Rebecca Taylor, Ph.D.



Ph.D.       2009       Montana State University-Bozeman, MT         Fish and Wildlife Biology
M.S.        2008        Montana State University-Bozeman, MT        Statistics
M.S.        2001        Montana State University-Bozeman, MT        Land Resources and Environmental Sciences
B.S.        1991         University of Wisconsin-Madison, WI             Natural Science with majors in Wildlife Ecology and Zoology


As a Principle Investigator and Research Statistician, I create science to support critical management decisions for hard to study species in a changing environment. Such situations produce data that tend to be sparse, biased and/or imprecise, and have large knowledge gaps. Thus, I specialize in modifying state-of-the-art analytical and computational approaches for complex problems and intractable data, as well as creating new statistical theory and techniques when existing methods are inadequate. I routinely use Bayesian and frequentist paradigms. 

Theme I:  Estimating demographic rates and abundance with emerging methods and multiple data types

Recent work under this theme includes evaluation of survival rate estimators based on standing age structure data that relax the (often used but generally unrealistic) stable age structure assumption, and integrated population modeling to estimate demographic rates using four data types, sparsely scattered over a multi-decade timespan. For example, the integrated population models have provided the only rigorous, robust estimates of Pacific walrus demographic rates and population trend to date. Current projects include work with age at death distributions, state-misclassification (as opposed to state-uncertainty) in multi-event mark recapture models, developing explicit maximum likelihood estimators that combine capture-mark-recapture data with other data types, and close kin mark recapture estimation.

Theme II:  Mechanistic models and causal inference methods to understand and predict effects of environmental change and anthropogenic disturbance on wildlife populations

Mechanistic models under this theme link environmental change and anthropogenic influences to 1) animal movement and behavior, 2) bioenergetics and body condition and 3) demography and population dynamics. Causal inference methods focus on obtaining unbiased estimates of a single link in the chain in the presence of multiple confounding factors: they use a combination of treatment and outcome modeling, including techniques such as propensity score-based matching that are rarely used in wildlife studies.  This theme is part of the Changing Arctic Ecosystems Initiative: recent work has forecasted effects of sea ice loss on Pacific walruses and evaluated effects of increased vessel traffic (which occurs secondary to sea ice loss), also on walruses. 

Species Studied

My career-long species affiliations have varied across the plant and animal kingdoms, but my USGS research centers on marine mammals. I maintain a strong, decade-long collaboration with walrus researchers at USGS and USFWS, and I have recently expanded my work to include collaborative sea otter and polar bear research.

Three-year goals

My highest priority goals include five different projects to estimate Pacific walrus abundance (while also refining estimates of their demographic rates and population trend). The Department of Interior needs population size and status  information to manage this trust species which is an important resource for native subsistence hunters, is protected and managed under the Marine Mammal Protection Act, and was an endangered species candidate until the 2017 decision not to list—a decision which is being litigated by the Center for Biological Diversity.  I also have ongoing demographic work using age-at-death distributions to estimate vital rates, in addition to mechanistic modeling for other species of concern.