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Classifying forest inventory data into species-based forest community types at broad extents： exploring tradeoffs among supervised and unsupervised approaches

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Forest Ecosystems 2018年第1期5卷 91-107页

作者： jennifer k.costanza don faber-langendoen john w.coulston david n.wear Department of Forestry and Environmental Resources North C arolina State University3041 Cornwalis RdResearch Triangle ParkRaleighNC 27709USA natureserve 4600 N.Fairfax Dr7th FloorArlingtonVA 22203USA Southern Research Station USDA Forest ServiceBlacksburgVAUSA Southern Research Station USDA Forest ServiceRaleighNCUSA

Background： Knowledge of the different kinds of tree communities that currently exist can provide a baseline for assessing the ecological attributes of forests and monitoring future changes. Forest inventory data can facilitate the development of this baseline knowledge across broad extents, but they first must be classified into forest community types. Here, we compared three alternative classifications across the United States using data from over 117,000 U.S. Department of Agriculture Forest Service Forest Inventory and Analysis （FIA） plots. Methods： Each plot had three forest community type labels：（1）＂FIA＂ types were assigned by the FIA program using a supervised method; （2）＂USNVC＂ types were assigned via a key based on the U.S. National Vegetation Classification; （3）＂empirical＂ types resulted from unsupervised clustering of tree species information. We assessed the degree to which analog classes occurred among classifications, compared indicator species values, and used random forest models to determine how well the classifications could be predicted using environmental variables. Results： The classifications generated groups of classes that had broadly similar distributions, but often there was no one-to-one analog across the classifications. The Iongleaf pine forest community type stood out as the exception： it was the only class with strong analogs across all classifications. Analogs were most lacking for forest community types with species that occurred across a range of geographic and environmental conditions, such as Ioblolly pine types, indicator species metrics were generally high for the USNVC, suggesting that LJSNVC classes are floristically well-defined. The empirical classification was best predicted by environmental variables. The most important predictors differed slightly but were broadly similar across all classifications, and included slope, amount of forest in the surrounding landscape, average minimum temperature, and other climate variab

关键词： Big data Correspondence analysis Dominant species Forest communities Global change,Hierarchical classification Indicator species Random forests Species assemblages

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