Summary1. The many thousands of potential invasive species pose one of the greatest threats to global biodiversity world-wide. In this study we propose that assemblages of well-known global invasive pest species, irrespective of whether they arise by anthropogenic means, are non-random species groupings that contain hidden predictive information. Such information can assist the identification and prioritization of species that have the potential to pose an invasive threat in regions where they are not normally found. 2. Data comprising the presence and absence of 844 insect pest species recorded over 459 geographical regions world-wide were analysed using a self-organizing map (SOM), a well-known artificial neural network algorithm. The SOM analysis classified the high dimensional data into two-dimensional space such that geographical areas that had similar pest species assemblages were organized as neighbours on a map or grid. 3. The SOM analysis allowed each species to be ranked in terms of its risk of invasion in each area based on the strength of its association with the assemblage that was characteristic for each geographical region. A risk map for example species was produced to illustrate how such a map can be compared with the species' actual distribution and used with other information, such as the species' biotic characteristics and interactions with the abiotic environment, to improve pest risk assessments further. 4. Synthesis and applications . This study presents a new approach to the identification of potentially high-risk invasive pest species based on the hypothesis that global insect pest assemblages are non-random species groupings that can be subjected to traditional community analysis. A well-known data mining and knowledge discovery method for high dimensional data, SOM, was used to determine pest species assemblages for global regions. Species were ranked according to their potential for establishment based on their strength of association with the species assemblage that characterizes a particular region. Such an analysis can then be used to support additional risk assessment of potential invasive species, giving invasive species researchers, conservation managers, quarantine and biosecurity scientists a means for prioritizing species as candidates for further research.

Because invasive species threaten the integrity of natural ecosystems, a major goal in ecology is to develop predictive models to determine which species may become widespread and where they may invade. Indeed, considerable progress has been made in understanding the factors that influence the local pattern of spread for specific invaders and the factors that are correlated with the number of introduced species that have become established in a given region. However, few studies have examined the relative importance of multiple drivers of invasion success for widespread species at global scales. Here, we use a dataset of >5,000 presence/absence records to examine the interplay between climatic suitability, biotic resistance by native taxa, humanaided dispersal, and human modification of habitats, in shaping the distribution of one of the world's most notorious invasive species, the Argentine ant (Linepithema humile). Climatic suitability and the extent of human modification of habitats are primarily responsible for the distribution of this global invader. However, we also found some evidence for biotic resistance by native communities. Somewhat surprisingly, and despite the often cited importance of propagule pressure as a crucial driver of invasions, metrics of the magnitude of international traded commodities among countries were not related to global distribution patterns. Together, our analyses on the global-scale distribution of this invasive species provide strong evidence for the interplay of biotic and abiotic determinants of spread and also highlight the challenges of limiting the spread and subsequent impact of highly invasive species.iological invasions can disrupt ecosystem functioning, homogenize biota, and threaten global diversity (1). To mitigate the often dramatic consequences of many invasive species on native ecosystems and the services they provide, a fundamental goal for conservation biology is to be able to predict which species will invade and which areas are most vulnerable to their invasion (2). Despite considerable efforts at both local and regional scales to elucidate the relative roles of biotic and abiotic conditions on the spread and impact of introduced species (e.g., refs. 3-6), understanding which factors limit the global distribution of species is still a largely unanswered question (7).One approach that has been relatively successful is to relate the number of invasive species established in a given area to factors that describe the region. For example, Pyšek et al. recently used up-to-date information on the presence of alien species from a variety of taxa to identify general predictors of the level of invasion (e.g., number of established species) across Europe (8). They found an overwhelming influence of anthropogenic factors (i.e., wealth and demography) in determining the distribution of alien species. Few studies consider the influence of environmental and human-mediated factors in shaping the global distribution of invasive species (8, 9), particularly for single species...

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Aim The purpose of this study was to improve understanding of the relationship between the spatial patterns of an important insect pest, the aphid Myzus persicae, and aspects of its environment. The main objectives were to determine the predominant geographical, climatic and land use factors that are linked with the aphid's distribution, to quantify their role in determining that distribution, including their interacting effects and to explore the ability of artificial neural networks (ANNs) to provide predictive models. (Regio data base coverage); North-West Europe (i.e. Belgium, France and the United Kingdom); and England with Wales.Methods Multiple linear regression (MLR) was used to identify the variables in the Geographic location, Climate and Land use groups, that explained significant proportions of the variance in M. persicae total annual numbers and Julian date of first capture. A variance partitioning procedure was used to measure the fraction of the variation that can be explained by each environmental factor and of shared variation between the different factors. Finally, ANNs were employed as an alternative modelling approach for the two largest study areas, i.e. Europe and the Regio data base coverage, to determine whether the relationship between aphid and environmental variables was better described by more complex functions as well as their ability to generalize to new data. ResultsLand use variables are shown to play a significant role in explaining aphid numbers. The area of agricultural crops, in particular oilseed rape, is positively correlated with M. persicae annual numbers. Among the climatic variables, rainfall is negatively correlated with aphid numbers and temperature is positively correlated. The geographical components also explain a significant part of aphid annual numbers. However, the variance partitioning procedure indicates that while each group has an effect, none is dominant. Aphid first capture is mainly explained by climate where rainfall tends to delay migration and warmer conditions tend to advance it. Climate accounts for the greatest part of the variance when considered separately from the other factors. The geographical and land use components also have a significant effect on first capture at each scale, but their direct contribution is negligible. The ability of the ANN models to generalize to new total numbers and phenological data compared with MLR models was less for Europe (9 and 6% increase in the variance accounted for, respectively) than for the Regio data coverage where an increase of 44% in the variance accounted for was observed.

The characteristics of spread for an invasive species should influence how environmental authorities or government agencies respond to an initial incursion. High-resolution predictions of how, where, and the speed at which a newly established invasive population will spread across the surrounding heterogeneous landscape can greatly assist appropriate and timely risk assessments and control decisions. The Argentine ant (Linepithema humile) is a worldwide invasive species that was inadvertently introduced to New Zealand in 1990. In this study, a spatially explicit stochastic simulation model of species dispersal, integrated with a geographic information system, was used to recreate the historical spread of L. humile in New Zealand. High-resolution probabilistic maps simulating local and human-assisted spread across large geographic regions were used to predict dispersal rates and pinpoint at-risk areas. The spatially explicit simulation model was compared with a uniform radial spread model with respect to predicting the observed spread of the Argentine ant. The uniform spread model was more effective predicting the observed populations early in the invasion process, but the simulation model was more successful later in the simulation. Comparison between the models highlighted that different search strategies may be needed at different stages in an invasion to optimize detection and indicates the influence that landscape suitability can have on the long-term spread of an invasive species. The modeling and predictive mapping methodology used can improve efforts to predict and evaluate species spread, not only in invasion biology, but also in conservation biology, diversity studies, and climate change studies.