Using graph theory to define data-driven ecoregion and bioregion maps
This is an idea proposed in 2025 as a good starter project, and is available for being worked on. It may be co-supervised with Daniele Baisero and Michael Dales.
Maps of biologically driven regionalization (e.g. ecoregions and bioregions) are useful in conservation science and policy as they help identify areas with similar ecological characteristics, allowing for more targeted, efficient, and ecosystem-specific management strategies. These regions provide a framework for prioritizing conservation efforts, monitoring biodiversity, and aligning policies across political boundaries based on ecological realities rather than arbitrary lines. However these products have historically been "hand drawn" by experts and are mostly based on plant distribution data only.
Graph theory offers numerous tools to analyse and highlight the relation between data points and has been used to study spatially explicit datasets. However, these tools have never been applied to global-scale systematic species distribution datasets. The Key Biodiversity Areas (KBA) Secretariat has compiled such a comprehensive dataset that includes Range and Area Of Habitat (AOH) information for all species currently mapped on the IUCN Red List (92,255 species; each species modelled for both its breeding and non-breeding distribution), along ~85 million hexagonal 6 km2 cells that cover the entire globe. The entire dataset is comprised of 32 billion spatially explicit data records.
The summer project
We aim to use clustering analysis for community detection on a combination of species co-occurrence and cell proximity, to create a data-driven spatial regionalization of the world based on all spatially described species. The project will involve compiling all this data into a graph database, identifying suitable clustering approaches for community detection, and analysing results to identify informative clustering thresholds.
This is a good summer project for a computer science student who wants to get more familiar with graph databases, data science and environmental/biodiversity approaches.
ideasRelated News
- Mapping LIFE on Earth / Jan 2023