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iForest -Biogeosciences and Forestry
IntroductionExotic species invasions pose a substantial threat to biological integrity and sustainability. In our increasingly global economy, the rate of species' invasions is increasing exponentially, compromising biodiversity and altering ecosystem function. Non-native invasive species can affect plant, animal, and human health, and these invasions can have devastating economic impacts (Schnase et al. 2002). A critical challenge for invasive species management is gaining a complete understanding of the invasion process. Predicting the spatial and temporal dynamics of newly established invaders is crucial to understanding their proliferation and mitigating their impacts.Emerging technologies such as remote sensing improve our ability to better understand factors influencing an invasion, including the invasability of an area, the dynamics of an invasion, predictions about invasiveness (Schnase et al. 2002, Holcombe et al. 2007, and mitigation of these invasions. The combined use of Global Positioning Systems (GPS) and Geographic Information System (GIS) offers a powerful set of tools to record movement and describe the behavior of invasive organisms. In the last few decades GIS-based analyses have been used effectively to investigate the pattern of dispersal of many diverse organisms. Hyperspectral images, GPS collected data and GIS have been used to locate and map invasive plants in California (Underwood et al. 2003), and combined with spatial regression analysis, to identify the parameters affecting their spread (Dark 2004). Remotely sensed data and GIS have also been used to understand invasions by naturalized horticultural imports (Lemke et al. 2011) and woody plants (Rouget et al. 2004).To predict the impacts of the invasive hemlock woolly adelgid, Adelges tsugae Annand (Hemiptera: Adelgidae), in eastern North America, remote sensing and spatial analysis has been used to map the occurrence of the highly susceptible eastern hemlock, Tsuga canadensis (L.) Carr. (Clark et al. 2012). These technologies have also proven effective in modeling the spread of windborne and flying insects (Riley 1989, Reynolds & Riley 2002. Remote sensing and GIS have been used to locate bark beetle (Coleoptera: Curculionidae) populations, characterize the scope and magnitude of infestations, and predict their spread (Wulder et al. 2006). Similarly, gypsy moth, Lymantria dispar L. (Lepidoptera: Lymantriidae), distribution in North America has been described and predicted using GIS and spatial analysis (Liebhold et al. 1992). These technologies have also been used to monitor pests in cropping systems. Carriere et al. (2006) modeled the movement of the lygus bug, Lygus hesperus Knight (Heteroptera: Myridae), through diverse settings. They tracked lygus bug populations within different crops over time, and were able to distinguish between source and sink locations. Clearly GIS-based spatial analyses allow a greater understanding of the biology, behavior and ecology of insects and how they interact...