Combining animal movements and behavioural data to detect behavioural states
Animal movement paths show variation in space caused by qualitative shifts in behaviours. I present a method that (1) uses both movement path data and ancillary sensor data to detect natural breakpoints in animal behaviour and (2) groups these segments into different behavioural states. The method c...
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| Veröffentlicht in: | Ecology letters 2014-10, Vol.17 (10), p.1228-1237 |
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| container_title | Ecology letters |
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| creator | Nams, Vilis O Moorcroft, Paul |
| description | Animal movement paths show variation in space caused by qualitative shifts in behaviours. I present a method that (1) uses both movement path data and ancillary sensor data to detect natural breakpoints in animal behaviour and (2) groups these segments into different behavioural states. The method can also combine analyses of different path segments or paths from different individuals. It does not assume any underlying movement mechanism. I give an example with simulated data. I also show the effects of random variation, # of states and # of segments on this method. I present a case study of a fisher movement path spanning 8 days, which shows four distinct behavioural states divided into 28 path segments when only turning angles and speed were considered. When accelerometer data were added, the analysis shows seven distinct behavioural states divided into 41 path segments. |
| doi_str_mv | 10.1111/ele.12328 |
| format | Article |
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I present a method that (1) uses both movement path data and ancillary sensor data to detect natural breakpoints in animal behaviour and (2) groups these segments into different behavioural states. The method can also combine analyses of different path segments or paths from different individuals. It does not assume any underlying movement mechanism. I give an example with simulated data. I also show the effects of random variation, # of states and # of segments on this method. I present a case study of a fisher movement path spanning 8 days, which shows four distinct behavioural states divided into 28 path segments when only turning angles and speed were considered. When accelerometer data were added, the analysis shows seven distinct behavioural states divided into 41 path segments.</description><identifier>ISSN: 1461-023X</identifier><identifier>EISSN: 1461-0248</identifier><identifier>DOI: 10.1111/ele.12328</identifier><identifier>PMID: 25040789</identifier><language>eng</language><publisher>England: Blackwell Science</publisher><subject>Algorithms ; Animal behavior ; Animal migration ; Animal movement ; Animals ; Behavior, Animal ; behavioural states ; Biogeography ; breakpoints ; case studies ; Computer Simulation ; correlated random walk ; Models, Biological ; Motor Activity ; segments ; spatial scale ; turning angles</subject><ispartof>Ecology letters, 2014-10, Vol.17 (10), p.1228-1237</ispartof><rights>2014 John Wiley & Sons Ltd/CNRS</rights><rights>2014 John Wiley & Sons Ltd/CNRS.</rights><rights>Copyright © 2014 John Wiley & Sons Ltd/CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4488-6a6c60436066eff09566fac836099c0947a7999c279a391bf80e84d397aa8cad3</citedby><cites>FETCH-LOGICAL-c4488-6a6c60436066eff09566fac836099c0947a7999c279a391bf80e84d397aa8cad3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fele.12328$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fele.12328$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25040789$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Moorcroft, Paul</contributor><contributor>Moorcroft, Paul</contributor><creatorcontrib>Nams, Vilis O</creatorcontrib><creatorcontrib>Moorcroft, Paul</creatorcontrib><title>Combining animal movements and behavioural data to detect behavioural states</title><title>Ecology letters</title><addtitle>Ecol Lett</addtitle><description>Animal movement paths show variation in space caused by qualitative shifts in behaviours. I present a method that (1) uses both movement path data and ancillary sensor data to detect natural breakpoints in animal behaviour and (2) groups these segments into different behavioural states. The method can also combine analyses of different path segments or paths from different individuals. It does not assume any underlying movement mechanism. I give an example with simulated data. I also show the effects of random variation, # of states and # of segments on this method. I present a case study of a fisher movement path spanning 8 days, which shows four distinct behavioural states divided into 28 path segments when only turning angles and speed were considered. When accelerometer data were added, the analysis shows seven distinct behavioural states divided into 41 path segments.</description><subject>Algorithms</subject><subject>Animal behavior</subject><subject>Animal migration</subject><subject>Animal movement</subject><subject>Animals</subject><subject>Behavior, Animal</subject><subject>behavioural states</subject><subject>Biogeography</subject><subject>breakpoints</subject><subject>case studies</subject><subject>Computer Simulation</subject><subject>correlated random walk</subject><subject>Models, Biological</subject><subject>Motor Activity</subject><subject>segments</subject><subject>spatial scale</subject><subject>turning angles</subject><issn>1461-023X</issn><issn>1461-0248</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkk1P3DAQhi1UVOjSQ_9AicSlPQTs2PHHEVbLFmlVhCgqN2s2mdDQfEDs5ePfd2hgpVZCwhePZp555ZnXjH0SfF_QOcAG90UmM7vBtoXSIuWZsu_WsbzcYh9CuOZcZM6I92wry7nixrpttpj27bLu6u4qga5uoUna_g5b7GKgRJks8Rfc1f1qoEoJEZLYJyVGLOI_pRAhYthhmxU0AT8-3xN2cTz7Mf2WLk7nJ9PDRVooZW2qQReaK6m51lhV3OVaV1BYSjhXcKcMGEdRZhxIJ5aV5WhVKZ0BsAWUcsK-jLo3Q3-7whB9W4cCmwY67FfBCxK0KldKvgUVgtZBz5mwvf_Qaxquo0GeKG64VsoR9XWkiqEPYcDK3wy0t-HRC-6f3PDkhv_rBrGfnxVXyxbLNfmyfgIORuC-bvDxdSU_W8xeJNOxow4RH9YdMPz22kiT-5_f5_7Izs-Pjs-mXhO_O_IV9B6uhjr4i_OMC0WfQebSCPkHvhupuQ</recordid><startdate>201410</startdate><enddate>201410</enddate><creator>Nams, Vilis O</creator><creator>Moorcroft, Paul</creator><general>Blackwell Science</general><general>Blackwell Publishing Ltd</general><scope>FBQ</scope><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7SS</scope><scope>7U9</scope><scope>C1K</scope><scope>H94</scope><scope>M7N</scope><scope>7X8</scope></search><sort><creationdate>201410</creationdate><title>Combining animal movements and behavioural data to detect behavioural states</title><author>Nams, Vilis O ; Moorcroft, Paul</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4488-6a6c60436066eff09566fac836099c0947a7999c279a391bf80e84d397aa8cad3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Algorithms</topic><topic>Animal behavior</topic><topic>Animal migration</topic><topic>Animal movement</topic><topic>Animals</topic><topic>Behavior, Animal</topic><topic>behavioural states</topic><topic>Biogeography</topic><topic>breakpoints</topic><topic>case studies</topic><topic>Computer Simulation</topic><topic>correlated random walk</topic><topic>Models, Biological</topic><topic>Motor Activity</topic><topic>segments</topic><topic>spatial scale</topic><topic>turning angles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nams, Vilis O</creatorcontrib><creatorcontrib>Moorcroft, Paul</creatorcontrib><collection>AGRIS</collection><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Virology and AIDS Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>MEDLINE - Academic</collection><jtitle>Ecology letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nams, Vilis O</au><au>Moorcroft, Paul</au><au>Moorcroft, Paul</au><au>Moorcroft, Paul</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Combining animal movements and behavioural data to detect behavioural states</atitle><jtitle>Ecology letters</jtitle><addtitle>Ecol Lett</addtitle><date>2014-10</date><risdate>2014</risdate><volume>17</volume><issue>10</issue><spage>1228</spage><epage>1237</epage><pages>1228-1237</pages><issn>1461-023X</issn><eissn>1461-0248</eissn><abstract>Animal movement paths show variation in space caused by qualitative shifts in behaviours. 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| identifier | ISSN: 1461-023X |
| ispartof | Ecology letters, 2014-10, Vol.17 (10), p.1228-1237 |
| issn | 1461-023X 1461-0248 |
| language | eng |
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| source | MEDLINE; Access via Wiley Online Library |
| subjects | Algorithms Animal behavior Animal migration Animal movement Animals Behavior, Animal behavioural states Biogeography breakpoints case studies Computer Simulation correlated random walk Models, Biological Motor Activity segments spatial scale turning angles |
| title | Combining animal movements and behavioural data to detect behavioural states |
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