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Detecting Compensatory Growth in Silviculture Trials: Empirical Evidence From Three Case Studies Across Canada
Chao Li1, Hugh Barclay2, Shongming Huang3
1Canadian Wood Fibre Centre, Canadian Forest Service, Edmonton, AB, Canada.
Frontiers in Plant Science
|May 23, 2022
Summary
Compensatory growth, or accelerated forest growth after unfavorable conditions, is common. This phenomenon, including overcompensation, can enhance forest productivity and carbon storage, offering benefits for forest management.
Area of Science:
- Forestry science
- Ecology
- Silviculture
Background:
- Compensatory growth (CG) is accelerated growth following unfavorable conditions, potentially increasing biomass beyond undisturbed levels.
- In forestry, CG can lead to exact compensation or overcompensation, where forest stands exceed original biomass.
- Overcompensation offers potential benefits like enhanced productivity and carbon storage, crucial for sustainable forest management.
Purpose of the Study:
- To investigate the prevalence of compensatory growth in forest stands.
- To evaluate the utility of legacy silviculture trial data for detecting CG.
- To identify reliable indicators for assessing CG in different forest datasets.
Main Methods:
- Analysis of three case studies with varying data structures from historical silviculture trials.
- Evaluation of stand-level CG using relative growth (RG) as a universal indicator.
- Assessment of individual tree-based volume and value indicators for CG detection.
Main Results:
- Compensatory growth was observed in all three investigated forest stand cases.
- Relative growth (RG) proved effective as a universal indicator for CG in long-term datasets.
- Lumber value-based indicators were more sensitive than volume indicators for detecting overcompensation.
Conclusions:
- Compensatory growth appears to be a common phenomenon in forest stands.
- Long-term silviculture data, even if not originally designed for CG studies, can reveal its presence.
- Relative growth and value-based indicators are valuable tools for detecting and understanding CG in forest management.
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