Quantifying the Biodiversity Impact of Land Management Practices using Bioacoustics and Machine Learning

In recent years, acoustic monitoring has developed as a promising technique to survey wildlife populations, due to its ability to survey a wide range of species at low cost. However, the potential and applicability of acoustic monitoring to accurately assess population changes of species is poorly understood. This project will assess and develop machine learning methods for species classification to quantify changes in wildlife population densities and community structure from acoustic surveys, and apply this in a rewilding and restoration framework.

Conservation and land management practices will play a key role in mitigating the biodiversity crisis, though their impacts may only be measured over long time frames. Accurate and scalable monitoring of wildlife is therefore essential to target these conservation activities effectively. Passive acoustic monitoring potentially allows the impact of different land management practices on biodiversity to be quantified over much larger spatial and temporal scales, at lower cost than any other technique.

This project will focus on how to analyse the complex relationship between observed bird call rates and species abundance by using datasets of audio recordings paired with physical bird surveys. While call rates can be extracted from audio recordings using convolutional neural networks such as BirdNET, the relationship between call rate and species abundance is complicated by many environmental factors (e.g. vegetation density and wind / rain) that influence how much an individual bird calls or how audible the calls are. By estimating species abundance, this research will facilitate the development of evidence-based conservation strategies and sustainable land management decisions.