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