Get access

Accounting for multi-scale spatial autocorrelation improves performance of invasive species distribution modelling (iSDM)

Authors

  • Tomáš Václavík,

    Corresponding author
    1. Center for Applied GIScience, Department of Geography and Earth Sciences, University of North Carolina, 9201 University City Blvd, Charlotte, NC 28223, USA
      Tomáš Václavík, Department of Ecology and Environmental Sciences, Faculty of Science, Palacký University, tř. Svobody 26, 771 46 Olomouc, Czech Republic and Department of Computational Landscape Ecology, UFZ – Helmholtz Centre for Environmental Research, Permoserstraße 15, 04318 Leipzig, Germany.
      E-mail: tova@email.cz
    Search for more papers by this author
  • John A. Kupfer,

    1. Department of Geography, University of South Carolina, Columbia, SC 29208, USA
    Search for more papers by this author
  • Ross K. Meentemeyer

    1. Center for Applied GIScience, Department of Geography and Earth Sciences, University of North Carolina, 9201 University City Blvd, Charlotte, NC 28223, USA
    Search for more papers by this author

Tomáš Václavík, Department of Ecology and Environmental Sciences, Faculty of Science, Palacký University, tř. Svobody 26, 771 46 Olomouc, Czech Republic and Department of Computational Landscape Ecology, UFZ – Helmholtz Centre for Environmental Research, Permoserstraße 15, 04318 Leipzig, Germany.
E-mail: tova@email.cz

Abstract

Aim  Analyses of species distributions are complicated by various origins of spatial autocorrelation (SAC) in biogeographical data. SAC may be particularly important for invasive species distribution models (iSDMs) because biological invasions are strongly influenced by dispersal and colonization processes that typically create highly structured distribution patterns. We examined the efficacy of using a multi-scale framework to account for different origins of SAC, and compared non-spatial models with models that accounted for SAC at multiple levels.

Location  We modelled the spatial distribution of an invasive forest pathogen, Phytophthora ramorum, in western USA.

Methods  We applied one conventional statistical method (generalized linear model, GLM) and one nonparametric technique (maximum entropy, Maxent) to a large dataset on P. ramorum occurrence (= 3787) to develop four types of model that included environmental variables and that either ignored spatial context or incorporated it at a broad scale using trend surface analysis, a local scale using autocovariates, or multiple scales using spatial eigenvector mapping. We evaluated model accuracies and amounts of explained spatial structure, and examined the changes in predictive power of the environmental and spatial variables.

Results  Accounting for different scales of SAC significantly enhanced the predictive capability of iSDMs. Dramatic improvements were observed when fine-scale SAC was included, suggesting that local range-confining processes are important in P. ramorum spread. The importance of environmental variables was relatively consistent across all models, but the explanatory power decreased in spatial models for factors with strong spatial structure. While accounting for SAC reduced the amount of residual autocorrelation for GLM but not for Maxent, it still improved the performance of both approaches, supporting our hypothesis that dispersal and colonization processes are important factors to consider in distribution models of biological invasions.

Main conclusions  Spatial autocorrelation has become a paradigm in biogeography and ecological modelling. In addition to avoiding the violation of statistical assumptions, accounting for spatial patterns at multiple scales can enhance our understanding of dynamic processes that explain ecological mechanisms of invasion and improve the predictive performance of static iSDMs.

Get access to the full text of this article

Ancillary