Influence of number of individuals and observations per individual on a model of community structure

Co-roosting relationships are consistent across years in a bat maternity group

Long-lived, group living animals have the potential to form multiyear relationships. In some temperate bat species, maternity groups break apart and rejoin both daily, as females depart to forage and select day roosts to use, and annually, as bats leave for and return from hibernation. Here, we investigated whether bats have persistent social preferences by testing whether relationships between dyads in a focal year could be predicted by previous years. We also hypothesized that experience influences social preferences and predicted that an individual's age would influence its network position, while familiarity with bats of the same cohort would drive persistent social preferences. We quantified roost co-occurrence in little brown myotis (Myotis lucifugus) in Salmonier Nature Park, Newfoundland, Canada both within and among years. We found that roost co-occurrence patterns of previous years still had predictive value even when accounting for potential roost fidelity. However, we found no evidence that cohort familiarity or age explained any of the variation. Overall, we found long-term patterns of association in this temperate bat species that suggest levels of social complexity akin to other large mammal species.

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

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.