A novel method for simulation of telemetry data based on Canadian Lynx
Master's Project (M.S.) University of Alaska Fairbanks, 2023 In movement ecology, one frequently encounters situations in which a test statistic is easy to define but its distribution is difficult or impossible to compute in closed form. As such, it is of interest to find methods for simulating...
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ftunivalaska:oai:scholarworks.alaska.edu:11122/14738 2023-11-12T04:13:05+01:00 A novel method for simulation of telemetry data based on Canadian Lynx Bishop, Andrew E. McIntyre, Julie Kielland, Knut Barry, Ron Short, Margaret Goddard, Scott 2023-05 http://hdl.handle.net/11122/14738 en_US eng http://hdl.handle.net/11122/14738 Department of Mathematics and Statistics Master of Science in Statistics Master's Project ms 2023 ftunivalaska 2023-10-26T18:04:10Z Master's Project (M.S.) University of Alaska Fairbanks, 2023 In movement ecology, one frequently encounters situations in which a test statistic is easy to define but its distribution is difficult or impossible to compute in closed form. As such, it is of interest to find methods for simulating data which can be used to approximate the null distribution of such test statistics. In this paper, we describe a motivating scenario for simulating data involving Canadian lynx collared by researchers in the Alaskan arctic. We initially use a hidden Markov model (HMM) to model the behavioral patterns of these animals, and use kernel density estimation to describe their usage distributions. We then describe a novel method for simulating animal tracks based on these telemetry data, which closely preserves the HMM and kernel density estimate (KDE) while removing any causal dependency between them. Finally, we apply this method to identify relationships between an individual’s behavioral state and location within its home range. Master Thesis Arctic Alaska Lynx University of Alaska: ScholarWorks@UA Arctic Fairbanks |
institution |
Open Polar |
collection |
University of Alaska: ScholarWorks@UA |
op_collection_id |
ftunivalaska |
language |
English |
topic |
Master of Science in Statistics |
spellingShingle |
Master of Science in Statistics Bishop, Andrew E. A novel method for simulation of telemetry data based on Canadian Lynx |
topic_facet |
Master of Science in Statistics |
description |
Master's Project (M.S.) University of Alaska Fairbanks, 2023 In movement ecology, one frequently encounters situations in which a test statistic is easy to define but its distribution is difficult or impossible to compute in closed form. As such, it is of interest to find methods for simulating data which can be used to approximate the null distribution of such test statistics. In this paper, we describe a motivating scenario for simulating data involving Canadian lynx collared by researchers in the Alaskan arctic. We initially use a hidden Markov model (HMM) to model the behavioral patterns of these animals, and use kernel density estimation to describe their usage distributions. We then describe a novel method for simulating animal tracks based on these telemetry data, which closely preserves the HMM and kernel density estimate (KDE) while removing any causal dependency between them. Finally, we apply this method to identify relationships between an individual’s behavioral state and location within its home range. |
author2 |
McIntyre, Julie Kielland, Knut Barry, Ron Short, Margaret Goddard, Scott |
format |
Master Thesis |
author |
Bishop, Andrew E. |
author_facet |
Bishop, Andrew E. |
author_sort |
Bishop, Andrew E. |
title |
A novel method for simulation of telemetry data based on Canadian Lynx |
title_short |
A novel method for simulation of telemetry data based on Canadian Lynx |
title_full |
A novel method for simulation of telemetry data based on Canadian Lynx |
title_fullStr |
A novel method for simulation of telemetry data based on Canadian Lynx |
title_full_unstemmed |
A novel method for simulation of telemetry data based on Canadian Lynx |
title_sort |
novel method for simulation of telemetry data based on canadian lynx |
publishDate |
2023 |
url |
http://hdl.handle.net/11122/14738 |
geographic |
Arctic Fairbanks |
geographic_facet |
Arctic Fairbanks |
genre |
Arctic Alaska Lynx |
genre_facet |
Arctic Alaska Lynx |
op_relation |
http://hdl.handle.net/11122/14738 Department of Mathematics and Statistics |
_version_ |
1782331250999033856 |