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Enhanced science–stakeholder communication to improve ecosystem model performances for climate change impact assessments

In recent years, climate impact assessments of relevance to the agricultural and forestry sectors have received considerable attention. Current ecosystem models commonly capture the effect of a warmer climate on biomass production, but they rarely sufficiently capture potential losses caused by pests, pathogens and extreme weather events. In addition, alternative management regimes may not be integrated in the models. A way to improve the quality of climate impact assessments is to increase the science-stakeholder collaboration, and in a two-way dialog link empirical experience and impact modelling with policy and strategies for sustainable management. In this paper we give a brief overview of different ecosystem modelling methods, discuss how to include ecological and management aspects, and highlight the importance of science-stakeholder communication. By this, we hope to stimulate a discussion among the science-stakeholder communities on how to quantify the potential for climate change adaptation by improving the realism in the models.
- University of Copenhagen Denmark
- University of Copenhagen Denmark
- Lund University Sweden
- University of Copenhagen Denmark
Conservation of Natural Resources, Ecology, Information Dissemination, Climate Change, Geography, Planning and Development, Agriculture, Forestry, Models, Theoretical, 333, Adaptation strategies, Nature conservation, Perspective, Environmental Chemistry, Sustainable management, Ecosystem
Conservation of Natural Resources, Ecology, Information Dissemination, Climate Change, Geography, Planning and Development, Agriculture, Forestry, Models, Theoretical, 333, Adaptation strategies, Nature conservation, Perspective, Environmental Chemistry, Sustainable management, Ecosystem
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).16 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).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
