Generation and applications of simulated datasets to integrate social network and demographic analyses

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1002/ece3.9871. This is version 3 of this Preprint.

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.

Downloads

Download Preprint

Supplementary Files
Authors

Matthew Silk , Olivier Gimenez

Abstract

Social networks are tied to population dynamics; interactions are driven by population density and demographic structure, while social relationships can be key determinants of survival and reproductive success. However, difficulties integrating models used in demography and network analysis have limited research at this interface. We introduce the R package genNetDem for simulating integrated network-demographic datasets. It can be used to create longitudinal social network and/or capture-recapture datasets with known properties. It incorporates the ability to generate populations and their social networks, generate grouping events using these networks, simulate social network effects on individual survival, and flexibly sample these longitudinal datasets of social associations. By generating co-capture data with known statistical relationships it provides functionality for methodological research. We demonstrate its use with case studies testing how imputation and sampling design influence the success of adding network traits to conventional Cormack-Jolly-Seber (CJS) models. We show that incorporating social network effects in CJS models generates qualitatively accurate results, but with downward-biased parameter estimates when network position influences survival. Biases are greater when fewer interactions are sampled or fewer individuals observed in each interaction. While our results indicate the potential of incorporating social effects within demographic models, they show that imputing missing network measures alone is insufficient to accurately estimate social effects on survival, pointing to the importance of incorporating network imputation approaches. genNetDem provides a flexible tool to aid these methodological advancements and help researchers testing other sampling considerations in social network studies.

DOI

https://doi.org/10.32942/osf.io/h5d89

Subjects

Behavior and Ethology, Ecology and Evolutionary Biology, Life Sciences, Population Biology

Keywords

co-capture data, Demography, hidden Markov model, population dynamics, social networks, stochastic block model, survival

Dates

Published: 2022-07-27 01:17

Last Updated: 2023-01-10 12:30

Older Versions
License

CC-By Attribution-ShareAlike 4.0 International