Social Network Analysis and Nutritional Behavior: An Integrated Modeling Approach.
Identifieur interne : 002417 ( PubMed/Curation ); précédent : 002416; suivant : 002418Social Network Analysis and Nutritional Behavior: An Integrated Modeling Approach.
Auteurs : Alistair M. Senior [Australie] ; Mathieu Lihoreau [France] ; Jerome Buhl [Australie] ; David Raubenheimer [Australie] ; Stephen J. Simpson [Australie]Source :
- Frontiers in psychology [ 1664-1078 ] ; 2016.
Abstract
Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments.
DOI: 10.3389/fpsyg.2016.00018
PubMed: 26858671
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Simpson</name><affiliation wicri:level="1"><nlm:affiliation>Charles Perkins Centre, The University of SydneySydney, NSW, Australia; School of Life and Environmental Sciences, The University of SydneySydney, NSW, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Charles Perkins Centre, The University of SydneySydney, NSW, Australia; School of Life and Environmental Sciences, The University of SydneySydney, NSW</wicri:regionArea></affiliation></author></analytic><series><title level="j">Frontiers in psychology</title><idno type="ISSN">1664-1078</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">26858671</PMID><DateCreated><Year>2016</Year><Month>02</Month><Day>09</Day></DateCreated><DateCompleted><Year>2016</Year><Month>02</Month><Day>09</Day></DateCompleted><DateRevised><Year>2017</Year><Month>02</Month><Day>20</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Print">1664-1078</ISSN><JournalIssue CitedMedium="Print"><Volume>7</Volume><PubDate><Year>2016</Year></PubDate></JournalIssue><Title>Frontiers in psychology</Title><ISOAbbreviation>Front Psychol</ISOAbbreviation></Journal><ArticleTitle>Social Network Analysis and Nutritional Behavior: An Integrated Modeling Approach.</ArticleTitle><Pagination><MedlinePgn>18</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.3389/fpsyg.2016.00018</ELocationID><Abstract><AbstractText>Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Senior</LastName><ForeName>Alistair M</ForeName><Initials>AM</Initials><AffiliationInfo><Affiliation>Charles Perkins Centre, The University of SydneySydney, NSW, Australia; School of Mathematics and Statistics, The University of SydneySydney, NSW, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lihoreau</LastName><ForeName>Mathieu</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Centre National de la Recherche Scientifique, Centre de Recherches sur la Cognition AnimaleToulouse, France; Centre de Recherches sur la Cognition Animale, Université Paul SabatierToulouse, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Buhl</LastName><ForeName>Jerome</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>School of Agriculture, Food and Wine, The University of Adelaide Adelaide, SA, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Raubenheimer</LastName><ForeName>David</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Charles Perkins Centre, The University of SydneySydney, NSW, Australia; Faculty of Veterinary Science, The University of SydneySydney, NSW, Australia; School of Life and Environmental Sciences, The University of SydneySydney, NSW, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Simpson</LastName><ForeName>Stephen J</ForeName><Initials>SJ</Initials><AffiliationInfo><Affiliation>Charles Perkins Centre, The University of SydneySydney, NSW, Australia; School of Life and Environmental Sciences, The University of SydneySydney, NSW, 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