comparison of sampling methods in low-gradient streams€¦ · do these tools work in low-gradient...
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![Page 1: Comparison of sampling methods in low-gradient streams€¦ · Do these tools work in low-gradient streams? ... San Gabriel Arroyo Seco Arroyo Seco. Background Sampling methods CSBP:](https://reader034.vdocument.in/reader034/viewer/2022050306/5f6f06a88fa96f3f95596860/html5/thumbnails/1.jpg)
Comparison of sampling methods in low-gradient streams
Raphael Mazor1 Andy Rehn2 Ken Schiff1
1Southern California Coastal Water Research Project, Westminster, CA2Aquatic Bioassessment Laboratory, Rancho Cordova, CA
November 29, 2006
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Intro
Initiation of regional biomonitoring program: Integrating data from SWAMP and NPDES programs.
Sampling methods and assessment tools (SoCal-IBI) have been proposed.
Do these tools work in low-gradient streams?
Low-gradient streams are common in southern California, and their health is of great public interest.
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Questions
1. Does the So-Cal IBI function well in low-gradient streams?
2. Which sampling methods are the most precise?
3. Do different sampling methods give similar results?
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Background
Sampling methods
CSBP: Targets richest habitats (riffles, margins)
San Gabriel Arroyo Seco Arroyo Seco
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Background
Sampling methods
CSBP: Targets richest habitats (riffles, margins)
?
San Gabriel Arroyo Seco Arroyo Seco
? ?
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Background
Sampling methods
MH: Multi-habitat (25%, 50%, and 75% of channel width)
San Gabriel Arroyo Seco Arroyo Seco
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Background
Sampling methods
MH: Multi-habitat (25%, 50%, and 75% of channel width)
San Gabriel Arroyo Seco Arroyo Seco
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Background
Sampling methods
MCM: Margin-Center-Margin (also gets richest habitats)
San Gabriel Arroyo Seco Arroyo Seco
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Background
Sampling methods
MCM: Margin-Center-Margin (also gets richest habitats)
San Gabriel Arroyo Seco Arroyo Seco
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Methods
Low-gradient streams sampled in southern California:
-Santa Clara River (4 sites)
-Rio Hondo
-Santa Margarita River (2 sites)
-Santa Ana River
-Las Virgenes Creek
-Agua Hedionda
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Methods
Each method tested in each river, often sampled in triplicate.
500-count samples were sorted and identified.
Metrics and IBI scores were calculated for each sample.
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Results
Number of samples:
River CSBP MCM MHSanta Clara 5 5 6Agua Hedionda 2 3 3Rio Hondo 3 3 3Santa Margarita C 2 3 3Santa Margarita D 2 3 3Santa Ana 3 3 3Las Virgenes 2 3 3TOTAL 19 23 24
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Results
CSBP MCM MHRichness 18.7 19.9 16.3Individuals per sample* 453 481 377
*p < 0.05
Of 66 samples total, 16 had < 450 organisms, of which 10 were MHsamples
Sampling method does NOT affect richness, but it may result in small samples.
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Results
Very good
Good
Fair
Poor
Very poor
Sant
a C
lara
Riv
er
Agu
a H
edio
nda
Cre
ek
Rio
Hon
do
Sant
a M
arga
rita
C
Sant
a M
arga
rita
D
Sant
a A
na R
iver
Las
Virg
enes
Cre
ek
SC-IB
I
0
10
20
30
40
50
60
70
80
90
100
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Results
Very good
Good
Fair
Poor
Very poor
Sant
a C
lara
Riv
er
Agu
a H
edio
nda
Cre
ek
Rio
Hon
do
Sant
a M
arga
rita
C
Sant
a M
arga
rita
D
Sant
a A
na R
iver
Las
Virg
enes
Cre
ek
SC-IB
I
0
10
20
30
40
50
60
70
80
90
100CSBP MCM MH
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Results
SS d.f. F p
Method 39.9 2 0.6 0.575River 2641.3 5 14.8 <0.001Interaction 380.2 10 1.1 0.408Residuals 1426.1 40
Exclude Santa Margarita C
Two-way ANOVA on IBI Score
Method does NOT affect IBI score at most sites
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CSBP
0 5 10 15 20 25 30 35
MC
M
0
5
10
15
20
25
30
35
CSBP
0 5 10 15 20 25 30 35M
H0
5
10
15
20
25
30
35
SoCal-IBI
R2 = 0.52 R2 = 0.58Slope = 0.62 Slope = 0.77
Results
Good relationship between all methods.
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Results
CSBP: High variability at low-scoring sites.
Other methods: High variability at all scores.
CSBP
IBI
0 5 10 15 20 25 30 35
SD
0
2
4
6
8
10
MCM
IBI
0 5 10 15 20 25 30 350
2
4
6
8
10
MH
IBI
0 5 10 15 20 25 30 350
2
4
6
8
10
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CSBP
0 2 4 6 8 10
MC
M
0
2
4
6
8
10
CSBP
0 2 4 6 8 10
MH
0
2
4
6
8
10
EPT Taxa
R2 = 0.76
Slope = 0.56
R2 = 0.43
Slope = 0.38
Results
Better relationship between CSBP and MCM than CSBP and MH.
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CSBP
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
MC
M
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
CSBP
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7M
H0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
% Non-insect
R2 = 0.73
Slope = 0.60
R2 = 0.96
Slope = 0.85
Results
Better relationship between CSBP and MH than CSBP and MCM.
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Results - Precision
Comparisons among streamsTypical questions:
Are streams in San Diego of fair or better condition?
Are streams draining urban areas worse than streams draining open space?
Among-stream variability (SD of site averages):
CSBP 6.6
MCM 6.1
MH 4.2
MH << MCM < CSBP
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Results - Precision
Comparisons within streamsTypical questions:
Is this site in better condition following restoration?
Is this site above a biocriterion threshold?
Within-stream variability (average within-site SD):
CSBP 3.8
MCM 3.9
MH 4.1
All methods more-or-less the same.
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NMDS 1 (20%)-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
NM
DS
2 (2
9%)
-1.0
-0.5
0.0
0.5
1.0
1.5
Stress = 10.2
Santa Clara
Santa Margarita D
Rio Hondo
AguaHedionda
Las Virgenes
SantaMargarita C
Santa Ana
Geography strongly influences community structure.
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NMDS 1 (20%)-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
NM
DS
2 (2
9%)
-1.0
-0.5
0.0
0.5
1.0
1.5
CSBP MCM MH
Stress = 10.2
But sampling method does not.
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Conclusions
1. Does the So-Cal IBI function well in low-gradient streams?
All streams are in poor condition.
True status of low-gradient streams?
What about “reference” streams?
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Conclusions
2. Which sampling methods are the most precise?
All methods similar for within-stream comparisons.
MH best for among-stream comparisons.
But: low power for most applications.
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Conclusions
3. Do different sampling methods give similar results?
Geography, not sampling method, has the strongest influence on community structure and IBI scores.
Correlations between methods are good.
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Conclusions
Next steps:
“Better” reference sites (Central Coast).
Test other assessment techniques (e.g., RIVPACS).
Examine physical habitat data. What drives between-site differences?
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End