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| Hybrid Simulation Modelling: An Optimum Approach to Forest Ecosystem Management Modelling for Certification and Sustainability Analysis? |
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J.P. Kimmins
Dept. of Forest Sciences, University of B.C.
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Traditional forest modelling based on empirical patterns of historical stand growth and development can be the optimum forecasting strategy where future stand and environmental conditions remain similar to those of the past. Where future stand characteristics, environmental conditions and management practices are forecast to be significantly different from the past, such "historical bioassay" models may have limited accuracy and applicability. They are also inappropriate for the analysis of sustainability and stewardship, and therefore of restricted application in the certification of forestry.
Process simulation modelling can provide the flexibility needed to predict stand growth and development under changed and changing forest conditions, but such models have not proven popular in forest management planning. This is because of either their high input data requirement, their complexity if they are truly ecosystem models, or their lack of ecological complexity and reality if they are merely light-driven tree population models.
Hybrid simulation models combine the empirical, historical bioassay foundation of the traditional stand model with sufficient process modelling to give the flexibility needed to assess the possible flow of forest values and environmental services that can be expected to result over time from alternative stand management strategies.
This paper describes the application of the hybrid simulation modelling at three levels of stand modelling: 1, FORECAST, a non-spatial, multi-value, ecosystem management model; 2, FORCEE, a spatial, individual tree stand model with on-screen, 3-D, interactive screen graphics suitable for the analysis of complex stands; and 3, LLEMS, a spatial, local landscape management model suitable for the analysis of variable retention harvesting systems and paradigms of "ecosystem management". A brief description of these models is given, together with some examples of their application as decision support, scenario analysis and educational tools.
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