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Modeling ecological niches of species is a promising approach for predicting the geographic potential of invasive species in new environments. Argentine ants (Linepithema humile) rank among the most successful invasive species: native to South America, they have invaded broad areas worldwide. Despite their widespread success, little is known about what makes an area susceptible--or not--to invasion. Here, we use a genetic algorithm approach to ecological niche modeling based on high-resolution remote-sensing data to examine the roles of niche similarity and difference in predicting invasions by this species. Our comparisons support a picture of general conservatism of the species' ecological characteristics, in spite of distinct geographic and community contexts.

Ecological niche modeling provides a means for predicting the potential future distribution of a nonindigenous species based on environmental characteristics of the species' native range. We applied this method to the Chinese mitten crab (Eriocheir sinensis), a catadromous crustacean with a long history of invasion in Europe. We used genetic algorithm for rule-set prediction to predict the potential European distribution of mitten crab based on its distribution in 42 locations in its native Asia. The climatic variables, air temperature, number of days, amount of precipitation, and wetness index, contributed significantly to predictions of native distribution limits. Although the genetic algorithm for rule-set prediction model was developed for the native range, the species' extensive distribution in Europe (n =434) allowed independent validation of the predictions. Application of the model to Europe was successful, with 84% of occurrences in regions predicted to be suitable by >80% of the models and <4% of occurrences in areas predicted suitable by <50% of the models (mainly along the northern range). At the watershed scale, areas with established mitten crab populations had significantly higher habitat matching than sites that were not invaded. The independent validation of the Asian-based model by the European distribution revealed that predictions were highly accurate. The model also identified large areas of Europe, particularly along the Mediterranean coast, as vulnerable to future invasion. These predictions can be used to develop strategies to control the spread of mitten crab by preventing introductions into vulnerable areas.

Invasive species are known to influence the structure and function of invaded ecological communities, and preventive measures appear to be the most efficient means of controlling these effects. However, management of biological invasions requires use of adequate tools to understand and predict invasion patterns in recently introduced areas. The present study: (1) estimates the potential geographic distribution and ecological requirements of the Argentine ant (Linepithema humile Mayr), one of the most conspicuous invasive species throughout the world, in the Iberian Peninsula using ecological niche modeling, and (2) provides new insights into the process of selection of consensual areas among predictions from several modeling methodologies. Ecological niche models were developed using 5 modeling techniques: generalized linear models (GLM), generalized additive models (GAM), generalized boosted models (GBM), Genetic Algorithm for Rule-Set Prediction (GARP), and Maximum Entropy (Maxent). Models for the eastern and western portions of the Iberian Peninsula were built using subsets of occurrence and environmental data to investigate the potential for ecological niche differences between the invading populations. Our results indicate geographic differences between predictions of different approaches, and the utility of ensemble predictions in identifying areas of uncertainty regarding the species' invasive potential. More generally, our models predict coastal areas and major river corridors as highly suitable for Argentine ants, and indicate that western and eastern Iberian Peninsula populations occupy similar environmental conditions.

Two correlative approaches to the challenge of ecological niche modeling (genetic algorithm, maximum entropy) were used to estimate the potential global distribution of the invasive fruit fly, Bactrocera invadens, based on associations between known occurrence records and a set of environmental predictor variables. The two models yielded similar estimates, largely corresponding to Equatorial climate classes with high levels of precipitation. The maximum entropy approach was somewhat more conservative in its evaluation of suitability, depending on thresholds for presence/absence that are selected, largely excluding areas with distinct dry seasons; the genetic algorithm models, in contrast, indicate that climate class as partly suitable. Predictive tests based on independent distributional data indicate that model predictions are quite robust. Field observations in Benin and Tanzania confirm relationships between seasonal occurrences of this species and humidity and temperature.

Habitat distribution models are increasingly used to predict the potential distributions of invasive species and to inform monitoring. However, these models assume that species are in equilibrium with the environment, which is clearly not true for most invasive species. Although this assumption is frequently acknowledged, solutions have not been adequately addressed. There are several potential methods for improving habitat distribution models. Models that require only presence data may be more effective for invasive species, but this assumption has rarely been tested. In addition, combining modeling types to form \"ensemble\" models may improve the accuracy of predictions. However, even with these improvements, models developed for recently invaded areas are greatly influenced by the current distributions of species and thus reflect near- rather than long-term potential for invasion. Larger scale models from species' native and invaded ranges may better reflect long-term invasion potential, but they lack finer scale resolution. We compared logistic regression (which uses presence/absence data) and two presence-only methods for modeling the potential distributions of three invasive plant species on the Olympic Peninsula in Washington, USA. We then combined the three methods to create ensemble models. We also developed climate envelope models for the same species based on larger scale distributions and combined models from multiple scales to create an index of near- and long-term invasion risk to inform monitoring in Olympic National Park (ONP). Neither presence-only nor ensemble models were more accurate than logistic regression for any of the species. Larger scale models predicted much greater areas at risk of invasion. Our index of near- and long-term invasion risk indicates that <4% of ONP is at high near-term risk of invasion while 67–99% of the Park is at moderate or high long-term risk of invasion. We demonstrate how modeling results can be used to guide the design of monitoring protocols and monitoring results can in turn be used to refine models. We propose that, by using models from multiple scales to predict invasion risk and by explicitly linking model development to monitoring, it may be possible to overcome some of the limitations of habitat distribution models.

The Chinese mitten crab (Eriocheir sinensis) is native to east Asia, is established throughout Europe, and is introduced but geographically restricted in North America. We developed and compared two separate environmental niche models using genetic algorithm for rule set prediction (GARP) and mitten crab occurrences in Asia and Europe to predict the species' potential distribution in North America. Since mitten crabs must reproduce in water with ≥15% salinity, we limited the potential North American range to freshwater habitats within the highest documented dispersal distance (1260 km) and a more restricted dispersal limit (354 km) from the sea. Applying the higher dispersal distance, both models predicted the lower Great Lakes, most of the eastern seaboard, the Gulf of Mexico and southern extent of the Mississippi River watershed, and the Pacific northwest as suitable environment for mitten crabs, but environmental match for southern states (below 35° N) was much lower for the European model. Use of the lower range with both models reduced the expected range, especially in the Great Lakes, Mississippi drainage, and inland areas of the Pacific Northwest. To estimate the risk of introduction of mitten crabs, the amount of reported ballast water discharge into major United States ports from regions in Asia and Europe with established mitten crab populations was used as an index of introduction effort. Relative risk of invasion was estimated based on a combination of environmental match and volume of unexchanged ballast water received (July 1999-December 2003) for major ports. The ports of Norfolk and Baltimore were most vulnerable to invasion and establishment, making Chesapeake Bay the most likely location to be invaded by mitten crabs in the United States. The next highest risk was predicted for Portland, Oregon. Interestingly, the port of Los Angeles/Long Beach, which has a large shipping volume, had a low risk of invasion. Ports such as Jacksonville, Florida, had a medium risk owing to small shipping volume but high environmental match. This study illustrates that the combination of environmental niche- and vector-based models can provide managers with more precise estimates of invasion risk than can either of these approaches alone.

To predict and compare potential geographical distributions of the Mediterranean fruit fly (Ceratitis capitata) and Natal fruit fly (Ceratitis rosa). Africa, southern Europe, and worldwide. Two correlative ecological niche modelling techniques, genetic algorithm for rule-set prediction (GARP) and a technique based on principal components analysis (PCA), were used to predict distributions of the two fly species using distribution records and a set of environmental predictor variables. The two species appear to have broadly similar potential ranges in Africa and southern Europe, with much of sub-Saharan Africa and Madagascar predicted as highly suitable. The drier regions of Africa (central and western regions of southern Africa and Sahelian zone) were identified as being less suitable for C. rosa than for C. capitata. Overall, the proportion of the region predicted to be highly suitable is larger for C. capitata than for C. rosa under both techniques, suggesting that C. capitata may be tolerant of a wider range of climatic conditions than C. rosa. Worldwide, tropical and subtropical regions are highlighted as highly suitable for both species. Differences in overlap of predictions from the two models for these species were observed. An evaluation using independent records from the adventive range for C. capitata and comparison with other predictions suggest that GARP models offer more accurate predictions than PCA models. This study suggests that these species have broadly similar potential distributions worldwide (based on climate), although the potential distribution appears to be broader for C. capitata than for C. rosa. Ceratitis capitata has become invasive throughout the world, whereas C. rosa has not, despite both species having broadly similar potential distributions. Further research into the biology of these species and their ability to overcome barriers is necessary to explain this difference, and to better understand invasion risk.

Ecological niche modeling, a new methodology for predicting the geographic course of species' invasions, was tested based on four invasive plant species (garlic mustard, sericea lespedeza, Russian olive, and hydrilla) in North America. Models of ecological niches and geographic distributions on native distributional areas (Europe and Asia) were highly statistically significant. Projections for each species to North America—effectively predictions of invasive potential—were highly coincident with areas of known invasions. Hence, in each case, the geographic invasive potential was well summarized in a predictive sense; this methodology holds promise for development of control and eradication strategies and for risk assessment for species' invasions.

Background: Cryptococcus gattii emerged on Vancouver Island, British Columbia (BC), Canada, in 1999, causing human and animal illness. Environmental sampling for C. gattii in southwestern BC has isolated the fungal organism from native vegetation, soil, air, and water. Objectives: Our aim was to help public health officials in BC delineate where C. gattii is currently established and forecast areas that could support C. gattii in the future. We also examined the utility of ecological niche modeling (ENM) based on human and animal C. gattii disease surveillance data. Methods: We performed ENM using the Genetic Algorithm for Rule-set Prediction (GARP) to predict the optimal and potential ecological niche areas of C. gattii in BC. Human and animal surveillance and environmental sampling data were used to build and test the models based on 15 predictor environmental data layers. Results: ENM provided very accurate predictions (> 98% accuracy, p-value < 0.001) for C. gattii in BC. The models identified optimal C. gattii ecological niche areas along the central and south eastern coast of Vancouver Island and within the Vancouver Lower Mainland. Elevation, biogeoclimatic zone, and January temperature were good predictors for identifying the ecological niche of C. gattii in BC. Conclusions: The use of human and animal case data for ENM proved useful and effective in identifying the ecological niche of C. gattii in BC. These results are shared with public health to increase public and physician awareness of cryptococcal disease in regions at risk of environmental colonization of C. gattii.

Climate change and invasive species pose important conservation issues separately, and should be examined together. We used existing long term climate datasets for the US to project potential climate change into the future at a finer spatial and temporal resolution than the climate change scenarios generally available. These fine scale projections, along with new species distribution modeling techniques to forecast the potential extent of invasive species, can provide useful information to aide conservation and invasive species management efforts. We created habitat suitability maps for Pueraria montana (kudzu) under current climatic conditions and potential average conditions up to 30 years in the future. We examined how the potential distribution of this species will be affected by changing climate, and the management implications associated with these changes. Our models indicated that P. montana may increase its distribution particularly in the Northeast with climate change and may decrease in other areas.