Carie Carie PigeonPigeonThe Pennsylvania State UniversityMaster of Geographic Information Systems Program

Geography 596ACapstone Project PresentationDecember 17, 2013Patrick Drohan, Advisor

USDA

BackgroundStudy Area and Resource

Management BackgroundGoals and ObjectivesDataProposed MethodologyTimelineAnticipated Results

Invasive Exotics Adverse Effects▪ Competition▪ Difficulty in Restoration▪ Economic and Environmental Losses of About

$137 billion in 2000. (Davies and Johnson, 2011)

DNR - IL Wikipedia

Dyer’s woad History Native to southeastern Russia

Noxious Weed Characteristics Biennial or monocarpic perennial Accelerated growth rate Average of 383 seeds

Idaho Weed Awareness

Wapi Lava Field

Park Background Volcanic in origin Sagebrush steppe

Resource Mgmt. Background Monitoring Chemical treatment

1. Dyer’s woad Movement Between 2009-2013

2. Estimated Numbers of Dyer’s woad had Herbicide not Been Used

3. Investigate Relationships Between Dyer’s woad and Independent Variables Using Regression Analysis

4. Model of Predicted Infestation Areas Based on Geographically Weighted Regression and the GWR Predictions Tool

Data Source AccuracyWeed point 2009-2013 (point)

Craters of the Moon NM and Preserve

Collected in the field with a Trimble GPS unit

Roads, Tracks, and Railroad Tracks (line)

Craters of the Moon NM and Preserve

Soil Type (polygon) SSURGO, USDA, NRCS 1 inch = 1,000 feetNED, Elevation (raster)

USGS 30 meter

Range Allotment ( Livestock Grazing) (Disturbance) (polygon)

GeoCommunicator, BLM

Land Cover (raster) USGS 30 meter

•Convert all rasters to polygons or points for joining later in the process

Slope Raster CreatedFlow Accumulation Raster CreatedWetness Index Raster Created

Ln((“FLOWACC”*900)/Tan(“SLOPE”))▪ (Cooley, 2013)

Movement Between 2009-2013 Annual Dyer’s woad distribution maps

Estimated Number of Dyer’s woad had Herbicide not Been Used. 2009 Data

NS = 383 * 0.865NF = [(VP * 0.175) + (NS * 0.01)] – MP

(Pokorny and Krueger-Mangold, 2007)

Investigate Relationships Between Dyer’s woad and Independent Variables Set Up▪ Break up study area into smaller pieces (polygons)▪ Dummy Variables for Categorical Data▪ Spatial Join (in order to get all variables in the

same table) Testing Independent Variables▪ Soil Type, Livestock Grazing (disturbance),

Elevation, Land Cover, and Wetness Index▪ Best Fit Model

Ordinary Least Squares (OLS) Regression▪ Test individual variables▪ Adjusted R-Squared value

OLS Regression Tool with All Inputs

1. Coefficients have the Expected Sign Expected positive or negative relationship with

dependent variable 2. No Redundancy Among Independent

Variables One or more variables telling the same story

3. Coefficients are Statistically Significant 4. Residuals are Normally Distributed

Jarque-Bera test 5. Strong Adjusted R-squared Value 6. Residuals are not Spatially

Autocorrelated Spatial Autocorrelation tool

ArcGIS Resources

Geographically Weighted Regression Tool Output of Model Residuals Neighbors Coefficient values

GWR Prediction Tool (different than geographically weighted regression tool) Prediction model showing areas that could

be infested based on chosen variables

Possible

Dyer’s woad Areas

January 15, 2014: Have all of the necessary data needed for the project

January 15 – March 1, 2014: Results Established

March 1 – April 1, 2014: Draft Paper April 8 – April 12, 2014: Present Paper at

Annual AAG Meeting in Tampa, FL April 13 – May 7: Complete Final Paper!

A series of annual species movement maps will be created for a 5 year period of Dyer’s woad point data

Priority zones for treatment will be established based on current levels and locations of infestation

The relationships between Dyer’s woad and independent variables (soil type, elevation, livestock grazing, wetness index, and disturbance will be explored and results shown)

Predictive Model for Land Managers

Cooley, Skye. 2013. Hillslope Wetness Index. Retrieved from http://gis4geomorphology.com/hillslope-wetness-index

Coutts, Shaun. R, van Klinken, Rieks. D, Yokomizo, Hiroyuki., Buckley, Yvonne. M (2010). What are the key drivers of spread in invasive plants: dispersal, demography or landscape: and how can we use this knowledge to aid management?. Biological Invasions, 13 (7) 1649-1661.

Davies, Kirk. W and Johnson, Dustin. D (2011). Are We “Missing the Boat” on Preventing the Spread of Invasive Plants in Rangelands?. Invasive Plant Science and Management, 4(1), 166-171. Retrieved from http://www.bioone.org/doi/full/10.1614/IPSM-D-10-00030.1

Dupont. 2009. Dupont Telar XP herbicide: Invasive Weed Management. Retrieved from http://www.weedcenter.org/technicalwebinars/documents/Written%20Content/DuPont/Invasive%20Weed%20Control%20with%20Telar%20XP%20herbicide.pdf

Pokorny, Monica. L and Krueger-Mangold, Jane. M (2007). Evaluating Montana’s Woad (Isatis Tinctoria) Cooperative Eradication Project. Weed Technology, 21(1), 262-269. Retrieved from http://www.bioone.org/doi/full/10.1614/WT-06-048.1

U.S. Department of the Interior, National Park Service and Bureau of Land Management. 2007. Craters of the Moon National Monument and Preserve Monument Management Plan, [Online]. Retrieved from http://www.fs.fed.us/database/feis/[2013, October 19]

Zouhar, Kris. 2009. Isatis tinctoria. In: Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory (Producer). Retrieved from http://www.fs.fed.us/database/feis/[2013, November 10]