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Animal movement models for multiple individuals

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Abstract Statistical models for animal movement provide tools that help ecologists and biologists learn how animals interact with their environment and each other. Efforts to develop increasingly realistic, implementable, and scientifically valuable methods for analyzing remotely observed trajectories have provided practitioners with a wide selection of models to help them understand animal behavior. Increasingly, researchers are interested in studying multiple animals jointly, which requires methods that can account for dependence across individuals. Dependence can arise for many reasons, including shared behavioral tendencies, familial relationships, and direct interactions on the landscape. We provide a synopsis of recent statistical methods for animal movement data applicable to settings in which inference is desired across multiple individuals. Highlights of these approaches include the ability to infer shared behavioral traits across a group of individuals and the ability to infer unobserved social networks summarizing dynamic relationships that manifest themselves in movement decisions. This article is categorized under: Statistical Models > Bayesian Models Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data Data: Types and Structure > Social Networks
Selection of the 21 possible pairs of seven individuals in the killer whale study. The plots displayed are for all (inter‐type) pairs of killer whales that include the sole individual of type B1. The dotted line shows the proximity‐based network defined such that individuals are deemed connected whenever they are separated by a distance less than 10 km. The solid line in each plot shows the inferred probability of a social connection during the 1 week study period (i.e., Pr(Aij(t) = 1)), and the gray region depicts uncertainty as one standard deviation above and below the probability computed using samples from the posterior distribution (Reprinted with permission from Scharf et al. (). Copyright 2016 Institute of Mathematical Statistics)
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Posterior means and 95% equi‐tailed credible intervals for the individual‐ (each named row) and population‐level (μ) effects of distance to nearest potential kill site on cougar motility. Population‐level effects represent the mean coefficient across all individuals (Reprinted with permission from Buderman, Hooten, Alldredge, et al. (). Copyright 2018 BioMed Central Ltd)
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Population‐level spatial quantities of residence time (a,b), speed (c,d), and tortuosity (e,f). For reference, county boundaries and major roads are shown for Colorado (a,c,e). Not included are rare movements to eastern states (Nebraska, Kansas, and Iowa) (Reprinted with permission from Buderman, Hooten, Ivan, and Shenk (). Copyright 2018 John Wiley & Sons Ltd; and refers to quantities related to Canada lynx movement)
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Points and lines in the bottom of the figure represent the effects of attraction (dashed line) and alignment (dotted line) between individual i and its “ego‐network” (polygons), defined as the collection all individuals that share a connection with i (Reprinted with permission from Scharf et al. (). Copyright 2016 Institute of Mathematical Statistics)
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Location uncertainty for five individual cranes over time indicated by the radius of the 95% credible circle for μ on the y‐axis. The dashed lines represent the uncertainty inferred from models fit independently to each individual, whereas the solid lines represent the uncertainty resulting from the migratory network model. Rug plots show observation times (Reprinted with permission from Hooten et al. (). Copyright 2018 John Wiley & Sons Ltd)
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Group movement paths. (a) Plotted paths of a shoal of 10 guppies from Bode et al. (). (b) Plotted paths of a simulated realization from the CTCRW model without interactions. (c) Plotted paths of a simulated realization from the DPPI model with the attraction‐repulsion point process interaction function (Reprinted with permission from Russell et al. (); Copyright 2016 Springer Nature)
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Data: Types and Structure > Social Networks
Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data
Statistical Models > Bayesian Models
Applications of Computational Statistics > Clinical Trials

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