Bayesian Belief Network-based assessment of nutrient regulating ecosystem services in Northern Germany

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Bicking, S.; Burkhard, B.; Kruse M.; Müller, F.: Bayesian Belief Network-based assessment of nutrient regulating ecosystem services in Northern Germany. In: PLoS ONE 14 (2019), Nr. 4. DOI: https://doi.org/10.1371/journal.pone.0216053

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/8615

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Sum total of downloads: 101




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Abstract: 
This study aims to assess the potential supply of the ecosystem service (ES) nutrient regulation on two spatial scales, the federal German state of Schleswig-Holstein (regional) and the Bornhöved Lakes District (local), exemplarily for the nutrient nitrogen. The methodology was developed using the ES matrix approach, which can be applied to evaluate and map ES at different geospatial units such as land use/land cover classes. A Bayesian Belief Network (BBN) was constructed in order to include additional spatial information on environmental characteristics in the assessment. The integration of additional data, which describes site-specific characteristics such as soil type and slope, resulted in shifted probability distributions for the nutrient regulation ES potential. The focal objective of the study was of methodological nature: to test the application of a BBN as an integrative modelling approach combining the information from the ES matrix with additional data sets. In the process, both study areas were assessed with a regional differentiation with regard to the predominant landscape types. For both study areas, regional differences could be detected. Furthermore, the results indicate a spatial mismatch between ES demand and supply of the nutrient regulation potential. Land management and agricultural practices seem not to be in harmony with the spatial patterns of the environmental characteristics in the study areas. The assessment on the local scale, which comprised higher resolution input data, emphasized these circumstances even more clearly.
License of this version: CC BY 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2019
Appears in Collections:Naturwissenschaftliche Fakultät

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pos. country downloads
total perc.
1 image of flag of Germany Germany 48 47.52%
2 image of flag of United States United States 33 32.67%
3 image of flag of China China 5 4.95%
4 image of flag of Netherlands Netherlands 4 3.96%
5 image of flag of No geo information available No geo information available 3 2.97%
6 image of flag of Vietnam Vietnam 1 0.99%
7 image of flag of Ukraine Ukraine 1 0.99%
8 image of flag of Taiwan Taiwan 1 0.99%
9 image of flag of India India 1 0.99%
10 image of flag of Canada Canada 1 0.99%
    other countries 3 2.97%

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