Cooperative Fish and Wildlife Research Units Program: Colorado
Education, Research and Technical Assistance for Managing Our Natural Resources

Colorado Project


Graphics and Analysis for Marine Mammal Research

December 2015 - July 2018


Personnel

Participating Agencies

  • NOAA

Problem statement: Data associated with marine mammal tracking are often collected but the measures associated with data and the underlying movement process make it challenging to understand space use, interaction among individuals, and to visualize the movement patterns. So What? Why this research matters: The ability to formally make inferences about, and visualize, marine mammal space use and movement can lead to improved understanding of these important aquatic species. Collaboration/Partners: This project is in collaboration with scientists at the National Oceanic and Atmospheric Administration. Research That Informs Decisions: Formal statistical models that account for the various sources of uncertainty in marine mammal telemetry data will lead to a better understanding of mechanisms that aid in the management of these aquatic wildlife populations.

Research Publications Publication Date
Brost, B.M., M.B. Hooten, and R.J. Small. (2020). Model-based clustering reveals patterns in central place use of a marine top predator. Ecosphere, 11: e03123. 2020-05-31
Brost, B.M., M.B. Hooten, and R.J. Small. (2017). Leveraging constraints and biotelemetry data to pinpoint repetitively used spatial features. Ecology, 98: 12-20. 2017-01-31
Conn, P.B., D.S. Johnson, P.J. Williams, S. Melin, and M.B. Hooten. (2018). A guide to Bayesian model checking for ecologists. Ecological Monographs, 88: 526-542. 2018-01-31
Hanks, E.M., D.S. Johnson, and M.B. Hooten. (2017). Reflected stochastic differential equation models for constrained animal movement. Journal of Agricultural, Biological, and Environmental Statistics, 22: 353-372. 2017-09-30
Scharf, H.R., M.B. Hooten, and D.S. Johnson. (2017). Imputation approaches for animal movement modeling. Journal of Agricultural, Biological, and Environmental Statistics, 22: 335-352. 2017-06-30
Scharf, H.R., M.B. Hooten, D.S. Johnson, and J. Durban. (2018). Process convolution approaches for modeling interacting trajectories. Environmetrics, 29: e2487. 2018-02-28
Scharf, H.R., M.B. Hooten, B.K. Fosdick, D.S. Johnson, J.M. London, and J.W. Durban. (2016). Dynamic social networks based on movement. Annals of Applied Statistics,10: 2182-2202. 2016-12-31