Community Detection from a Species Interaction Network

Supervisor: Lise Stork (

“Biodiversity researchers have focused on diversity at the cost of ignoring the networks of interactions between organisms that characterize ecosystems.” - Kevin McCann, 2007


A lot of research in the biodiversity domain investigates the variety among different organisms that inhabit our planet, and how these are geologically distributed across our planet. However, mapping out species variations does not tell us all there is to know about ecology and life on our planet. Biologists have started gathering species-interaction data: e.g., which species eat one another, which species host which other species, who preys on who. By analysing these relationships, one can better understand and thereby better preserve our planet’s ecosystems.


The goal of this project is to extract interesting communities in species interaction networks using a clustering/community detection algorithm such as OSLOM:, and or detect interesting logic rules that relate to these communities. A (made-up) example: community: an organism that belongs to the suborder Pronocephalata, AND is from the Nicaraguan part of the Caribbean Sea, AND is a parasite of the species Pomacanthus arcuatus. Rule: a species from community 1 ​​is also likely a parasite of a species from the family of Cheloniidae.


We will collaborate with experts from the Naturalis Biodiversity Center.