Abstract
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.
Highlights
Social network analysis permits an in-depth understanding of population-level social structure and has been widely applied to study the organization of animal groups [1,2,3]
When looking at other global network metrics including graph density, the number of connections compared to the number of possible connections, and clustering coefficient, a metric based on the connections among triads, discrepancies from the observed network decreased most notably when including more observations per individual, while including more individuals reduced the variance in estimates
Our results suggest that when selecting a social network metric to determine the presence of communities for populations that are under-sampled, Q may be a robust choice in the face of missing information, while measures such as clustering coefficient showed little accuracy with limited numbers of observations, and great uncertainty with limited numbers of individuals
Summary
Social network analysis permits an in-depth understanding of population-level social structure and has been widely applied to study the organization of animal groups [1,2,3]. Social groups may vary in size, composition, and structure, and large groups may show hierarchical organization, with higher order groups comprised of subgroups [4,5]. These subgroups are not always static in structure, demonstrating fission-fusion dynamics, as they merge and split through space and time, and consistencies in subgroup composition may result in the delineation of social communities [6,7,8]. Community structure can still be delineated, when sufficient data exists, based on repeated or consistent subgroup composition and associations among individuals. Testing hypotheses about social structure in groups with fission-fusion dynamics requires sufficient data on interactions among individuals to reliably assign community membership [3]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
