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Evaluation of a mechanistic approach to mixedwood modelling

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FORESTRY CHRONICLE
卷 84, 期 2, 页码 181-193

出版社

CANADIAN INST FORESTRY
DOI: 10.5558/tfc84181-2

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FORECAST; mixedwood management; model testing; process-based model; resource competition

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Mixed conifer-broadleaf forests (mixedwoods), covering more than a third of the productive forest landbase in BC, are highly valuable both as sources of fibre and as areas rich in biodiversity. In recognition of the multiple benefits of this forest type, management paradigms have transitioned from a focus on promoting conifer plantations in mixedwood areas to the management of intimate mixtures. The exceptionally dynamic growth properties and species interactions in mixedwood forests present a challenge for projecting the growth and development of different types of mixedwoods and their response to different silviculture systems. Here we evaluate the ability of a mechanistic forest growth model (FORECAST) to project patterns of stand growth and dynamics in two mixedwood forest types subjected to different silvicultural. treatments. Model output is compared against field measurements from long-term silviculture trials in the Sub Boreal Spruce (SBS)-18 years, and Interior Cedar Hemlock (ICH)-10 years, biogeoclimatic zones in British Columbia, Canada. FORECAST was able to reproduce patterns of growth response in both mixedwood forest types with reasonable accuracy. An analysis of the simulated relative impact of light and nutrient competition on growth dynamics and treatment response is provided. Results suggest that competition for both light and nutrients are important factors in the dynamics of these mixedwood forest types and that long-term response data and modelling are required to adequately assess the rotation-length effects of treatments on stand development. The analysis described herein provides a level of confidence for the use of the model as a decision-support tool in these ecosystem types, but more validation work should be conducted across a range of different mixedwood forest types and management interventions as long-term datasets become available.

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