Forest structure as a predictor of tree species diversity in the North Carolina Piedmont

Questions Can forest structure significantly predict tree species diversity in the forests of the North Carolina Piedmont? If so, which structural attributes are most correlated with it, and how effective are they when used in concert in a generalized predictive model of tree species diversity? Location North Carolina Piedmont, USA. Methods Using a set of geographically distributed Forest inventory and analysis (FIA) plots (n = 972), we analysed Spearman correlations between 15 measures of forest structure and five indices of tree species diversity. We predict tree species diversity based on structural predictors using support vector regression (SVR) models, assessing model fit via ten-fold cross-validation. Results Results show a consistent and significant relationship between most structural attributes and indices of tree species diversity. Among all structural predictors, maximum height, basal area size inequality (basal area Gini coefficient) and skewness of the basal area distribution (Weibull shape) exhibited the strongest correlations with indices of tree species diversity. Predictive SVR models trained solely with structural attributes explained 44–61% of the variance in tree species diversity in the full Piedmont data set, and 22–71% of the variance in subsets defined by stand origin and forest type. Conclusions Results confirm that forest structure alone was able to predict a substantial portion of the variance in tree species diversity without accounting for other known predictors of diversity in the North Carolina Piedmont, such as environment, soil conditions and site history. Beyond the theoretical implications of unravelling primary patterns underlying tree species diversity, these findings highlight the empirical basis and potential for utilizing forest structure in predictive models of tree species diversity over large geographic regions.

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