DOI: 10.5061/dryad.q83bk3jgn
title: Influence of number of individuals and observations per individual on a model of community structure

description: Social network analysis is increasingly applied to understand animal
 groups. However, it is rarely feasible to observe every interaction among
 all individuals in natural populations. Studies have assessed how missing
 information affects estimates of individual network positions, but less
 attention has been paid to metrics that characterize overall network
 structure such as modularity, clustering coefficient, and density. In
 cases such as groups displaying fission-fusion dynamics, where subgroups
 break apart and rejoin in changing conformations, missing information may
 affect estimates of global network structure differently than in groups
 with distinctly separated communities due to the influence single
 individuals can have on the connectivity of the network. Using a bat
 maternity group showing fission-fusion dynamics, we quantify the effect of
 missing data on global network measures including community detection. In
 our system, estimating the number of communities was less reliable than
 detecting community structure. Further, reliably assorting individual bats
 into communities required fewer individuals and fewer observations per
 individual than to estimate the number of communities. Specifically, our
 metrics of global network structure (i.e., graph density, clustering
 coefficient, Rcom) approached the ‘real’ values with increasing numbers of
 observations per individual and, as the number of individuals included
 increased, the variance in these estimates decreased. Similar to previous
 studies, we recommend that more observations per individual should be
 prioritized over including more individuals when resources are limited. We
 recommend caution when making conclusions about animal social networks
 when a substantial number of individuals or observations are missing, and
 when possible, suggest subsampling large datasets to observe how estimates
 are influenced by sampling intensity. Our study serves as an example of
 the reliability, or lack thereof, of global network measures with missing
 information, but further work is needed to determine how estimates will
 vary with different data collection methods, network structures, and
 sampling periods.
description: This dataset contains recordings of little brown
 myotis (<em>Myotis lucifugus</em>) that were implanted with
 passive integrated transponder (PIT) tags at 11 artificial roost boxes in
 Salmonier Nature Park, Newfoundland in 2016. Data were filtered such that
 only individuals with at least 40 observations over the course of the
 summer were included. For individuals with more than 40 observations, 40
 observations were randomly selected to create a balanced network. All
 animal handling protocol was approved by the animal care committee of
 Saint Mary’s University, Halifax, Nova Scotia (AUP #16-12). Wildlife
 scientific research permits were also obtained from the Government of
 Newfoundland and Labrador, Department of Fisheries and Land Resources,
 Forestry and Wildlife Branch (# WLR2016-12).
description: It is important to note that this dataset does not contain all
 observations of PIT tagged bats in 2016 and has been substantially
 subsetted from the available data. Sample code for randomizing these data
 and generating the figures appearing in the manuscript can be found
 at https://github.com/juliasunga/sample_size_community_models