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Title: Assessing knowledge ambiguity in the creation of a model based on expert knowledge and comparison with the results of a landscape succession model in central Labrador. Chapter 10.
Author: Doyon, Frederik; Sturtevant, Brian; Papaik, Michael J.; Fall, Andrew; Miranda, Brian; Kneeshaw, Daniel D.; Messier, Christian; Fortin, Marie-Josee.; James, Patrick M.A.
Publication: In: Perera, Ajith H.; Drew, C. Ashton; Johnson, Chris J., eds. Expert Knowledge and Its Application in Landscape Ecology. Springer: 189-210.
Abstract: Sustainable forest management (SFM) recognizes that the spatial and temporal patterns generated at different scales by natural landscape and stand dynamics processes should serve as a guide for managing the forest within its range of natural variability. Landscape simulation modeling is a powerful tool that can help encompass such complexity and support SFM planning. Forecasting the complex behaviors of a forested landscape involving patterns and processes that interact at multiple temporal and spatial scales poses significant challenges. Empirical evidence for the functioning of key elements, such as succession and disturbance regimes, is crucial for model parameterization. However, reliable empirical data about the forest vegetation dynamics that arise in response to forest management and other disturbances may be scarce, particularly in remote areas where harvesting activity has been historically limited. Expert knowledge-based (EKB) modeling is receiving more attention as a companion approach to empirical modeling, and attempts are now being made to formalize the process of eliciting and including expert knowledge during the development of decision-support systems. Forestry experts with local knowledge collectively have considerable knowledge about forest succession and disturbance. Such collective knowledge can contribute greatly to our understanding of the vegetation transitions within a landscape that are so critical for informed SFM planning.
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