Alien Plant Survey Protocol Design

Following a survey effort to describe alien plant invasion of anthropogenically disturbed areas, this project set the stage for inventorying areas of natural disturbance in Yosemite National Park. Sampling protocols were designed to maximize the number of species likely to be found in an inventory of alien plants in burned and riparian areas. Contractors used existing plot data to identify environmental factors that were correlated with the percent cover of non-native species and then developed a predictive model using the Genetic Algorithm for Rule-set Production (GARP) technique. They selected elevation, slope, and vegetation structure as inputs into the GARP model and calculated prediction accuracy as 76%. Distribution maps of predicted occurrence can be used by management to stratify monitoring of non-native species, to prioritize inventory and control efforts according to the likelihood of non-native occurrences, and to inform decisions relating to the management of non-native species in postfire environments. With the expected increase in the spread of non-native plants, this approach provides a tool for assisting decision makers in managing non-native species and can be adapted to target non-native species in other locations. The broader implication of this research is that similar predictive models can be developed for other national parks where suitable plot data exist to increase the efficiency and reduce the costs of field work. Other products stemming from the project include data structures proposed for the collection of field survey data, predictive model coverages, references on alien plant species and disturbance, a final report, shape files obtained from the park or created as part of the project, and shape file metadata. A publication stemming from the project is: Underwood, E. C., R. Klinger and P. E. Moore. 2004. Predicting patterns of non-native plant invasions in Yosemite National Park, California, USA. Diversity and Distributions 10:447–459. doi.org/10.1111/j.1366-9516.2004.00093.x

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