Introduction
Beaver rewilding is expanding rapidly in the UK, yet there is limited predictive capacity to assess how their dam-building alters hydrology, biodiversity and ecosystem services across contrasting river types.
This project will use field data, ecological modelling and AI methods to quantify and forecast the ecological and hydrological impacts of beaver reintroductions in different UK catchments (e.g. Bircham Valley, surface-fed urban beaver enclosure in Plymouth, and the River Meon, chalk stream rural beaver enclosure in Southampton).
Research Challenge:
Beavers provide biodiversity and hydrological benefits (e.g. flood attenuation, increased habitat complexity; Kemp et al. 2012, Brazier et al. 2020, Grudzinski et al. 2022), but may also create trade-offs (e.g. local flooding of farmland, sediment accumulation, reduced connectivity for some taxa; Auster et al. 2020, Dgebuadze et al. 2021, Wilson et al. 2025). Understanding when these changes tip from beneficial to detrimental is central to sustainable management.
The PhD will address three interlinked components:
(i) Data collection, with field surveys of biodiversity (macroinvertebrates, riparian vegetation, fish via eDNA), hydrological conditions (pond depth, water quality, sediment, temperature) across beaver-modified and control reaches (Antognazza et al. 2021). These will give the student a good understanding of what is living where and the characteristics of the environments associated with that.
(ii) Individual-based modelling to simulate ecological responses to habitat changes induced by beaver activity, focusing on key fish species (e.g. salmonids). These models uses species traits (e.g. growth, fecundity) and movement traits (e.g. probability of dispersal, behavioural reasoning for choosing a particular habitat) to map the distribution of species in dynamics landscapes (in this case, landscapes that are exposed to changes due to beaver activity through damming, channel changes, etc.; Dominguez Almela et al. 2020, 2022).
(iii) AI integration: Apply machine learning to detect thresholds, forecast ecological responses and develop a decision-support prototype for managers to evaluate rewilding trade-offs under different scenarios. Methods like Random Forest or supervised machine learning algorithms could be used to achieve this component (e.g. Cordier et al. 2017, Zong et al. 2024).
Team combines senior and early career expertise across two academic institutions and one practitioner: Vicky Dominguez and Paul Kemp (University of Southampton) will bring modelling and fish expertise respectively, Demetra Andreou (Bournemouth University) will add eDNA skills and Jane Ashford (Plymouth Council) will share her knowledge on managing beaver enclosures. The student will spend time embedded across all three institutions, which will strength their networks and expose them to different working environments.
References:
References on beavers:
Kemp, P.S., Worthington, T.A., Langford, T.E.L., Tree, A.R.J. and Gaywood, M.J. (2012), Qualitative and quantitative effects of reintroduced beavers on stream fish. Fish and Fisheries, 13: 158-181.
Brazier RE, Puttock A, Graham HA, Auster RE, Davies KH, Brown CML. Beaver: Nature's ecosystem engineers. WIREs Water. (2021); 8:e1494.
Grudzinski, B.P., Fritz, K., Golden, H.E., Newcomer-Johnson, T.A., Rech, J.A., Levy, J., Fain, J., McCarty, J.L., Johnson, B., Vang, T.K. and Maurer, K., (2022). A global review of beaver dam impacts: Stream conservation implications across biomes. Global ecology and conservation, 37, p.e02163.
Auster, R. E., Barr, S., & Brazier, R. (2020). Alternative perspectives of the angling community on Eurasian beaver (Castor fiber) reintroduction in the River Otter Beaver Trial. Journal of Environmental Planning and Management, 64(7), 1252–1270.
Dgebuadze, Y.Y., Bashinskiy, I.V. & Osipov, V.V. (2021). The influence of Eurasian beaver Castor fiber activity on fish assemblages in small steppe rivers in Russia. Environ Biol Fish 104, 689–700.
Wilson, K., Bryce, J., Skilbeck, A., Needham, R., McAllister, S., and Campbell-Palmer, R. (2025). How to create woodlands that are resilient in the presence of beaver (Castor fiber): a review of current evidence. NatureScot Research Report 1368.
References on methods:
Antognazza CM, Britton RJ, Read DS, et al. Application of eDNA metabarcoding in a fragmented lowland river: Spatial and methodological comparison of fish species composition. Environmental DNA. 2021; 3: 458–471.
Dominguez Almela, V., Palmer, S.C.F., Gillingham, P.K. et al. (2020). Integrating an individual-based model with approximate Bayesian computation to predict the invasion of a freshwater fish provides insights into dispersal and range expansion dynamics. Biol Invasions 22, 1461–1480.
Dominguez Almela, V., Palmer, S. C. F., Andreou, D., Gillingham, P. K., Travis, J. M. J., & Britton, J. R. (2022). Predicting the influence of river network configuration, biological traits and habitat quality interactions on riverine fish invasions. Diversity and Distributions, 28, 257–270.
Cordier, T., Esling, P., Lejzerowicz, F., Visco, J., Ouadahi, A., Martins, C., Cedhagen, T. and Pawlowski, J., (2017). Predicting the ecological quality status of marine environments from eDNA metabarcoding data using supervised machine learning. Environmental science & technology, 51(16), pp.9118-9126.
Zong, S., Brantschen, J., Zhang, X., Albouy, C., Valentini, A., Zhang, H., Altermatt, F. and Pellissier, L. (2024), Combining environmental DNA with remote sensing variables to map fish species distributions along a large river. Remote Sens Ecol Conserv, 10: 220-235.