Methods for identifying green infrastructure

• DOI: 10.1007/s42452-020-03575-4
• Publication Date: 2020-10-28T23:02:41Z
• Authors: Erica Honeck; Arthur Sanguet; Martin A. Schlaepfer; Nicolas Wyler; Anthony Lehmann
• Keywords: ddc:333.7-333.9; 0106 biological sciences; info:eu-repo/classification/ddc/333.7-333.9; Connectivity; info:eu-repo/classification/ddc/550; 9. Industry and infrastructure; info:eu-repo/classification/ddc/580; Green infrastructure; Biodiversity; 15. Life on land; 01 natural sciences; Spatial conservation prioritization; ddc:580; 13. Climate action; 11. Sustainability; ddc:550; Ecosystem services

AbstractNature forms interdependent networks in a landscape, which is key to the survival of species and the maintenance of genetic diversity. Nature provides crucial socio-economic benefits to people, but they are typically undervalued in political decisions. This has led to the concept of Green Infrastructure (GI), which defines an interlinked network of (semi-)natural areas with high ecological values for wildlife and people, to be conserved and managed in priority to preserve biodiversity and ecosystem services. This relatively new concept has been used in different contexts, but with widely diverging interpretations. There is no apparent consensus in the scientific literature on the methodology to map and implement GI. This paper serves as an informed primer for researchers that are new to GI mapping understand the key principles and terminology for the needs of their own case-study, and as a framework for more advance researchers willing to contribute to the formalization of the concept. Through a literature review of articles on creating GI networks, we summarized and evaluated commonly used methods to identify and map GI. We provided key insights for the assessment of diversity, ecosystem services and landscape connectivity, the three ‘pillars’ on which GI identification is based according to its definition. Based on this literature review, we propose 5 theoretical levels toward a more complex, reliable and integrative approach to identify GI networks. We then discuss the applications and limits of such method and point out future challenges for GI identification and implementation.