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Spatial distribution of invasive species: an extent of occurrence approach

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  • Alberto Rodríguez-Casal

    (Universidade de Santiago de Compostela)

  • Paula Saavedra-Nieves

    (Universidade de Santiago de Compostela)

Abstract

Ecological Risk Assessment faces the challenge of determining the impact of invasive species on biodiversity conservation. Although many statistical methods have emerged in recent years in order to model the evolution of the spatio-temporal distribution of invasive species, the notion of extent of occurrence, formally defined by the International Union for the Conservation of Nature, has not been properly handled. In this work, a novel and flexible reconstruction of the extent of occurrence from occurrence data will be established from nonparametric support estimation theory. Mathematically, given a random sample of points from some unknown distribution, we establish a new data-driven method for estimating its probability support S in general dimension. Under the mild geometric assumption that S is $$r-$$ r - convex, the smallest $$r-$$ r - convex set which contains the sample points is the natural estimator. A stochastic algorithm is proposed for determining an optimal estimate of r from the data under regularity conditions on the density function. The performance of this estimator is studied by reconstructing the extent of occurrence of an assemblage of invasive plant species in the Azores archipelago.

Suggested Citation

  • Alberto Rodríguez-Casal & Paula Saavedra-Nieves, 2022. "Spatial distribution of invasive species: an extent of occurrence approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 416-441, June.
  • Handle: RePEc:spr:testjl:v:31:y:2022:i:2:d:10.1007_s11749-021-00783-x
    DOI: 10.1007/s11749-021-00783-x
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    References listed on IDEAS

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    1. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    2. Christopher R. Genovese & Marco Perone-Pacifico & Isabella Verdinelli & Larry Wasserman, 2012. "The Geometry of Nonparametric Filament Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 788-799, June.
    3. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
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