A multilevel statistical toolkit to study animal social networks: the Animal Network Toolkit Software (ANTs) R package

Abstract

The possible role played by individual attributes, sociodemographic characteristics and/or ecological pressures in the interaction between animals and the development of social relationships between them is of great interest in animal ecology and evolutionary biology. Social network Analysis is an ideal tool to study these types of questions. the Animal network toolkit Software (Ants) R package was specifically developed to provide all the different social network analysis techniques currently used in the study of animal social networks. This global package enables users to (1) compute global, polyadic and nodal network measures; (2) perform data randomisation: data stream and network (node and link) permutations; (3) perform statistical permutation tests for static or temporal network analyses, and (4) visualise networks. ANTs allows researchers to perform multilevel network analyses ranging from individual network measures to interaction patterns and the analysis of the overall network structure, and carry out static or temporal network analyses without switching between different R packages, thus making a substantial contribution to advances in the study of animal behaviour. Ants outperforms existing R packages for the computation speed of network measures and permutations.

Hierarchical networks of food exchange in the black garden antLasius niger

“…The network was directed and weighted. We then calculated different individual and global measures using the ANTs (Sosa et al ., 2018) and igraph (Csardi & Nepusz, 2006) R packages. We avoided measures that were not interpretable with the directionality of the edges (see Sueur et al ., 2011; Mersch, 2016; Sosa et al ., 2020); instead, we selected those that allowed us to make expectations based on our three aforementioned hypotheses.…”

Social interactions change with season and age in Northern Bald Ibis

“…To assess whether associations among pairs of individuals deviated from random variation, we performed prenetwork randomizations using data stream permutations for focal sampling data collection protocols and compared the strength of associations of the original and permuted data sets via t-test with the Animal Network Toolkit Software (ANTs) R package (Sosa et al 2020). Data stream permutations swap a single association in each permutation and this permutation can be controlled according to the focal observation number.…”

Animal social networks: Towards an integrative framework embedding social interactions, space and time

“…Consequently, an important challenge for ASNA studies is the need to consider the robustness of current methodological approaches. In this joint Special Feature, Sosa et al (2020) consider network measures and their variants, and highlight the necessity for future research to state the variant used as this may skew the interpretation of the results. Once a network measure is selected, taking care to avoid multiple hypothesis testing (Webber, Schneider, & Vander Wal, 2020), it is essential to ensure the reliability of statistical tests.…”

“…This is important not only for exploring the mechanisms driving individual heterogeneity in sociality but also for understanding how plasticity manifests at both the individual and the group level (Ilany & Akçay, 2016; Montiglio, McGlothlin, & Farine, 2018). The study of social dynamics, such as how individuals sociality changes in response to demographic changes (Borgeaud et al., 2017), is made possible thanks in part to the application of time‐aggregated network analysis (Hobson, Avery, & Wright, 2013) for which specialised analysis packages exist now (Bonnell & Vilette, 2020; Sosa et al., 2020). Several studies in this joint Special Feature explore these aspects by addressing, for example, how mechanistic factors allow animals to cope with demographic changes (Farine, 2020), how networks are shaped by group phenotypic composition (Dakin et al, 2020) and environmental conditions (Burns et al, 2020), and how inter‐group encounters shape overall network structure (Preston et al, 2020).…”