Summary
Summary
This project aims to develop and test novel modelling frameworks to explore the drivers of ecosystem services at different spatial scales in order to predict and map their delivery.
Research objectives
Our role on the project is to:
- Co-develop and apply the modelling frameworks
- Work with the National Forest Inventory (NFI) team to deliver detailed information on the function and service delivery of UK forest ecosystems
- Extract landscape composition and structure variables measured at different scales
Results so far
We are currently extracting and compiling data with the NFI and exploring modelling approaches with Southampton University.
Status
SCALEFORES is a 5-year project (July 2016 to July 2021).
Contact
Funders and partners
This project is funded by an ERC Starting Grant to Felix Eigenbrod at Southampton University. Forest Research and the Centre for Ecology & Hydrology are subcontracted to work on this project in collaboration with Felix’s research team.
Latest Update
The SCALEFORES project team have developed and published an analytical framework that identifies both why and where management actions are most effective for enhancing natural capital across large geographic areas (Spake et al., 2019, free read access link below). We illustrate the framework’s generality by applying it to two examples for Britain: pond water quality and invasion of forests by rhododendron.
We are now applying the framework to the NFI data to better understand various other aspects of woodlands and the benefits they provide.
General Content
What’s of interest
ResearchGate SCALEFORES project page
Land Use and Ecosystem Services
Further information
An analytical framework for spatially targeted management of natural capital
Spake, R., Bellamy, C., Graham, L.J., Watts, K., Wilson, T., Norton, L.R., Wood, C.M., Schmucki, R., Bullock, J.M. and Eigenbrod, F., 2019. An analytical framework for spatially targeted management of natural capital. Nature Sustainability, 2(2), p.90-97. Free read access: https://rdcu.be/bmc3V