Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation
1. Animal tracking data are indispensable for understanding the ecology, behaviour and physiology of mobile or cryptic species. Meaningful signals in these data can be obscured by noise due to imperfect measurement technologies, requiring rigorous quality control as part of any comprehensive analysi...
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ftzenodo:oai:zenodo.org:7478650 2024-09-15T18:04:44+00:00 Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation Jonsen, Ian Grecian, James Phillips, Lachlan Carroll, Gemma McMahon, Clive Harcourt, Robert Hindell, Mark Patterson, Toby 2022-12-23 https://doi.org/10.5061/dryad.qz612jmjw unknown Zenodo https://doi.org/10.5281/zenodo.7425388 https://doi.org/10.1111/2041-210x.14060 https://zenodo.org/communities/dryad https://doi.org/10.5061/dryad.qz612jmjw oai:zenodo.org:7478650 info:eu-repo/semantics/openAccess Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode animal movement biologging bio-telemetry movement behaviour move persistence Random walk simulation state-space model ecological modeling Ecology Evolution Behavior and Systematics info:eu-repo/semantics/other 2022 ftzenodo https://doi.org/10.5061/dryad.qz612jmjw10.5281/zenodo.742538810.1111/2041-210x.14060 2024-07-26T07:51:56Z 1. Animal tracking data are indispensable for understanding the ecology, behaviour and physiology of mobile or cryptic species. Meaningful signals in these data can be obscured by noise due to imperfect measurement technologies, requiring rigorous quality control as part of any comprehensive analysis. 2. State-space models are powerful tools that separate signal from noise. These tools are ideal for quality control of error-prone location data and for inferring where animals are and what they are doing when they record or transmit other information. However, these statistical models can be challenging and time-consuming to fit to diverse animal tracking data sets. 3. The R package aniMotum eases the tasks of conducting quality control on and inference of changes in movement from animal tracking data. This is achieved via: 1) a simple but extensible workflow that accommodates both novice and experienced users; 2) automated processes that alleviate complexity from data processing and model specification/fitting steps; 3) simple movement models coupled with a powerful numerical optimization approach for rapid and reliable model fitting. 4. We highlight aniMotum 's capabilities through three applications to real animal tracking data. Full R code for these and additional applications are included as Supporting Information so users can gain a deeper understanding of how to use aniMotum for their own analyses. This datafile contains the data used by Jonsen et al. (in Applications 3.2 & 3.3) to highlight the capabilities of the aniMotum R package for analysis of animal tracking data. The lipe_gps_ex32.csv file contains GPS-derived locations for 4 little penguins, recorded by AxyTrek data loggers. The lipe_pc_ex32.csv file contains processed prey capture events inferred from 3-D accelerometry data, recorded by AxyTrek data loggers. The sese_extra.csv file contains Argos-derived locations for 4 southern elephant seals (Jonsen et al. Appendix 3), transmitted by SMRU CTD satellite relay data loggers. The hase_ex33.csv ... Other/Unknown Material Elephant Seals Southern Elephant Seals Zenodo |
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collection |
Zenodo |
op_collection_id |
ftzenodo |
language |
unknown |
topic |
animal movement biologging bio-telemetry movement behaviour move persistence Random walk simulation state-space model ecological modeling Ecology Evolution Behavior and Systematics |
spellingShingle |
animal movement biologging bio-telemetry movement behaviour move persistence Random walk simulation state-space model ecological modeling Ecology Evolution Behavior and Systematics Jonsen, Ian Grecian, James Phillips, Lachlan Carroll, Gemma McMahon, Clive Harcourt, Robert Hindell, Mark Patterson, Toby Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation |
topic_facet |
animal movement biologging bio-telemetry movement behaviour move persistence Random walk simulation state-space model ecological modeling Ecology Evolution Behavior and Systematics |
description |
1. Animal tracking data are indispensable for understanding the ecology, behaviour and physiology of mobile or cryptic species. Meaningful signals in these data can be obscured by noise due to imperfect measurement technologies, requiring rigorous quality control as part of any comprehensive analysis. 2. State-space models are powerful tools that separate signal from noise. These tools are ideal for quality control of error-prone location data and for inferring where animals are and what they are doing when they record or transmit other information. However, these statistical models can be challenging and time-consuming to fit to diverse animal tracking data sets. 3. The R package aniMotum eases the tasks of conducting quality control on and inference of changes in movement from animal tracking data. This is achieved via: 1) a simple but extensible workflow that accommodates both novice and experienced users; 2) automated processes that alleviate complexity from data processing and model specification/fitting steps; 3) simple movement models coupled with a powerful numerical optimization approach for rapid and reliable model fitting. 4. We highlight aniMotum 's capabilities through three applications to real animal tracking data. Full R code for these and additional applications are included as Supporting Information so users can gain a deeper understanding of how to use aniMotum for their own analyses. This datafile contains the data used by Jonsen et al. (in Applications 3.2 & 3.3) to highlight the capabilities of the aniMotum R package for analysis of animal tracking data. The lipe_gps_ex32.csv file contains GPS-derived locations for 4 little penguins, recorded by AxyTrek data loggers. The lipe_pc_ex32.csv file contains processed prey capture events inferred from 3-D accelerometry data, recorded by AxyTrek data loggers. The sese_extra.csv file contains Argos-derived locations for 4 southern elephant seals (Jonsen et al. Appendix 3), transmitted by SMRU CTD satellite relay data loggers. The hase_ex33.csv ... |
format |
Other/Unknown Material |
author |
Jonsen, Ian Grecian, James Phillips, Lachlan Carroll, Gemma McMahon, Clive Harcourt, Robert Hindell, Mark Patterson, Toby |
author_facet |
Jonsen, Ian Grecian, James Phillips, Lachlan Carroll, Gemma McMahon, Clive Harcourt, Robert Hindell, Mark Patterson, Toby |
author_sort |
Jonsen, Ian |
title |
Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation |
title_short |
Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation |
title_full |
Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation |
title_fullStr |
Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation |
title_full_unstemmed |
Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation |
title_sort |
data from: animotum, an r package for animal movement data: rapid quality control, behavioural estimation and simulation |
publisher |
Zenodo |
publishDate |
2022 |
url |
https://doi.org/10.5061/dryad.qz612jmjw |
genre |
Elephant Seals Southern Elephant Seals |
genre_facet |
Elephant Seals Southern Elephant Seals |
op_relation |
https://doi.org/10.5281/zenodo.7425388 https://doi.org/10.1111/2041-210x.14060 https://zenodo.org/communities/dryad https://doi.org/10.5061/dryad.qz612jmjw oai:zenodo.org:7478650 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode |
op_doi |
https://doi.org/10.5061/dryad.qz612jmjw10.5281/zenodo.742538810.1111/2041-210x.14060 |
_version_ |
1810442354035785728 |