Bevan, Peggy Alice; (2025) Challenges to biodiversity monitoring across spatial and ecological scales. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

In the face of a biodiversity loss crisis, quantifying the impact of human-induced pressures on ecological communities is crucial for future conservation efforts. In this thesis, I investigate methods for measuring wildlife responses to anthropogenic pressure and address some of the challenges of a global biodiversity monitoring framework.. In my first analysis, I use a meta-analytical approach to reveal the unique responses of biodiversity to land-use change across biome, realm and taxonomic group. These results suggest that monitoring across regionally separated biomes (regional biomes) could improve global monitoring efforts. In the next chapters, I use a regional biome case-study in Asian sub-tropical dry forest to show nuanced responses of species to anthropogenic pressure. In Chapter 3, I use a dataset of 150 camera traps (CTs) covering a gradient of pressure in Nepal’s terai region to show that variation in species’ threat responses is producing novel co-occurrence networks. As habitat availability reduces, some species alter their diel activity patterns to reduce interspecific overlap, with implications for long-term fitness and community resilience. Collecting nuanced biodiversity metrics like this on a global scale would require efficient data collection using minimal resources. Automated analysis tools, such as deep learning image classifiers, provide a solution for rapidly processing vast amounts of data. In Chapter 4, I use two CT data sets from Kenya and Nepal to show how manipulating aspects of a deep learning classification pipeline, such as training set size or model architecture, has a low impact on the accuracy of ecological metrics. In Chapter 5, I use an unsupervised deep learning pipeline to show that ecological patterns can be extracted from CT data with no species labelling, demonstrating the potential of automated tools to transform ecological monitoring. Through the research presented here, I have identified major gaps in global biodiversity monitoring and explored innovative solutions for addressing these gaps.

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