Incorporating periodic variability in hidden Markov models for animal movement

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• 作者：Michael Li ; Benjamin M. Bolker
• 关键词：Hidden Markov model ; Animal movement ; Temporal autocorrelation ; Temporal heterogeneity ; Florida panther
• 刊名：Movement Ecology
• 出版年：2017
• 出版时间：December 2017
• 年：2017
• 卷：5
• 期：1
• 全文大小：663KB
• 刊物主题：Animal Ecology; Conservation Biology/Ecology; Terrestial Ecology;
• 出版者：BioMed Central
• ISSN：2051-3933
• 卷排序：5

文摘

BackgroundClustering time-series data into discrete groups can improve prediction and provide insight into the nature of underlying, unobservable states of the system. However, temporal variation in probabilities of group occupancy, or the rates at which individuals move between groups, can obscure such signals. We use finite mixture and hidden Markov models (HMMs), two standard clustering techniques, to model long-term hourly movement data from Florida panthers (Puma concolor coryi). Allowing for temporal heterogeneity in transition probabilities, a straightforward but little-used extension of the standard HMM framework, resolves some shortcomings of current models and clarifies the movement patterns of panthers.