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A guide to pre‐processing high‐throughput animal tracking data

1. Modern, high‐throughput animal tracking increasingly yields ‘big data’ at very fine temporal scales. At these scales, location error can exceed the animal's step size, leading to mis‐estimation of behaviours inferred from movement. ‘Cleaning’ the data to reduce location errors is one of the...

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Autores principales: Gupte, Pratik Rajan, Beardsworth, Christine E., Spiegel, Orr, Lourie, Emmanuel, Toledo, Sivan, Nathan, Ran, Bijleveld, Allert I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299236/
https://www.ncbi.nlm.nih.gov/pubmed/34657296
http://dx.doi.org/10.1111/1365-2656.13610
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author Gupte, Pratik Rajan
Beardsworth, Christine E.
Spiegel, Orr
Lourie, Emmanuel
Toledo, Sivan
Nathan, Ran
Bijleveld, Allert I.
author_facet Gupte, Pratik Rajan
Beardsworth, Christine E.
Spiegel, Orr
Lourie, Emmanuel
Toledo, Sivan
Nathan, Ran
Bijleveld, Allert I.
author_sort Gupte, Pratik Rajan
collection PubMed
description 1. Modern, high‐throughput animal tracking increasingly yields ‘big data’ at very fine temporal scales. At these scales, location error can exceed the animal's step size, leading to mis‐estimation of behaviours inferred from movement. ‘Cleaning’ the data to reduce location errors is one of the main ways to deal with position uncertainty. Although data cleaning is widely recommended, inclusive, uniform guidance on this crucial step, and on how to organise the cleaning of massive datasets, is relatively scarce. 2. A pipeline for cleaning massive high‐throughput datasets must balance ease of use and computationally efficiency, in which location errors are rejected while preserving valid animal movements. Another useful feature of a pre‐processing pipeline is efficiently segmenting and clustering location data for statistical methods while also being scalable to large datasets and robust to imperfect sampling. Manual methods being prohibitively time‐consuming, and to boost reproducibility, pre‐processing pipelines must be automated. 3. We provide guidance on building pipelines for pre‐processing high‐throughput animal tracking data to prepare it for subsequent analyses. We apply our proposed pipeline to simulated movement data with location errors, and also show how large volumes of cleaned data can be transformed into biologically meaningful ‘residence patches’, for exploratory inference on animal space use. We use tracking data from the Wadden Sea ATLAS system (WATLAS) to show how pre‐processing improves its quality, and to verify the usefulness of the residence patch method. Finally, with tracks from Egyptian fruit bats Rousettus aegyptiacus, we demonstrate the pre‐processing pipeline and residence patch method in a fully worked out example. 4. To help with fast implementation of standardised methods, we developed the R package atlastools, which we also introduce here. Our pre‐processing pipeline and atlastools can be used with any high‐throughput animal movement data in which the high data‐volume combined with knowledge of the tracked individuals' movement capacity can be used to reduce location errors. atlastools is easy to use for beginners while providing a template for further development. The common use of simple yet robust pre‐processing steps promotes standardised methods in the field of movement ecology and leads to better inferences from data.
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spelling pubmed-92992362022-07-21 A guide to pre‐processing high‐throughput animal tracking data Gupte, Pratik Rajan Beardsworth, Christine E. Spiegel, Orr Lourie, Emmanuel Toledo, Sivan Nathan, Ran Bijleveld, Allert I. J Anim Ecol Research Methods Guide 1. Modern, high‐throughput animal tracking increasingly yields ‘big data’ at very fine temporal scales. At these scales, location error can exceed the animal's step size, leading to mis‐estimation of behaviours inferred from movement. ‘Cleaning’ the data to reduce location errors is one of the main ways to deal with position uncertainty. Although data cleaning is widely recommended, inclusive, uniform guidance on this crucial step, and on how to organise the cleaning of massive datasets, is relatively scarce. 2. A pipeline for cleaning massive high‐throughput datasets must balance ease of use and computationally efficiency, in which location errors are rejected while preserving valid animal movements. Another useful feature of a pre‐processing pipeline is efficiently segmenting and clustering location data for statistical methods while also being scalable to large datasets and robust to imperfect sampling. Manual methods being prohibitively time‐consuming, and to boost reproducibility, pre‐processing pipelines must be automated. 3. We provide guidance on building pipelines for pre‐processing high‐throughput animal tracking data to prepare it for subsequent analyses. We apply our proposed pipeline to simulated movement data with location errors, and also show how large volumes of cleaned data can be transformed into biologically meaningful ‘residence patches’, for exploratory inference on animal space use. We use tracking data from the Wadden Sea ATLAS system (WATLAS) to show how pre‐processing improves its quality, and to verify the usefulness of the residence patch method. Finally, with tracks from Egyptian fruit bats Rousettus aegyptiacus, we demonstrate the pre‐processing pipeline and residence patch method in a fully worked out example. 4. To help with fast implementation of standardised methods, we developed the R package atlastools, which we also introduce here. Our pre‐processing pipeline and atlastools can be used with any high‐throughput animal movement data in which the high data‐volume combined with knowledge of the tracked individuals' movement capacity can be used to reduce location errors. atlastools is easy to use for beginners while providing a template for further development. The common use of simple yet robust pre‐processing steps promotes standardised methods in the field of movement ecology and leads to better inferences from data. John Wiley and Sons Inc. 2021-11-16 2022-02 /pmc/articles/PMC9299236/ /pubmed/34657296 http://dx.doi.org/10.1111/1365-2656.13610 Text en © 2021 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Research Methods Guide
Gupte, Pratik Rajan
Beardsworth, Christine E.
Spiegel, Orr
Lourie, Emmanuel
Toledo, Sivan
Nathan, Ran
Bijleveld, Allert I.
A guide to pre‐processing high‐throughput animal tracking data
title A guide to pre‐processing high‐throughput animal tracking data
title_full A guide to pre‐processing high‐throughput animal tracking data
title_fullStr A guide to pre‐processing high‐throughput animal tracking data
title_full_unstemmed A guide to pre‐processing high‐throughput animal tracking data
title_short A guide to pre‐processing high‐throughput animal tracking data
title_sort guide to pre‐processing high‐throughput animal tracking data
topic Research Methods Guide
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299236/
https://www.ncbi.nlm.nih.gov/pubmed/34657296
http://dx.doi.org/10.1111/1365-2656.13610
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