A guide to null models for animal social network analysis.

ABSTRACT: Null models are an important component of the social network analysis toolbox. However, their use in hypothesis testing is still not widespread. Furthermore, several different approaches for constructing null models exist, each with their relative strengths and weaknesses, and often testing different hypotheses.In this study, I highlight why null models are important for robust hypothesis testing in studies of animal social networks. Using simulated data containing a known observation bias, I test how different statistical tests and null models perform if such a bias was unknown.I show that permutations of the raw observational (or 'pre-network') data consistently account for underlying structure in the generated social network, and thus can reduce both type I and type II error rates. Howe

SUBMITTER: Farine DR 

PROVIDER: S-EPMC5656331 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature