Hidden Markov movement models reveal diverse seasonal movement patterns in two North American ungulates
Abstract Animal movement is the mechanism connecting landscapes to fitness, and understanding variation in seasonal animal movements has benefited from the analysis and categorization of animal displacement. However, seasonal movement patterns can defy classification when movements are highly variab...
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| Language: | English |
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Wiley
2023-07-01
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| Series: | Ecology and Evolution |
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| Online Access: | https://doi.org/10.1002/ece3.10282 |
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| author | John Terrill Paterson Aaron N. Johnston Anna C. Ortega Cody Wallace Matthew Kauffman |
| author_facet | John Terrill Paterson Aaron N. Johnston Anna C. Ortega Cody Wallace Matthew Kauffman |
| author_sort | John Terrill Paterson |
| collection | DOAJ |
| description | Abstract Animal movement is the mechanism connecting landscapes to fitness, and understanding variation in seasonal animal movements has benefited from the analysis and categorization of animal displacement. However, seasonal movement patterns can defy classification when movements are highly variable. Hidden Markov movement models (HMMs) are a class of latent‐state models well‐suited to modeling movement data. Here, we used HMMs to assess seasonal patterns of variation in the movement of pronghorn (Antilocapra americana), a species known for variable seasonal movements that challenge analytical approaches, while using a population of mule deer (Odocoileus hemionus), for whom seasonal movements are well‐documented, as a comparison. We used population‐level HMMs in a Bayesian framework to estimate a seasonal trend in the daily probability of transitioning between a short‐distance local movement state and a long‐distance movement state. The estimated seasonal patterns of movements in mule deer closely aligned with prior work based on indices of animal displacement: a short period of long‐distance movements in the fall season and again in the spring, consistent with migrations to and from seasonal ranges. We found seasonal movement patterns for pronghorn were more variable, as a period of long‐distance movements in the fall was followed by a winter period in which pronghorn were much more likely to further initiate and remain in a long‐distance movement pattern compared with the movement patterns of mule deer. Overall, pronghorn were simply more likely to be in a long‐distance movement pattern throughout the year. Hidden Markov movement models provide inference on seasonal movements similar to other methods, while providing a robust framework to understand movement patterns on shorter timescales and for more challenging movement patterns. Hidden Markov movement models can allow a rigorous assessment of the drivers of changes in movement patterns such as extreme weather events and land development, important for management and conservation. |
| format | Article |
| id | doaj-art-d6974ac375bc455b807c574d7635fe21 |
| institution | DOAJ |
| issn | 2045-7758 |
| language | English |
| publishDate | 2023-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecology and Evolution |
| spelling | doaj-art-d6974ac375bc455b807c574d7635fe212025-08-20T03:01:35ZengWileyEcology and Evolution2045-77582023-07-01137n/an/a10.1002/ece3.10282Hidden Markov movement models reveal diverse seasonal movement patterns in two North American ungulatesJohn Terrill Paterson0Aaron N. Johnston1Anna C. Ortega2Cody Wallace3Matthew Kauffman4U.S. Geological Survey Northern Rocky Mountain Science Center Bozeman Montana USAU.S. Geological Survey Northern Rocky Mountain Science Center Bozeman Montana USAWyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology University of Wyoming Laramie Wyoming USAWyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology University of Wyoming Laramie Wyoming USAWyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology University of Wyoming Laramie Wyoming USAAbstract Animal movement is the mechanism connecting landscapes to fitness, and understanding variation in seasonal animal movements has benefited from the analysis and categorization of animal displacement. However, seasonal movement patterns can defy classification when movements are highly variable. Hidden Markov movement models (HMMs) are a class of latent‐state models well‐suited to modeling movement data. Here, we used HMMs to assess seasonal patterns of variation in the movement of pronghorn (Antilocapra americana), a species known for variable seasonal movements that challenge analytical approaches, while using a population of mule deer (Odocoileus hemionus), for whom seasonal movements are well‐documented, as a comparison. We used population‐level HMMs in a Bayesian framework to estimate a seasonal trend in the daily probability of transitioning between a short‐distance local movement state and a long‐distance movement state. The estimated seasonal patterns of movements in mule deer closely aligned with prior work based on indices of animal displacement: a short period of long‐distance movements in the fall season and again in the spring, consistent with migrations to and from seasonal ranges. We found seasonal movement patterns for pronghorn were more variable, as a period of long‐distance movements in the fall was followed by a winter period in which pronghorn were much more likely to further initiate and remain in a long‐distance movement pattern compared with the movement patterns of mule deer. Overall, pronghorn were simply more likely to be in a long‐distance movement pattern throughout the year. Hidden Markov movement models provide inference on seasonal movements similar to other methods, while providing a robust framework to understand movement patterns on shorter timescales and for more challenging movement patterns. Hidden Markov movement models can allow a rigorous assessment of the drivers of changes in movement patterns such as extreme weather events and land development, important for management and conservation.https://doi.org/10.1002/ece3.10282Hidden Markov movement modelmigrationmovementmule deerpronghorn |
| spellingShingle | John Terrill Paterson Aaron N. Johnston Anna C. Ortega Cody Wallace Matthew Kauffman Hidden Markov movement models reveal diverse seasonal movement patterns in two North American ungulates Ecology and Evolution Hidden Markov movement model migration movement mule deer pronghorn |
| title | Hidden Markov movement models reveal diverse seasonal movement patterns in two North American ungulates |
| title_full | Hidden Markov movement models reveal diverse seasonal movement patterns in two North American ungulates |
| title_fullStr | Hidden Markov movement models reveal diverse seasonal movement patterns in two North American ungulates |
| title_full_unstemmed | Hidden Markov movement models reveal diverse seasonal movement patterns in two North American ungulates |
| title_short | Hidden Markov movement models reveal diverse seasonal movement patterns in two North American ungulates |
| title_sort | hidden markov movement models reveal diverse seasonal movement patterns in two north american ungulates |
| topic | Hidden Markov movement model migration movement mule deer pronghorn |
| url | https://doi.org/10.1002/ece3.10282 |
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