A guide to null models for animal social network analysis

By Damien Farine. Methods in Ecology and Evolution, 2017

347 citations recorded by the source corpus.

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. However, permutations of pre‐network data remain relatively uncommon in animal social network analysis because they are challenging to implement for certain data types, particularly those from focal follows and GPS tracking. I explain simple routines that can easily be implemented across different types of data, and supply R code that applies each type of null model to the same simulated dataset. The R code can easily be modified to test hypotheses with empirical data. Widespread use of pre‐network data permutation methods will benefit researchers by facilitating robust hypothesis testing.

Subfields: Genetics, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3c1179c10bcf95a27680ae3671c2e19f203ff1df

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. A. Maldonado‐Chaparro
2. Peng He
3. E. Vander Wal
4. Q. Webber
5. Damien Farine

In Sociology and Political Science

1. E. Vander Wal
2. Q. Webber
3. A. Maldonado‐Chaparro
4. Peng He
5. Damien Farine

In Statistical and Nonlinear Physics

1. A. Maldonado‐Chaparro
2. Peng He
3. E. Vander Wal
4. Q. Webber
5. Damien Farine

Citations in the corpus

• The role of habitat configuration in shaping social structure: a gap in studies of animal social complexity (2019, methodology): “…to quantify complexity in animal populations (see Weiss et al. 2019, topical collection on Social complexity), which can easily be compared to spatially explicit null models (see Farine 2017) to quantify the effects of habitat configuration on animal social complexity (see also Aplin et al. 2015b).”
• Trends and perspectives on the use of animal social network analysis in behavioural ecology: a bibliometric approach (2019, background): “Methods for conducting randomizations for various types of data have also improved (see Farine, 2017), but it is clear greater transparency about the type and extent of randomizations used is required.”