Analysis of sparse animal social networks DOI Creative Commons
Helen K. Mylne, Jackie Abell, Colin M. Beale

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Abstract Low-density social networks can be common in animal societies, even among species generally considered to highly social. Social network analysis is commonly used analyse societal structure, but edge weight (strength of association between two individuals) estimation methods designed for dense produce biased measures when applied low-density networks. Frequentist suffer data availability low, because they contain an inherent flat prior that will accept any possible value, and no uncertainty their output. Bayesian alternative priors, so provide more reliable weights include a measure uncertainty, only reduce bias sensible values are selected. Currently, neither accounts zero-inflation, estimates towards stronger associations than the true network, which seen through diagnostic plots quality against output estimate. We address this by adding zero-inflation model, demonstrate process using group-based from population male African savannah elephants. show approach performs better frequentist caused these problems, though requires careful consideration priors. recommend use framework, with conditional allows modelling zero-inflation. This reflects fact derivation two-step process: i) probability ever interacting, ii) frequency interaction those who do. Additional priors could added where biology it, example society strong community such as female elephants kin structure would create additional levels clustering. Although was inspired reducing observed sparse networks, it have value all densities.

Language: Английский

The biology of aging in a social world: Insights from free-ranging rhesus macaques DOI Creative Commons
Laura Newman, Camille Testard,

Alex R. DeCasien

et al.

Neuroscience & Biobehavioral Reviews, Journal Year: 2023, Volume and Issue: 154, P. 105424 - 105424

Published: Oct. 11, 2023

Language: Английский

Citations

14
The biology of aging in a social world: insights from free-ranging rhesus macaques DOI Creative Commons
Laura Newman, Camille Testard,

Alex R. DeCasien

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 29, 2023

Social adversity can increase the age-associated risk of disease and death, yet biological mechanisms that link social adversities to aging remain poorly understood. Long-term naturalistic studies nonhuman animals are crucial for integrating observations behavior throughout an individual's life with detailed anatomical, physiological, molecular measurements. Here, we synthesize body research from one such study system, Cayo Santiago Island, which is home world's longest continuously monitored free-ranging population rhesus macaques. We review recent age-related variation in morphology, gene regulation, microbiome composition, immune function. also discuss ecological modifiers age-markers this population. In particular, summarize how a major natural disaster, Hurricane Maria, affected macaque physiology structure highlight context-dependent domain-specific nature modifiers. Finally, conclude by providing directions future study, on elsewhere, will further our understanding across different domains modifies processes.

Language: Английский

Citations

3
Analysis of sparse animal social networks DOI Creative Commons
Helen K. Mylne, Jackie Abell, Colin M. Beale

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Abstract Low-density social networks can be common in animal societies, even among species generally considered to highly social. Social network analysis is commonly used analyse societal structure, but edge weight (strength of association between two individuals) estimation methods designed for dense produce biased measures when applied low-density networks. Frequentist suffer data availability low, because they contain an inherent flat prior that will accept any possible value, and no uncertainty their output. Bayesian alternative priors, so provide more reliable weights include a measure uncertainty, only reduce bias sensible values are selected. Currently, neither accounts zero-inflation, estimates towards stronger associations than the true network, which seen through diagnostic plots quality against output estimate. We address this by adding zero-inflation model, demonstrate process using group-based from population male African savannah elephants. show approach performs better frequentist caused these problems, though requires careful consideration priors. recommend use framework, with conditional allows modelling zero-inflation. This reflects fact derivation two-step process: i) probability ever interacting, ii) frequency interaction those who do. Additional priors could added where biology it, example society strong community such as female elephants kin structure would create additional levels clustering. Although was inspired reducing observed sparse networks, it have value all densities.

Language: Английский

Citations

0