Multi-mode movement decisions across widely ranging behavioral processes.
Movement of organisms plays a fundamental role in the evolution and diversity of life. Animals typically move at an irregular pace over time and space, alternating among movement states. Understanding movement decisions and developing mechanistic models of animal distribution dynamics can thus be co...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0272538 |
| _version_ | 1798002629123506176 |
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| author | Marie-Caroline Prima Thierry Duchesne Jerod A Merkle Simon Chamaillé-Jammes Daniel Fortin |
| author_facet | Marie-Caroline Prima Thierry Duchesne Jerod A Merkle Simon Chamaillé-Jammes Daniel Fortin |
| author_sort | Marie-Caroline Prima |
| collection | DOAJ |
| description | Movement of organisms plays a fundamental role in the evolution and diversity of life. Animals typically move at an irregular pace over time and space, alternating among movement states. Understanding movement decisions and developing mechanistic models of animal distribution dynamics can thus be contingent to adequate discrimination of behavioral phases. Existing methods to disentangle movement states typically require a follow-up analysis to identify state-dependent drivers of animal movement, which overlooks statistical uncertainty that comes with the state delineation process. Here, we developed population-level, multi-state step selection functions (HMM-SSF) that can identify simultaneously the different behavioral bouts and the specific underlying behavior-habitat relationship. Using simulated data and relocation data from mule deer (Odocoileus hemionus), plains bison (Bison bison bison) and plains zebra (Equus quagga), we illustrated the HMM-SSF robustness, versatility, and predictive ability for animals involved in distinct behavioral processes: foraging, migrating and avoiding a nearby predator. Individuals displayed different habitat selection pattern during the encamped and the travelling phase. Some landscape attributes switched from being selected to avoided, depending on the movement phase. We further showed that HMM-SSF can detect multi-modes of movement triggered by predators, with prey switching to the travelling phase when predators are in close vicinity. HMM-SSFs thus can be used to gain a mechanistic understanding of how animals use their environment in relation to the complex interplay between their needs to move, their knowledge of the environment and navigation capacity, their motion capacity and the external factors related to landscape heterogeneity. |
| first_indexed | 2024-04-11T11:56:42Z |
| format | Article |
| id | doaj.art-7c955d4fde1a4cbaad72334501becde5 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-11T11:56:42Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-7c955d4fde1a4cbaad72334501becde52022-12-22T04:25:10ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01178e027253810.1371/journal.pone.0272538Multi-mode movement decisions across widely ranging behavioral processes.Marie-Caroline PrimaThierry DuchesneJerod A MerkleSimon Chamaillé-JammesDaniel FortinMovement of organisms plays a fundamental role in the evolution and diversity of life. Animals typically move at an irregular pace over time and space, alternating among movement states. Understanding movement decisions and developing mechanistic models of animal distribution dynamics can thus be contingent to adequate discrimination of behavioral phases. Existing methods to disentangle movement states typically require a follow-up analysis to identify state-dependent drivers of animal movement, which overlooks statistical uncertainty that comes with the state delineation process. Here, we developed population-level, multi-state step selection functions (HMM-SSF) that can identify simultaneously the different behavioral bouts and the specific underlying behavior-habitat relationship. Using simulated data and relocation data from mule deer (Odocoileus hemionus), plains bison (Bison bison bison) and plains zebra (Equus quagga), we illustrated the HMM-SSF robustness, versatility, and predictive ability for animals involved in distinct behavioral processes: foraging, migrating and avoiding a nearby predator. Individuals displayed different habitat selection pattern during the encamped and the travelling phase. Some landscape attributes switched from being selected to avoided, depending on the movement phase. We further showed that HMM-SSF can detect multi-modes of movement triggered by predators, with prey switching to the travelling phase when predators are in close vicinity. HMM-SSFs thus can be used to gain a mechanistic understanding of how animals use their environment in relation to the complex interplay between their needs to move, their knowledge of the environment and navigation capacity, their motion capacity and the external factors related to landscape heterogeneity.https://doi.org/10.1371/journal.pone.0272538 |
| spellingShingle | Marie-Caroline Prima Thierry Duchesne Jerod A Merkle Simon Chamaillé-Jammes Daniel Fortin Multi-mode movement decisions across widely ranging behavioral processes. PLoS ONE |
| title | Multi-mode movement decisions across widely ranging behavioral processes. |
| title_full | Multi-mode movement decisions across widely ranging behavioral processes. |
| title_fullStr | Multi-mode movement decisions across widely ranging behavioral processes. |
| title_full_unstemmed | Multi-mode movement decisions across widely ranging behavioral processes. |
| title_short | Multi-mode movement decisions across widely ranging behavioral processes. |
| title_sort | multi mode movement decisions across widely ranging behavioral processes |
| url | https://doi.org/10.1371/journal.pone.0272538 |
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