Local Variations of Biodiversity Index

Why is it important to measure biodiversity accurately?

The UN Sustainable Development Goals (SDGs) mandate that we “protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss.” Understanding the nature and the extent of changes in biodiversity, and making accurate predictions will be crucial in our journey to mitigating the impact of human actions and climate change. Specifically, an accurate evaluation of the global as well as local biodiversity will help inform the decision makers and communities to implement suitable plans to maintain good habitats for the respective species whilst also providing green amenities.

Limitations of the existing indices of diversity

Existing indices such as the Gini-Simpson Index (Gini 1912) and Shannon’s Measure of Information (SMI) tend to be adopted from other disciplines. They are not necessarily designed to give an accurate measurement of the diversity of species at the microscale, especially when the local variations in the underlying conditions for formulating specific habitats vary considerably. Use of such indices and their interpretation could therefore be misleading for decision makers involved in helping to achieve the UN SDGs.

What this project aims to achieve

This project builds on the recent development of a new index (Augousti et al. 2021) which offers more intuitive and realistic values of biodiversity measurements, especially when there are limitations such as low count of organisms and species. The aim is to incorporate the notion of local geographical variations (Murkin et al. 2023) to account for the variation in the underlying conditions. Specifically, we intend to develop an index that can help understand the local contexts and the different weighting of the contributing factors therein. The index will be derived using a machine-learning approach to train the model and it will be applied to a series of different contexts and datasets. The successful development of this approach will lead to a measure that has much wider applicability than hitherto and is more robust to sampling variations such as small numbers of individuals of particular species, providing a more realistic measure of biodiversity.

What we are looking for in the PhD applicant

We are looking for an ambitious, forward-looking person with a background in applied physics, quantitative geography and/or data science, who has a wide-ranging interest in cross-disciplinary research that can make an impact at a global scale.