Data

Optimising sample sizes for animal distribution analysis using tracking data

Australian Ocean Data Network
Australian Institute of Marine Science (AIMS)
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://apps.aims.gov.au/metadata/view/d986f5ef-9d99-4b0e-b717-9caa68f3a6b8&rft.title=Optimising sample sizes for animal distribution analysis using tracking data&rft.identifier=https://apps.aims.gov.au/metadata/view/d986f5ef-9d99-4b0e-b717-9caa68f3a6b8&rft.publisher=Australian Institute of Marine Science (AIMS)&rft.description=Knowledge of the spatial distribution of populations is fundamental to management plans for any species. When tracking data are used to describe distributions, it is sometimes assumed that the reported locations of individuals delineate the spatial extent of areas used by the target population. Here we examine existing approaches to validate this assumption, highlight caveats, and propose a new method for a more informative assessment of the number of tracked animals (i.e. sample size) necessary to identify distribution patterns. We show how this assessment can be achieved by considering the heterogeneous use of habitats by a target species using the probabilistic property of a utilisation distribution. Our methods are compiled in the r package SDLfilter. We illustrate and compare the protocols underlying existing and new methods using conceptual models and demonstrate an application of our approach using a large satellite tracking dataset of flatback turtles Natator depressus tagged with accurate Fastloc-GPS tags (n = 69). Our approach has applicability for the post hoc validation of sample sizes required for the robust estimation of distribution patterns across a wide range of taxa, populations and life-history stages of animals.Maintenance and Update Frequency: asNeeded&rft.creator=Australian Institute of Marine Science (AIMS) &rft.date=2024&rft.coverage=westlimit=141.87744140625003; southlimit=-24.86650252692691; eastlimit=153.69873046875003; northlimit=-10.206813072484595&rft.coverage=westlimit=141.87744140625003; southlimit=-24.86650252692691; eastlimit=153.69873046875003; northlimit=-10.206813072484595&rft_rights= http://creativecommons.org/licenses/by-nc/3.0/au/&rft_rights=http://i.creativecommons.org/l/by-nc/3.0/au/88x31.png&rft_rights=WWW:LINK-1.0-http--related&rft_rights=License Graphic&rft_rights=Creative Commons Attribution-NonCommercial 3.0 Australia License&rft_rights=http://creativecommons.org/international/au/&rft_rights=WWW:LINK-1.0-http--related&rft_rights=WWW:LINK-1.0-http--related&rft_rights=License Text&rft_rights=Use Limitation: All AIMS data, products and services are provided as is and AIMS does not warrant their fitness for a particular purpose or non-infringement. While AIMS has made every reasonable effort to ensure high quality of the data, products and services, to the extent permitted by law the data, products and services are provided without any warranties of any kind, either expressed or implied, including without limitation any implied warranties of title, merchantability, and fitness for a particular purpose or non-infringement. AIMS make no representation or warranty that the data, products and services are accurate, complete, reliable or current. To the extent permitted by law, AIMS exclude all liability to any person arising directly or indirectly from the use of the data, products and services.&rft_rights=Attribution: Format for citation of metadata sourced from Australian Institute of Marine Science (AIMS) in a list of reference is as follows: Australian Institute of Marine Science (AIMS). (2022). Optimising sample sizes for animal distribution analysis using tracking data. https://apps.aims.gov.au/metadata/view/d986f5ef-9d99-4b0e-b717-9caa68f3a6b8, accessed[date-of-access].&rft_rights=Creative Commons Attribution-NonCommercial 3.0 Australia License http://creativecommons.org/licenses/by-nc/3.0/au&rft_subject=oceans&rft.type=dataset&rft.language=English Access the data
Cite Saved to MyRDA Save to MyRDA

Licence & Rights:

Non-Commercial Licence view details
CC-BY-NC

http://creativecommons.org/licenses/by-nc/3.0/au/

Creative Commons Attribution-NonCommercial 3.0 Australia License
http://creativecommons.org/licenses/by-nc/3.0/au

http://i.creativecommons.org/l/by-nc/3.0/au/88x31.png

WWW:LINK-1.0-http--related

License Graphic

Creative Commons Attribution-NonCommercial 3.0 Australia License

http://creativecommons.org/international/au/

WWW:LINK-1.0-http--related

WWW:LINK-1.0-http--related

License Text

Use Limitation: All AIMS data, products and services are provided "as is" and AIMS does not warrant their fitness for a particular purpose or non-infringement. While AIMS has made every reasonable effort to ensure high quality of the data, products and services, to the extent permitted by law the data, products and services are provided without any warranties of any kind, either expressed or implied, including without limitation any implied warranties of title, merchantability, and fitness for a particular purpose or non-infringement. AIMS make no representation or warranty that the data, products and services are accurate, complete, reliable or current. To the extent permitted by law, AIMS exclude all liability to any person arising directly or indirectly from the use of the data, products and services.

Attribution: Format for citation of metadata sourced from Australian Institute of Marine Science (AIMS) in a list of reference is as follows: "Australian Institute of Marine Science (AIMS). (2022). Optimising sample sizes for animal distribution analysis using tracking data. https://apps.aims.gov.au/metadata/view/d986f5ef-9d99-4b0e-b717-9caa68f3a6b8, accessed[date-of-access]".

Access:

Open

Brief description

Knowledge of the spatial distribution of populations is fundamental to management plans for any species. When tracking data are used to describe distributions, it is sometimes assumed that the reported locations of individuals delineate the spatial extent of areas used by the target population. Here we examine existing approaches to validate this assumption, highlight caveats, and propose a new method for a more informative assessment of the number of tracked animals (i.e. sample size) necessary to identify distribution patterns. We show how this assessment can be achieved by considering the heterogeneous use of habitats by a target species using the probabilistic property of a utilisation distribution. Our methods are compiled in the r package SDLfilter. We illustrate and compare the protocols underlying existing and new methods using conceptual models and demonstrate an application of our approach using a large satellite tracking dataset of flatback turtles Natator depressus tagged with accurate Fastloc-GPS tags (n = 69). Our approach has applicability for the post hoc validation of sample sizes required for the robust estimation of distribution patterns across a wide range of taxa, populations and life-history stages of animals.

Lineage

Maintenance and Update Frequency: asNeeded

Notes

Credit
Australia Pacific LNG
Credit
Gladstone Port Corporation, QLD
Credit
Shell's QGC Business
Credit
Santos GLNG
Credit
Coupled Animal and Artificial Sensing for Sustainable Ecosystems (CAASE), James Cook University
Credit
Queensland Department of Environment and Science

Modified: 10 08 2024

This dataset is part of a larger collection

Click to explore relationships graph
Help

Related People

153.69873,-10.20681 153.69873,-24.8665 141.87744,-24.8665 141.87744,-10.20681 153.69873,-10.20681

147.7880859375,-17.536657799706

text: westlimit=141.87744140625003; southlimit=-24.86650252692691; eastlimit=153.69873046875003; northlimit=-10.206813072484595

Subjects
oceans |

User Contributed Tags    

Login to tag this record with meaningful keywords to make it easier to discover

Other Information
Shimada, T., Thums, M., Hamann, M., Limpus, C. J., Hays, G. C., FitzSimmons, N. N., Wildermann, N. E., Duarte, C. M., & Meekan, M. G. (2021). Optimising sample sizes for animal distribution analysis using tracking data. Methods in Ecology and Evolution, 12(2), 288–297. https://doi.org/10.1111/2041-210X.13506

doi : https://doi.org/10.1111/2041-210X.13506

Methods in Ecology and Evolution - Publication Data

uri : https://repository.kaust.edu.sa/handle/10754/667709

Shimada, Takahiro et al. (2020), Data from: Optimising sample sizes for animal distribution analysis using tracking data, Dryad, Dataset, https://doi.org/10.5061/dryad.x69p8czgh

doi : https://doi.org/10.5061/dryad.x69p8czgh

Identifiers
  • global : d986f5ef-9d99-4b0e-b717-9caa68f3a6b8