Imputation Approaches for Animal Movement Modeling
Abstract The analysis of telemetry data is common in animal ecological studies. While the collection of telemetry data for individual animals has improved dramatically, the methods to properly account for inherent uncertainties (e.g., measurement error, dependence, barriers to movement) have lagged...
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ftrepec:oai:RePEc:spr:jagbes:v:22:y:2017:i:3:d:10.1007_s13253-017-0294-5 2023-05-15T15:43:54+02:00 Imputation Approaches for Animal Movement Modeling Henry Scharf Mevin B. Hooten Devin S. Johnson http://link.springer.com/10.1007/s13253-017-0294-5 unknown http://link.springer.com/10.1007/s13253-017-0294-5 article ftrepec 2020-12-04T13:30:42Z Abstract The analysis of telemetry data is common in animal ecological studies. While the collection of telemetry data for individual animals has improved dramatically, the methods to properly account for inherent uncertainties (e.g., measurement error, dependence, barriers to movement) have lagged behind. Still, many new statistical approaches have been developed to infer unknown quantities affecting animal movement or predict movement based on telemetry data. Hierarchical statistical models are useful to account for some of the aforementioned uncertainties, as well as provide population-level inference, but they often come with an increased computational burden. For certain types of statistical models, it is straightforward to provide inference if the latent true animal trajectory is known, but challenging otherwise. In these cases, approaches related to multiple imputation have been employed to account for the uncertainty associated with our knowledge of the latent trajectory. Despite the increasing use of imputation approaches for modeling animal movement, the general sensitivity and accuracy of these methods have not been explored in detail. We provide an introduction to animal movement modeling and describe how imputation approaches may be helpful for certain types of models. We also assess the performance of imputation approaches in two simulation studies. Our simulation studies suggests that inference for model parameters directly related to the location of an individual may be more accurate than inference for parameters associated with higher-order processes such as velocity or acceleration. Finally, we apply these methods to analyze a telemetry data set involving northern fur seals (Callorhinus ursinus) in the Bering Sea. Supplementary materials accompanying this paper appear online. Animal movement models, Hierarchical models, Telemetry data, Multiple imputation Article in Journal/Newspaper Bering Sea Callorhinus ursinus RePEc (Research Papers in Economics) Bering Sea |
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Open Polar |
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RePEc (Research Papers in Economics) |
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Abstract The analysis of telemetry data is common in animal ecological studies. While the collection of telemetry data for individual animals has improved dramatically, the methods to properly account for inherent uncertainties (e.g., measurement error, dependence, barriers to movement) have lagged behind. Still, many new statistical approaches have been developed to infer unknown quantities affecting animal movement or predict movement based on telemetry data. Hierarchical statistical models are useful to account for some of the aforementioned uncertainties, as well as provide population-level inference, but they often come with an increased computational burden. For certain types of statistical models, it is straightforward to provide inference if the latent true animal trajectory is known, but challenging otherwise. In these cases, approaches related to multiple imputation have been employed to account for the uncertainty associated with our knowledge of the latent trajectory. Despite the increasing use of imputation approaches for modeling animal movement, the general sensitivity and accuracy of these methods have not been explored in detail. We provide an introduction to animal movement modeling and describe how imputation approaches may be helpful for certain types of models. We also assess the performance of imputation approaches in two simulation studies. Our simulation studies suggests that inference for model parameters directly related to the location of an individual may be more accurate than inference for parameters associated with higher-order processes such as velocity or acceleration. Finally, we apply these methods to analyze a telemetry data set involving northern fur seals (Callorhinus ursinus) in the Bering Sea. Supplementary materials accompanying this paper appear online. Animal movement models, Hierarchical models, Telemetry data, Multiple imputation |
format |
Article in Journal/Newspaper |
author |
Henry Scharf Mevin B. Hooten Devin S. Johnson |
spellingShingle |
Henry Scharf Mevin B. Hooten Devin S. Johnson Imputation Approaches for Animal Movement Modeling |
author_facet |
Henry Scharf Mevin B. Hooten Devin S. Johnson |
author_sort |
Henry Scharf |
title |
Imputation Approaches for Animal Movement Modeling |
title_short |
Imputation Approaches for Animal Movement Modeling |
title_full |
Imputation Approaches for Animal Movement Modeling |
title_fullStr |
Imputation Approaches for Animal Movement Modeling |
title_full_unstemmed |
Imputation Approaches for Animal Movement Modeling |
title_sort |
imputation approaches for animal movement modeling |
url |
http://link.springer.com/10.1007/s13253-017-0294-5 |
geographic |
Bering Sea |
geographic_facet |
Bering Sea |
genre |
Bering Sea Callorhinus ursinus |
genre_facet |
Bering Sea Callorhinus ursinus |
op_relation |
http://link.springer.com/10.1007/s13253-017-0294-5 |
_version_ |
1766378110439129088 |