LIAH MCPHERSON
Master's student
My research interests include the behavior, ecology and conservation of cetaceans. I’m passionate about the use of novel technologies such as unoccupied aerial systems to address questions in marine mammal science. In August of 2023, I defended my master’s thesis on the abundance and demographic parameters of spinner dolphins (Stenella longirostris subsp.) off Oʻahu’s Waiʻanae Coast. This work provided the first systematic estimates of abundance and demographic parameters for spinner dolphins off Waiʻanae, imparting valuable information for monitoring and management decisions.
email: liahlm@hawaii.edu
For publication pdfs, please visit the following links:
Biography
Liah is a master's student in the Marine Biology Graduate Program at the University of Hawaiʻi at Manoa. Hailing from the cozy beach town of Kitty Hawk, North Carolina, she began studying dolphins in 2009 with the Outer Banks Center for Dolphin Research, assisting in photo-identification efforts, and later, skin lesion analysis. Since 2017, Liah has worked in the Bahamas as a field assistant with the Wild Dolphin Project, contributing to their longitudinal study of free-ranging Atlantic spotted dolphins. She received undergraduate degrees in Biology and Animal Behavior (interdisciplinary) from the University of North Carolina at Chapel Hill in 2019, where she completed an honors thesis on the applications of unmanned aerial systems for measuring delphinid behavior. An avid freediver, photographer, and science communicator, Liah is always searching for opportunities to explore and protect marine environments.
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.