Spatial distribution of invasive species: an extent of occurrence approach
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DOI: 10.1007/s11749-021-00783-x
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- 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.
- 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.
- 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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Keywords
Ecological risk; Extent of occurrence (EOO); Invasive species; Spacing; Testing $$r-$$ r - convexity;All these keywords.
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