Development of GIS Based Maps for the “Top 10” Invasive Weeds in Crook
County, Oregon – Final Report
Summary and Results of the Weed Project
Work Performed:
Conducted field research and mapping efforts in the Beaver Creek sub-basin and South
Fork of Beaver Creek, and the upper Crooked River watersheds. These new sources of
data will fill in gaps in the counties' invasive weeds mapping efforts and lead to improved
statistical and spatial analyses. Efforts to gather and analyze current invasive weed data
collected in the field by local, state, and federal agencies will also continue.
Secondly, county GIS assisted in organizing and deploying weed discovery field
personnel. These personnel will be equipped with hand-held Trimble GeoXT GPS/GIS
units to gather spatio-temporal invasive data. Field teams used statistically relevant
sampling methodologies reviewed and recommended by the Forest Service, Forestry and
Range Sciences Lab.
Finally, the data gathered from all sources in 2007 were used to improve County weed
modeling efforts. This sampling effort will used the same methodology as the discovery
field teams. All the information was analyzed statistically and communicated to the
public via an updated weeds Web and FTP site and in the form of new maps distributed
to team members and posted for free downloading.
Website & Maps Update:
As part of our MOD 2 deliverables the Crook County GIS & WWETAC invasive weeds
website has been updated with the latest weed locations and model predictions
derived from USFS, BLM, BOR, Crook County and the Crooked River Weed
Management Area data for 2005, 2006 and 2007. The website itself has also been
redesigned and updated. The interactive mapping tool initially displays general weed
distributions. These layers are replaced by more detailed weed distributions when
the user zooms into an area of interest.
Geostatistical Analysis Summary of Project:
Habitat Suitability Analysis (HSA):
In this study point, line and polygon weed locations in Crook County, Oregon were
generalized to 1000ft2 grid cells for geostatistical analysis. The goal is to try and
predict the distribution of an invasive species using spatial and environmental
variables derived from known infestations. We used the following environmental
parameters as inputs for our predictive model:
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Max and Min elevation (based on USGS 30m DEM)
Slope (based on USGS 30m DEM)
Vegetation (GAP analysis 1:100,000 Northwest Habitat Group)
Geology (USGS, 1:500,000)
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Rainfall (NRCS)
Figure 1: Species distribution maps & model data
Whitetop
Cardaria
draba
Diffuse
Knapweed
Centaurea
diffusa
Russian
Knapweed
Acroptilon
repens
Hounds
Tongue
Cynoglossu
m officinale
Leafy
Spurge
Euphorbia
esula
Scotch
Thistle
Onopordum
acanthium
Medusa
Head
Taeniatheru
m caputmedusae
Spotted
Knapweed
Centaurea
biebersteinii
Figure 2 indicates the distribution of the remaining eight species where habitat
suitability analysis was not possible due to small number of recorded locations or
limited spatial distributions.
Figure 2: Species distribution maps – remaining eight species
Musk Thistle
Carduus nutans
Yellow Star
Thistle
Centaurea
solstitialis
Yellow Flag
Iris
Iris
pseudacorus
Perennial
Pepperweed
Lepidium
latifolium
Dalmatian
Toadflax
Linaria
dalmatica
Yellow
Toadflax
Linaria
vulgaris
Mediterranean
sage
Salvia aethiopis
Tansy
Ragwort
Senecio
jacobaea
Regression Tree Analysis (RTA):
With many types of spatial analyses the individual input variables often interact in
unpredictable ways. To minimize these effects we need to be able to constrain the
variables used in our spatial analyses to those that have the most statistical
significance in explaining weed distributions. The alternative is that we try to
manually identify the effects each variable has on our analysis. Two RTA models
were developed. Both used the same predictors but differed in the use of class
variables. The first used class variables representing presence ‘1’ and absence ‘0’ by
species. The second used an abundance class variable representing the % cover of
each grid cell between 0 and 100%. Rainfall, vegetation code, geology, type and
aspect were used as categorical predictors and slope and elevation were used as
continuous predictors.
Crooked River Weed Management Area (CRWMA) Analysis:
One of the main problems with the HSA method initially described is that the
predictions are based on where the weeds were found. This limits the scope of the
model. MOD 2 expanded the project and included weed surveys on private and
public lands conducted by the CRWMA. Field crews collected weed presence, absence
and abundance data along roads and driveways, see Figure 4. Sample sites were
visited in a systematically random manner. The resulting data set was analyzed
using RTA.
Figure 4: CRWMA
road sample sites
For this analysis we were limited by sample size. Although many locations were
sampled (n=390) weeds were only found at a few sites. For example whitetop
(CADR) was the most common species detected but it was only recorded at 35 sites.
Sample size was further reduced as 10% of the observations were held back for
testing the predictions of the model. Because there were so few sites with weeds
present the dataset reserved for testing was not representative.
Due to sample size RTA could only be conducted on whitetop data (see Figure 5).
When categorical abundance was applied the first predictor of weed absence was an
eastern aspect (accounts for 13% of the observations and a confidence of 96%).
The second predictor was a southwest aspect (accounts for 8% of the observations
and a confidence of 97%) and the third predictor was a flat slope (accounts for 7%
of the observations and a confidence of 100%). The remaining predictors were not
statistically significant.
When using presence and absence data the only predictor for absence of whitetop
was Elevation >= 3672ft, which supported 83% of observations with a 95%
confidence level.
These results tell us very little in themselves but they highlight the power and
potential for this kind of statistical analysis method providing a large enough sample
and control set can be collected.
Limiting the HSA using the RTA rules:
Our RTA unfortunately relied on an abundance of sample points with a low presence
of weeds (35 of 375 or 9%). Because of this it is difficult to make reliable
predictions over large areas. When using elevation < 3672ft as a predictor of
whitetop the model only identified 18 of 35 sites correctly, a 51% success rate.
Expanding the RTA analysis to use the 1000ft2 grid cells the model correctly assessed
692 out of 1864 (44%) grid cells. When slope is not equal to ‘East’ or ‘Flat’ the
model identifies 1327 of 1564 cells correctly, an 84.8% agreement with the 1000ft2
grid.
From the previous results we might expect elevation to be a better predictor as it
supported 83% of the observations in the RTA. However, this was using presence
and absence data which can be highly variable. For instance, a single plant could be
found in multiple locations and never develop into a larger infestation. Greater
differences might be detected using abundance data as the analysis would compare
like areas. From our data we could surmise that established weed colonies are
present and growing because of positive environmental conditions. This could be
why aspect fits the model better and suggests that abundance (rather than presence
and absence) is a critical piece of information to collect when attempting to define
weed distribution.
It would be difficult to produce a predictive model of a widely distributed weed
species as they are often opportunistic in nature and could be found in low densities
in many areas. Through a combination of HSA and RTA modeling it may be possible
to locate areas that have a higher probability of more dense infestations occurring.
Time is also a critical component that needs to be incorporated into a more advanced
model and the data collection process.
Figure 6 shows the HSA model for whitetop where the rules “slope is not equal to an
eastern aspect or a flat slope” are applied. The black area is the actual weed
distribution; light gray represents the results of the original habitat suitability
analysis; and dark gray represents the habitat restriction based on the aspect rules.
Figure 6: HSA for Whitetop (CADR) where slope is not equal to ‘East’ or
‘Flat’
Future Recommendations:
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Environmental variables differ widely across the county so it would be
advantageous to work with a number of smaller control areas and carry out
separate analyses in each area.
Examine distance from known infestations to study how weed distribution
decreases spatially.
Conduct further temporal analysis building up the sample and control data
sets.
Test the RTA rules being developed. Use these rules to predict weed presence
based on representative habitat locations.
Expand project to surrounding counties to predict weed presence on a wider
basin scale