In the past few years interest in statistical modeling has rapidly increased for scientists
in many different fields. With new technologies and the ability to collect larger amounts of
data they sought a tool which would help them to get a better understanding, and eventually,
prediction of behavior of subjects in their range of study. For biologists and ecologists
habitat data is necessary to develop effective conservation and management strategies, and
help determine what is behind the change in the population of different species.
Our research is focused on the moose habitat behavior statistics. Moose, Alces alces,
are the largest of all deer species. Male moose are recognizable by their huge antlers,
which can spread up to 6 feet wide. Because of their tall body, they prefer to browse
higher shrubs and their typical habitat is a dense mixed boreal forest in North America,
including the northern United States, Canada, Alaska, and in Scandinavia and Russia.
Despite their large bodies, moose are good swimmers and are often seen in lakes and rivers
feeding on aquatic plants both at and below the surface. One of the reasons why
moose habitat behavior is the subject of study by many biologists is recent changes in
population in North America. Since the 1990’s, the moose population in northern Minnesota
has decline significantly. Based on a moose population survey from 2017, the
population in northeastern Minnesota has dropped from about 8; 000 moose to a stable
population of just under 4; 000 moose over the last 4 years. Meanwhile, the northwestern
Minnesotan population practically disappeared after declining from 4; 000 to fewer than
100. The reason behind this steep drop is unknown. Many scientists believe that it could
be caused by climate change. Shorter winters and longer falls give more time for parasites,
especially winter ticks, to find a host. For purposes of research, moose wore GPS collars,
which allow biologists to track their location and collect essential data for future work.
In some cases, moose received a tiny transmitter which monitored their heart rate and
temperature and notified biologists when the moose died. This work intends to utilize the Bayesian hierarchic model with spatially varying coefficients
to obtain better insights into moose habitat behavior in Northern Minnesota.
A project submitted to the faculty of University of Minnesota in partial fulfillment of the requirements for the degree of Master of Science. June, 2017. Major: Mathematics and Statistics. Advisor: Xuan Li. 1 computer file (PDF); vii, 66 pages, appendices I-III
Statistical Analysis of Moose Habitat Behaviors Using Bayesian Hierarchical Model with Spatially Varying Coefficients.
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