Rate My Professor Peter Dillingham

Associate Professor Peter Dillingham serves in the Department of Mathematics and Statistics at the University of Otago, where he holds the position of Director of Studies for 200- and 300-level Statistics courses and acts as Theme Leader for the Coastal People: Southern Skies Centre of Research Excellence. Originally from southern California, he earned a BSc in Mathematics and an MSc in Mathematics and Statistics from the University of Alaska Fairbanks. He completed his PhD in Statistics at the University of Otago, with a thesis on population modelling of seabirds. His career trajectory includes five years as a researcher at the University of Washington, a year as a stay-at-home dad in Dublin, stints at Clark University in Massachusetts, and leading the Statistics group at the University of New England in New South Wales, prior to returning to Otago in 2017.

Dillingham's research specializations encompass ecological, environmental, and Bayesian statistics. His work applies quantitative methods to marine systems, multi-factor experiments for ocean global change biology, tools for managing fisheries impacts on marine megafauna, and statistical models for chemical sensors in environmental monitoring, with secondary interests in health outcomes research. Notable publications include Dillingham, P. W., Collins, S., Comeau, S., et al. (2025). The MEDDLE data analysis guides as a living resource for multiple-driver marine research. Limnology & Oceanography Bulletin; Coakley, J. B., Dillingham, P. W., & Lamare, M. D. (2025). A new approach to quantifying growth in sea stars through the application of tetracycline tag-recapture methods. Marine Biology, 172, 173; Rayment, W. J., Bennington, S., et al. (2024). Long- and short-term impacts of vessels on Hector’s dolphins at Te Pātaka-o-Rākaihautū/Banks Peninsula. DOC Research and Development Series 372; Bennington, S. M., Bourke, S. D., et al. (2024). New insights into the population structure of Hector's dolphin revealed using environmental DNA. Environmental DNA, 6, e70024; Vance, J.M., Currie, K., et al. (2022). An empirical MLR for estimating surface layer DIC and a comparative assessment to other gap-filling techniques for ocean carbon time series. Biogeosciences 19(1): 241-269; Dillingham, P.W., Alsaedi, B.S.O., et al. (2020). Establishing meaningful limits of detection for ion-selective electrodes and other non-linear sensors. ACS Sensors 5(1): 250-257; Dillingham, P.W., Moore, J.E., et al. (2016). Improved estimation of intrinsic growth r max: integrating matrix models and allometry. Ecological Applications 26:322-333. He has also developed R packages such as ISEtools for ion-selective electrode data analysis.