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    Author(s): Brian MirandaBrian R. Sturtevant; Isabelle Schmelzer; Frederik Doyon; Peter Wolter
    Date: 2016
    Source: Canadian Journal of Forest Research. 46: 1009-1018.
    Publication Series: Scientific Journal (JRNL)
    Station: Northern Research Station
    PDF: Download Publication  (1.0 MB)


    Understanding vegetation recovery patterns following wildfire and logging disturbance is essential for long-term planning in sustainable forestry. Plot-scale studies indicate differences in revegetation rates and postdisturbance composition in Labrador, Canada, following fire in comparison with harvest but do not necessarily capture the full range of relevant landscape variability. Using a satellite-based land cover classification that distinguishes forest, woodland, shrub, lichen, and bare ground, we applied partial least-squared regression (PLS) to derive empirical models of vegetation dynamics following fire and harvest. Forest recovery rates were found to be generally slow and sensitive to predisturbance land condition and site quality (potential productivity). We found that, although disturbance type was not specifically retained in the model, estimated rates of vegetation recovery were faster for a typical harvest compared with a typical fire (i.e., 50% recovery at 14 years versus 33 years, respectively). Indeed, the model predicts important regeneration delay following fire that appears sensitive to both site quality and area burned. Understanding factors affecting broad-scale vegetation recovery relationships can help guide future sustainable forestry and wildlife habitat initiatives in the region, in part by parameterizing landscape simulation models used for strategic decision support.

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    Miranda, Brian R.; Sturtevant, Brian R.; Schmelzer, Isabelle; Doyon, Frédérik; Wolter, Peter. 2016. Vegetation recovery following fire and harvest disturbance in central Labrador — a landscape perspective. Canadian Journal of Forest Research. 46: 1009-1018.


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    Canada, disturbance patch attributes, land cover, partial least-squared regression (PLS), site productivity

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