Documenting, Mapping, and Predicting Invasive Species Using the Fort Collins Science Center's RAM (Resource for Advanced Modeling)
The Resource for Advanced Modeling room provides a collaborative working environment for up to 20 scientists, supported with networked, wireless computing capability for running and testing various scientific models (e.g., Maxent, Boosted Regression Trees, Logistic Regression, MARS, Random Forest) at a variety of spatial scales, from county to global levels. Models use various predictor layers that can include current and future climate layers (near- and long-term projections), remote-sensing derivatives (such as MODIS phenology metrics), land cover, topography, and anthropogenic features.
Early detection and rapid assessment (ED/RA) is a crucial aspect of our national approach to the invasive species threat, exemplified by the USGS-led Brown Treesnake Rapid Response Team based on Guam. FORT research through the USGS National Institute of Invasive Species Science is striving to create and maintain a national capability to guide effective early detection, rapid assessment, and forecasting of harmful plants, animals, and diseases. FORT also is the physical home of the Resource for Advanced Modeling (RAM), formed to develop cooperative approaches for invasive species science that meet the urgent needs of land managers and the public.
Our mission at the RAM is to coordinate data and research from many sources and to predict and reduce the effects of harmful non-native plants, animals, and diseases in natural areas and throughout the United States. With a strategic approach to information management, research, modeling, technical assistance, and outreach, scientists at the RAM are meeting research goals. FORT researchers are investigating state and transition models, using them to help evaluate the costs and benefits of alternative management strategies for invasive species. For example, FORT has been working with a team on buffelgrass (Pennisetum ciliare) in Arizona, developing models to determine the potential spread of the invasive grass and the risks of inaction. Researchers plan to integrate these techniques into the suite of models already available through the RAM. Other efforts include continuing to develop new methods to integrate disparate data sets to feed into models. This work is in partnership with Colorado State University through development of the International Biological Information System (IBIS) and the Global Invasive Species Information Network (GISIN).
The Resource for Advanced Modeling room provides a collaborative working environment for up to 20 scientists, supported with networked, wireless computing capability for running and testing various scientific models (e.g., Maxent, Boosted Regression Trees, Logistic Regression, MARS, Random Forest) at a variety of spatial scales, from county to global levels. Models use various predictor layers that can include current and future climate layers (near- and long-term projections), remote-sensing derivatives (such as MODIS phenology metrics), land cover, topography, and anthropogenic features.
Early detection and rapid assessment (ED/RA) is a crucial aspect of our national approach to the invasive species threat, exemplified by the USGS-led Brown Treesnake Rapid Response Team based on Guam. FORT research through the USGS National Institute of Invasive Species Science is striving to create and maintain a national capability to guide effective early detection, rapid assessment, and forecasting of harmful plants, animals, and diseases. FORT also is the physical home of the Resource for Advanced Modeling (RAM), formed to develop cooperative approaches for invasive species science that meet the urgent needs of land managers and the public.
Our mission at the RAM is to coordinate data and research from many sources and to predict and reduce the effects of harmful non-native plants, animals, and diseases in natural areas and throughout the United States. With a strategic approach to information management, research, modeling, technical assistance, and outreach, scientists at the RAM are meeting research goals. FORT researchers are investigating state and transition models, using them to help evaluate the costs and benefits of alternative management strategies for invasive species. For example, FORT has been working with a team on buffelgrass (Pennisetum ciliare) in Arizona, developing models to determine the potential spread of the invasive grass and the risks of inaction. Researchers plan to integrate these techniques into the suite of models already available through the RAM. Other efforts include continuing to develop new methods to integrate disparate data sets to feed into models. This work is in partnership with Colorado State University through development of the International Biological Information System (IBIS) and the Global Invasive Species Information Network (GISIN).