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Community Ecology

, Volume 10, Issue 2, pp 215–224 | Cite as

Modelling the spatial distribution of tree species with fragmented populations from abundance data

  • L. Scarnati
  • F. AttorreEmail author
  • A. Farcomeni
  • F. Francesconi
  • M. De Sanctis
Article
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Abstract

Spatial distribution modelling can be a useful tool for elaborating conservation strategies for tree species characterized by fragmented and sparse populations. We tested five statistical models—Support Vector Regression (SVR), Multivariate Adaptive Regression Splines (MARS), Gaussian processes with radial basis kernel functions (GP), Regression Tree Analysis (RTA) and Random Forests (RF)—for their predictive performances. To perform the evaluation, we applied these techniques to three tree species for which conservation measures should be elaborated and implemented: one Mediterranean species (Quercus suber) and two temperate species (Ilex aquifolium and Taxus baccata). Model evaluation was measured by MSE, Goodman-Kruskal and sensitivity statistics and map outputs based on the minimal predicted area criterion. All the models performed well, confirming the validity of this approach when dealing with species characterized by narrow and specialized niches and when adequate data (more than 40–50 samples) and environmental and climatic variables, recognized as important determinants of plant distribution patterns, are available. Based on the evaluation processes, RF resulted the most accurate algorithm thanks to bootstrap-resampling, trees averaging, randomization of predictors and smoother response surface.

Keywords

Ilex aquifolium Gaussian processes with radial basis kernel functions Multivariate adaptive regression splines Potential areas Random forest Regression tree analysis Quercus suber Spatial modelling Support vector regression Taxus baccata 

Abbreviations

GP

Gaussian processes with radial basis kernel functions

IV

Importance Value

MARS

Multivariate Adaptive Regression Splines

RF

Random Forests

RTA

Regression Tree Analysis

SVR

Support Vector Regression

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Copyright information

© Akadémiai Kiadó, Budapest 2009

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • L. Scarnati
    • 1
  • F. Attorre
    • 1
    Email author
  • A. Farcomeni
    • 2
  • F. Francesconi
    • 1
  • M. De Sanctis
    • 1
  1. 1.Department of Plant BiologySapienza University of RomeRomeItaly
  2. 2.Department of Experimental Medicine – Statistics UnitSapienza University of RomeRomeItaly

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