An Information-Theoretic Approach to Detect the Associations of GPS-Tracked Heifers in Pasture

Sensor technologies, such as the Global Navigation Satellite System (GNSS), produce huge amounts of data by tracking animal locations with high temporal resolution. Due to this high resolution, all animals show at least some co-occurrences, and the pure presence or absence of co-occurrences is not s...

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Autores principales:	Cornelia Meckbach, Sabrina Elsholz, Caroline Siede, Imke Traulsen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	social networks pointwise mutual information association measure information theory sensor-tracked animals Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/98749ba7c46f41769857f5864e2a0687
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spelling	oai:doaj.org-article:98749ba7c46f41769857f5864e2a06872021-11-25T18:57:40ZAn Information-Theoretic Approach to Detect the Associations of GPS-Tracked Heifers in Pasture10.3390/s212275851424-8220https://doaj.org/article/98749ba7c46f41769857f5864e2a06872021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7585https://doaj.org/toc/1424-8220Sensor technologies, such as the Global Navigation Satellite System (GNSS), produce huge amounts of data by tracking animal locations with high temporal resolution. Due to this high resolution, all animals show at least some co-occurrences, and the pure presence or absence of co-occurrences is not satisfactory for social network construction. Further, tracked animal contacts contain noise due to measurement errors or random co-occurrences. To identify significant associations, null models are commonly used, but the determination of an appropriate null model for GNSS data by maintaining the autocorrelation of tracks is challenging, and the construction is time and memory consuming. Bioinformaticians encounter phylogenetic background and random noise on sequencing data. They estimate this noise directly on the data by using the average product correction procedure, a method applied to information-theoretic measures. Using Global Positioning System (GPS) data of heifers in a pasture, we performed a proof of concept that this approach can be transferred to animal science for social network construction. The approach outputs stable results for up to 30% missing data points, and the predicted associations were in line with those of the null models. The effect of different distance thresholds for contact definition was marginal, but animal activity strongly affected the network structure.Cornelia MeckbachSabrina ElsholzCaroline SiedeImke TraulsenMDPI AGarticlesocial networkspointwise mutual informationassociation measureinformation theorysensor-tracked animalsChemical technologyTP1-1185ENSensors, Vol 21, Iss 7585, p 7585 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	social networks pointwise mutual information association measure information theory sensor-tracked animals Chemical technology TP1-1185
spellingShingle	social networks pointwise mutual information association measure information theory sensor-tracked animals Chemical technology TP1-1185 Cornelia Meckbach Sabrina Elsholz Caroline Siede Imke Traulsen An Information-Theoretic Approach to Detect the Associations of GPS-Tracked Heifers in Pasture
description	Sensor technologies, such as the Global Navigation Satellite System (GNSS), produce huge amounts of data by tracking animal locations with high temporal resolution. Due to this high resolution, all animals show at least some co-occurrences, and the pure presence or absence of co-occurrences is not satisfactory for social network construction. Further, tracked animal contacts contain noise due to measurement errors or random co-occurrences. To identify significant associations, null models are commonly used, but the determination of an appropriate null model for GNSS data by maintaining the autocorrelation of tracks is challenging, and the construction is time and memory consuming. Bioinformaticians encounter phylogenetic background and random noise on sequencing data. They estimate this noise directly on the data by using the average product correction procedure, a method applied to information-theoretic measures. Using Global Positioning System (GPS) data of heifers in a pasture, we performed a proof of concept that this approach can be transferred to animal science for social network construction. The approach outputs stable results for up to 30% missing data points, and the predicted associations were in line with those of the null models. The effect of different distance thresholds for contact definition was marginal, but animal activity strongly affected the network structure.
format	article
author	Cornelia Meckbach Sabrina Elsholz Caroline Siede Imke Traulsen
author_facet	Cornelia Meckbach Sabrina Elsholz Caroline Siede Imke Traulsen
author_sort	Cornelia Meckbach
title	An Information-Theoretic Approach to Detect the Associations of GPS-Tracked Heifers in Pasture
title_short	An Information-Theoretic Approach to Detect the Associations of GPS-Tracked Heifers in Pasture
title_full	An Information-Theoretic Approach to Detect the Associations of GPS-Tracked Heifers in Pasture
title_fullStr	An Information-Theoretic Approach to Detect the Associations of GPS-Tracked Heifers in Pasture
title_full_unstemmed	An Information-Theoretic Approach to Detect the Associations of GPS-Tracked Heifers in Pasture
title_sort	information-theoretic approach to detect the associations of gps-tracked heifers in pasture
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/98749ba7c46f41769857f5864e2a0687
work_keys_str_mv	AT corneliameckbach aninformationtheoreticapproachtodetecttheassociationsofgpstrackedheifersinpasture AT sabrinaelsholz aninformationtheoreticapproachtodetecttheassociationsofgpstrackedheifersinpasture AT carolinesiede aninformationtheoreticapproachtodetecttheassociationsofgpstrackedheifersinpasture AT imketraulsen aninformationtheoreticapproachtodetecttheassociationsofgpstrackedheifersinpasture AT corneliameckbach informationtheoreticapproachtodetecttheassociationsofgpstrackedheifersinpasture AT sabrinaelsholz informationtheoreticapproachtodetecttheassociationsofgpstrackedheifersinpasture AT carolinesiede informationtheoreticapproachtodetecttheassociationsofgpstrackedheifersinpasture AT imketraulsen informationtheoreticapproachtodetecttheassociationsofgpstrackedheifersinpasture
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An Information-Theoretic Approach to Detect the Associations of GPS-Tracked Heifers in Pasture

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