Statistical models of landscape pattern metrics,with applications to regional scale dynamic forest simulations |
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| Authors: | Cumming Steve Vervier Pierre |
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| Institution: | (1) Boreal Ecosystems Research Ltd, 6915 – 106 Street, T6H 2W1 Edmonton, Canada;(2) Centre for Applied Conservation Biology, Forest Sciences Department, University of British Columbia, 3004-2424 Main Mall, V6T 1Z4 Vancouver, Canada |
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| Abstract: | Forest managers in Canada need to model landscape pattern or spatial configurationoverlarge (100,000 km2) regions. This presents a scalingproblem, as landscape configuration is measured at a high spatial resolution,but a low spatial resolution is indicated for regional simulation. We present astatistical solution to this scaling problem by showing how a wide range oflandscape pattern metrics can be modelled from low resolution data. Our studyarea comprises about 75,000 km2 of boreal mixedwoodforest in northeast Alberta, Canada. Within this area we gridded a sample of 84digital forest cover maps, each about 9500 ha in size, to aresolution of 1 ha and used FRAGSTATS to compute a suite oflandscape pattern metrics for each map. We then used multivariate dimensionreduction techniques and canonical correlation analysis to model therelationship between landscape pattern metrics and simpler stand table metricsthat are easily obtained from non-spatial forest inventories. These analyseswere performed on four habitat types common in boreal mixedwood forests: youngdeciduous, old deciduous, white spruce, and mixedwood types. Using only threelandscape variables obtained directly from stand attribute tables (totalhabitatarea, and the mean and standard deviation of habitat patch size), ourstatistical models explained more than 73% of the joint variation in fivelandscape pattern metrics (representing patch shape, forest interior habitat,and patch isolation). By PCA, these five indices captured much of the totalvariability in the rich set of landscape pattern metrics that FRAGSTATS cangenerate. The predictor variables and strengths of association were highlyconsistent across habitat classes. We illustrate the potential use of suchstatistical relationships by simulating the regional, cumulative effects ofwildfire and forest management on the spatial arrangement of forest patches,using non-spatial stand attribute tables.This revised version was published online in May 2005 with corrections to the Cover Date. |
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| Keywords: | Boreal mixedwood forest Alberta Canada Canonical correlation analysis Forest configuration Forest fragmentation Forest inventory data Fragstats Landscape models Landscape pattern metrics Principal components analysis |
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