Accounting for phenology in the analysis of animal movement
The analysis of animal tracking data provides an important source of scientific understanding and discovery in ecology. Observations of animal trajectories using telemetry devices provide researchers with information about the way animals interact with their environment and each other. For many spec...
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ftdatacite:10.48550/arxiv.1806.09473 2023-05-15T15:54:37+02:00 Accounting for phenology in the analysis of animal movement Scharf, Henry R. Hooten, Mevin B. Wilson, Ryan R. Durner, George M. Atwood, Todd C. 2018 https://dx.doi.org/10.48550/arxiv.1806.09473 https://arxiv.org/abs/1806.09473 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Methodology stat.ME Applications stat.AP FOS Computer and information sciences Preprint Article article CreativeWork 2018 ftdatacite https://doi.org/10.48550/arxiv.1806.09473 2022-04-01T09:40:06Z The analysis of animal tracking data provides an important source of scientific understanding and discovery in ecology. Observations of animal trajectories using telemetry devices provide researchers with information about the way animals interact with their environment and each other. For many species, specific geographical features in the landscape can have a strong effect on behavior. Such features may correspond to a single point (e.g., dens or kill sites), or to higher-dimensional subspaces (e.g., rivers or lakes). Features may be relatively static in time (e.g., coastlines or home-range centers), or may be dynamic (e.g., sea ice extent or areas of high-quality forage for herbivores). We introduce a novel model for animal movement that incorporates active selection for dynamic features in a landscape. Our approach is motivated by the study of polar bear (Ursus maritimus) movement. During the sea ice melt season, polar bears spend much of their time on sea ice above shallow, biologically productive water where they hunt seals. The changing distribution and characteristics of sea ice throughout the late spring through early fall means that the location of valuable habitat is constantly shifting. We develop a model for the movement of polar bears that accounts for the effect of this important landscape feature. We introduce a two-stage procedure for approximate Bayesian inference that allows us to analyze over 300,000 observed locations of 186 polar bears from 2012--2016. We use our proposed model to answer a particular question posed by wildlife managers who seek to cluster polar bears from the Beaufort and Chukchi seas into sub-populations. : Correction to caption of Figure 4 Report Chukchi polar bear Sea ice Ursus maritimus DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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Methodology stat.ME Applications stat.AP FOS Computer and information sciences |
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Methodology stat.ME Applications stat.AP FOS Computer and information sciences Scharf, Henry R. Hooten, Mevin B. Wilson, Ryan R. Durner, George M. Atwood, Todd C. Accounting for phenology in the analysis of animal movement |
topic_facet |
Methodology stat.ME Applications stat.AP FOS Computer and information sciences |
description |
The analysis of animal tracking data provides an important source of scientific understanding and discovery in ecology. Observations of animal trajectories using telemetry devices provide researchers with information about the way animals interact with their environment and each other. For many species, specific geographical features in the landscape can have a strong effect on behavior. Such features may correspond to a single point (e.g., dens or kill sites), or to higher-dimensional subspaces (e.g., rivers or lakes). Features may be relatively static in time (e.g., coastlines or home-range centers), or may be dynamic (e.g., sea ice extent or areas of high-quality forage for herbivores). We introduce a novel model for animal movement that incorporates active selection for dynamic features in a landscape. Our approach is motivated by the study of polar bear (Ursus maritimus) movement. During the sea ice melt season, polar bears spend much of their time on sea ice above shallow, biologically productive water where they hunt seals. The changing distribution and characteristics of sea ice throughout the late spring through early fall means that the location of valuable habitat is constantly shifting. We develop a model for the movement of polar bears that accounts for the effect of this important landscape feature. We introduce a two-stage procedure for approximate Bayesian inference that allows us to analyze over 300,000 observed locations of 186 polar bears from 2012--2016. We use our proposed model to answer a particular question posed by wildlife managers who seek to cluster polar bears from the Beaufort and Chukchi seas into sub-populations. : Correction to caption of Figure 4 |
format |
Report |
author |
Scharf, Henry R. Hooten, Mevin B. Wilson, Ryan R. Durner, George M. Atwood, Todd C. |
author_facet |
Scharf, Henry R. Hooten, Mevin B. Wilson, Ryan R. Durner, George M. Atwood, Todd C. |
author_sort |
Scharf, Henry R. |
title |
Accounting for phenology in the analysis of animal movement |
title_short |
Accounting for phenology in the analysis of animal movement |
title_full |
Accounting for phenology in the analysis of animal movement |
title_fullStr |
Accounting for phenology in the analysis of animal movement |
title_full_unstemmed |
Accounting for phenology in the analysis of animal movement |
title_sort |
accounting for phenology in the analysis of animal movement |
publisher |
arXiv |
publishDate |
2018 |
url |
https://dx.doi.org/10.48550/arxiv.1806.09473 https://arxiv.org/abs/1806.09473 |
genre |
Chukchi polar bear Sea ice Ursus maritimus |
genre_facet |
Chukchi polar bear Sea ice Ursus maritimus |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1806.09473 |
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1766389857470382080 |