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Quantifying Ecosystem Service Trade-Offs to Inform Spatial Identification of Forest Restoration
Specific forest restoration aims to maximum ecosystem services (ESs); however, the complex trade-offs among ecosystem services pose considerable challenges for fulfilling such goals. Based on forest restoration on Hainan Island, China, we integrated spatially explicit models of ecosystem services and spatial prioritization techniques based on the efficiency frontier between habitat quality and plantation revenue to analyze the impacts of decision-makers’ preferences on optimal configurations of forest restoration. We then investigated the effects of different optimal restoration schemes on water purification, soil retention, carbon sequestration, and coastal hazard mitigation. Based on our results, plantation revenue and habitat quality exhibited an obvious trade-off during the process of restoration. Forest restoration patterns also varied with the degree of preference for plantation yield or habitat quality, indicating that understanding ecosystem service tradeoffs can support the optimal selection of forest restoration schemes under different preferences. However, when the values of multiple ecosystem services associated with forest restoration were considered (e.g., water purification, soil retention, carbon sequestration, and coastal hazard mitigation), the optimal solution choice varied. Our results suggest the application of the efficiency frontier can deepen quantitative understanding of ecosystem service trade-offs, and the addition of multi-benefit evaluation based on optimal solutions can provide a more detailed and broader picture of forest restoration plans. Integrated efficiency frontier assessment with the valuation of ecosystem services associated with forest restoration provides a quantitative approach for optimal forest restoration, which can be applied in broad forest restoration programs.
Quantifying Ecosystem Service Trade-Offs to Inform Spatial Identification of Forest Restoration
Specific forest restoration aims to maximum ecosystem services (ESs); however, the complex trade-offs among ecosystem services pose considerable challenges for fulfilling such goals. Based on forest restoration on Hainan Island, China, we integrated spatially explicit models of ecosystem services and spatial prioritization techniques based on the efficiency frontier between habitat quality and plantation revenue to analyze the impacts of decision-makers’ preferences on optimal configurations of forest restoration. We then investigated the effects of different optimal restoration schemes on water purification, soil retention, carbon sequestration, and coastal hazard mitigation. Based on our results, plantation revenue and habitat quality exhibited an obvious trade-off during the process of restoration. Forest restoration patterns also varied with the degree of preference for plantation yield or habitat quality, indicating that understanding ecosystem service tradeoffs can support the optimal selection of forest restoration schemes under different preferences. However, when the values of multiple ecosystem services associated with forest restoration were considered (e.g., water purification, soil retention, carbon sequestration, and coastal hazard mitigation), the optimal solution choice varied. Our results suggest the application of the efficiency frontier can deepen quantitative understanding of ecosystem service trade-offs, and the addition of multi-benefit evaluation based on optimal solutions can provide a more detailed and broader picture of forest restoration plans. Integrated efficiency frontier assessment with the valuation of ecosystem services associated with forest restoration provides a quantitative approach for optimal forest restoration, which can be applied in broad forest restoration programs.
Quantifying Ecosystem Service Trade-Offs to Inform Spatial Identification of Forest Restoration
Ruida Li (author) / Ruonan Li (author) / Hua Zheng (author) / Yanzheng Yang (author) / Zhiyun Ouyang (author)
2020
Article (Journal)
Electronic Resource
Unknown
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