Supplementary material from "Using network analysis to study behavioural phenotypes: an example using domestic dogs"
Phenotypic integration describes the complex interrelationships between organismal traits, traditionally focusing on morphology. Recently, research has sought to represent behavioural phenotypes as composed of quasi-independent latent traits. Concurrently, psychologists have opposed latent variable...
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ftdatacite:10.6084/m9.figshare.c.3500385.v1 2023-05-15T15:50:27+02:00 Supplementary material from "Using network analysis to study behavioural phenotypes: an example using domestic dogs" Goold, Conor Vas, Judit Olsen, Christine Newberry, Ruth C. 2016 https://dx.doi.org/10.6084/m9.figshare.c.3500385.v1 https://figshare.com/collections/Supplementary_material_from_Using_network_analysis_to_study_behavioural_phenotypes_an_example_using_domestic_dogs_/3500385/1 unknown Figshare https://dx.doi.org/10.1098/rsos.160268 https://dx.doi.org/10.6084/m9.figshare.c.3500385 CC-BY https://creativecommons.org/licenses/by/4.0 CC-BY 60801 Animal Behaviour FOS Biological sciences Collection article 2016 ftdatacite https://doi.org/10.6084/m9.figshare.c.3500385.v1 https://doi.org/10.1098/rsos.160268 https://doi.org/10.6084/m9.figshare.c.3500385 2021-11-05T12:55:41Z Phenotypic integration describes the complex interrelationships between organismal traits, traditionally focusing on morphology. Recently, research has sought to represent behavioural phenotypes as composed of quasi-independent latent traits. Concurrently, psychologists have opposed latent variable interpretations of human behaviour, proposing instead a network perspective envisaging interrelationships between behaviours as emerging from causal dependencies. Network analysis could also be applied to understand integrated behavioural phenotypes in animals. Here, we assimilate this cross-disciplinary progression of ideas by demonstrating the use of network analysis on survey data collected on behavioural and motivational characteristics of police patrol and detection dogs ( Canis lupus familiaris ). Networks of conditional independence relationships illustrated a number of functional connections between descriptors, which varied between dog types. The most central descriptors denoted desirable characteristics in both patrol and detection dog networks, with ‘Playful’ being widely correlated and possessing mediating relationships between descriptors. Bootstrap analyses revealed the stability of network results. We discuss the results in relation to previous research on dog personality, and benefits of using network analysis to study behavioural phenotypes. We conclude that a network perspective offers widespread opportunities for advancing the understanding of phenotypic integration in animal behaviour. Article in Journal/Newspaper Canis lupus DataCite Metadata Store (German National Library of Science and Technology) |
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60801 Animal Behaviour FOS Biological sciences |
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60801 Animal Behaviour FOS Biological sciences Goold, Conor Vas, Judit Olsen, Christine Newberry, Ruth C. Supplementary material from "Using network analysis to study behavioural phenotypes: an example using domestic dogs" |
topic_facet |
60801 Animal Behaviour FOS Biological sciences |
description |
Phenotypic integration describes the complex interrelationships between organismal traits, traditionally focusing on morphology. Recently, research has sought to represent behavioural phenotypes as composed of quasi-independent latent traits. Concurrently, psychologists have opposed latent variable interpretations of human behaviour, proposing instead a network perspective envisaging interrelationships between behaviours as emerging from causal dependencies. Network analysis could also be applied to understand integrated behavioural phenotypes in animals. Here, we assimilate this cross-disciplinary progression of ideas by demonstrating the use of network analysis on survey data collected on behavioural and motivational characteristics of police patrol and detection dogs ( Canis lupus familiaris ). Networks of conditional independence relationships illustrated a number of functional connections between descriptors, which varied between dog types. The most central descriptors denoted desirable characteristics in both patrol and detection dog networks, with ‘Playful’ being widely correlated and possessing mediating relationships between descriptors. Bootstrap analyses revealed the stability of network results. We discuss the results in relation to previous research on dog personality, and benefits of using network analysis to study behavioural phenotypes. We conclude that a network perspective offers widespread opportunities for advancing the understanding of phenotypic integration in animal behaviour. |
format |
Article in Journal/Newspaper |
author |
Goold, Conor Vas, Judit Olsen, Christine Newberry, Ruth C. |
author_facet |
Goold, Conor Vas, Judit Olsen, Christine Newberry, Ruth C. |
author_sort |
Goold, Conor |
title |
Supplementary material from "Using network analysis to study behavioural phenotypes: an example using domestic dogs" |
title_short |
Supplementary material from "Using network analysis to study behavioural phenotypes: an example using domestic dogs" |
title_full |
Supplementary material from "Using network analysis to study behavioural phenotypes: an example using domestic dogs" |
title_fullStr |
Supplementary material from "Using network analysis to study behavioural phenotypes: an example using domestic dogs" |
title_full_unstemmed |
Supplementary material from "Using network analysis to study behavioural phenotypes: an example using domestic dogs" |
title_sort |
supplementary material from "using network analysis to study behavioural phenotypes: an example using domestic dogs" |
publisher |
Figshare |
publishDate |
2016 |
url |
https://dx.doi.org/10.6084/m9.figshare.c.3500385.v1 https://figshare.com/collections/Supplementary_material_from_Using_network_analysis_to_study_behavioural_phenotypes_an_example_using_domestic_dogs_/3500385/1 |
genre |
Canis lupus |
genre_facet |
Canis lupus |
op_relation |
https://dx.doi.org/10.1098/rsos.160268 https://dx.doi.org/10.6084/m9.figshare.c.3500385 |
op_rights |
CC-BY https://creativecommons.org/licenses/by/4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.c.3500385.v1 https://doi.org/10.1098/rsos.160268 https://doi.org/10.6084/m9.figshare.c.3500385 |
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1766385386587684864 |