The biogeography of invasive alien plants in California: an application of GIS and spatial regression analysis

Abstract

The spatial distribution of invasive alien plants has been poorly documented in California. However, with the increased availability of GIS software and spatially explicit data, the distribution of invasive alien plants can be explored. Using bioregions as defined in Hickman (1993), I compared the distribution of invasive alien plants (n = 78) and noninvasive alien plants (n = 1097). The distribution of both categories of alien plants was similar with the exception of a higher concentration of invasive alien plants in the North Coast bioregion. Spatial autocorrelation analysis using Moran's I indicated significant spatial dependence for both invasive and noninvasive alien plant species. I used both ordinary least squares (OLS) and spatial autoregressive (SAR) models to assess the relationship between alien plant species distribution and native plant species richness, road density, population density, elevation, area of sample unit, and precipitation. The OLS model for invasive alien plants included two significant effects; native plant species richness and elevation. The SAR model for invasive alien plants included three significant effects; elevation, road density, and native plant species richness. The SAR model for noninvasive alien plants resulted in the same significant effects as invasive alien plants. Both invasive and noninvasive alien plants are found in regions with low elevation, high road density, and high native‐plant species richness. This is in congruity with previous spatial pattern studies of alien plant species. However, the similarity in effects for both categories of alien plants alludes to the importance of autecological attributes, such as pollination system, dispersal system and differing responses to disturbance in the distribution of invasive plant species. In addition, this study emphasizes the critical importance of testing for spatial autocorrelation in spatial pattern studies and using SAR models when appropriate.

Conservation Biogeography: assessment and prospect

“…For instance, we have not explicitly discussed the many technical issues concerned with the proper analysis of spatially distributed data, and especially the concerns over spatial autocorrelation, and other artefacts such as the so-called geometric constraints problem (see, e.g. Diniz-Filho et al, 2003;Colwell et al, 2004;Dark, 2004).…”

Stray cats in Auckland, New Zealand: Discovering geographic information for exploratory spatial analysis

“…When results of global regression analysis such as OLS insufficiently describe the spatial relationships or when spatial autocorrelation (inherent in geographic data with nearby features more likely to be similar than farther ones hence violating assumed independence in error distribution) is present, local regression using geographically weighted regression (GWR) is normally employed (Fotheringham, Brunsdon, & Charlton, 2002;Legendre, 1993). GWR addresses the assumption in traditional statistics that the relationship is spatially constant with variables remaining constant over spatial distances (Dark, 2004;Mitchell, 2005;Shi et al, 2006). GWR includes spatial influences by deriving the local regression of each location and calibrating parameters using distance weighted neighbourhood observations.…”

Spatial autocorrelation of mammalian richness and abundance along the Cerrado-Pantanal corridor, Central Brazil

“…Understanding the spatial structure of abundance and species richness has been traditionally one of the main concerns of ecological research (Krebs, 1994), both to understand ecological and evolutionary processes underlying these patterns and, more recently, to use these pieces of information to drive conservation efforts Rouget et al, 2003;Dark, 2004). Handling spatial data, thus, becomes a central issue in conservation programs worldwide, at different scales.…”

“…This way, autocorrelation analyses can be used as a powerful tool to describe spatial patterns in ecological variables. At the same time, testing statistical hypotheses using standard methods (e.g., ANOVA, correlation and regression) in the presence of spatially autocorrelated data deserves special concern, since the standard errors of estimates are usually underestimated and, consequently, Type I errors may be strongly inflated (Legendre, 1993;Dark, 2004). Recent papers have discussed the importance of measuring spatial autocorrelation when evaluating problems in different fields of ecological research, including the analysis of latitudinal gradients in species richness, the relationship between local and regional richness, spatial patterns in community structure, spatial synchrony in population dynamics and conservation biology (see Koenig, 1998Koenig, , 1999Koenig and Knops, 1998;Diniz-Filho and Telles, 2002;Manel et al 2003;Diniz-Filho et al 2003 andEscudero et al, 2003, for recent reviews).…”