The influence of landscape structure and connectivity on ecosystem services: trade-offs and synergies in urban areas.

Ecosystem services are the benefits that humans derive their well-being. Globally, 60 per cent of ecosystem services assessed are either being degraded or being used unsustainably. Urban ecosystem services provide a range of services such as climate regulation, erosion control, flood control, food production, urban cooling, recreation and cultural values. Ecosystem services have been found to cluster to form ecosystem service (ES) bundles. Landscape structure has been shown to affect the provision of multiple ecosystem services. However, there is limited knowledge on whether the landscape configuration and connectivity of urban green space affect the provision of ES. This research aims to understand whether ecosystem services form ES bundles at a 2 m spatial resolution in the combined built-up areas of three large towns Milton Keynes, Bedford, and Luton, UK and to analyse the trade-offs and synergies between ecosystem services. Multivariate techniques were used to identify the bundles and analyse the trade-offs and synergies between ES. This study has assessed whether landscape configuration and connectivity influence ES trade-offs and synergies. A Bayesian Belief Network (BBN) modelling approach was used to test whether landscape configuration characteristics and connectivity drive ES trade-offs and synergies. Circuit theory was used to model landscape connectivity. Overall, this study found that ES cluster to form bundles and exhibited distinct geographic patterns. There were specific spatial trade-offs and synergies between ecosystem services. BBN models proved to be suitable for predicting ES trade-offs of urban green space. Landscape configuration and connectivity influence predicted ES trade-offs and synergies. Key findings indicated clearly that landscape configuration characteristics and landscape connectivity drive ES trade-offs and synergies. Core area and connectivity were strong predictors of ES trade-offs. Evidence suggests that ES trade-offs and synergies are influenced by green space configuration characteristics, landscape connectivity and ecological processes and influence the provision of multiple ES. Low connectivity was found to be associated with high trade-off predictions. Therefore, the use of ES modelling tools, Geographical Information Systems (GIS), Bayesian Belief Networks and connectivity modelling tools provide significant insights into understanding the influence of landscape structure and connectivity of urban green space on ES trade-offs and synergies and the underlying mechanisms for trade-offs and synergies. This study supports the conviction that enhancing green infrastructure increases ES provision and confers resilience to ES. A better understanding of the mechanisms for trade-offs and synergies can be beneficial for planning and management of ES because it can help to predict where and when trade-offs might take place, reduce the undesirable trade-offs and enhance the desirable synergies, and improve the provision of multiple ecosystem services across the landscape.