PREDICTING MOOSE BROWSE PRODUCTION USING THE NORTH-WESTERN ONTARIO FOREST ECOSYSTEM CLASSIFICATION
High browse production is a key factor for quality moose habitat, and in this study we test the utility of a forest ecosystem classification system to predict the density and biomass of browse in standing timber, and the regrowth response of browse species following timber harvest. The 38 vegetation- and 22 soil-types of the Northwestern Ontario Forest Ecosystem Classification were grouped a priori into Treatment Units (TUs) according to their expected ability to produce browse, and we tested the hypothesis that all groups produced browse equally, before and after harvest. We sampled for browse density using a nearest-neighbour, plotless sampling technique, and extrapolated current annual growth by using regressions of plant dimensions to clipped annual growth dry weight. We ranked each TU according to observed density and inferred preference of browse species, and also tested if existing and commonly available forest resource inventory (FRI) data could successfully assign forest stands to Tus without the need for on-ground data collection. TU 1 (hardwood and mixedwood) and TU 2 (balsam fir-white spruce conifer and mixedwood) has highest browse density (P < 0.0001), current annual growth (P < 0.001), and overall browse habitat suitability. The magnitude of response of browse density to timber harvest differed across TUs, with browse density in TUs 1 and 2 increasing 2-3 times following harvest, but increasing little or not at all for other TUs. Substantial congruence between the field-determined TUs and airphoto-based FRI estimated TU classifications (83% overlap), although the classifications did differ (P = 0.018). Some TUs could not be differentiated because of the absence of information on understory vegetation in the FRI data.
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