Peter J. Holden 1, Eric Ben-David2, Karyn Wilde1, Kerie Hammerton1, David J. Stone1, Robert Russell1 and John Foster2

1Australian Nuclear Science and Technology Organisation, Australia

2School of Biotechnology and Biomolecular Sciences, University of New South Wales, Australia

Figure 3: Redundancy analysis biplots based on the first two axes (a) PLFA mol% data with respect to pH (b) TP and pH and (c) Biolog data with respect to SO4, Cl and pH.

Study Site and MeasurementsThe Dawesley Creek system is located near the city of Adelaide in South Australia and is effected by ARD from the abandoned pyrite mine at Brukunga, nutrients from rural and urban run-off and dry land salinity.

Water samples were taken and analysed for heavy metals, pH, total P and N, conductivity, SO4 and Cl and sediments were analysed for their heavy metal and organic matter content (Loss on Ignition – LOI)

The use of microbial measures to characterise impact of acid rock drainage on Australian rivers

Statistically significant association between the PLFA profiles and the variables was obtained but only after the removal of two outliers (sites 8 and 17). The resulting RDA (Fig. 3a) identified pH as the single most influential variable accounting for 44.4% of the variation. ARD impacted sites immediately downstream of the mine (4,7 and 8 u/s) clustered separately along axis 1 and along axis 2 separation of salinity affected sites (16, 18 and 19) from the remaining sites. A very similar biplot was obtained with data obtained using the Biolog technique and pH as the main explanatory variable when site 18 was removed (data not shown).

The best combination of variables for explaining variation in the PLFA data was pH and total P which led to a separation of the eutrophied sites form those impacted by ARD immediately downstream of the mine and a third group downstream of the diluting influence of Nairne Creek (Fig. 3b). Biolog data inclusive of site 18 was best explained by SO4, ph, and to a lesser extent Cl (Fig. 3c).

RDA ordination of PLFA against sediment quality parameters (metals) did not give good separation with all sites except 4 and 14 clustering.

Conclusions1. Cultivation based techniques (agar plates and Biolog) correlated well with each other, and with exoenzyme activity to a lesser extent.

2. Total PLFA did nor correlate well with other methods on a univariate basis but does capture real abundance as the extraction of biomarkers from samples does not rely on a biological response in the laboratory.

3. PLFA and Biolog both lend themselves well to a multivariate approach which in this case led to a separation of sites basis of the impact of ARD, salinity and eutrophication.

4. Redundancy analysis against water quality parameters proved better for discrimination of sites in this study than did analysis against sediment quality parameters.

Introduction

References

Sediment samples were investigated for impact on sediment microbial communities using various biological techniques including analysis of extracted phospholipid fatty acids (PLFAs) (1), microbial carbon substrate utilisation activity using Biolog plates (2), traditional cultivation of bacteria and fungi, and measurement of the activity of various enzymes involved in biogeochemical cycles [exoglucanase, Β-glucosidase, aminopeptidase and phosphatase using fluorochrome (MUF) labelled substrates, and DMSO reductase as a measure of microbial respiration (3)].

The pollution of aquatic ecosystems with heavy metals and acid derived from the oxidation of sulfidic mine wastes (Acid Rock Drainage – ARD) can have devastating effects on the health of affected waterways as reflected by large reductions in the diversity and abundance of biota. In Australia, at least 54 mine sites manage appreciable quantities (> 10 Mt) of acid generating wastes (29).

Multivariate Analysis of Impact at Brukunga

Redundancy Analysis (RDA) ordination of PLFA relative abundance and water quality data for 19 selected variables (pH, Al, Ca, Cd, Cl, Cu, dissolved oxygen, Fe, K, Mn, Na, SO4, salinity, TDS, temperature, TN, TP, turbidity, and Zn) was used to identify the water parameters which had the greatest bearing on the PLFA profiles.

In addition, these methods are likely to be less labour intensive and more rapid than more traditional methods.

This study reports a comparison of a number of methods including biomarker (phospholipid fatty acid), activity (enzyme) and growth (Biolog) based measurements of the impact of ARD on both microbial biota in both stream sediments and water.

Biological measures assessing the impact of pollution on aquatic ecosystems have been increasingly used over the last ten years, in preference to sole reliance on water chemistry, to examine ecosystem health. The focus, however, has been on diversity and abundance of higher organisms such as fish, frogs and macroinvertebrates and it is desirable that such measures be made across all trophic levels of the ecosystem. The development of microbial measures has been motivated by the concept that microorganisms’ position at the base of the aquatic ecosystem, and their crucial roles in biogeochemical cycles may facilitate use as appropriate indicators of unacceptable impact on the system as a whole.

PC

2: 1

0.0%

(p

=0.

005)

PC1: 23.9% (p=0.005)

pH

TP

4

78u/s

18

19

1516

20

2

14

9

3

PC

2: 4

.9%

PC1: 67.8%

pH Cl

4

7

8u/s

18

15

16

2

14

9

3SO4

8

PC

2:

24.3

%

PC1: 20.1% (p=0.01)

pH

4

7

8u/s

18

15

16

19

20

3

9 2

14

Statistical Comparison of MethodsUnivariate Analysis

The statistical agreement between cultivation , PLFA, Biolog (as average activity against total substrate range), and the various types of enzyme activity was examined in a pair-wise fashion based on significant difference in Pearson Correlation Coefficients. The results indicated significant correlation between: cultured based estimates of viable bacteria and fungi (r = 0.76 at p< 0.01); cultured bacteria and Biolog (r = 0.79 at p< 0.05); Biolog substrate ranges designed for Gram negative Bacteria and yeast (r = 0.89 at p< 0.01); Biolog and aminopeptidase activity (r = 1.00 at p< 0.05); and between Biolog and glucosidase activity (r = 0.76 at p< 0.01).

The table below indicates differences in which techniques show correlations with water or sediment quality parameters. In view of the wealth of information available in the PLFA and Biolog data that was not utilised in the univariate approach as the data below was given as total PLFA (not species) or averaged activity across all substrates, respectively, it was decided to examine multivariate analysis of the data.

In addition, these methods are likely to be less labour intensive and more rapid than more traditional methods.

Total Dissolved Metal Extractable Response variable

pH

Salinity Temp. Pa Ala Cua Cda Fea Zna SO4

a Ala Cda Cua Zna TOC

PLFAa

NS NS NS NS NS NS NS NS NS NS NS 0.88

*** NS 0.68

** NS

Bacteriaa NS 0.52

** NS NS NS 0.39

** 0.42

** NS 0.39

** NS NS NS NS NS NS

Viable Count

Fungia NS 0.54 ***

0.63 ***

0.54 **

NS NS NS NS NS NS NS NS NS NS NS

BiologTM GN 0.51 **

0.40 **

NS NS 0.59 ***

0.44 **

NS 0.48 **

0.49 **

0.69 **

0.55 **

NS NS NS NS

Exoglucanase

NS 0.41

**

**

NS NS NS NS NS NS NS NS NS NS NS NS 0.71

** ** p < 0.05 *** p < 0.01

1. Ben-David, E.A., Holden, P.J., Stone, D.J.M., Harch, B.D. and L.J. Foster, 2005. Microbial Ecology 48(3): 300-315.

2. Garland, J.L.,1997. FEMS Microbiology Ecology 24: 289-300.

3. Griebler, C., and Slezak, D., 2001. Applied & Environmental Microbiology 67: 100-109.

Fig. 2. Map showing the location of the Dawesley Creek system and the sites used for sampling sediment and water