University of Oxford
11a Mansfield Rd
OX1 3SZ
UK
Postdoctoral Researcher
Ella Browning
Background
As an interdisciplinary ecologist with a core background in zoology and conservation, I work on providing evidence for effective biodiversity restoration measures. Prior to joining ICCS, I was a postdoc at University College London (UCL), and a Research Scientist at the Bat Conservation Trust. I completed my PhD at UCL and the Institute of Zoology, London, where I used citizen science data to investigating temporal and spatial trends in bat populations, identifying drivers using spatiotemporal statistical modelling methods. I was introduced to fascinating hidden world of bats during my undergraduate Zoology degree, and I was hooked. I have been studying bats ever since, using them to understand impacts of anthropogenic environmental change on biodiversity.
Bats’ use of echolocation led me to a deeper exploration of the use of passive acoustic monitoring techniques for gathering vital ecological data, and I co-wrote the first guidelines for conservation practitioners on using passive acoustics. I continue to work on developing and improving tools for collecting passive acoustic data and extracting ecologically information from audio using machine learning methods. I am more widely interested in combining conservation technologies, Internet of Things systems, and AI tools for enabling automation and real-time monitoring, to improve biodiversity data collection efficiency, and for interdisciplinary applications, such as for monitoring mosquitoes which pose global public health concerns. Ultimately, I hope my research improves biodiversity and human health.
Bats’ use of echolocation led me to a deeper exploration of the use of passive acoustic monitoring techniques for gathering vital ecological data, and I co-wrote the first guidelines for conservation practitioners on using passive acoustics. I continue to work on developing and improving tools for collecting passive acoustic data and extracting ecologically information from audio using machine learning methods. I am more widely interested in combining conservation technologies, Internet of Things systems, and AI tools for enabling automation and real-time monitoring, to improve biodiversity data collection efficiency, and for interdisciplinary applications, such as for monitoring mosquitoes which pose global public health concerns. Ultimately, I hope my research improves biodiversity and human health.
Research Interests
My research interests focus on improving understanding of drivers of biodiversity trends and patterns in species occurrences or abundances. I have a special interest in bats and their use of echolocation, evolutionary adaptations, and use as bioindicator species. I am also interested in the impacts on biodiversity loss and climate change on public health risks, such as the increasing global health threat posed by arboviruses and zoonotic disease spillover. I investigate these by:
- Improving methods for biodiversity and ecological data collection, primarily using passive acoustic monitoring methods, and I am also interested in citizen science approaches and other conservation technologies.
- Developing pipelines for increasingly large biodiversity data management, automated ecological information extraction, and robust ecological inference.
- Using statistical methods, such as Bayesian hierarchical modelling approaches, able to account for biases in complex biodiversity datasets to identify spatial and temporal population trends and their drivers.
- Using interdisciplinary approaches and collaborations.
Current Research
Currently, I am using passive acoustic monitoring data collected on and around cattle farms in Brazil to understand how farming practices impact biodiversity, and, conversely, effects on livestock health. This will help inform recommendations for sustainable beef production, minimising environmental impacts, whilst maximising livestock health and yield. I use machine learning methods to extract bat activity and community diversity information from ultrasonic acoustic data and broader biodiversity estimates from audible soundscape data.
Brief CV
2022 – 2024 Postdoctoral Research Fellow (part-time), People and Nature Lab, CBER & Centre for Digital Public Health in Emergencies, IRDR, University College London
2021 – 2024 Research Scientist (part-time), Bat Conservation Trust, London
2021 – 2022 Postdoctoral Research Assistant, CBER, University College London
2016 – 2021 PhD candidate, University College London & Institute of Zoology, Zoological Society of London
2015 – 2016 Postgraduate Research Assistant, University College London
2014 – 2015 MRes Biodiversity, Evolution, and Conservation, University College London
2010 – 2013 BSc Zoology, University of Leeds
Publications
Browning, E., Christie, M., Czajkowski, M., Chalak, A., Drummond, R., Hanley, N., Jones, K. E., Kuyer, J., Provins A. (2024) Valuing the economic benefits of species recovery programmes. People and Nature 6 (2) 894-905
Martines Balvanera, S., Mac Aodha, O., Weldy, M.J., Pringle, H., Browning, E., Jones, K.E. (2023). Whombat: An open-source annotation tool for machine learning development in bioacoustics. arXiv
Musah, A., Browning, E., Aldosery, A., Valerio Graciano Borges, I., Ambrizzi, T., Tunali, M., Başibüyük, S., Yenigün, O., Machado Magalhaes Moreno, G., Lins de Lima, C., Gomes da Silva, A., Pinheiro dos Santos, W., Massoni, T., Cintra Campos, L., Kostkova, P. (2023). Coalescing disparate data sources for 2 the geospatial prediction of mosquito abundance, using Brazil as a motivating case study. Frontiers in Tropical diseases
Mac Aodha, O., Martínez Balvanera, S., Damstra, E., Cooke, M., Eichinski, P., Browning, E., Barataud, M., Boughey, K., Coles, R., Giacomini, G., M. Cristina Mac Swiney G., Obrist, M.K., Parsons, S., Sattler, T., Jones, K.E. (2022) Towards a General Approach for Bat Echolocation Detection and Classification. bioRxiv
Browning, E. Freeman, R., Boughey, K., Isaac, N.J.B. and K.E. Jones (2022). Accounting for spatial and environmental biases are important in estimating long-term population trends from citizen science data. Ecological Indicators.
Browning, E., Barlow, K., Walters, C., Eaton, M., Boughey, K., (2021). Drivers of bat population change: a review reveals evidence gaps. Mammal Review 51 (3) 353-368 Isaac, N.J.B., Jarzyna, M.A., Kiel, P., Dambly, L.I., Boersch-Supan, P.H., Browning, E., Freeman, S.N., Goulding, N., Guillera-Arroita, G., Henrys., P.A., Jarvis, S., Lahoz-Monfort, J., Pagel, J., Pescott, O.L., Schmucki, R., Simmonds, E.G., O’Hara, R.B., (2020). Data Integration for Large-Scale Models of Species Distributions. Trends in Ecology & Evolution 35 (1) 56-67
Gibb, R.*and Browning, E.,* Glover-Kapfer P., Jones, K.E., (2019). Emerging opportunities and challenges for passive acoustics in ecological assessments Methods in Ecology and Evolution 10 (2), 169-185 *Denotes joint first authorship.
Browning, E., Gibb, R., Glover-Kapfer, P., Jones, K.E., (2018). Passive acoustic monitoring in ecology and conservation. WWF Conservation Technology Series 1 (2), 1-75
Mac Aodha, O., Gibb, R., Barlow, K.E., Browning, E., Firman, M., Freeman, E., Harder, B., Kinsey, L., Mead, G.R., Newson, S., Pandourski, I., Parsons, S., Russ, J., Szodoray-Paradi, A., Szodoray-Paradi, F., Tilova, E., Girolami, M., Brostow, G., Jones., K.E., (2018). Bat detective – Deep learning tools for bat acoustic signal detection PloS Computational Biology 14 (3) e1005995
Browning, E., Bolton, M., Owen, E., Shoji, A., Guilford, T., Freeman, R., (2017). Using deep-learning to predict behaviour in seabirds: GPS data alone is an accurate predictor of diving. Methods in Ecology and Evolution 9 (3), 681-692