimating co–occurrence between generalist brood parasites and host communities. Paper presented at the EURING Analytical Meeting. Barcelona, Spain. June 2017
  6. Patton, P. T. et al. Multi–species occupancy models that incorporate false positive and false negative sampling errors. Paper presented at The Wildlife Society Conference. Raleigh, NC, USA. October 2016
  7. Patton, P. T. et al. Joint host–parasite occurrence models can improve predictions and reveal ecological traps. Paper presented at the International Statistical Ecology Conference. Seattle, WA, USA. July 2016

Teaching Experience

  • Teaching Assistant, Principles of Wildlife Science (FW 453), North Carolina State, Spring 2016
  • Teaching Assistant, Introduction to Probability and Statistics (APM 391), SUNY ESF, Fall 2012
  • Tutor, Calculus I (APM 105), Academic Support Services, SUNY ESF, 2011 to 2013

Professional Development

  • An Introduction to Close-Kin Mark-Recapture, EURING Analytical Meeting
  • C++ Virtual Training, NOAA Fisheries
  • Bayesian Model Selection and Decision Theory for Ecologists, International Statistical Ecology Conference
  • Flexible Programming with NIMBLE, International Statistical Ecology Conference
  • Introduction to Structured Decision Making, National Conservation Training Center

Professional Service

  • Referee: Wildlife Society Bulletin, Marine Mammal Science
  • Member: British Ecological Society, The Wildlife Society (biometrics working group), The Ecological Society of America (statistical ecology section)
  • Representative to the Faculty, Marine Biology Graduate Program, University of Hawaiʻi at Mānoa
  • Representative to the Graduate Student Organization, Marine Biology Graduate Program, University of Hawaiʻi at Mānoa