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

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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.

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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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Authors and Affiliations

  1. Department of Natural Resources, Isfahan University of Technology, 84156-83111, Isfahan, Iran

    Mostafa Tarkesh

  2. Institute of Ecology, University of Jena, Dornburger Straße 159, 07743, Jena, Germany

    Gottfried Jetschke

Authors
  1. Mostafa Tarkesh
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  2. Gottfried Jetschke
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Correspondence to Mostafa Tarkesh.

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Tarkesh, M., Jetschke, G. Comparison of six correlative models in predictive vegetation mapping on a local scale. Environ Ecol Stat 19, 437–457 (2012). https://doi.org/10.1007/s10651-012-0194-3

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  • DOI: https://doi.org/10.1007/s10651-012-0194-3

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