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Land_Plant Path

Predictive Modeling of the Role of an Insect-vectored Pathogen in Exotic Plant Invasions

The goal of this study is to investigate the use of Kepler workflows to develop and test models exploring the impacts of abiotic factors (real-time light, temperature, and rainfall measurements) on the dynamics of plant host populations and their susceptibility to viral pathogens. A large-scale invasion of non-native grasses is occurring in the western United States, involving the displacement of native perennial grasslands by exotic annual grasses and forbs. This displacement has altered the dynamics of a globally distributed complex of viral pathogens on the grasses, which appears to be allowing the exotic grasses to further extend their range. Aphids are the insect vectors which are facilitating the spread of the viral pathogen. This work is, to our knowledge, the first field-parameterized, system-specific model suggesting that disease may have tipped the competitive balance among a species complex, to allow a widespread invasion. Much remains unknown about this pathogen-host community, and we will investigate how the REAP workflow tools can provide an effective framework to access relevant data and develop appropriate models to better resolve several research questions within this area. A network of monitoring sites ranging from Canada to Mexico has already been established, and the REAP applications provide an outstanding opportunity to develop the needed real-time data collection and monitoring capabilities to parameterize integrated models of the plant-pathogen-insect vector systems. The models developed in support of this research effort will represent state-of-the-art approaches to disease ecology. This use case has relevance to clarifying the interactions among altered biogeochemical cycles, species diversity, and species invasions, four areas identified as Grand Challenges within the NEON program

Terrestrial Use Cases Discussed in the Requirements Workshop

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This page last changed on 26-Nov-2007 14:30:18 PST by uid=barseghian,o=NCEAS.