Abstract
Alpine treelines can be explained by lower temperatures with increasing elevation at global scales, but regional and local treelines vary because of additional constraints. We examine data from treelines across the western USA, divided into subregions to elucidate the patterns of these other constraints. We determined the best predictor of elevation for 930 treeline sites in 26 mountain ranges by finding a climate variable with least variance. We used the variance at each site from this predictor to compute an elevation anomaly that became the dependent variable in correlation and regression analyses. We derived independent variables from digital elevation models and regional climate interpolations. A "best subsets" regression, which compares all possible combinations, revealed that at the continental scale, latitude and longitude stand out among many significant but weak correlations between the elevation anomaly and the other variables; these retain importance within regions. Our results show variables related to water and energy play a secondary role. We found that the division of the observations into regions and interpretation of spatial patterns within regions was necessary to interpret the relations between the potential predictor variables and the elevation anomaly.
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