Revisiting The Analysis of the Condition Of Streams In The Primary Region Of Mountaintop...
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Transcript of Revisiting The Analysis of the Condition Of Streams In The Primary Region Of Mountaintop...
Revisiting The Analysis of the Condition Of Streams In The Primary Region Of Mountaintop Mining/Valley Fill (MTM/VF) Coal Mining
G. Pond and M. Passmore (USEPA Region 3)
BACKGROUND
In 1999 and 2000, EPA R3 characterized and compared the ecological condition of unmined, valley-filled, mined, residential and filled-residential streams in the MTM/VF coal fields of southern WV for a programmatic EIS using a family-level Stream Condition Index (WVSCI). Since that time, EPA has worked with WVDEP to develop a genus-level index (GLIMPSS).
The vouchered EIS macroinvertebrate samples were re-identified to genus level and reanalyzed using the GLIMPSS. Relationships between the ecological condition and various physical, chemical and watershed characteristics were examined using descriptive and multivariate statistics. Results are shown here for the filled, mined and unmined sites. Note that the mined sites have some mining, but no valley fills. The amount of mining in these watersheds tends to be very small as most large scale surface mining is associated with valley fills.
The genus-level index offers many refinements over the family-level index, which are summarized in table 1. These refinements offer a more sensitive, and therefore accurate, characterization of stream condition and causes of impairment.
BACKGROUND
In 1999 and 2000, EPA R3 characterized and compared the ecological condition of unmined, valley-filled, mined, residential and filled-residential streams in the MTM/VF coal fields of southern WV for a programmatic EIS using a family-level Stream Condition Index (WVSCI). Since that time, EPA has worked with WVDEP to develop a genus-level index (GLIMPSS).
The vouchered EIS macroinvertebrate samples were re-identified to genus level and reanalyzed using the GLIMPSS. Relationships between the ecological condition and various physical, chemical and watershed characteristics were examined using descriptive and multivariate statistics. Results are shown here for the filled, mined and unmined sites. Note that the mined sites have some mining, but no valley fills. The amount of mining in these watersheds tends to be very small as most large scale surface mining is associated with valley fills.
The genus-level index offers many refinements over the family-level index, which are summarized in table 1. These refinements offer a more sensitive, and therefore accurate, characterization of stream condition and causes of impairment.
CCA variable scores
Axis 2
Axis 1
Acentrella
Acroneuria
Agapetus
Ameletus
AmphinemuraBaetis
Bezzia/Palpomyia
CambarusCapniidae
Ceratopogonidae
Chaetocladius
Chelifera
Cheumatopsyche
Chimarra
ChloroperlidaeCinygmula
Clinocera
Constempellina
Cricotopus
Diamesa
Diplectrona
Diploperla
Dolichopodidae
Dolophilodes
Drunella
Ectopria
Empididae
Epeorus
Ephemerella
Eukiefferiella
Gomphidae
Haploperla
Helichus
Hemerodromia
Hydropsyche
Hydroptila
Isoperla
Leptophlebiidae
Leuctra
Micropsectra
Neophylax
Neozavrelia
Oligochaeta
Optioservus
Orthocladius
Oulimnius
Parakiefferiella
Paraleptophlebia
Parametriocnemus
Peltoperla
Perlidae
Perlodidae
Plauditus
PolycentropusPolypedilum
Psephenus
Pseudolimnophila
Pteronarcys
Remenus
Rheotanytarsus
Rhyacophila
Simulium
Stempellinella
Stilocladius
Taeniopteryx
Tanytarsus
Thienemanniella
ThienemannimyiaTipula
Tvetenia
Yugus
Zavrelimyia
Epifaunal Substrate
EmbeddednessSediment Deposition
Total RBP Score
ALKALINITY
Conductivity
pH
HARDNESS
IRON
MANGANESE, DISSOLVED
NITRATE+NITRITE
POTASSIUM
SELENIUM
SODIUM
SULFATE
ZINC
Vector scaling: 1.73
CCA case scoresUnmined
Filled
MinedA
xis 2
Axis 1
MT02
MT03
MT13
MT39MT42
MT50MT51
MT91
MT95
MT103MT104
MT14
MT15
MT18
MT25B
MT32
MT34B
MT52
MT60
MT64
MT86MT87MT98
MT45
MT79
MT81
Epifaunal Substrate
Embeddedness
Sediment Deposition
Total RBP Score
ALKALINITY
Conductivity
pH
HARDNESS
IRON
MANGANESE, DISSOLVED
NITRATE+NITRITE
POTASSIUM
SELENIUM
SODIUM
SULFATE
ZINC
Vector scaling: 2.74
Intraset correlations between env. variables and constrained sitescores.
Envi. Axis 1 Envi. Axis 2
Epifaunal Substrate -0.57 -0.37
Embeddedness/Pool Substrate -0.27 -0.63
Sediment Deposition -0.67 -0.57
Total RBP Score -0.68 -0.38
ALKALINITY 0.82 0.08
Field Conductivity 0.82 -0.26
Field pH 0.68 -0.38
HARDNESS, TOTAL 0.85 -0.23
IRON, TOTAL 0.23 0.34
MANGANESE, DISSOLVED 0.54 0.27
NITRATE/NITRITE NITROGEN 0.79 0.19
POTASSIUM, TOTAL 0.92 0.07
SELENIUM, TOTAL 0.83 0.08
SODIUM, TOTAL 0.59 0.03
SULFATE 0.85 -0.26
ZINC, TOTAL -0.17 0.43
Partial PCA of EPA MTM/VF EIS DATA
Unmined
Filled
Mined
Axi
s 2
Axis 1
-0.03-0.06-0.09 0.03 0.06 0.09 0.12 0.15ALKALINITY
CONDUCTIVITYpH HARDNESS, TOTAL
IRON, TOTAL
MANGANESE, DISS.
NITRATE+NITRITE
POTASSIUM, TOTALSELENIUM, TOTAL
SODIUM, TOTAL
SULFATE
ZINC, TOTAL
Vector scaling: 0.19
67_69Sp
0 10 20 30 40 50 60 70 80 90 100
WVSCI
0
10
20
30
40
50
60
70
80
90
100
GLI
MP
SS
GLI
MP
SS
20 30 40 50 60 70 80 90 100
WVSCI
20
30
40
50
60
70
80
90
Unmined
Mined
Filled
5th %ile
5th %
ile
COMPARISON OF GLIMPSS AND WVSCI ASSESSMENTS
Although the WVSCI and the GLIMPSS are highly correlated, the WVSCI often fails to detect impairment that the GLIMPSS can detect. The graph on the left indicates all data in WV dataset from the spring/mountain season/region. The graph on the right indicates the EISSpring 2000 data from the southern coal fields. Sites in the lower right quadrant are rated as not impaired by the WVSCI but impaired by the GLIMPSS. Most of the filled sites that the WVSCI indicated as not impaired were impaired using the GLIMPSS.
Filled Mined Unmined15
20
25
30
35
40
45
# T
OT
AL
TA
XA
Filled Mined Unmined0
5
10
15
20
25
# IN
TO
LE
RA
NT
Filled Mined Unmined0
2
4
6
8
10
12
# M
AY
FL
Y
Filled Mined Unmined0
5
10
15
# S
TO
NE
FL
Y
Filled Mined Unmined0
5
10
15
20
# C
LIN
GE
R
Filled Mined Unmined2
3
4
5
6
7
HB
I
Filled Mined Unmined0
10
20
30
40
50
60
70
% O
RT
HO
CL
AD
Filled Mined Unmined20
30
40
50
60
70
% 5
DO
MIN
AN
T
Filled Mined Unmined0
10
20
30
40
50
% M
AY
FL
IES
-Bae
tis
COMPONENT METRICS OF THE GLIMPSS FOR THE MTM/VF REGION
The genus-level data allows much better resolution of tolerant and intolerant taxa and habit designations (e.g. % clinger) than the family level data. At the family level, several genera may be present, and if those genera vary in terms of tolerance or habit, the family level tolerance ratings may not represent the true tolerance of the actual taxa that are present. The genus level data also offer more range in the taxa richness measures than the family level data. Some of the metrics don’t change from family to genus level (e.g. % mayflies).
The graphs below indicate the values of the component metrics of the GLIMPSS for the Spring 2000 EIS data. Note that there is excellent discrimination between the filled and unmined sites for all metrics except the # of stonefly taxa. Stonefly taxa appear to be more tolerant to the alkaline mine drainage.
CONCLUSIONS
The genus level index (GLIMPSS) indicates similar patterns to the family level index (WVSCI), except that the more sensitive and accurate genus-level index indicates more of the filled sites are impaired. Note that during the EIS, one of the criticisms was that the family level index may not give an accurate protrayal of the condition of filled sites. This analysis indicates the WVSCI painted a more optimistic picture of ecological conditions in filled streams.
The multivariate analysis indicates that the patterns in the raw taxa data are best explained by a subset of the water quality and habitat parameters. The water quality parameters are all associated with coal mining. This analysis supports further research on the effects of total dissolved solids (conductivity and associated ions) and selenium. Selenium was the only parameter in the EIS to exceed numeric water quality criteria. The other parameters of concern do not have numeric water quality criteria for aquatic life.
MULTIVARIATE ANALYSIS OF TAXANOMIC DATA WITH CHEMICAL AND PHYSICAL HABITAT VARIABLES
Principal Components Analysis (PCA) was used to explore the multivariate character of a subset of the water chemistry variables at the sites. Water quality variables dominated by non-detects or with little variation were not included in this analysis. The PCA graph indicates the sites in multidimensional space so that the longest axis (the axis with the most variance) is the first PCA axis, and the second longest axis is the second PCA axis, perpendicular to the first. The first few PCA axes indicate the greatest amount of variation in the dataset and should contain some significant patterns. In this case, the first axis explained 71% of the variance in the sites, and the second axis only explained an additional 12% of the variance. In the EIS dataset, potassium, selenium, sulfate, hardness, alkalinity, conductivity, and sodium all had high positive component loadings on axis 1. Note that the filled sites are clearly separated from the unmined sites along axis 1. The mined sites plotted closer to the unmined sites in this multivariate space, indicating their water quality is more similar to the unmined sites.
Canonical Correspondence Analysis (CCA) was used to relate the biotic variables (genera) to abiotic variables (RBP habitat and median water chemistry values). This analysis is a multivariate, direct-gradient analysis method. The axes of the final ordination are restricted to be linear combinations of the environmental variables and the taxa data. In this CCA, the first two axes accounted for 42% of the variation in the data, the first was 26% and the second was 16%.
The results of the CCA are presented with the environmental variables plotted as arrows and the taxa (called variables) and sites (called cases) plotted as points in 2-dimensional space. The sites are identified as mined, unmined and filled by symbol. Each site lies at the centroid of the points for species that occur in those samples. The arrows indicate the direction of maximum change for that environmental variable and the length of the arrow is proportional to the rate of change. The case (or site) axis scores can be correlated to the original environmental variables to further quantify the relationship between where the site is positioned and the individual variables. In the CCA case diagram, the filled sites clearly contain very different taxa from the unmined sites and the arrows indicate several chemical or habitat variables associated with those taxa differences. Filled sites tended to have worse water quality and habitat. In the CCA variable diagram, the taxa associated with those chemical-physical gradients are shown. The taxa associated with the “filled” space are clearly more tolerant taxa than those occupying the “unmined” space. This ordination also indicates the lack of mayfly taxa in the space associated with mine effluent or the ‘filled” space. This space is dominated by tolerant caddisflies (Hydropsyche and Cheumatopsyche) and midge taxa (e.g. Cricotopus).
MULTIVARIATE ANALYSIS OF TAXANOMIC DATA WITH CHEMICAL AND PHYSICAL HABITAT VARIABLES
Principal Components Analysis (PCA) was used to explore the multivariate character of a subset of the water chemistry variables at the sites. Water quality variables dominated by non-detects or with little variation were not included in this analysis. The PCA graph indicates the sites in multidimensional space so that the longest axis (the axis with the most variance) is the first PCA axis, and the second longest axis is the second PCA axis, perpendicular to the first. The first few PCA axes indicate the greatest amount of variation in the dataset and should contain some significant patterns. In this case, the first axis explained 71% of the variance in the sites, and the second axis only explained an additional 12% of the variance. In the EIS dataset, potassium, selenium, sulfate, hardness, alkalinity, conductivity, and sodium all had high positive component loadings on axis 1. Note that the filled sites are clearly separated from the unmined sites along axis 1. The mined sites plotted closer to the unmined sites in this multivariate space, indicating their water quality is more similar to the unmined sites.
Canonical Correspondence Analysis (CCA) was used to relate the biotic variables (genera) to abiotic variables (RBP habitat and median water chemistry values). This analysis is a multivariate, direct-gradient analysis method. The axes of the final ordination are restricted to be linear combinations of the environmental variables and the taxa data. In this CCA, the first two axes accounted for 42% of the variation in the data, the first was 26% and the second was 16%.
The results of the CCA are presented with the environmental variables plotted as arrows and the taxa (called variables) and sites (called cases) plotted as points in 2-dimensional space. The sites are identified as mined, unmined and filled by symbol. Each site lies at the centroid of the points for species that occur in those samples. The arrows indicate the direction of maximum change for that environmental variable and the length of the arrow is proportional to the rate of change. The case (or site) axis scores can be correlated to the original environmental variables to further quantify the relationship between where the site is positioned and the individual variables. In the CCA case diagram, the filled sites clearly contain very different taxa from the unmined sites and the arrows indicate several chemical or habitat variables associated with those taxa differences. Filled sites tended to have worse water quality and habitat. In the CCA variable diagram, the taxa associated with those chemical-physical gradients are shown. The taxa associated with the “filled” space are clearly more tolerant taxa than those occupying the “unmined” space. This ordination also indicates the lack of mayfly taxa in the space associated with mine effluent or the ‘filled” space. This space is dominated by tolerant caddisflies (Hydropsyche and Cheumatopsyche) and midge taxa (e.g. Cricotopus).
ABIOTIC CHARACTERISTICS AND CORRELATIONS WITH GLIMPSS
Filled Mined Unmined130
140
150
160
170
TO
TA
L R
BP
SC
OR
E
Filled Mined Unmined0
500
1000
1500
SP
CO
ND
Filled Mined Unmined0
10
20
30
40
50
60
70
% M
inin
g
Filled Mined Unmined20
30
40
50
60
70
80
90
GL
IMP
SS
0 500 1000 1500SP COND
20
30
40
50
60
70
80
90
GLI
MP
SS
UnminedMinedFilled
0 10 20 30 40 50 60 70% Mining
20
30
40
50
60
70
80
90
GLI
MP
SS
These box and whisker plots indicate the distribution of total RBP habitat scores, conductivity, % mining in the watershed and GLIMPSS scores. The mined and unmined sites tended to have better physical habitat and better water quality than the filled sites. The mined sites (some surface mining but no valley fills) tended to have very small amounts of mining in their watersheds. Most large scale surface mining is associated with valley fills in this region.
These scatter plots show the relationship between the GLIMPSS scores and % mining in the watershed and field conductivity for the Spring of 2000. There is a strong and positive correlation between the GLIMPSS scores and % mining and conductivity. This is similar to what was found with the WVSCI.
REFINEMENTS WVSCI GLIMPSS
Taxonomy family genus
Season 1 (spring through fall) 2 (spring and fall)
Region 1 (statewide) 2 (mountains and plateau)
Set of Metrics 1 set of metrics 4 sets of metrics (each season/region)
Tolerant Taxa in Metrics? could not be removed removed from certain metrics
Table 1. Refinements offered by the genus-leve GLIMPSS