The effects of military tank traffic on prairie: A management model |
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Authors: | Scott D. Wilson |
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Affiliation: | (1) Department of Botany, University of Manitoba, R3T 2N2 Winnipeg, Manitoba, Canada;(2) Present address: Department of Ecology and Behavioral Biology, University of Minnesota, 318 Church St. S.E., 55455 Minneapolis, MN, USA |
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Abstract: | The effect of various frequencies and seasons of military tank traffic on native mixed-grass prairie was examined in a randomized and replicated field experiment. Vegetation (in 10×10 m plots) was subjected to tank traffic at the following rates: (a) one pass per day of training from May until August; (b) one pass per day in May and June; (c) one pass per day in July and August; (d) one pass every three weeks from May until August; (e) zero (control). Species composition and the amount of bare ground were found to vary significantly with traffic frequency. Plant species alien to North America invaded plots subjected to spring driving. Regression analysis showed spring driving to produce more bare ground than summer driving. The regression models suggested that much higher intensities of training could be conducted without damage if spring driving were avoided. Regression models were also used to estimate the frequency of traffic associated with a significant change in species composition, where species composition was expressed as the ratio ofBouteloua gracilis toStipa spartea, an indicator of disturbance-induced change in prairie vegetation. This relationship predicted the capacity of the vegetation of a training area of any given width to support tank traffic without changing species composition. The predictive ability of the model was tested by comparing predicted traffic capacities with the amount of traffic actually applied to two training areas in 1986. Where traffic capacity was exceeded, the model successfully predicted a significantly higher frequency of bare ground and ratio ofBouteloua gracilis toStipa spartea. |
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Keywords: | Prairie Military tanks Experiment Predictive model Management |
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