Understanding the importance of landscape configuration on ecosystem service bundles at a high resolution in urban landscapes in the UK

Context Landscape structure is thought to affect the provision of ecosystem service bundles. However, studies of the influence of landscape configuration on ecosystem service trade-offs and synergies in urban areas are limited. This study used Bayesian Belief Networks to predict ecosystem service trade-offs and synergies in the urban area comprising the towns of Milton Keynes, Bedford and Luton, UK. Objectives The objectives of this study were to test (1) a Bayesian Belief Network approach for predicting ecosystem service trade-offs and synergies in urban areas and (2) assess whether landscape configuration characteristics affect ecosystem service trade-offs and synergies. Methods Bayesian Belief Network models were used to test the influence of landscape configuration on ecosystem service interactions. The outputs of a Principal Component Analysis (PCA) on six ecosystem services and landscape configuration metrics were used as response and explanatory variables, respectively. We employed Spearman's rank correlation and principal component analysis to identify redundancies between landscape metrics. Results We found that landscape configuration affects ecosystem service trade-offs and synergies. A sensitivity analysis conducted on the principal components showed that landscape configuration metrics core area (CORE) and effective mesh size (MESH) are strong influential determinants of ecosystem service trade-offs and synergies. Conclusions This study demonstrates that landscape configuration characteristics affect ecosystem service trade-offs and synergies and that a core set of metrics could be used to assess ecosystem service (ES) trade-offs and synergies. The findings may be relevant to planning and urban design and improved ecosystem management.

Automated summary

The study shows that landscape configuration affects the trade-offs and synergies of ecosystem services in urban areas, with core area and effective mesh size as strong influential determinants. These findings have implications for planning, urban design, and ecosystem management.