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

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 into 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.

aniSNA : An R package to assess bias and uncertainty in social networks obtained from animals sampled via direct observations or satellite telemetry

“…In R (R Core Team 2022), packages have played a crucial role in the analysis of animal social networks by providing a comprehensive set of tools, functions, and methodologies tailored to network analysis. Apart from the general packages available to analyse social networks such as igraph (Csardi and Nepusz 2006), sna (Butts 2020), network (Butts 2015(Butts , 2008, statnet (Handcock, Hunter, Butts, Goodreau, Krivitsky, and Morris 2018;Hunter, Handcock, Butts, Goodreau, and Morris 2008), specialised software packages developed to analyse animal networks have surfaced (Farine 2013;Bonnell 2023;Silk, McDonald, Delahay, Padfield, and Hodgson 2020;Sosa, Puga-Gonzalez, Hu, Pansanel, Xie, and Sueur 2020;Ross, McElreath, and Redhead 2023;Silk and Gimenez 2023). Ecologists now have a very solid platform to undertake many types of studies and develop networks from data obtained in various ways owing to this softwares.…”