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Evaluating the Performance of a Forest Succession Model to Predict the Long-Term Dynamics of Tree Species in Mixed Boreal Forests Using Historical Data in Northern Ontario, Canada
Environmental concerns and economic pressures on forest ecosystems have led to the development of sustainable forest management practices. As a consequence, forest managers must evaluate the long-term effects of their management decisions on potential forest successional pathways. As changes in forest ecosystems occur very slowly, simulation models are logical and efficient tools to predict the patterns of forest growth and succession. However, as models are an imperfect representation of reality, it is desirable to evaluate them with historical long-term forest data. Using remeasured tree and stand data from three data sets from two ecoregions in northern Ontario, the succession gap model ZELIG-CFS was evaluated for mixed boreal forests composed of black spruce (Picea mariana [Mill.] B.S.P.), balsam fir (Abies balsamea [L.] Mill.), jack pine (Pinus banksiana L.), white spruce (Picea glauca [Moench] Voss), trembling aspen (Populus tremuloides Michx.), white birch (Betula papyrifera Marsh.), northern white cedar (Thuja occidentalis L.), American larch (Larix laricina [Du Roi] K. Koch), and balsam poplar (Populus balsamefera L.). The comparison of observed and predicted basal areas and stand densities indicated that ZELIG-CFS predicted the dynamics of most species consistently for periods varying between 5 and 57 simulation years. The patterns of forest succession observed in this study support gap phase dynamics at the plot scale and shade-tolerance complementarity hypotheses at the regional scale.
Evaluating the Performance of a Forest Succession Model to Predict the Long-Term Dynamics of Tree Species in Mixed Boreal Forests Using Historical Data in Northern Ontario, Canada
Environmental concerns and economic pressures on forest ecosystems have led to the development of sustainable forest management practices. As a consequence, forest managers must evaluate the long-term effects of their management decisions on potential forest successional pathways. As changes in forest ecosystems occur very slowly, simulation models are logical and efficient tools to predict the patterns of forest growth and succession. However, as models are an imperfect representation of reality, it is desirable to evaluate them with historical long-term forest data. Using remeasured tree and stand data from three data sets from two ecoregions in northern Ontario, the succession gap model ZELIG-CFS was evaluated for mixed boreal forests composed of black spruce (Picea mariana [Mill.] B.S.P.), balsam fir (Abies balsamea [L.] Mill.), jack pine (Pinus banksiana L.), white spruce (Picea glauca [Moench] Voss), trembling aspen (Populus tremuloides Michx.), white birch (Betula papyrifera Marsh.), northern white cedar (Thuja occidentalis L.), American larch (Larix laricina [Du Roi] K. Koch), and balsam poplar (Populus balsamefera L.). The comparison of observed and predicted basal areas and stand densities indicated that ZELIG-CFS predicted the dynamics of most species consistently for periods varying between 5 and 57 simulation years. The patterns of forest succession observed in this study support gap phase dynamics at the plot scale and shade-tolerance complementarity hypotheses at the regional scale.
Evaluating the Performance of a Forest Succession Model to Predict the Long-Term Dynamics of Tree Species in Mixed Boreal Forests Using Historical Data in Northern Ontario, Canada
Guy R. Larocque (author) / F. Wayne Bell (author)
2021
Article (Journal)
Electronic Resource
Unknown
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