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Practical application of spatial ecosystem service models to aid decision support
Grazia Zulian1, Erik Stange2, Helen Woods3
1Joint Research Centre, via Fermi 1, 21020 Ispra Varese, Italy.
Summary
Ecosystem service (ES) mapping tools like ESTIMAP can be adapted for local use, but data limitations and expertise needs remain challenges for effective environmental sustainability policies.
Area of Science:
- Environmental science
- Geographic Information Systems (GIS)
- Ecosystem services assessment
Background:
- Ecosystem service (ES) spatial modelling is crucial for integrated assessments supporting environmental sustainability.
- The ESTIMAP (Ecosystem Service Mapping Tool) provides spatially explicit models for policy support, initially at the European scale.
Purpose of the Study:
- To analyze the adaptation of ESTIMAP models to local scales across 10 case studies.
- To evaluate the "precision differential" of models and gather stakeholder opinions on their usefulness.
- To propose a protocol for adapting ESTIMAP to local conditions.
Main Methods:
- Application of 3 ES models from the ESTIMAP collection across 10 diverse case studies at local scales.
- Analysis of model adaptation processes, the "precision differential" (model-generated variation vs. spatial reality), and stakeholder feedback.
- Qualitative assessment of stakeholder perceptions regarding model utility and identified constraints.
Main Results:
- Stakeholders generally found the adapted ESTIMAP approach useful for discussion and communication.
- The "precision differential" was presented as a method to assess model output variation based on adaptation levels.
- Key constraints identified include insufficient spatial data detail and the required technical expertise for model implementation.
Conclusions:
- Adapting ES spatial models like ESTIMAP to local scales is feasible and can enhance communication.
- Addressing data availability and user expertise is critical for improving the practical application of these models in environmental management.
- The "precision differential" offers a valuable metric for understanding model performance and adaptation impacts.
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