A multilayer network approach combines different network layers, which are
connected by interlayer edges, to create a single mathematical object. These
networks can contain a variety of information types and represent different
aspects of a system. However, the process for selecting which information to
include is not always straightforward. Using data on two agonistic behaviors in
a captive population of monk parakeets (Myiopsitta monachus), we developed a
framework for investigating how pooling or splitting behaviors at the scale of
dyadic relationships (between two individuals) affects individual- and
group-level social properties. We designed two reference models to test whether
randomizing the number of interactions across behavior types results in similar
structural patterns as the observed data. Although the behaviors were
correlated, the first reference model suggests that the two behaviors convey
different information about some social properties and should therefore not be
pooled. However, once we controlled for data sparsity, we found that the
observed measures corresponded with those from the second reference model.
Hence, our initial result may have been due to the unequal frequencies of each
behavior. Overall, our findings support pooling the two behaviors. Awareness of
how selected measurements can be affected by data properties is warranted, but
nonetheless our framework disentangles these efforts and as a result can be
used for myriad types of behaviors and questions. This framework will help
researchers make informed and data-driven decisions about which behaviors to
pool or separate, prior to using the data in subsequent multilayer network
analyses.Comment: accepted for Current Zoolog
