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

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 (n = 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.

Climatic niche shift of aquatic plant invaders between native and invasive ranges: a test using 10 species across different biomes on a global scale

“…MAXENT (ver.3.3.3; http:// www.cs.princeton.edu/∼schapire/maxent/; accessed in August 2016) was used as a common ENM to model native and invasive niches of APIs in climatic space. This was done based on bioclimatic variables and occurrence localities using native (native model), invasive ranges (invasive model), and both native and invasive ranges (all model) (Warren et al, 2008;Václavík et al, 2012). All pixels were regarded as parts of the possible climate space of native and invasive ranges for the globally distributed APIs (Kolanowska, 2013).…”

“…All pixels were regarded as parts of the possible climate space of native and invasive ranges for the globally distributed APIs (Kolanowska, 2013). A logistic output format was used to visualize the identified potential niches of these APIs in the climate space of native and invasive ranges based on MAXENT modeling (Václavík et al, 2012). For climatic niche maps based on native and invasive ranges, each pixel had a value ranging from 0 to 1, with 0 representing the lowest climatic suitability of (not suitable at all) and representing 1 the highest climatic suitability (completely suitable) (Phillips et al, 2006;Warren et al, 2008).…”

Incorporating movement in species distribution models

“…For example, Václavík et al (2012) used autocovariate logistic regression and spatial eigenvector modelling to incorporate spatial information into the model outputs in order to constrain projections of Sudden Oak Death in California, and found that these proxies for dispersal better predicted the presence of the pathogen compared to models calibrated only on abiotic factors. Invasive organism distributions are often driven by factors beyond environmental controls, and so a direct set of abiotic and biotic predictors may not plausible.…”

Accounting for and Predicting the Influence of Spatial Autocorrelation in Water Quality Modeling

“…Many spatial approaches have been developed in order to overcome such limitations of non-spatial counterparts. These approaches include, but are not restricted to, regression kriging, simultaneous autoregressive modeling, conditional autoregressive modeling, spatial lag modeling, spatial error modeling, spatial eigenvector mapping, and geographically weighted regression [8,9,[17][18][19][20][21][22][23][24][25][26].…”