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Integrating Expert Knowledge into Mapping Ecosystem Services Trade-offs for Sustainable Forest Management

Mountain ecosystems are highly sensitive to global change. In fact, the continued capacity of mountain regions to provide goods and services to society is threatened by the impact of environmental changes on ecosystems. Although mapping ecosystem services values is known to support sustainable resource management, the integration of spatially explicit local expert knowledge on ecosystem dynamics and social responses to global changes has not yet been integrated in the modeling process. This contribution demonstrates the importance of integrating local knowledge into the spatially explicit valuation of ecosystem services. Knowledge acquired by expert surveys flows into a GIS-based Bayesian Network for valuing forest ecosystem services under a land-use and a climate change scenario in a case study in the Swiss Alps. Results show that including expert knowledge in ecosystem services mapping not only reduces uncertainties considerably, but also has an important effect on the ecosystem services values. Particularly the iterative process between integrating expert knowledge into the modeling process and mapping ecosystem services guarantees a continuous improvement of ecosystem services values maps while opening a new way for mutual learning between scientists and stakeholders which might support adaptive resource management.
- WSL Institute for Snow and Avalanche Research SLF Switzerland
- WSL Institute for Snow and Avalanche Research SLF Switzerland
Ecology, QH301-705.5, forest management, expert survey, land-use change, Bayesian network, climate change, trade-offs, mapping, Biology (General), ecosystem services, mountain ecosystem, uncertainty, QH540-549.5
Ecology, QH301-705.5, forest management, expert survey, land-use change, Bayesian network, climate change, trade-offs, mapping, Biology (General), ecosystem services, mountain ecosystem, uncertainty, QH540-549.5
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).81 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
