Conception, Idea, and Writing: Patrick Lauer and Urs Kalbitzer. Realization and Programming: Patrick Lauer.
Introduction Animals forage to meet their nutritional needs while avoiding potentially harmful components, such as toxins. Geometric Framework Models, which have gained popularity in recent years, provide a framework that takes into account that there is rarely one single resource that satisfies an animal’s nutritional requirements and animals have to balance the consumption of different food resources with different nutritional compositions (Lambert & Rothman, 2015; Simpson & Raubenheimer, 2011).
Interested in the distribution of animal species on a global or continental level? This can be easily illustrated with R using the data provided by the IUCN Red List. In this post, I will focus on mammals in Africa, the continent where I have conducted most of my field work. But the code below can be easily adapted to other areas of the world and other taxonomic groups of species, or only species at low or high risk of extinction (though the completeness of the IUCN data is variable across different groups).
In the previous post, I simulated clustered networks with varying strength of within-group, between-close-groups, and between-group social relationships. In part 2, I am using some of the methods provided by the igraph package to test how well these simulated clusters can be (re)-detected.
2. Determine clustering using different methods and compare the results 2.1 Prepare R rm(list = ls()) library(tidyverse) library(tidygraph) library(ggraph) library(igraph) In addition to these packages, this notebook requires the installation of the following packages: DT, cowplot, ggrepel.
Social networks often exhibit some kind of clustering (or community structure), such as distinct social groups in animal societies, or kin groups (or families) within social groups. Individuals within such clusters are more likely to interact with each other than individuals from different clusters.
There are many algorithms to detect clusters in social networks, and one might work better than another under some circumstances (see, e.g., Emmons et al. 2016).