|
Activity Number:
|
207
- Ecology and Animal Movement
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
|
|
Sponsor:
|
Section on Statistics and the Environment
|
|
Abstract #313229
|
|
|
Title:
|
A Bayesian Non-Parametric Approach for Animal Movement: Application to Elk Movement in the GYE
|
|
Author(s):
|
Sahar Zarmehri* and Ephraim Hanks and Lin Lin
|
|
Companies:
|
Penn State and The Pennsylvania State University and The Pennsylvania State University
|
|
Keywords:
|
Animal movement;
Spatio-temporal model;
Bayesian non- parametric approach
|
|
Abstract:
|
In this paper, we propose a Bayesian non-parametric approach to model animal movement. This model extends a continuous time Markov chain (CTMC) animal movement model in a non-parametric approach using Dirichlet process priors to better estimate movements rates that matches the empirical movement in areas with many observations. This results in a flexible model with varying coefficients at grid cell and time as well as individual level, similar to a random slope model. This non-parametric approach will return movement patterns that are very similar to empirical movements whenever we have GPS data, and will also estimate a population level model for spatial regions with no GPS observations. We apply this model to elk GPS data in the Greater Yellowstone Ecosystem (GYE) which includes the GPS locations of 1197 collared elk from the year 2001 to 2015.
|
Authors who are presenting talks have a * after their name.
