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Environmental and Ecological Statistics

, Volume 19, Issue 3, pp 437–457 | Cite as

Comparison of six correlative models in predictive vegetation mapping on a local scale

  • Mostafa TarkeshEmail author
  • Gottfried Jetschke
Article

Abstract

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.

Keywords

Predictive vegetation mapping BIOCLIM GARP MAXENT MARS NPMR LRT Teucrio-Seslerietum Local scale 

Abbreviations

PVM

Predictive vegetation mapping

GARP

Genetic algorithm rule-set prediction

MAXENT

Maximum entropy

MARS

Multivariate adaptive regression spline

NPMR

Nonparametric multiplicative regression

LRT

Logistic regression tree

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Natural ResourcesIsfahan University of TechnologyIsfahanIran
  2. 2.Institute of EcologyUniversity of JenaJenaGermany

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