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Trees in urban environments provide multiple benefits (pollution reduction, water regulation, carbon sequestration, amenity value etc.) which are increasingly well understood. Tools for assessing and valuing these benefits have been developed and their use in small and large-scale ecosystem-services evaluations is increasing. A key challenge is building understanding and acceptance of the value of urban trees among wider society. Citizen science provides an opportunity to actively engage individuals and organisations in data collection and information generation. In the context of urban trees in the UK, Treezilla – the Monster Map of Trees (www.treezilla.org) exists to engage citizens in these data collection efforts. Treezilla estimates multiple ecosystem services values of individual trees based on calculations from models of the way these ecosystem services vary with size and the characteristics of individual species. These models, developed in the US have been translated to be UK-relevant, but there is scope for them to be refined.

This project will aim to collect data on UK urban trees to refine existing ecosystem service models. In parallel it will carry out a programme of citizen engagement and evaluate it in order to assess the use of Treezilla by citizens. Ultimately it will use data within Treezilla and models of ecosystem services provision to model the environmental benefits of urban trees at small and large scales.

A veteran urban oak tree, over 200 years old, in Milton Keynes.


The project will investigate tree ecosystem services at individual and UK-wide scales. A sample of urban trees in Milton Keynes will be identified for detailed individual monitoring through the course of the PhD. Large-scale ecosystem services provision from urban trees will be assessed at town/city scale UK-wide from data collated through the Treezilla app.

Tree growth and physiology will be assessed using standard tools such as dendrometers, PAR sensors and LAI assessments. Tree location data will be collated through the Treezilla app and database which will form the primary means of citizen engagement. Spatial modelling of ecosystem services provision will use existing urban forest effects models in combination with a range of existing environmental datasets.

Training and Skills

Training will be provided in statistical computing and ecological data analysis as well as practical field skills in environmental monitoring.

NERC CENTA students are required to complete 45 days training throughout their PhD including a 10 day work placement. In the first year, students will be trained as a single cohort on environmental science, research methods and core skills. Throughout the PhD, training will progress from core skills sets to master classes specific to CENTA research themes.


Year 1: Build knowledge and expertise in ecosystem services models of urban trees. Core training in GIS and data analysis. Produce review paper on citizen science and ecosystem services. Set up citizen science evaluation. Set up urban tree growth monitoring sites.

Year 2: Monitoring urban tree growth; Evaluating citizen science participation. Draft paper/thesis chapter on citizen science participation.

Year 3: Assessing and refining models of urban ecosystem services provision. Draft paper on urban tree growth. International conference presentation. Submit thesis.


Partners and collaboration (including CASE)

The project is part of a long-running partnership between the Open University, Forest Research and others.

Further Details

Students should have a strong background in ecology or environmental science. Experience of environmental modelling and managing large datasets or databases would be valuable, though full training will be given. An enthusiasm for fieldwork and data analysis is required. Experience of GIS is essential. The student will join a well-established team researching trees, ecosystem services and citizen science at the Open University.

Please contact Phil Wheeler philip.wheeler@open.ac.uk for further information.

Applications should include:

Applications should be sent to


by 5 pm on Monday 22nd January 2018