Working on nature’s digital twin (strong quantitative skills required)

One of the major challenges in animal population monitoring is that we cannot be everywhere all the time, and extensive population census operations such as aerial surveys or parallel counts from the ground are very labour-intensive and costly. Many nature conservation organisations do not have the resources to do this consistently over time, and therefore they often have poor insight in the status of the animal populations that they are trying to protect. Let alone the information they need to diagnose what is happening when animal populations are declining. This is why we are working on Digital Twins of nature (DTNs). DTNs are live models of natural systems that mirror the life and behaviour of their real life counterparts. These DTNs use all available data on relevant parameters about animal populations, combined with the best available physical models of population behaviour. The result is a live DTN simulation that visualises in real-time the spatial location and movements of each individual animal of the species to be monitored.

DTNs are very large and complicated models, and consist of many different modules that can be worked on and improved. Topics to work on include:

1. predicting animal movement using data of GPS radio collared animals,
2. wildlife census simulations to elicit the relationship between animal distributions and wildlife counts,
3. and data assimilation techniques for merging a wide range of biodiversity monitoring data.

Strong quantitative skills are required if you like to work on this topic, as it involves a lot of modelling, simulations and machine learning.