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Six empirical classes and five FIA classes in the eastern U.S. had low similarity in species composition on average with all classes in the other classification.
Examples of those classes are the black willow and cedar elm empirical classes, and the other eastern softwoods and other hardwoods FIA classes.
Those included the honey mesquite-Pinchot juniper and chokecherry-Pacific dogwood empirical classes, and the North American Boreal Conifer Poor Swamp USNVC class, likely because those empirical classes were largely determined by western species.
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In terms of average species composition, the highest similarity among pairs of classes in the empirical and USNVC classifications was between the slash pine-longleaf pine empirical class and the Longleaf Pine Woodland USNVC class (Fig. 4a).
When comparing the FIA and empirical classes in the eastern U.S. in terms of species composition, similar analogs stand out.
For assigning plots to empirical classes, calculating species composition similarity, and analyzing indicator and dominant species, we needed information on species composition within each plot.
The Utah juniper-two needle pinyon and alligator juniper-Arizona white oak empirical classes were associated with the pinyon/juniper FIA class, and three empirical groups containing western oak species were associated with the western oak FIA class.
Considering this, it is not surprising that recent environmental data would predict the empirical classes better than the FIA classes.
The largest empirical classes in terms of numbers of plots had relatively high similarities in species composition with more than one FIA class.
The empirical classes are the result of an unsupervised cluster analysis of all tree species in FIA plots (Costanza et al. 2017).
Fig. 5 Variable importance plots for each random forest model: (a) FIA classes in the eastern U.S.; (b) empirical classes in the eastern U.S.; (c) USNVC classes; (d) FIA classes across the U.S.; (e) empirical classes across the U.S. See Table 2 for a description of each variable.
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