Classification trees: An alternative non‐parametric approach for predicting species distributions

Classification trees: An alternative non‐parametric approach for predicting species distributions Abstract. The use of Generalized Linear Models (GLM) in vegetation analysis has been advocated to accommodate complex species response curves. This paper investigates the potential advantages of using classification and regression trees (CART), a recursive partitioning method that is free of distributional assumptions. We used multiple logistic regression (a form of GLM) and CART to predict the distribution of three major oak species in California. We compared two types of model: polynomial logistic regression models optimized to account for non‐linearity and factor interactions, and simple CART‐models. Each type of model was developed using learning data sets of 2085 and 410 sample cases, and assessed on test sets containing 2016 and 3691 cases respectively. The responses of the three species to environmental gradients were varied and often non‐homogeneous or context dependent. We tested the methods for predictive accuracy: CART‐models performed significantly better than our polynomial logistic regression models in four of the six cases considered, and as well in the two remaining cases. CART also showed a superior ability to detect factor interactions. Insight gained from CART‐models then helped develop improved parametric models. Although the probabilistic form of logistic regression results is more adapted to test theories about species responses to environmental gradients, we found that CART‐models are intuitive, easy to develop and interpret, and constitute a valuable tool for modeling species distributions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Vegetation Science Wiley

Classification trees: An alternative non‐parametric approach for predicting species distributions

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Publisher
Wiley
Copyright
2000 IAVS ‐ the International Association of Vegetation Science
ISSN
1100-9233
eISSN
1654-1103
DOI
10.2307/3236575
Publisher site
See Article on Publisher Site

Abstract

Abstract. The use of Generalized Linear Models (GLM) in vegetation analysis has been advocated to accommodate complex species response curves. This paper investigates the potential advantages of using classification and regression trees (CART), a recursive partitioning method that is free of distributional assumptions. We used multiple logistic regression (a form of GLM) and CART to predict the distribution of three major oak species in California. We compared two types of model: polynomial logistic regression models optimized to account for non‐linearity and factor interactions, and simple CART‐models. Each type of model was developed using learning data sets of 2085 and 410 sample cases, and assessed on test sets containing 2016 and 3691 cases respectively. The responses of the three species to environmental gradients were varied and often non‐homogeneous or context dependent. We tested the methods for predictive accuracy: CART‐models performed significantly better than our polynomial logistic regression models in four of the six cases considered, and as well in the two remaining cases. CART also showed a superior ability to detect factor interactions. Insight gained from CART‐models then helped develop improved parametric models. Although the probabilistic form of logistic regression results is more adapted to test theories about species responses to environmental gradients, we found that CART‐models are intuitive, easy to develop and interpret, and constitute a valuable tool for modeling species distributions.

Journal

Journal of Vegetation ScienceWiley

Published: Oct 1, 2000

References

  • Continuum concept, ordination methods, and niche theory
    Austin, Austin
  • Determining alternative models for vegetation response analysis: a non‐parametric approach
    Bio, Bio; Alkemade, Alkemade; Barendregt, Barendregt
  • Predicting the distribution of shrub species in southern California from climate and terrain‐derived variables
    Franklin, Franklin

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