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Background: In recent studies, mixed forests were found to be more productive than monocultures with everything else remaining the same.Methods: To find out if this productivity is caused by tree species richness, by a more heterogeneous stand structure or both, we analyzed the effects of forest structure and tree species richness on stand productivity, based on inventory data of temperate forests in the United States of America and Germany.Results: Having accounted for effects such as tree size and stand density, we found that: (I) tree species richness increased stand productivity in both countries while the effect of tree size heterogeneity on productivity was negative in Germany but positive in the USA; (II) productivity was highest at sites with an intermediate amount of precipitation; and (III) growth limitations due water scarcity or low temperature may enhance structural heterogeneity.Conclusions: In the context of forest ecosystem goods and services, as well as future sustainable forest resource management, the associated implications would be:Tree species richness is vital for maintaining forest productivity.As an optimum amount of precipitation is accompanied by the highest productivity, changes in climatic conditions should be considered when planning.Resource limitations enhance structural heterogeneity, which in turn can have positive or negative effects on stand productivity.Furthermore, we discuss the difficulties encountered when analyzing different national forest inventories and large data sets. [ABSTRACT FROM AUTHOR]

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 longleaf 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 loblolly pine types. Indicator species metrics were generally high for the USNVC, suggesting that USNVC 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 variables.Conclusions: The classifications have similarities and differences that reflect their differing approaches and objectives. They are most consistent for forest community types that occur in a relatively narrow range of environmental conditions, and differ most for types with wide-ranging tree species. Environmental variables at a variety of scales were important for predicting all classifications, though strongest for the empirical and FIA, suggesting that each is useful for studying how forest communities respond to of multi-scale environmental processes, including global change drivers. [ABSTRACT FROM AUTHOR]