Performing a National Threatened and Endangered Species Risk assessment for each pesticide registration action is daunting. The scale, both geographically, temporally, and the sheer number of species, presents a challenge. Add to this the fact

that data sets and approaches will be refined moving forward, but current registration efforts cannot be delayed. To effectively perform Proximity Analyses at Step 1, a process which is computationally efficient, technically accurate for the scale,

and robust enough to handle changing data sets must be developed. This poster demonstrates an approach to address these challenges. Using authoritative geospatial data from government sources, a National Potential Pesticide Use Site Layer is generated which can be easily updated as new data are released annually. The potential pesticide use site layer is used in a framework that allows for Flexible Action Area Definition and subsequent National Proximity Analysis of 100’s of Species at a Time. Furthermore, Downstream Transport must be accounted for when calculating proximity to Aquatic Species. By using existing Automation and Scripting methods within ArcGIS and SQL Server, the framework can be re-

applied as needed (such as availability of newer data), and also provides a source of documentation at each step of the process. This poster illustrates an approach to Implementing National-Scale Proximity Analyses with Efficiency & Reliability and Results.

Implementing National-Scale Proximity Analyses with Efficiency & Reliability Joshua J. Amos, Vivienne L. Sclater, and Christopher M. Holmes Waterborne Environmental, Inc., Leesburg, VA, USA

Steve Kay Pyxis Regulatory Consulting, Inc., Generic Endangered Species Task Force (GESTF) Gig Harbor, WA, USA

Define Potential Pesticide Use Sites

For more info, contact: Joshua Amos, (amosj@waterborne-env.com, +1.703.777.0005) 897-B Harrison Street, Leesburg, VA 20175

Figure 1: Aggregating individual NASS Cropland

Data Layer CDL crops into 13 broad crop categories like “Vegetables/Ground Fruit”

improves the certainty in the crop classification.

However, using a single year of CDL may not

sufficiently account for agriculture rotation and

potential expansion through years.

Figure 7: Distribution of Orchards/Vineyards in

relation to FWS Critical Habitats for all species

with spatial data.

Conduct National Screening Level Proximity Assessment Summarize and Interpret Proximity Assessment Results

Figure 3: Further conservatism can be applied in

counties where the multiple years of CDL do not

fully account for acreage reported in the Census

of Agriculture. This figure presents the Selective Expansion distance required for each county to

meet/exceed Census of Agriculture, county-level

acreage.

2013 NASS CDL Vegetable/Ground Fruit Crops 5 Yr. Composite (2009-2013) NASS CDL Vegetable Ground Fruit Crops

Veg/Ground Fruit: 5 Year Composite Percent of Ag Census

# o

f C

ounties

0500

1000

2000

3000

<25%

25%

-50%

50%

-75%

75%

-99.

9%

100%

-125

%

125%

-150

%

150%

-175

%

175%

-200

%

200%

-250

%

250%

-300

%

300%

-400

%

400%

-500

%

>500%

% of Census of Agriculture Acreage

0%

20%

40%

60%

80%

100%

Veg/Ground Fruit: Selective Expansion Percent of Ag Census

# o

f C

ounties

0500

1000

2000

3000

<25%

25%

-50%

50%

-75%

75%

-99.

9%

100%

-125

%

125%

-150

%

150%

-175

%

175%

-200

%

200%

-250

%

250%

-300

%

300%

-400

%

400%

-500

%

>500%

% of Census of Agriculture Acreage

0%

20%

40%

60%

80%

100%

Figure 2: Combining multiple years of CDL expands the crop footprint to ensure that the

evolving agricultural landscape is consistently

represented.

Figure 4: Further conservatism can be applied in

counties where the multiple years of CDL do not

fully account for acreage reported in the Census

of Agriculture. A distance raster calculated

outwards from multi-year CDL crop locations,

into only other agriculture, provides a framework

for Selectively Expanding the base crop

footprint by accumulating adjacent crop pixels

until the crop footprint meets or exceeds the

acreage reported in the Census of Agriculture.

Figure 5: The national distribution of

counties with their acreage of ‘Vegetables/

Ground Fruit’ crops before & after

Selectively Expanding the footprint to meet

the Census of Agriculture reported acreage.

Figure 6: The ‘Vegetables/ Ground Fruit’

pesticide use site layer was expanded 270 meters into adjacent agriculture in order to meet the Census of Agriculture acreage reported for Monterey County.

Figure 9: Standard automation tools in ArcGIS are used to calculate proximity between every

Endangered Species Location (ESL) and

potential pesticide use sites. Operations are

conducted in raster at 10 meter resolution at the

ESL/County intersection level.

Figure 10: Effects of rasterizing ESL’s (that are

natively vector) to 10 meters are negligible when

considered from the perspective of a screening

level assessment with thousands of

measurements.

Figure 8: Euclidean Distance raster is used to

measure the distance outward from every

potential pesticide use site pixel.

Figure 11: ESL’s overlain with Orchard/Vineyard Use Site Euclidean Distance Raster highlights

the range of potential proximity distances

between the two datasets.

Figure 12: Map highlights that distributions of

proximity distances are calculated for every

unique ESL polygon /county intersection. In this

way, it’s possible to generate a distribution of proximity distances for every ESL adding

further insight into the potential for interaction.

Figure 13: The proximity results table stores the distance between each ESL polygon /county

intersection and the potential pesticide use site. The Nearest distance, as well as “Percentiles”, are

recorded using the ArcGIS model in Fig 9.

Figure 14. The proximity results can be

summarized to present the number of species in

each Taxa within specific proximity distances to

potential pesticide use sites, enabling the risk manager to quickly garner the magnitude of potential interaction.

Figure 15. Proximity results can be presented as charts.

Aquatic Endangered Species Screening Level Proximity Assessment

Aquatic Species locations require an additional step for determining proximity since pesticides can be transported downstream from off-target

application sites.

0

20

40

60

80

100

0 100 250 750 >750

Amphibians

Arachnids

Birds

Conifers andCycadsCrustaceans

Dicots

Ferns andAlliesFish

0

20

40

60

80

100

0 100 250 750 >750

Amphibians

Arachnids

Birds

Conifers andCycadsCrustaceans

Dicots

Ferns and Allies

Fish

Insects

Proximity Distance From Orchards/Vineyard Use Sites

Nu

mb

er

of

En

da

ng

ere

d S

pe

cie

s in

ea

ch

Ta

xa

TAXA

NUMBER OF SPECIES IN PROXIMITY DISTANCE TO

ORCHARD/VINEYARD USE SITES

0

feet

1-100

feet

101 – 250

feet

451 – 750

feet

>750

feet

Number of

Species in

Taxa

Amphibians 12 1 0 2 1 36

Arachnids 4 0 3 0 2 13

Birds 20 1 3 0 4 65

Conifers and

Cycads 0 1 1 1 0 5

Crustaceans 2 0 1 4 4 20

Dicots 66 18 9 5 4 216

Ferns and Allies 1 4 0 4 0 14

Fish 88 6 3 0 3 191

Insects 2 5 6 9 2 55

Lichens 0 4 0 1 0 6

Mammals 16 0 9 0 6 63

Molluscs 55 22 14 4 2 212

Monocots 3 2 14 0 1 37

Reptiles 5 9 6 1 0 41

Number of

Species in

Proximity

Distance

274 73 69 31 29 498

Figure 16. NHDPlus AquaXpos analyzes the stream network to find

places, downstream from potential

pesticide use site, where the

chemical’s impact no longer exceeds a specific Level of Concern using a dilution approach for

estimating pesticide transport.

Common Name

Scientific

Name

Critical

Habitat

Polygon

Unique ID STATE County

Area of

Polygon

(M2)

NEAREST

USE SITE

DISTANCE

(Meters)

5% of Polygon

is Within

(Meters)

25% of

Polygon is

Within

(Meters)

50% of

Polygon is

Within

(Meters)

75% of

Polygon is

Within

(Meters)

100% of

Polygon is

Within

(Meters)

California red-legged frog Rana draytonii 10001 CA Alameda 463,436,186 0 365 1,026 2,044 3,606 7,991

California red-legged frog Rana draytonii 10002 CA Merced 135,365,164 0 283 805 1,440 2,362 3,567

California red-legged frog Rana draytonii 10003 CA San Benito 12,595,823 0 134 2,880 3,933 4,705 5,328

California red-legged frog Rana draytonii 10004 CA San Joaquin 100,564,396 0 481 1,405 2,373 4,160 7,006

California red-legged frog Rana draytonii 10005 CA Santa Clara 891,182,960 0 228 890 1,943 3,267 5,014

California red-legged frog Rana draytonii 10006 CA Stanislaus 59,112,383 268 1,537 3,144 4,241 5,931 7,360

California red-legged frog Rana draytonii 10007 CA San Mateo 395,250,129 0 170 582 1,134 1,987 3,878

California red-legged frog Rana draytonii 10008 CA Santa Clara 183,886 127 240 679 815 886 960

California red-legged frog Rana draytonii 10009 CA Santa Cruz 285,290,722 0 108 365 745 1,333 3,798

California red-legged frog Rana draytonii 10010 CA Santa Barbara 535,140,367 0 216 735 1,570 2,630 4,432

California red-legged frog Rana draytonii 10011 CA Ventura 51,210,538 0 547 1,448 2,289 3,045 3,808

California red-legged frog Rana draytonii 10012 CA Monterey 483,785,152 0 127 576 1,374 2,480 4,435