Implementing landscape fragmentation as an indicator in the Swiss Monitoring System Of Sustainable Development (Monet)
- PMID: 17524552
- DOI: 10.1016/j.jenvman.2007.03.043
Implementing landscape fragmentation as an indicator in the Swiss Monitoring System Of Sustainable Development (Monet)
Abstract
There is an increasing need and interest in including indicators of landscape fragmentation in monitoring systems of sustainable landscape management. Landscape fragmentation due to transportation infrastructure and urban development threatens human and environmental well-being by noise and pollution from traffic, reducing the size and viability of wildlife populations, facilitating the spread of invasive species, and impairing the scenic and recreational qualities of the landscape. This paper provides the rationale, method, and data for including landscape fragmentation in monitoring systems, using as an example the Swiss Monitoring System of Sustainable Development (Monet). We defined and compared four levels of fragmentation analysis, or fragmentation geometries (FGs), each based on different fragmenting elements, e.g., only anthropogenic, or combinations of anthropogenic and natural elements. As each FG has specific strengths and weaknesses, the most appropriate choice of FG depends on the context and objectives of a study. We present data on the current degree of landscape fragmentation for the five ecoregions and 26 cantons in Switzerland for all four FGs. Our results show that the degree of landscape fragmentation as quantified by the effective mesh size method is strongly supported by the postulates and indicator selection criteria of Monet, and we identify the most suitable FG focusing on the land area below 2,100 m (e.g., excluding lakes) and allowing for an equitable comparison of fragmentation degrees among regions that differ in area covered by lakes and high mountains. For a more detailed analysis of landscape fragmentation in the context of environmental impact assessments and strategic environmental assessments, a combination of all four FGs may provide a more informative tool than any single FG.
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