Synthesizing and Spatially Linking Disparate Datasets Catherine Crosier 1,2, Gregory J. Newman 1, and Thomas J. Stohlgren 1,2 1 Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colorado Fort Collins Science Center, US Geological Survey, Fort Collins, Colorado Acknowledgements DATA SOURCEDATA TYPE 1. Biota of North America ProgramCounty species list 2. U.S. Geological SurveyMulti-scale plot data 3. U.S. Forest ServiceMulti-scale plot data (2 national programs) 4. Graduate studentsMulti-scale plot data 5. San Luis Valley GIS AuthorityPoint, line, and polygon data 6. Larimer CountyPoint, line, and polygon data (noxious weeds) 7. Center for the Environmental Management of Military Lands (CEMML) Vegetation monitoring transects 8. The Nature ConservancyPolygon, point, and transect data 9. Colorado Natural Heritage ProgramPolygon and point data 10. State of ColoradoQuarter quad data for 20 species; DOT weed GIS data 11. CSU Research ScientistsPoint data and control data 12. National Park ServicePark species lists and GIS data 13. U.S. Fish and Wildlife ServiceRefuge manager survey results 14. LTERPlot data 15. Bureau of Land ManagementPoint, polygon, and control data Problem: Lack of knowledge 1.Ecological studies often are conducted over short temporal scales in specific areas. 2.Species occurrence and abundance data do not exist for many public land units (e.g. Fish and Wildlife Refuges), making it hard to make sound management decisions. Solution: Synthesize smaller studies 1.Addresses additional and more complex questions 2.Capitalizes on resources already expended 3.Creates probable species lists and distribution maps Introduction Figure 2: Dataset Bias 1.Obtain datasets from partners (Table 1). 2.Synthesize pre-existing datasets, including multiple scale (i.e., 1-m 2 to entire county) information on non-native vascular plant species in Colorado obtained from individuals, organizations, and agencies at all levels (i.e., federal to local) in a geodatabase (Figure 1). 3.Use on-line GIS technology to disseminate information on non-native species locations to land managers and the public in a user-friendly and easily understood manner (Figure 1). Objectives Table 1: Datasets Used, Pending Figure 3: Species Added Extensive surveys to obtain species distribution data are expensive and time consuming. In addition, many land managers do not know what species occur within their management boundaries, let alone adjacent to them. Yet, there is a wealth of data collected by government agencies and non-government organizations that, when synthesized, maximize the use of existing data on species locations without incurring large costs. We obtained datasets on non-native species including species lists, plot data, transect data, point data, and individual species polygon data that have been collected using various methods at different spatial scales to meet different objectives. We then created a geodatabase with ESRI’s ArcGIS and Microsoft Access 2000 that relates these different datasets spatially and hierarchically and allows us to use them in concert. When combined and spatially linked, the data improve the completeness of each individual dataset. As an example, species reported in a specific area by one dataset were not reported by a second dataset for the same location. The creation of a synthesized, spatially linked database allows development of predictive models and maps for the distribution and abundance of non- native species. These products can be used by land managers at all levels to aid in the early detection, management, and control of non-native species. Keywords: Data synthesis, Data synergy, Spatial linking, Non-native species, Ecological databases Abstract 1.Create three linked geodatabases to synthesize datasets using nested locations (i.e., plot located in a park located in a county) and standardized NRCS plant codes (Figure 1). The three geodatabases are general enough to accommodate plot data; GIS point, line and polygon data; and species lists for large geographic units. 2.Compare datasets at smaller scales to those at larger scales where they are nested. Datasets include 63 county lists, two National Park unit lists, 493 plots, and 15,666 GIS points, lines, and polygons. Methods Dataset synthesis indicates biases in individual datasets. For example, the county dataset is based on herbarium records that are biased towards specific areas (Figure 2). Two hundred seventy four new species records (including 35 Colorado noxious weeds and 99 unique species) were added to 47 of 48 counties containing nested locations (Figure 3). Cardaria draba (Hoary cress) was added to six county lists. In addition, Cardaria draba was not listed in parks and other areas within some counties listing it, indicating managers should be vigilant for these species (Figure 4). Results These results indicate that data synergy can be used to improve the quality of individual datasets. Bias of county datasets can be diminished by supporting them with data from other sources. Land managers can use synthesized non-native datasets to set priorities for early detection and prevention efforts. In addition, the geodatabase can be used to create predictive spatial models for hotspots of invasion in the state or in a specific land management unit and to create distribution surfaces for individual species. These models can then be served on-line to distribute non-native species information to land mangers at all levels (Figure 5). Conclusions and Future Steps San Luis Valley GIS/GPS Figure 4: Dataset Synergy Figure 1: Data Management Figure 5: On-line Interface