Data from: A comprehensive analysis of autocorrelation and bias in home range estimation
Dryad version number: 1 Version status: submitted Dryad curation status: Published Sharing link: https://datadryad.org/stash/share/wFYV98M7IdYpyA4nBl6nKWqB4kAj5c79puIOUUDkbMY Storage size: 24175161 Visibility: public Usage notes Empirical GPS tracking data Anonymised, empirical tracking data used to...
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The University of British Columbia
2018
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Online Access: | https://doi.org/10.14288/1.0397835 https://doi.org/10.5061/dryad.v5051j2 https://doi.org/10.5683/SP2/OAJTAO |
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fttriple:oai:gotriple.eu:50|dedup_wf_001::59920b9f324ed133c965e0c25951a743 |
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openpolar |
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Open Polar |
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Unknown |
op_collection_id |
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unknown |
topic |
Aepyceros melampus Anthropocene Eulemur rufifrons Madoqua guentheri Beatragus hunteri Bycanistes bucinator GPS Ursus arctos Gyps coprotheres Glyptemys insculpta Cerdocyon thous Ovis canadensis Sus scrofa Propithecus verreauxi Life sciences medicine and health care Other envir stat |
spellingShingle |
Aepyceros melampus Anthropocene Eulemur rufifrons Madoqua guentheri Beatragus hunteri Bycanistes bucinator GPS Ursus arctos Gyps coprotheres Glyptemys insculpta Cerdocyon thous Ovis canadensis Sus scrofa Propithecus verreauxi Life sciences medicine and health care Other envir stat Noonan, Michael J. Tucker, Marlee A. Fleming, Christen H. Akre, Tom S. Alberts, Susan C. Ali, Abdullahi H. Altmann, Jeanne Antunes, Pamela C. Belant, Jerrold L. Beyer, Dean Blaum, Niels Böhning-Gaese, Katrin Cullen Jr., Laury De Paula Cunha, Rogerio Dekker, Jasja Drescher-Lehman, Jonathan Farwig, Nina Fichtel, Claudia Fischer, Christina Ford, Adam T. Goheen, Jacob R. Janssen, René Jeltsch, Florian Kauffman, Matthew Kappeler, Peter M. Koch, Flávia LaPoint, Scott Markham, A. Catherine Medici, Emilia Patricia Morato, Ronaldo G. Nathan, Ran Oliveira-Santos, Luiz Gustavo R. Olson, Kirk A. Patterson, Bruce D. Paviolo, Agustin Ramalho, Emiliano E. Rosner, Sascha Schabo, Dana G. Selva, Nuria Sergiel, Agnieszka Da Silva, Marina X. Spiegel, Orr Thompson, Peter Ullmann, Wiebke Zięba, Filip Zwijacz-Kozica, Tomasz Fagan, William F. Mueller, Thomas Calabrese, Justin M. Data from: A comprehensive analysis of autocorrelation and bias in home range estimation |
topic_facet |
Aepyceros melampus Anthropocene Eulemur rufifrons Madoqua guentheri Beatragus hunteri Bycanistes bucinator GPS Ursus arctos Gyps coprotheres Glyptemys insculpta Cerdocyon thous Ovis canadensis Sus scrofa Propithecus verreauxi Life sciences medicine and health care Other envir stat |
description |
Dryad version number: 1 Version status: submitted Dryad curation status: Published Sharing link: https://datadryad.org/stash/share/wFYV98M7IdYpyA4nBl6nKWqB4kAj5c79puIOUUDkbMY Storage size: 24175161 Visibility: public Usage notes Empirical GPS tracking data Anonymised, empirical tracking data used to estimate home range areas based on various home range estimators. Anonymised_Data.zip Abstract Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive dataset of GPS locations from 369 individuals representing 27 species distributed across 5 continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function (AKDE), Silverman's rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ($\hat{N}_\mathrm{area}$) to quantify the information content of each dataset. We found that AKDE 95\% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the holdout sets by AKDE 95\% (or 50\%) estimates was 95.3\% (or ... |
author2 |
Federated Research Data Repository Dépôt fédéré de données de recherche |
format |
Dataset |
author |
Noonan, Michael J. Tucker, Marlee A. Fleming, Christen H. Akre, Tom S. Alberts, Susan C. Ali, Abdullahi H. Altmann, Jeanne Antunes, Pamela C. Belant, Jerrold L. Beyer, Dean Blaum, Niels Böhning-Gaese, Katrin Cullen Jr., Laury De Paula Cunha, Rogerio Dekker, Jasja Drescher-Lehman, Jonathan Farwig, Nina Fichtel, Claudia Fischer, Christina Ford, Adam T. Goheen, Jacob R. Janssen, René Jeltsch, Florian Kauffman, Matthew Kappeler, Peter M. Koch, Flávia LaPoint, Scott Markham, A. Catherine Medici, Emilia Patricia Morato, Ronaldo G. Nathan, Ran Oliveira-Santos, Luiz Gustavo R. Olson, Kirk A. Patterson, Bruce D. Paviolo, Agustin Ramalho, Emiliano E. Rosner, Sascha Schabo, Dana G. Selva, Nuria Sergiel, Agnieszka Da Silva, Marina X. Spiegel, Orr Thompson, Peter Ullmann, Wiebke Zięba, Filip Zwijacz-Kozica, Tomasz Fagan, William F. Mueller, Thomas Calabrese, Justin M. |
author_facet |
Noonan, Michael J. Tucker, Marlee A. Fleming, Christen H. Akre, Tom S. Alberts, Susan C. Ali, Abdullahi H. Altmann, Jeanne Antunes, Pamela C. Belant, Jerrold L. Beyer, Dean Blaum, Niels Böhning-Gaese, Katrin Cullen Jr., Laury De Paula Cunha, Rogerio Dekker, Jasja Drescher-Lehman, Jonathan Farwig, Nina Fichtel, Claudia Fischer, Christina Ford, Adam T. Goheen, Jacob R. Janssen, René Jeltsch, Florian Kauffman, Matthew Kappeler, Peter M. Koch, Flávia LaPoint, Scott Markham, A. Catherine Medici, Emilia Patricia Morato, Ronaldo G. Nathan, Ran Oliveira-Santos, Luiz Gustavo R. Olson, Kirk A. Patterson, Bruce D. Paviolo, Agustin Ramalho, Emiliano E. Rosner, Sascha Schabo, Dana G. Selva, Nuria Sergiel, Agnieszka Da Silva, Marina X. Spiegel, Orr Thompson, Peter Ullmann, Wiebke Zięba, Filip Zwijacz-Kozica, Tomasz Fagan, William F. Mueller, Thomas Calabrese, Justin M. |
author_sort |
Noonan, Michael J. |
title |
Data from: A comprehensive analysis of autocorrelation and bias in home range estimation |
title_short |
Data from: A comprehensive analysis of autocorrelation and bias in home range estimation |
title_full |
Data from: A comprehensive analysis of autocorrelation and bias in home range estimation |
title_fullStr |
Data from: A comprehensive analysis of autocorrelation and bias in home range estimation |
title_full_unstemmed |
Data from: A comprehensive analysis of autocorrelation and bias in home range estimation |
title_sort |
data from: a comprehensive analysis of autocorrelation and bias in home range estimation |
publisher |
The University of British Columbia |
publishDate |
2018 |
url |
https://doi.org/10.14288/1.0397835 https://doi.org/10.5061/dryad.v5051j2 https://doi.org/10.5683/SP2/OAJTAO |
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ENVELOPE(-64.259,-64.259,-65.247,-65.247) |
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Ursus arctos |
genre_facet |
Ursus arctos |
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10.14288/1.0397835 10.5061/dryad.v5051j2 oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:111707 oai:easy.dans.knaw.nl:easy-dataset:111707 oai:dataverse.scholarsportal.info-dataverse-ubc:151195_150149 10.5683/sp2/oajtao 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 10|openaire____::55045bd2a65019fd8e6741a755395c8c 10|re3data_____::94816e6421eeb072e7742ce6a9decc5f 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 10|openaire____::e783372970a1dc066ce99c673090ff88 10|opendoar____::8b6dd7db9af49e67306feb59a8bdc52c |
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https://dx.doi.org/10.14288/1.0397835 http://dx.doi.org/10.5061/dryad.v5051j2 https://dx.doi.org/10.5061/dryad.v5051j2 http://dx.doi.org/10.5683/SP2/OAJTAO https://dx.doi.org/10.5683/sp2/oajtao |
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https://doi.org/10.14288/1.0397835 https://doi.org/10.5061/dryad.v5051j2 https://doi.org/10.5683/SP2/OAJTAO https://doi.org/10.5683/sp2/oajtao |
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fttriple:oai:gotriple.eu:50|dedup_wf_001::59920b9f324ed133c965e0c25951a743 2023-05-15T18:42:17+02:00 Data from: A comprehensive analysis of autocorrelation and bias in home range estimation Noonan, Michael J. Tucker, Marlee A. Fleming, Christen H. Akre, Tom S. Alberts, Susan C. Ali, Abdullahi H. Altmann, Jeanne Antunes, Pamela C. Belant, Jerrold L. Beyer, Dean Blaum, Niels Böhning-Gaese, Katrin Cullen Jr., Laury De Paula Cunha, Rogerio Dekker, Jasja Drescher-Lehman, Jonathan Farwig, Nina Fichtel, Claudia Fischer, Christina Ford, Adam T. Goheen, Jacob R. Janssen, René Jeltsch, Florian Kauffman, Matthew Kappeler, Peter M. Koch, Flávia LaPoint, Scott Markham, A. Catherine Medici, Emilia Patricia Morato, Ronaldo G. Nathan, Ran Oliveira-Santos, Luiz Gustavo R. Olson, Kirk A. Patterson, Bruce D. Paviolo, Agustin Ramalho, Emiliano E. Rosner, Sascha Schabo, Dana G. Selva, Nuria Sergiel, Agnieszka Da Silva, Marina X. Spiegel, Orr Thompson, Peter Ullmann, Wiebke Zięba, Filip Zwijacz-Kozica, Tomasz Fagan, William F. Mueller, Thomas Calabrese, Justin M. Federated Research Data Repository Dépôt fédéré de données de recherche 2018-01-01 https://doi.org/10.14288/1.0397835 https://doi.org/10.5061/dryad.v5051j2 https://doi.org/10.5683/SP2/OAJTAO undefined unknown The University of British Columbia https://dx.doi.org/10.14288/1.0397835 http://dx.doi.org/10.5061/dryad.v5051j2 https://dx.doi.org/10.5061/dryad.v5051j2 http://dx.doi.org/10.5683/SP2/OAJTAO https://dx.doi.org/10.5683/sp2/oajtao lic_creative-commons 10.14288/1.0397835 10.5061/dryad.v5051j2 oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:111707 oai:easy.dans.knaw.nl:easy-dataset:111707 oai:dataverse.scholarsportal.info-dataverse-ubc:151195_150149 10.5683/sp2/oajtao 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 10|openaire____::55045bd2a65019fd8e6741a755395c8c 10|re3data_____::94816e6421eeb072e7742ce6a9decc5f 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 10|openaire____::e783372970a1dc066ce99c673090ff88 10|opendoar____::8b6dd7db9af49e67306feb59a8bdc52c Aepyceros melampus Anthropocene Eulemur rufifrons Madoqua guentheri Beatragus hunteri Bycanistes bucinator GPS Ursus arctos Gyps coprotheres Glyptemys insculpta Cerdocyon thous Ovis canadensis Sus scrofa Propithecus verreauxi Life sciences medicine and health care Other envir stat Dataset https://vocabularies.coar-repositories.org/resource_types/c_ddb1/ 2018 fttriple https://doi.org/10.14288/1.0397835 https://doi.org/10.5061/dryad.v5051j2 https://doi.org/10.5683/SP2/OAJTAO https://doi.org/10.5683/sp2/oajtao 2023-01-22T16:53:24Z Dryad version number: 1 Version status: submitted Dryad curation status: Published Sharing link: https://datadryad.org/stash/share/wFYV98M7IdYpyA4nBl6nKWqB4kAj5c79puIOUUDkbMY Storage size: 24175161 Visibility: public Usage notes Empirical GPS tracking data Anonymised, empirical tracking data used to estimate home range areas based on various home range estimators. Anonymised_Data.zip Abstract Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive dataset of GPS locations from 369 individuals representing 27 species distributed across 5 continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function (AKDE), Silverman's rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ($\hat{N}_\mathrm{area}$) to quantify the information content of each dataset. We found that AKDE 95\% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the holdout sets by AKDE 95\% (or 50\%) estimates was 95.3\% (or ... Dataset Ursus arctos Unknown Thumb ENVELOPE(-64.259,-64.259,-65.247,-65.247) |