Monty Porter - Streams/Rivers Monitoring CoordinatorJason Childress – Biological Team Leader
Oklahoma Water Resources Board (OWRB) Water Quality Programs Division - Monitoring Section
Methods Comparability in Oklahoma’s Low Gradient Streams
Roadmap
• Oklahoma’s part in the National Wadeable Streams Assessment (NWSA) and Methods Comparability
• What does an Oklahoma low gradient stream look like?
• A look at the data and some answers—maybe??
• How can we do it better?
Questions???
• Do different sampling methods produce similar or different answers?
• Can the results of different sampling methods be combined to produce a national assessment?
• Is it realistic to expect states to change methods?
Oklahoma’s Part in the NWSA• Target Sites
– 20 sites visited with comparability on 18 (Red River site excluded and West Buffalo Creek had sample issues)
– 15 of these sites are low gradient
• Reference Site Selection– 10 stations visited with comparability
on 8 (Trader’s creek had sample issues)– 4 of these sites are low gradient– Station's were ecoregion-based– Used OCC reference site work and best
professional judgment
NWSA Parametric Coverage• Typical in-situ parameters such as pH, dissolved
oxygen, temperature, specific conductance• Nutrients• Major cations and anions• Variety of metals • Benthic Macroinvertebrates—single habitat• Periphyton• Physical Habitat• Stream Flow
Cimmaron River, Beaver County, Southwest Tablelands Ecoregion
Trail Creek, Dewey County, Central Great Plains Ecoregion
Red River, Tillman County, Central Great Plains Ecoregion
Grey Horse Creek, Osage County, Flint Hills Ecoregion
Hybarger Creek, McClain County, Central Great Plains Ecoregion
Unknown Creek in the Arbuckle Uplift, Johnston County, Central Oklahoma/Texas Plains Ecoregion
Methods Comparability Study (MCS)Methods Comparability Study (MCS)
MCS Design
• Spatially consists of Fourteen WSA Cooperators
• Incorporates both target and reference sites
• Side-by-side collections using multiple methods (Oklahoma compared W-EMAP to State RBP)
Objectives of the MCS
• Compare state to federal methods to determine the extent of the difference between the methods.
• Is needed in future studies to assess the condition of the Nation’s waters.
• Allays the need for standardized protocols across states
• Looked at both physical habitat and benthic macroinvertebrate sampling methodologies
Oklahoma’s Benthic Macroinvertebrate Collections• Source of Method
– Rapid Bioassessment Protocol adopted from Plafkin, et al (1999)– Method adopted by Oklahoma state agencies in OWRB Technical Document 99-3 (1999)– Use is codified into Oklahoma Administrative Code (OAC) through the Oklahoma Water Quality
standards (OAC 785:45) and the Use Support Assessment Protocols (OAC 785:46)• Oklahoma RBP Method
– Multi-habitat method targeting richest habitats in flowing water over a 400-800 meter reach– 500 uM nets and sieves are used– Composite Riffle—3 kicks in a fast, medium, and slow riffle– Streamside Vegetation—reachwide 3-minute collection of composited jabs– Woody Debris—reachwide 5-minute collection of composited scrapes/picks– 100-150 organism subsample with a large and rare scan– Identified to lowest practical taxonomic level
NWSA Methods—Benthic Macroinvertebrate Collections
• Sample taken reach wide
• Kick sample with modified D-frame net at 11 equidistant transects
• Work L, R, C
• Composite Sample
• Processed at any of a number of EPA contract labs
• 300-500 organism subsample
• Identified to lowest taxonomic level
MCS Results
• Meetings– National meeting of cooperators in Baltimore, MD– Regional meeting of cooperators in Lawrence, KS
• Have results to date on only 6 states• Focused comparability on macroinvertebrates
– Evaluate relationships of Indices of Biotic Integrity (IBIs)
– Evaluate relationships of condition class assessment– Evaluate relationships of pass-fail assessment– Investigate effects of natural slope gradient– Investigate effects of stressor gradient– Investigate relationships with biological condition
gradient
Data Analysis
• Data Sets– WSA_WSA IBI—is the WSA dataset processed through the
WSA IBI (Courtesy of Versar, Inc. and USEPA, OWOW)– WSA_Ok IBI—is the WSA dataset processed through the
Oklahoma IBI – OK_Ok IBI—is the OK dataset processed through the Oklahoma
IBI
• Oklahoma IBI– Metrics are Taxa Richness, EPT Taxa Richness, EPT
Abundance, Modified Hilsenhoff Biotic Index, EPT/Chironomidae Proportion, Percent Dominant Taxa, and Shannon-Weaver Diversity
– 5 condition classes including Reference (Good), Non-impaired (Good), Slightly Impaired (Fair), Moderately Impaired (Poor), and Severely Impaired (Poor)
Data Analysis (continued)
• Data Analysis
– Evaluate relationships of Indices of Biotic Integrity (IBIs)
– Evaluate relationships of condition class assessment
– Evaluate relationships of pass-fail assessment
– Make comparisons of IBI scores to some habitat and land use metrics
– Look at some simple boxplots of data to see where some variation may exist
• Data Issues
– Subsample counts not standardized between datasets
– Taxonomic resolution
– Did not compare Oklahoma Data to NSA IBI
– Sample size is small
– Data collection issues including sample handling and weather
– Oklahoma IBI is well used but application of reference was a first run
– Do no look at biological condition gradient in this analysis
Draft Interim Comparability Study. 2006. Versar, Inc. and USEPA OW
Relationships of Indices of Biotic Integrity (IBIs)
• Value of r2 for Oklahoma Low Gradient Stream Data– WSA_Ok IBI vs. Ok_Ok IBI
0.20
– WSA_WSA IBI vs. WSA_Ok IBI 0.68
– WSA_WSA IBI vs. OK_Ok IBI 0.28
Draft Interim Comparability Study. 2006. Versar, Inc. and USEPA OW
WSA_OK IBI Scores vs. OK_OK IBI Scores
R2 = 0.2796
0
20
40
60
80
100
120
140
160
0 20 40 60 80 100 120 140
WSA_OK IBI
OK
OK
_IB
I
WSA_OK IBI Scores vs. WSA_WSA IBI Scores
R2 = 0.667
0
10
20
30
40
50
60
70
0 20 40 60 80 100 120 140
WSA_OK IBI
WS
A_W
SA
IB
I
OK_OK IBI Scores vs. WSA_WSA IBI Scores
R2 = 0.1964
0
10
20
30
40
50
60
70
0 20 40 60 80 100 120 140 160
OK_OK IBI
WS
A_W
SA
IBI
WSA_WSA IBI WSA_OK IBI OK_OK IBI
mean 34.7 89.1 88.9median 36.1 100.0 89.0p25 23.8 79.0 72.4p75 47.3 111.0 106.1minimum 23.8 79.0 72.4maximum 65.1 118.0 135.7
Comparison of WSA_OK IBI and OK_OK IBI Scores to Various Habitat Metrics
IBI scores vs. % Fines
R2 = 0.0647
R2 = 0.0932
0
20
40
60
80
100
120
140
160
0.00 20.00 40.00 60.00 80.00 100.00 120.00
% Fines
IBI S
core
WSA_OK IBI OK_OK IBILinear (OK_OK IBI) Linear (WSA_OK IBI)
IBI scores vs. % Sand
R2 = 0.0046
R2 = 0.1381
0
20
40
60
80
100
120
140
160
0.00 20.00 40.00 60.00 80.00 100.00
% Sand
IBI S
core
WSA_OK IBI OK_OK IBILinear (WSA_OK IBI) Linear (OK_OK IBI)Linear (WSA_OK IBI)
IBI scores vs. % Pool
R2 = 0.0127
R2 = 0.3308
0
20
40
60
80
100
120
140
160
0.00 20.00 40.00 60.00 80.00 100.00 120.00
% Pool
IBI S
core
WSA_OK IBI OK_OK IBILinear (WSA_OK IBI) Linear (OK_OK IBI)
IBI scores vs. % Rock Substrate
R2 = 0.0001
R2 = 0.2465
0
20
40
60
80
100
120
140
160
0.00 20.00 40.00 60.00 80.00 100.00 120.00
% Rock Substrate
IBI S
core
WSA_OK IBI OK_OK IBILinear (WSA_OK IBI) Linear (OK_OK IBI)
Comparison of WSA_OK IBI and OK_OK IBI Scores to Several Land Use Metrics
• RHUM0—% of human land use at the site
• RAGT0—% of total agricultural land use at the site
• POPDENS—population density
IBI scores vs. RHUM0
R2 = 0.2765
R2 = 0.0928
0
20
40
60
80
100
120
140
160
0 10 20 30 40 50 60 70 80
RHUM0
IBI S
core
WSA_OK IBI OK_OK IBILinear (WSA_OK IBI) Linear (OK_OK IBI)
IBI scores vs. RAGT0
R2 = 0.2437
R2 = 0.0681
0
20
40
60
80
100
120
140
160
0 10 20 30 40 50 60 70 80
RAGT0
IBI S
core
WSA_OK IBI OK_OK IBILinear (WSA_OK IBI) Linear (OK_OK IBI)
IBI scores vs. POPDENS
R2 = 0.1327
R2 = 0.2476
0
20
40
60
80
100
120
140
160
0 5 10 15 20 25 30 35 40
POPDENS
IBI S
co
re
WSA_OK IBI OK_OK IBILinear (WSA_OK IBI) Linear (OK_OK IBI)
Relationship of Condition Class Assessment
• WSA Method/WSA IBI vs. OK Method/OK IBI– Not well compared – 8 of 18, or 44% have different class
• WSA Method/WSA IBI vs. WSA Method/OK IBI
– Not well compared – 9 of 18, or 50% have different class
• WSA Method/OK IBI vs. OK Method/OK IBI– Not well compared – 9 of 19, or 42% have different class– However, 50% of the divergence is at
the Fair/Good classifications
Draft Interim Comparability Study. 2006. Versar, Inc. and USEPA OW
WSA_OKIBI Poor Fair Good Total
Poor 1 1 1 3Fair 1 0 1 2Good 0 4 10 14
Total 2 5 12 19
OK_OK IBI
WSA_WSA IBI Poor Fair Good Total
Poor 3 1 4 8Fair 0 1 4 5Good 0 0 5 5
Total 3 2 13 18
WSA OK IBI
WSA_WSA IBI Poor Fair Good Total
Poor 2 3 3 8Fair 0 5 0 5Good 0 2 3 5
Total 2 10 6 18
OK_OK IBI
Comparison of WSA_OK IBI and OK_OK IBI Condition Classifications to Various Habitat Metrics
Condition Class vs. % Rock Substrate
R2 = 0.3289
R2 = 0.0055
0
1
2
3
4
0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00
% Rock Substrate
Con
ditio
n C
lass
WSA_OK IBI OK_OK IBILinear (OK_OK IBI) Linear (WSA_OK IBI)
Condition Class vs. % Pool
R2 = 5E-05 R2 = 0.3883
0
1
2
3
4
0.00 20.00 40.00 60.00 80.00 100.00 120.00
% Pool
Co
nd
itio
n C
lass
WSA_OK IBI OK_OK IBILinear (WSA_OK IBI) Linear (OK_OK IBI)
Condition Class vs. % Sand
R2 = 0.002
R2 = 0.2305
0
1
2
3
4
0.00 20.00 40.00 60.00 80.00 100.00
% Sand
Co
nd
itio
n C
lass
WSA_OK IBI OK_OK IBILinear (WSA_OK IBI) Linear (OK_OK IBI)
Condition Class vs. % Fines
R2 = 0.1313
R2 = 0.1329
0
1
2
3
4
0.00 20.00 40.00 60.00 80.00 100.00 120.00
% Fines
Co
nd
itio
n C
lass
WSA_OK IBI OK_OK IBILinear (WSA_OK IBI) Linear (OK_OK IBI)
Comparison of WSA_OK IBI and OK_OK IBI Condition Classifications to Several Land Use Metrics
• RHUM0—% of human land use at the site
• RAGT0—% of total agricultural land use at the site
• POPDENS—population density
Condition Class vs. RAGT0
R2 = 0.3456
R2 = 0.095
0
1
2
3
4
0 10 20 30 40 50 60 70 80
RAGT0
Co
nd
itio
n C
lass
WSA_OK IBI OK_OK IBILinear (WSA_OK IBI) Linear (OK_OK IBI)
Condition Class vs. RHUM0
R2 = 0.3812
R2 = 0.132
0
1
2
3
4
0 10 20 30 40 50 60 70 80
RHUM0
Co
nd
itio
n C
lass
WSA_OK IBI OK_OK IBILinear (WSA_OK IBI) Linear (OK_OK IBI)
Condition Class vs. POPDENS
R2 = 0.1735
R2 = 0.2904
0
1
2
3
4
0 5 10 15 20 25 30 35 40
POPDENS
Co
nd
itio
n C
lass
WSA_OK IBI OK_OK IBILinear (WSA_OK IBI) Linear (OK_OK IBI)
Relationship of Pass/Fail Assessment
• WSA Method/WSA IBI vs. OK Method/OK IBI– Not well compared– 6 of 18, or 33% have different status– More sites impaired per the WSA IBI
• WSA Method/WSA IBI vs. WSA Method/OK IBI
– Not well compared– 5 of 18, or 28% have different status– More sites impaired per the WSA IBI
• WSA Method/OK IBI vs. OK Method/OK IBI– Well compared– 3 of 19, or 16% have different status
Draft Interim Comparability Study. 2006. Versar, Inc. and USEPA OW
WSA_WSA IBI Fail Pass Total
Fail 2 6 8Pass 0 10 10
Total 2 16 18
OK_OK IBI
WSA_WSA IBI Fail Pass Total
Fail 3 5 8Pass 0 10 10
Total 3 15 18
WSA OK IBI
WSA_OKIBI Fail Pass Total
Fail 1 2 3Pass 1 15 16
Total 2 17 19
OK_OK IBI
OKNSA
35
25
15
5
Dataset
Num
ber
of ta
xa
OKNSA
7
6
5
4
Dataset
Mod
Hils
enho
ff B
iotic
Ind
OKNSA
4
3
2
Dataset
Sha
nnon
-Wea
ver
dive
rsity
inde
x
OKNSA
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Dataset
EP
T/to
tal
OKNSA
15
10
5
0
Dataset
EP
T ta
xa
OKNSA
1.0
0.5
0.0
Dataset
EP
T/E
PT
+ C
hiro
nom
idae
OKNSA
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Dataset
Chi
rono
mid
ae/to
tal
Answers???• Do different sampling methods produce similar or different answers?
• Can the results of different sampling methods be combined to produce a national assessment?
• The r2 values between IBI scores show little to some relationship
• When use different IBI’s see poor comparability between both condition classes and pass/fail designations
• Using same IBI
– See poor comparability at the condition class level but 50% comes at the fair/good classifications
– However, when use same IBI see a little better comparability between pass/fail designations
– Relationship of datasets to habitat and land use metrics is a mixed bag
Answer
• Is it realistic to expect states to change methods?– No– Not economically feasible– Not politically practical– Most states have developed, adopted and
revised methods that fit that state– Are following RBP’s adopted and pushed
long ago by the EPA – RBP’s have been used effectively by the
states to both screen waterbodies and determine impairment status of streams
So where can we go from here?
• Need to invest money in comparability work
• Investigate comparability at all levels from sampling techniques to metric and index development
• In the end need to decide if the same final answer can be obtained
• Studies need to be more tightly designed and controlled
• Need to continue developing the good working relationship that came out of the national study and continues into the National Lakes Study
Questions?Questions?
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