Evaluating Invasive Plants in Eau Claire County

Evaluating Terrestrial Invasive
Plants in Eau Claire County,
Wisconsin
David Hon

1

Evaluating Terrestrial Invasive Plants in Eau Claire County, Wisconsin

Outline

• Introduction to Python
• Applications of Python to GIS
• Primer to the project
• Research questions
• Data
• Descriptive analysis
• Predictive analysis with Python
• Conclusions

2


Introduction to Python
What is Python?

“Python is designed to be an easy-to-use,
easy-to-learn dynamic scripting language”
(Butler, 2005)

3


Applications of Python to GIS

Why Python?

• Increase efficiency
• Reduce error
• Customize processes
• Create script tools
• Formalize a process

4


Introduction to Python

How to use Python in GIS (or at least how I did!):
• Learn Python basics
• Learn Python the Hard Way, by Zed Shaw
• Plan
• Psuedocode
• Get help, or Help
• Import modules (gp. and arcpy. important)
• Define variables
• Execute tools
• Go crazy with comments
• Make it work
• Make it better
• Make your tool and use it!

5


Project Primer

• Document impacts of terrestrial invasive plants
(aka TIPS)
• Forest productivity
• Biodiversity
• Endangered species
• Create a scientific collection process (yes, we
made it up!)
• Gather rich baseline data
• Engage citizen scientists
• Describe human and environmental characteristics
of TIPS
• Create a basic predictive habitat suitability model
to prioritize areas for future monitoring

6


Literature Review
• Terrestrial invasive plant species = TIPS
• Defining TIPS
• Long natural history (Maron et al, 2004)
• Many negative effects (Pimental, 2000; Chornesky et al, 2003; Hedja et al,
2009; Lodge et al, 2006; Mascaro et al, 2007; Wisconsin Department of Natural
Resources, 2010)
• Humans facilitate invasions (Robbins
2004)
• Invasions exist through a process
(Lodge et al, 2007; Wisconsin Department of Natural
Resources, 2010; Theoharides et al, 2007; Blumenthal,
2005)
• TIPS difficult to control (Anderson et al,
2003; DiTomasao et al, 2006; Kirby et al, 2000;
Wisconsin Department of Natural Resources, 2010)
• Policy change best for controlling
TIPS (Hauser et al, 2009; Wisconsin Department of
Natural Resources, 2009; Shine and Doody, 2011)

7
Lodge et al, 2007


Research Questions

8


Other thanks to:
Matt Moris Aaron McEachern
Judy Schwarzmeier
Josh Ruttschow
Anna Mares
Jeanette Kelly
Paula Kleinjes-Neff
Sean Hartnett
9
(Hon, 2011)


Region of Interest

10


Reed Canary Species Used
Grass
In Analysis
Orange/Yellow Hawkweed
Leafy Spurge

Common St.
John’s Wort
Spotted Knapweed Bush
Honeysuckles

11
(Hon, 2011)


Data

Understanding the Field Data (Summer 2011)

12


Data: Base Layers

13


Data: Invasive Species in GIS
Orange/Yellow Hawkweed Spotted Knapweed Leafy Spurge

Common St. John’s Wort Reed Canary Grass Bush Honeysuckles

14


Human Environmental
Data: Controls
Factors Factors

Mixed Factors

Based on field
observations!
15


Methods: GIS Data Flow for Descriptive Model
Generate
factor layers
Split species
by
infestation
Merge with intensity
points
Lines to
points

Join point
data with
factor layers
16


Results

Total Number % Infestations % Infestations % Infestations
Used in % Infestations Classified as Classified as Classified as
Species Analysis (n) Classified as Rare Infrequent/Occasional Locally Abundant Dominant

Orange and Yellow
Hawkweed 729.00 13.44 52.81 33.33 0.41

Leafy Spurge 114.00 1.75 29.82 59.65 8.77

Common St. John's
Wort 46.00 36.96 34.78 28.26 0.00

Bush Honeysuckles 137.00 43.80 25.55 30.66 0.00

Spotted Knapweed 412.00 20.63 30.58 41.50 7.28

Reed Canary Grass 123.00 13.01 53.66 31.71 1.63 17


Results

Mean Mean Mean Mean
Distance Distance Road Distance
Mean Soil Mean from from Density from
Species Drainage Slope Cities (m) Roads (m) (m/m^2) Rivers (m) Mode Land Cover

Orange and Yellow
Hawkweed 27.8 1.20 9401.4 452.3 0.001042 248.0 Coniferous Forest

Leafy Spurge 20.8 1.15 9431.3 99.0 0.001256 277.8 Coniferous Forest

Common St. John's Wort 43.0 1.85 11219.1 632.8 0.000655 230.7 Deciduous Forest

Bush Honeysuckles 40.3 1.36 5896.4 278.3 0.003204 113.1 Deciduous Forest

Forest Edge or
Spotted Knapweed 25.3 1.31 9054.5 361.0 0.001152 221.0 Forest Transition

Reed Canary Grass 48.2 1.29 10216.4 536.7 0.000959 277.8 Deciduous Forest
18


Results

Distance from Rivers
600.00

500.00

400.00
Distance (m)

300.00

200.00 Rare
Infrequent/Occasional
100.00
Locally Abundant
Dominant
0.00

Terrestrial Invasive Species

19


Results

Summary of Controls on Infestation
Species Intensity

Orange/Yellow Hawkweed Slope, distance from rivers, road density
Soil drainage, distance from rivers, distance from
Leafy Spurge roads, land cover

Soil drainage, slope, distance from rivers, distance from
Spotted Knapweed roads, distance from cities, road density, land cover

Common St. John's Wort Soil drainage, distance from cities, road density, land cover

Bush Honeysuckles cities, road density, land cover

Reed Canary Grass cities, road density, land cover 20


Timeline: Predictive Habitat Suitability
Model
The “Hon Method”

• Learn Python basics (Sept. to Nov.)
• Learn Python the Hard Way, by Zed Shaw
• Plan (Week 1 Nov.)
• Psuedocode (Week 1 Nov.)
• Create a simple mimic-tool (Week 2 Nov)
• Get help, or Help (Week 2 Nov.)
• Import modules (gp. and arcpy. important) (Week 2
Nov – Dec)
• Define variables (Week 2 Nov – Dec)
• Execute tools (Week 2 Nov – Dec)
• Go crazy with comments (Week 2 Nov – Dec)
• Make it work (Week 1 Dec.)
• Make it better (Weeks 1-2) Dec.)
• Make your tool and use it! (Week 3 Dec.)
21


Predictive Habitat Suitability Model

The Plan

• Use known locations of invasive species
Data issues:
• Points represent areas
• Environmental and human influences all
assumed to be equal
• Samples not even across landscape
• Samples not stratified
• Python because of replicability
• Use loops for point data
• Find unique values of all factor data
• Binary model (Yes/No)
• Reclass by table
• Add binary layers to create surface

Result – deterministic index (0-7) model of invasive
species habitat 22


Mimic tool: multiclip 10.5.11
# Import arcpy module
import arcpy
import os

# Declared variables:
inFCs = arcpy.GetParameterAsText(0) #input feature classes
clip_FC = arcpy.GetParameterAsText(1) #clip feature
output_WS = arcpy.GetParameterAsText(2) #output workspace

inFCs = inFCs.split(";") #splits features into multiple features

for inFC in inFCs:
(filePath, fileName) = os.path.split(inFC)
dotInd = fileName.find(".") #finds the extension name e.g. .shp
if dotInd <> -1: #looks at what is before the extension name
newFC = fileName[0:dotInd] #declares the filename that is
before the extension filename
outFC = newFC + "_clip" #adds "_clip" to the new features
else:
outFC = fileName + "_clip"

# execute clip tool
arcpy.Clip_analysis(inFC, clip_FC, output_WS + “//" + outFC)
23



Pseudocode sample

If:
IP exists on LULC 1, 3, 5
Then:
LULC layer 1, 3, 5 = 1
LULC layer 2, 4, 6, 7, 8 = 0

24



Get Help!

25



Import modules

import arcpy
from arcpy import env
from arcpy.sa import *
import sys, os
import arcgisscripting
gp = arcgisscripting.create(9.3)

26



Define variables
invasives = arcpy.GetParameterAsText (0)
#invasive species fcs;multivalue
#variable
#definition
#comment
dfc = arcpy.GetParameterAsText (1)
dff = arcpy.GetParameterAsText (2)
dfr = arcpy.GetParameterAsText (3)
DI = arcpy.GetParameterAsText (4)
slp = arcpy.GetParameterAsText (5)
rdn = arcpy.GetParameterAsText (6)
rlu = arcpy.GetParameterAsText (7)
#inraster1-7 are the factors used for
reclassing
out_ws = arcpy.GetParameterAsText (8)
#output workspace 27



Execute tools

for invasive in invasives:
(filePath, fileName) = os.path.split(invasive)
dotInd = fileName.find(".") #finds the extension name e.g.
.shp
if dotInd <> -1: #looks at what is before the extension
name
newFC = fileName[0:dotInd] #declares the
filename that is before the extension filename
out_inv = newFC + "_" #adds "_" to the new
features' filename
else:
out_inv = fileName + "_"

#loops through all of the invasive species
gp.addmessage("finding unique values for reclass
tables...")
freq_dfc = arcpy.Frequency_analysis(invasive, out_ws +
“//" + out_inv + "dfc", "dfrmcities2_slice"
...

28


Results: Predictive Habitat Suitability Model

Eau Claire
Altoona

Fall Creek

Augusta

Fairchild
29


Conclusions

• Disconnect between citizens, scientists, policymakers, and
foresters
• Leafy spurge
• Improvements for next time
• Python and predictive modeling for other projects
• Location analysis
• Hazard mapping
• Population growth
• Urbanization
• What else?

30


Any questions?

31
(Hon, 2011)


References
Anderson, G. L., E. S. Delfosse, N. R. Spencer, C. W. Prosser, and R. D. Richard. 2003. Lessons in
Developing Successful Invasive Weed Control Programs. Journal of Range Management 56
(1):2-12.
Blumenthal, Dana. 2005. Interrelated Causes of Plant Invasion. Science 310 (5746):243-244.
Bonnet, Alastair. 2008. What is Geography? 2455 Teller Road, Thousand Oaks, California: Sage Publications
Inc.
Butler, Howard. 2005. A guide to the Python universe for ESRI users. ArcUser Apr-May 2005.
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Chornesky, Elizabeth A., and John M. Randall. 2003. The Threat of Invasive Alien Species to Biological
Diversity: Setting a Future Course. Annals of the Missouri Botanical Garden 90 (1):67-76.
Ditomaso, Joseph M., Matthew L. Brooks, Edith B. Allen, Ralph Minnich, Peter M. Rice, and Guy B. Kyser.
2006. Control of Invasive Weeds with Prescribed Burning. Weed Technology 20 (2):535-548.
Hauser, Cindy E., and Michael A. McCarthy. 2009. Streamlining ‘search and destroy’: cost-effective
surveillance for invasive species management: Wiley-Blackwell.
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References

Lodge, David M., Susan Williams, Hugh J. MacIsaac, Keith R. Hayes, Brian Leung, Sarah Reichard, Richard
N. Mack, Peter B. Moyle, Maggie Smith, David A. Andow, James T. Carlton, and Anthony
McMichael. 2006. Biological Invasions: Recommendations for U.S. Policy and Management. Ecological
Applications 16 (6):2035-2054.
Maron, John L., Montserrat Vilà, Riccardo Bommarco, Sarah Elmendorf, and Paul Beardsley. 2004. Rapid
Evolution of an Invasive Plant. Ecological Monographs 74 (2):261-280.
Mascaro, Joseph, and Stefan A. Schnitzer. 2007. Rhamnus cathartica L. (Common Buckthorn) as an
Ecosystem Dominant in Southern Wisconsin Forests. Northeastern Naturalist 14 (3):387-402.
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September/October 1990: 202-206.
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of Nonindigenous Species in the United States. BioScience 50 (1):53.
Robbins, Paul. 2004. Comparing Invasive Networks: Cultural and Political Biographies of Invasive Species.
Geographical Review 94 (2):139-156.
Shine, Richard and Doody, J Sean. 2011. Invasive species control: understanding conflicts between researchers
and the general community. Frontiers in ecology and the environment 9 (7):400-406.
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of Workforce Needs and Expectations. The Professional Geographer 60(3): 356-373.
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nonindigenous (accessed October 11th 2011).
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success during four stages of invasion. New Phytologist 176 (2):256-273.
Wisconsin Department of Natural Resources. 2009. Wisconsin's Forestry Best Management Practices for
Invasive Species. http://dnr.wi.gov/invasives/species.asp?filterBy=Terrestrial&filterVal=Y
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Zelinsky, Wilber. 2001. The Geographer as Voyeur. Geographical Review 91(1/2): 1-8.

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