David Benz

David Benz

My DPhil research concerns the use of artificial intelligence to improve ecosystem service provision in England’s public forests. I’m running SWAT hydrological modelling software to assess the effect of conifer-broadleaf mix on flooding likelihood. I’m also using LASSO regularisation in a regression model to identify the factors driving forest visitation during the Coronavirus pandemic of 2020. My findings will be incorporated in a multi-objective search and optimisation model to determine the management practices that maximise flood mitigation and recreational amenity in each parcel of public woodland.

My work is supported by the NERC DTP and by a CASE partnership with Forestry England.

I have a professional background in GIS and remote sensing, and I teach courses on these topics.

Selected Publications

Long, P R, S Nogué, D Benz, and K J Willis (2021). Devising a method to remotely model and map the distribution of natural landscapes in Europe with the greatest recreational amenity value (cultural services). Frontiers of Biogeography 13(1), doi:10.21425/F5FBG47737.

Long, P R, D Benz, A C Martin, P W A Holland, M Macias-Fauria, A W R Seddon, R Hagemann, T K Frost, A C Simpson, D J Power, M A Slaymaker, and K J Willis (2018). LEFT – a web-based tool for the remote measurement and estimation of ecological value across global landscapes. Methods in Ecology and Evolution 9, 571-579, doi:10.1111/2041-210X.12924.

Seddon, A. W. R., M. Macias-Fauria, P. R. Long, D. Benz, K. J. Willis (2016).  Sensitivity of global terrestrial ecosystems to climate variability.  Nature 531(7593):229–232, doi:10.1038/nature16986.

Macias-Fauria, M., A. W. R. Seddon, D. Benz, P. R. Long, and K. J. Willis (2014).  Spatiotemporal patterns of warming.  Nature Climate Change 4(10):845–846, doi:10.1038/nclimate2372.