Comparison of six correlative models in predictive vegetation mapping on a local scale
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A variety of statistical techniques has been used in predictive vegetation modelling (PVM) that attempt to predict occurrence of a given community or species in respect to environmental conditions. We compared the performance of three profile models, BIOCLIM, GARP and MAXENT with three nonparametric models of group discrimination techniques, MARS, NPMR and LRT. The two latter models are relatively new statistical techniques that have just entered the field of PVM. We ran all models on a local scale for a given grassland community (Teucrio-Seslerietum) using the same input data to examine their performance. Model accuracy was evaluated both by Cohen’s kappa statistics (κ) and by area under receiver operating characteristics curve based both on resubstitution of training data and on an independent test data set. MAXENT of profile models and MARS of group discrimination techniques achieved the best prediction.
KeywordsPredictive vegetation mapping BIOCLIM GARP MAXENT MARS NPMR LRT Teucrio-Seslerietum Local scale
Predictive vegetation mapping
Genetic algorithm rule-set prediction
Multivariate adaptive regression spline
Nonparametric multiplicative regression
Logistic regression tree
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