Non-parametric habitat models with automatic interactions

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  • Questions: Can a statistical model be designed to represent more directly the nature of organismal response to multiple interacting factors? Can multiplicative ernel smoothers be used for this purpose? What advantages does this approach have over more traditional habitat modelling methods? Methods: Non-parametric multiplicative regression (NPMR)was developed from the premises that: the response variable has a minimum of zero and a physiologically-determined maximum, species respond simultaneously to multiple ecological factors, the response to any one factor is conditioned by the values of other factors, and that if any of the factors is intolerable then the response is zero. Key features of NPMR are interactive effects of predictors, no need to specify an overall model form in advance, and built-in controls on overfitting. The effectiveness of the method is demonstrated with simulated and real data sets. Results: Empirical and theoretical relationships of species response to multiple interacting predictors can be represented effectively by multiplicative kernel smoothers. NPMR allows us to abandon simplistic assumptions about overall model form, while embracing the ecological truism that habitat factors interact.
  • Keywords: regression, NPMR, kernel smoothing, species response surface, nonparametric multiplicative regression, habitat model
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  • McCune, B. (2006), Non‐parametric habitat models with automatic interactions. Journal of Vegetation Science, 17: 819-830. doi:10.1111/j.1654-1103.2006.tb02505.x
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  • 17
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  • 6
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