Today’s global economy moves unprecedented quantities of people and products around the planet, increasing the probability that alien species will be introduced and successfully established beyond their native ranges. Invasive alien species (IAS) are the second most important cause of biodiversity loss, and pose additional threats to agriculture and human health. Together, IAS, habitat alteration and climate change are dramatically re-shaping biogeographic patterns across the globe. We need accessible data and analysis tools to assess the threats of IAS at multiple stages: to identify at-risk habitats before invasion occurs, to identify potential arrival sites, and to understand potential routes and rates of dispersal. Beyond threat assessment, data and tools are needed to create conservation strategies that mitigate these threats. In Latin America, economic losses from IAS amount to billions of dollars annually, but strategies to minimize the damage of IAS are generally underdeveloped. We describe an international collaboration using novel techniques to predict the potential distributions of IAS in South America.

Researchers from the California Academy of Sciences, The Nature Conservancy (TNC), and the Centro de Analisis Espacial of the Universidad Mayor de San Andres in Bolivia, are using ensemble distribution modeling to generate composite potential distribution maps for 300 of the most threatening IAS in South America. We are using species occurrence data, derived from both museum specimens and observations obtained from the TNC Invasive Species Initiative, the IABIN Invasive Species Information Network (I3N), and the Global Biodiversity Information Facility (GBIF). Global environmental data layers and higher resolution regional layers are being used to predict distributions of IAS. Seven distribution modeling algorithms are being run for each IAS: Bioclim, Minimum distance, Climate space model, Distance to average, Environmental distance, Garp and MaxEnt. The outputs are combined using a consensus method to produce an ensemble model. Composite maps reveal ‘hotspots’ of IAS susceptibility, depicting which regions of South America are most at risk from the threats of IAS. We are compiling a database of all the biological and spatial data input, as well as all the output models, which will be made publicly accessible.

Our future goals include: 1) creating web access to all project inputs and outputs, including the high-resolution regional environmental data layers we created specifically for this IAS modeling research; 2) building a website to support the collection and distribution of invasive species occurrence data in Bolivia, the only South American country not currently contributing to the I3N effort; and 3) incorporating estimates of future land use and climate change in predicting IAS distributions for South America.