Predicting vegetation types at treeline using topography and biophysical disturbance variables

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Abstract
The relationships between four vegetation types and variables representing topography and biophysical disturbance gradients were modeled for a study area in east‐central Glacier National Park, Montana. Four treeline transition vegetation types including closed‐canopy forest, open‐canopy forest, meadow, and unvegetated surfaces (e.g. rock, snow, and ice) were identified and mapped through classification of satellite data and subsequent field verification. Topographic characteristics were represented using a digital elevation model and three variables derived from topoclimatic potential models (solar radiation potential, snow accumulation potential, and soil saturation potential). A combination of generalized additive and generalized linear modeling (GAM and GLM, respectively) techniques was used to construct logistic regression models representing the distributions of the four vegetation types. The variables explained significant amounts of variation in the vegetation types, but high levels of variation remained unexplained. A comparison of ‘expected’ and ‘observed’ vegetation patterns suggested that some unexplained variation may have occurred at the basin scale. A suite of tools and techniques is presented that facilitates predicting landscape‐scale vegetation patterns and testing hypotheses about the spatial controls on those patterns.