Invasive Risk Assessment Research Articles

Evaluation of preoperative endoscopic ultrasonography for diagnosis and invasive risk assessment of gastric gastrointestinal stromal tumors: a single-center retrospective study

Objective To evaluate preoperative endoscopic ultrasonography(EUS)for diagnosis and invasive risk assessment of gastric gastrointestinal stromal tumors(GISTs). Methods A total of 248 patients diagnosed as having gastric GISTs based on preoperative EUS and with established pathologic results at our institution were recruited to the study. The coincidence rates for gastric GISTs between preoperative EUS and pathologic results were calculated. Univariate and multivariate analyses were used to explore the possible predictors for the coincidence rates. EUS features were compared according to histological risk classification of very low/low risk malignant potential or intermediate/high risk malignant potential. Results The overall coincidence rate for gastric GISTs between EUS and pathologic results was 67.7%(168/248). Univariate analysis showed that tumor diameter, mucosal bridge, heterogeneity, and calcification were significantly correlated with the coincidence rate for gastric GISTs between preoperative EUS and pathologic results. Multivariate analysis indicated that heterogeneity (OR=2.53, 95%CI: 1.23-5.18, P=0.011) and calcification(OR=2.25, 95%CI: 1.34-6.56, P=0.045) were the independent factors for the coincidence rate. A total of 168 patients were divided into four groups according to histological risk: very low risk(n=63), low risk(n=62), intermediate risk(n=26), and high risk(n=17). Gastric GISTs with heterogeneity, mucosal ulceration, and calcification on EUS suggested higher risk GISTs. Multivariate analysis indicated that only tumor diameter(P<0.000 1)could predict malignant potential of gastric GISTs. Conclusion Preoperative EUS plays an role in diagnosing and evaluating of invasive risk for gastric GISTs, which might aid the clinical management of gastric GISTs. Key words: Risk assessment; Mesenchymoma; Endosonography

Disregarding human pre-introduction selection can confound invasive crayfish risk assessments

Trait-based risk assessments of invasive species focus on identifying intrinsic biological or ecological traits associated with invasion success, which allows for a new species’ invasion risk to be assessed a priori, thus facilitating cost-effective prevention strategies. However, human preferences for species traits—preferences that might affect which species enter into different pathways of invasion—exist for taxa closely associated with people. Disregarding such preferences can confound correlations between species traits and invasion success. Here we develop a risk assessment for crayfish, a group of culturally and ecologically important decapod crustaceans with numerous harmful invasive species, that explicitly accounts for species traits as well as human preferences as they are expressed in different pathways (e.g., aquaculture, live angling bait for fishing, harvesting). Our results indicate that species traits and human preferences are confounded for introduction and establishment risk models, but subsequent spread risk is not associated with human preferences and can be predicted by clutch size. Although not commonly addressed, this study demonstrates that accounting for human preferences in trait-based risk assessments is important for taxa closely associated with people, as pre-introduction human selection of traits may bias such analyses.

Predicting alien herb invasion with machine learning models: biogeographical and life-history traits both matter

Identifying the variables associated with invasiveness is a core task for developing risk assessment models to predict invasion potential. However, quantitative models with both biogeographical and life-history variables for invasion risk assessment in China are limited. We hypothesized that (1) compared to statistical algorithms, some machine learning models could offer a promising quantitative approach with high accuracy for potential invader prediction; (2) native range distribution size, origins and life-history traits co-determine an alien plant’s performance in the latter invasion stage. In this study, we used four machine learning models [classification and regression tree (CART), multivariate adaptive regression spline (MARS), random forest (RF) and multiple additive regression tree (MART)] and two traditional statistical algorithms [logistic regression (LR) and linear discriminant analysis (LDA)] to assess the relative importance of biogeographical and trait variables in the naturalized-invasion stage of 150 invasive and 87 non-invasive herb plants in China. Our results showed that good performance was the case for all predictive models (AUROC ranges from 0.68 to 0.87), which had overall mean performance value ranging from 0.66 to 0.82. Compared with traditional statistical algorithms, MART and RF models have a consistently higher accuracy, indicating that these two models could be used as alternative quantitative approaches for risk assessment. Additionally, both biogeographical (native range distribution size) and life-history traits (seed weight) were screened out by the models, suggesting their high correlation with plant invasiveness and important roles in risk assessment.

Australian acacias as invasive species: lessons to be learnt from regions with long planting histories

Problems associated with invasiveness of non-native tree species used in forestry are increasing rapidly worldwide and are most severe in areas with a long history of plantings. Lessons learnt in areas with long histories of plantings and invasions may be applicable to areas with shorter planting histories. Most research towards understanding such tree invasions has focused on Pinus species, though all groups of trees that have been widely used in forestry are invasive to some extent. This paper explores the experience of Australian Acacia species (wattles). Unlike some other groups of trees, no particular set of traits clearly separates highly invasive from less- or non-invasive wattles. All species that have been widely planted over a long period have become invasive; the extent of invasions is largely a function of human usage. These findings imply that propagule pressure in concert with residence times are the main drivers of invasiveness in wattles (many factors mediate these drivers, including fire, forest clearance and soil disturbance). The massive extent of recent plantings of Australian Acacia species in South-east Asia is therefore likely to result in large-scale invasions unless proactive management is implemented. The history of wattles in South Africa highlights the need for such proactive management. Wattles were of considerable net value to the South African economy immediately after introduction. However, the costs of wattle invasions increased over time to such an extent that (certainly over the last few decades) these costs exceed the benefits derived from the forestry industry. Wattles now dominate many natural ecosystems. We recommend several interventions to prevent a similar pattern in South-east Asia and to ensure the sustainability of plantation forestry based on wattles in the region. A spatially explicit assessment of invasion risk is required, and a monitoring system should be implemented. Cost–benefit analyses (that consider the full suite of perspectives relating to costs and benefits) need to be applied to determine the need for sustainable mitigation methods. Options for reducing potential invasiveness should be implemented; these include biological control targeting seed production (very good success has been achieved in South Africa) and the use of sterile cultivars.

Invasive weed risk assessment of three potential bioenergy fuel species.

Bioenergy crops are potential renewable sources of bio-diesel which have low emission profiles, environmentally beneficial, and capable of substituting petro-diesel. However, since most of them are introduced or are not native, it is essential to reduce the ecological and economic consequences of invasive pest introductions and the potential invasiveness of species not yet introduced. The Australian Weed Risk Assessment (WRA) is a plant screening method and has the highest accuracy. The objective of this study was to conduct an agronomic and invasive weed risk assessment of three potential bioenergy fuel species namely: moringa (Moringa oliefera), physic nut (Jatropha curcas), and castor bean (Rincinus communis) for the Caribbean Islands. The WRA gave overall scores for moringa (0), jatropha (13) and castor oil (13). Based on their climatic adaptation and distribution, jatropha (5) and castor oil (5), the dispersal mechanism score was high (5) for both of them. The study revealed that jatropha and castor bean should not be considered as bioenergy crops within the ecological limits of the study, and that moringa should be further evaluated as bioenergy crop against invasiveness, given its agronomic potential as a high yielding oil crop. Key words: Weed risk assessment, Moringa oliefera, Jatropha curcas, Rincinus communis, bioenergy plants.

Resolving regulatory uncertainty: legislative language for potentially invasive bioenergy feedstocks

AbstractConcerns about invasions by novel bioenergy feedstocks are valid, given the parallels between the traits of energy crops and those of many common invasive plants. As the bioenergy industry is poised to introduce nonnative bioenergy crops to large acreages in the United States under state and federal mandates, it is important to consider these concerns – and not simply in an academic sense. Instead, the prevention of invasions should be codified in statutes and regulations pertaining to bioenergy production on both the state and federal level. Unfortunately, this is not occurring regularly or consistently at this time. The few existing regulations that do consider invasiveness in bioenergy systems suffer from vague terminology that could have major economic, environmental, and legal consequences. Here, we discuss existing regulatory challenges and provide solutions to address invasion potential of bioenergy crops. We provide model definitions and provisions to be included in revised or new state and federal regulations, including an invasion risk assessment process, a permit and bond system for high‐risk crops, and a risk mitigation provision for all novel crops. Our proposal provides a consistent and transparent system that will allow the industry to move forward with minimal risk of invasion by novel feedstocks.

Updated invasion risk assessment for Ponto-Caspian fishes to the Great Lakes

Majority of invasive species discovered in the Great Lakes since 1985 are native to the Ponto-Caspian region, including species that have had strong negative impacts in the Great Lakes (for example, dreissenid mussels and the round goby). The rich biota of the Ponto-Caspian region coupled with a high volume of commercial shipping traffic strongly suggests that this region will continue to be a major source of invasive species to the Great Lakes. To assess invasion risk by Ponto-Caspian fishes that have not been included in previous studies, we reviewed English-language publications and untranslated European literature (published primarily in Russian), focusing on physiological and ecological traits that have proven useful in previous risk assessments. We then used discriminant analysis to identify fishes that had a high probability of becoming established, spreading, and having significant negative impacts in the Great Lakes. Our updated listing of high-risk Ponto-Caspian fishes includes five species identified previously (the Black and Caspian Sea sprat, Eurasian minnow, big-scale sand smelt, European perch, and monkey goby) and five additional species (the Black sea shad, Caspian tyulka, Volga dwarf goby, Caspian bighead goby, and black-striped pipefish). Of these ten species, four (the monkey goby, big-scale sand smelt, Caspian tyulka, and black-striped pipefish) are likely to survive ballast water exchange as eggs, larvae, or adults based on salinity tolerances. Our results can be used to focus ongoing surveillance and rapid response efforts by highlighting Ponto-Caspian fishes that pose the greatest risk of becoming established and having significant negative impacts in the Great Lakes.

Abstract P332: Framingham Risk Score to Predict Subsequent Coronary Artery Calcification in a Young Adult Population

Introduction: Coronary artery calcification (CAC) has been identified as an independent predictor of coronary events, suggesting the potential use of CAC for risk assessment in asymptomatic individuals. However, ionizing radiation exposure associated with CAC CT scans remains a concern. If elevated risk for developing subclinical cardiovascular disease (CVD) could be identified based on less invasive risk assessment, such as the Framingham risk score, intensified prevention and screening services could be provided to this targeted population. Hypothesis: This study aims to assess the association between the Framingham risk score in early adulthood and subsequent subclinical CVD measured by CAC. Additional risk factors including demographics, socioeconomic status and health behaviors were tested in terms of their capabilities to enhance prediction of subclinical CVD beyond the Framingham risk score. Methods: This study used the Coronary Artery Risk Development in Young Adults (CARDIA) data, with a total of 5,115 Caucasian and African American males and females. Information collected at examination year 10 was used to calculate the Framingham risk score. CAC was measured ten years later (examination year 20). Participants’ demographics, health behaviors (alcohol consumption, BMI, and exercise), socioeconomic status and medical needs at year 10 were identified as potential risk factors associated with the subsequent presence of CAC beyond the Framingham risk score. Multiple logistic regression was used to examine the adjusted association between CAC, Framingham risk score and proposed risk factors. Model comparison was estimated using the area under the receiver operating characteristic curve (AUC) and Akaike information criterion (AIC). Results: By year 20, CAC was present in 19% of the CARDIA population. The Framingham risk score in young adulthood was strongly associated with the subsequent presence of CAC ten years later, regardless of race and gender. Overall, 42% of the CARDIA participants with elevated Framingham risk scores at year 10 had CAC at year 20, compared to 16% of participants with normal scores. The Framingham risk score may underestimate the risk of CAC for males compared to females (Negative Predictive Value: 75% vs. 91%). Beyond the Framingham risk score, the subsequent presence of CAC was associated with being overweight or obese in all populations, at-risk alcohol consumption in African American males, and having high school level or lower education and financial hardship in African American females. Conclusions: Our findings support the potential use of the Framingham risk score as a screening tool for subsequent subclinical atherosclerosis in a young adult population. However, other gender-specific risk factors beyond the Framingham risk score such as obesity also may be important to better predict subclinical CVD risk, especially in male populations.

Assessing Effects of Variation in Global Climate Data Sets on Spatial Predictions from Climate Envelope Models

Abstract Climate change poses new challenges for natural resource managers. Predictive modeling of species–environment relationships using climate envelope models can enhance our understanding of climate change effects on biodiversity, assist in assessment of invasion risk by exotic organisms, and inform life-history understanding of individual species. While increasing interest has focused on the role of uncertainty in future conditions on model predictions, models also may be sensitive to the initial conditions on which they are trained. Although climate envelope models are usually trained using data on contemporary climate, we lack systematic comparisons of model performance and predictions across alternative climate data sets available for model training. Here, we seek to fill that gap by comparing variability in predictions between two contemporary climate data sets to variability in spatial predictions among three alternative projections of future climate. Overall, correlations between monthly temperature and precipitation variables were very high for both contemporary and future data. Model performance varied across algorithms, but not between two alternative contemporary climate data sets. Spatial predictions varied more among alternative general-circulation models describing future climate conditions than between contemporary climate data sets. However, we did find that climate envelope models with low Cohen's kappa scores made more discrepant spatial predictions between climate data sets for the contemporary period than did models with high Cohen's kappa scores. We suggest conservation planners evaluate multiple performance metrics and be aware of the importance of differences in initial conditions for spatial predictions from climate envelope models.

Spatial analysis facilitates invasive species risk assessment

Regional scale quantitative invasion risk analyses are needed to allow early detection and rapid response in order to effectively control the spread of exotic invasion. Most of the current invasion risk analyses are qualitative and ad hoc based. In this study, we used a spatial statistics based framework to assess the invasion risks of hemlock woolly adelgid (Adelges tsugae) with the following major steps: (1) invasion probability was first predicted by two widely used spatial statistics tools, maximum entropy (Maxent) and Mahalanobis distance (MD), based on known adelgid infestation locations and a set of environmental and anthropogenic related factors; (2) an ensemble of the above two models and a multi-threshold approach were employed to reduce prediction uncertainties; and (3) a spatial hotspot analysis were applied to enhance invasion prevention and management decision making. Among the factors investigated, variables representing corridors (e.g., trails and railroads) that are inadvertently spreading adelgid were important for the prediction of adelgid invasion. Large portion of the hemlock forests in the study area had a high adelgid invasion probability. The hotspot analysis based on the ensemble model showed three major clustered areas with high adelgid infestation probability. Our study demonstrated the feasibility of regional-scale quantitative invasion risk assessment with the application of a spatial statistics based framework, which can be used for effective and proactive invasion prevention and management.

Exotic Plants of Halimun Salak Corridor: Micro-Environment, Detection and Risk Analysis of Invasive Plants

The research of exotic plant species detection and invasive plant risk analysis conducted in Halimun Salak corridor area. This study aims to do inventory of exotic plant species in this area and perform invasive plant risk analysis to the exotic plants found. The invasion risk assessment of detected exotic plants analyzed using Weed Risk Assessment (WRA) method. Moreover, analysis of multi-dimensional scaling (MDS) based on inequality performed on relative humidity, light intensity, and soil pH. There are eleven exotic plant species which consist of three tree species and eight species of herbs / shrubs. Lantana camara and Camellia sinensis are the exotic species with biggest and smallest WRA score respectively. MDS analysis shows that exotic tree species have similar environmental variables. Moreover, environmental variables of Clidemia hirta are relatively different from other exotic species found in the Halimun Salak corridor.Recommendations for the management of invasive exotic plant species in the area are: immediate management implementation, priority of eradication to exotics that have not been abundant but have a high risk score, two management options (gradual eradication or containment) should be considered for exotics with very high WRA score such as Ageratina riparina, Chromolaena odorata and Lantana camara.

Spatial Risk Assessment of Alien Invasive Plants in China

The large-scale distribution patterns of alien invasive plants (AIP) can provide key information and a theoretical basis for management strategies, including the prevention of invasions, the control and eradication of established AIPs, and the identification of areas at high risk of invasion. This study aims to quantify distribution patterns of AIP in China, to develop approaches that measure the social, economic, and ecological impacts, and to identify areas that are at higher risk of plant invasion. Based on published literature, there were 384 AIPs in China, representing 233 genera from 66 families. Climatic factors were among the primary factors determining AIPs' overall distribution patterns. The majority of AIPs were tropically distributed in China, meaning that they were mainly restricted to southern China. Temperate-distributed AIPs, those distributed only or predominantly in northern China, were fewer but had higher average rates of spread than tropically distributed AIPs. Average ecological and economic impact per AIP was negatively correlated with AIP richness, meaning that areas with relatively few AIPs nevertheless have some of the most detrimental ones. Our comparative evaluation showed that the risk of invasion differed among regions of China, with high-risk areas in southern China (Yunnan, Guangxi, and Guangdong) and central coastal areas of eastern China (Shandong, Hebei, and Jiangsu). In the context of climate change, areas around latitudes of 33° N, including Hebei, Shandong, Henan, and Jiangsu, should be given more attention for the control and prevention of plant invasions. Predictions of high-risk areas for future invasions differed depending on the scale of aggregation and the evaluation index, indicating that invasive risk assessments should be based on multiple factors.

Invasion status and risk assessment for Salvia tiliifolia, a recently recognised introduction to China

SummaryThis study corrected the misidentification of an alien species, Salvia tiliifolia, which had been incorrectly identified as S. dugesii (a synonym of S. melissodora) in China. The distribution of S. tiliifolia in China was surveyed and it was inferred that it was probably introduced into Kunming, Yunnan in the 1990s and then spread to adjacent counties of Yunnan and south‐western Sichuan Province. The Australian weed risk assessment (WRA) was used to evaluate its invasive status. To determine the validity of Australian WRA in China, another 25 exotics representing casual alien plants, naturalised plants and invasive plants were tested. The Australian WRA was validated as a legitimate approach in China. Salvia tiliifolia scored 14, falling into the category of invasive plants. While the distribution of S. tiliifolia is currently restricted to Yunnan and a small part of Sichuan and the species has not displayed an adverse impact on local environments yet, the WRA results indicated that the species was a high risk plant. It was recommended that local land managers should monitor this species and take measures to stop its continuing expansion or eradicate it if possible.

Preeclampsia and MicroRNAs

Preeclampsia is a critical gestational condition that threatens the life of both mother and child. One of the most serious aspects of preeclampsia hampering both clinical management and scientific understanding is that there are, as yet, no early warning signs or risk markers. The discovery of microRNAs (miRNAs), tiny post-transcriptional regulators of gene expression, offers potentially fertile ground for developing such markers. The current state of knowledge about miRNAs in preeclampsia is presented along with information regarding miRNA detection in peripheral fluids that could lead to minimally invasive risk assessment.

Weed Risk Assessment for Aquatic Plants: Modification of a New Zealand System for the United States

We tested the accuracy of an invasive aquatic plant risk assessment system in the United States that we modified from a system originally developed by New Zealand’s Biosecurity Program. The US system is comprised of 38 questions that address biological, historical, and environmental tolerance traits. Values associated with each response are summed to produce a total score for each species that indicates its risk of invasion. To calibrate and test this risk assessment, we identified 39 aquatic plant species that are major invaders in the continental US, 31 species that have naturalized but have no documented impacts (minor invaders), and 60 that have been introduced but have not established. These species represent 55 families and span all aquatic plant growth forms. We found sufficient information to assess all but three of these species. When the results are compared to the known invasiveness of the species, major invaders are distinguished from minor and non-invaders with 91% accuracy. Using this approach, the US aquatic weed risk assessment correctly identifies major invaders 85%, and non-invaders 98%, of the time. Model validation using an additional 10 non-invaders and 10 invaders resulted in 100% accuracy for the former, and 80% accuracy for the latter group. Accuracy was further improved to an average of 91% for all groups when the 17% of species with scores of 31–39 required further evaluation prior to risk classification. The high accuracy with which we can distinguish non-invaders from harmful invaders suggests that this tool provides a feasible, pro-active system for pre-import screening of aquatic plants in the US, and may have additional utility for prioritizing management efforts of established species.

Spatial Analysis of the Distribution and Abundance Patterns of Chromolaena odorata in Ghana: An Invasion Risk Assessment

Spatial Analysis of the Distribution and Abundance Patterns of Chromolaena odorata in Ghana: An Invasion Risk Assessment

Predicting Invasive Fungal Pathogens Using Invasive Pest Assemblages: Testing Model Predictions in a Virtual World

Predicting future species invasions presents significant challenges to researchers and government agencies. Simply considering the vast number of potential species that could invade an area can be insurmountable. One method, recently suggested, which can analyse large datasets of invasive species simultaneously is that of a self organising map (SOM), a form of artificial neural network which can rank species by establishment likelihood. We used this method to analyse the worldwide distribution of 486 fungal pathogens and then validated the method by creating a virtual world of invasive species in which to test the SOM. This novel validation method allowed us to test SOM's ability to rank those species that can establish above those that can't. Overall, we found the SOM highly effective, having on average, a 96–98% success rate (depending on the virtual world parameters). We also found that regions with fewer species present (i.e. 1–10 species) were more difficult for the SOM to generate an accurately ranked list, with success rates varying from 100% correct down to 0% correct. However, we were able to combine the numbers of species present in a region with clustering patterns in the SOM, to further refine confidence in lists generated from these sparsely populated regions. We then used the results from the virtual world to determine confidences for lists generated from the fungal pathogen dataset. Specifically, for lists generated for Australia and its states and territories, the reliability scores were between 84–98%. We conclude that a SOM analysis is a reliable method for analysing a large dataset of potential invasive species and could be used by biosecurity agencies around the world resulting in a better overall assessment of invasion risk.

Macroecological drivers of alien conifer naturalizations worldwide

Understanding the factors that drive the global distribution of alien species is a pivotal issue in invasion biology. Here, we used data on naturalized conifers (Pinaceae, Cupressaceae) from sixty temperate and subtropical regions and five continents to test how environmental and socio‐economic conditions of recipient areas as well as introduction efforts affect naturalization probabilities. We collated 18 predictor variables for each region describing environmental, biogeographic and socio‐economic conditions as well as a measure of the macro‐climatic match with the species' native ranges, and the extent to which alien conifers are used in commercial forestry. Naturalization probabilities across all species and regions were then related to these predictor variables by means of generalized linear mixed models.For both Pinaceae and Cupressaceae, naturalization probabilities were generally higher in the Southern Hemisphere, and increased with indicators of habitat diversity of the recipient region. The match in macro‐climatic conditions between the native and introduced regions was a significant predictor of conifer naturalization, but socio‐economic variables were less powerful predictors. Only for Cupressaceae did a socio‐economic variable (human population density) affect naturalization probabilities. Key attributes facilitating naturalization were related to introduction effort. Moreover, usage in commercial forestry generally fostered naturalization, although the actual size of alien conifer plantations in a region was only correlated with the naturalization of Pinaceae.Our results suggest that climate matching, habitat diversity and introduction effort co‐determine the probability of naturalization, which additionally, is modulated by biogeographic features of the recipient area, such as incidence of natural enemies or competitors. To date, the most widely used tools for invasive plant risk assessment only account for climate match and rarely factor in other attributes of the recipient environment. Future tools should additionally consider biotic environment and introduction effort if risk assessment is to be effective.

Habitat invasion risk assessment based on Landsat 5 data, exemplified by the shrub Rosa rubiginosa in southern Argentina

Prediction of invasive species spread helps to plan management actions. We performed a risk assessment by quantifying habitat invasibility, predicted the potential distribution of an invasive species using the Maxent modelling program and confirmed patterns using detailed field studies. Our study was conducted in southern Argentina, Patagonia, where large areas are already invaded by the European shrub Rosa rubiginosa. A total of 163 R. rubiginosa locations served as ground truth data, and predictors were obtained from the spaceborne sensor Landsat 5. Based on the Maxent Method (area under the receiver operating characteristic curve 0.8), the habitat invasibility map covered about 5000 km2. Our model revealed that R. rubiginosa has the potential to invade 36% of the area along a steep precipitation gradient (target region 600–1400 mm per year). The Tasseled Cap brightness index and the normalized vegetation index explained most of the variance in our model, followed by the Tasseled Cap greenness and wetness indices, which can be interpreted as indicators of disturbance. Highest levels of invasibility were predicted for urban areas, along roads and rivers, on pastures, in Austrocedrus chilensis forests and inside Nothofagus dombeyi forest gaps. Detailed field assessments of rose cover performed in seven habitat types supported these results: rose cover significantly decreased with increasing tree cover (P < 0.01). Our data revealed that the occurrence of R. rubiginosa is not connected to a certain habitat type, but that it thrives in open patches following habitat disturbance. Our approach is a widely applicable, cost-free remote sensing method that can serve as a risk assessment tool for alien plant species invasion of habitats.

Using a self-organizing map to predict invasive species: sensitivity to data errors and a comparison with expert opinion

Summary 1. Predicting which species are more likely to invade a region presents significant difficulties to researchers and government agencies. Methods for estimating the risk of establishment are often qualitative and rely on consultation with experts and stakeholders. The inherent subjectivity of this process can lead to ambiguities in any estimate of a species’ risk of establishment. 2. Using global presence ⁄ absence data of insect crop pests employed a self-organizing map (SOM) to categorize regions based on similarities in species assemblages. This technique enabled them to generate a list of species and rank them based on an index of the risk of establishment. However, the sensitivity of this risk list to errors in the presence ⁄ absence data has never been tested. 3. We evaluated the sensitivity of the SOM method by altering the original presence ⁄ absence data by increasing amounts and compared estimates of risk with those generated by a national coordinating body (Plant Health Australia) utilizing expert stakeholder opinion. 4. The risk list was unaffected by alterations of up to 20% of data over all regions. The error rate we detected in the data was within these limits. 5. Comparison with the expert stakeholder methodology revealed significant differences in the estimates of establishment risk. Further analysis of the Australian data revealed that a number of regions with strong trade links to Australia supported species assemblages similar to those in Australia, suggesting they are possible sources of pest species with high probability of establishment. 6. Synthesis and applications. This analysis confirms that the SOM methodology is a robust tool in the quantification of risk of establishment. In addition, SOMs can deliver a level of objectivity, which can complement current consultative processes employed by many biosecurity agencies around the world, providing a better overall assessment of invasion risk. This assessment can inform research and development funding decisions and incursion management plans for both government and host industries. While SOMs are utilized in this work for the prioritization of pest insects they can potentially be applied to any taxa (pest or native) or at any scale in which the data are available.