BIOLOGICAL COMMUNITIES

AND WATER QUALITY

IN ACIDIC MINE LAKES

Tristan Derham

Student number 9923097

1st November, 2004

This dissertation is submitted as partial fulfilment of the requirements for

the Degree of Bachelor of Engineering (Environmental)

Biological Communities and Water Quality in Acidic Mine Lakes i

Acknowledgements

I would like to thank my supervisors, Dr. Anas Ghadouani and Dr. Carolyn Oldham, for their

unsurpassable guidance, enthusiasm and inspiration throughout this year. I would also like to

thank Dr. Ursula Salmon for her wonderful patience and wisdom.

Many thanks also to the Centre for Sustainable Mine Lakes for funding this study.

A big thank you to Greg Attwater, a field genius. Thanks also to Peter Chapman (CWR) for

collecting samples from Chicken Creek, Peter Buckley, Michael Davies and Ken Hitchcock from

the Water Corporation for allowing us access to Wellington dam and supplying data and Mr

Graham McIlleree for access to his property and Blue Waters.

Further thanks go to Dr. Clint McCullough (ECU), Geoff Wake (CWR), Stuart Simmonds (Iluka

Resources), Peter Riley (Wesfarmers Premier Coal), Halinka Lamparski (CWR), Tim Storer

(Curtin), Jeebus, and my friends at university who have turned an otherwise taxing year into a

fantastic experience.

My thanks to Pifa for showing me the way at a very early age.

Especial thanks to Halinka for all her love and her ability to endure just about anything – even me.

Finally I would like to thank my parents, Hugh and Lyn, for their unwavering love and support

throughout my university career. Perhaps they think I will move out now…

Biological Communities and Water Quality in Acidic Mine Lakes ii

Abstract

Most studies on acidic mine lake communities have concentrated on extremely acidic (pH < 3.5)

lakes. This study shows that moderately acidic (pH 3.5 – 5) mine lakes contain functioning pelagic

communities. These may have a significant impact on water quality and broaden options for future

use of the lakes.

The pelagic zones of four moderately acidic mine lakes (Blue Waters, Chicken Creek, Stockton and

WO5B; pH range 2.8 – 5) and a nearby reservoir (Wellington dam; pH ~7) were sampled in March

and July, 2004. Measures of phytoplankton biomass, zooplankton abundance and species diversity

were taken. These were compared with chemical and physical data to investigate the ecological

drivers of these communities.

Phytoplankton were found in all the mine lakes and all but the most acidic contained both primary

producers and consumers. Phytoplankton biomass varied within and between lakes, ranging from 0.1

µg.L-1 to over 4.5 µg.L-1 chlorophyll a. These values were comparable to nearby natural systems. The

lakes may be considered oligo- and ultraoligotrophic with respect to algal biomass and phosphorous.

No relationship between lake pH and phytoplankton biomass was observed. The lakes with highest

phytoplankton biomass also had the highest total phosphorous concentrations and the lowest

zooplankton abundance. It is likely that phytoplankton biomass is controlled by nutrient (inorganic

carbon and phosphorous) limitation as is the case in extremely acidic mine lakes. The overburden

around some mine lakes may be a significant source of nutrients for mine lake communities. Grazing

by zooplankton may also have an influence on phytoplankton biomass but is less likely.

Each of the major zooplankton groups: rotifers, copepods and calanoids, were represented in some

lakes and evidence for seasonal succession of algae was observed. The occurrence of major

zooplankton groups may be influenced by pH but local factors must also be considered. A positive

relationship was observed between plankton species diversity and pH, as has been found in acidic

natural lakes and extremely acidic mine lakes around the world.

iii

Glossary

Allochthonous Originating outside of the system.

Benthic Of the sediment of a water body.

Biomass The mass or weight of all living matter in a system.

Calanoid Small, usually planktonic, crustaceans of the order Copepoda

(suborder Calanoida). Calanoids have long antennae and carry eggs

in a single egg sac, anteriorly.

Cladoceran Small (0.2 – 3 mm) crustaceans of the order Cladocera, one of the

three main zooplankton groups in freshwater systems. Cladocerans

have a distinct head and a body covered by a bivalve cuticular

carapace.

Copepod A member of the order Copepoda (Class Crustacea). One of the three

main zooplankton groups in freshwater systems. The main copepod

groups are calanoids, cyclopoids and harpacticoids.

Filamentous Of algae: having cells arranged end to end in long ‘strings’.

Lake strata Horizontal layers of lake water when stratified due to density

differences. The lowest stratum is the hypolimnion, the highest the

epilimnion. The stratum in between these, containing the thermocline

(in thermally stratified lakes) is the metalimnion.

Littoral zone The shallow waters around the edges of a lake.

Mixotrophy The ability of an organism to ingest organic matter and use light-

driven production to obtain energy

Pelagic Belonging to the free open water of an aquatic system.

pH A measure of the intensity of protons in water, equal to the negative

log of the proton concentration. Acidic water has pH < 7, alkaline

water has pH > 7.

iv

Phytoplankton Plankton containing photosynthetic pigments.

Plankton Small organisms living in the water column with limited powers of

locomotion.

Planktovore An organism that consumes plankton, for example some fish species

Production The flow or flux of mass or energy over time. The increase in

biomass or new organic material formed over a period including any

losses (eg. to grazing, respiration etc.). In most systems, primary

production is production via photosynthesis.

Rotifer A member of the class Rotifera (phylum Aschelminthes). One of the

three main zooplankton groups in freshwater systems. Mostly sessile

but some 100 species are planktonic. Body is elongated with the

head, trunk and foot usually distinguishable.

Thermocline The plane of maximum rate of decrease of temperature with depth.

Trophic classification A system of classification of aquatic ecosystems usually based on

levels of primary production and the primary physical and chemical

determinants of that production. Oligotrophy: low production,

associated with low phosphorous (P) and nitrogen (N); Mesotrophy:

moderate production, P and N; Eutrophy: high production, P and N.

Ultraoligotrophy: exceptionally low production, P and N.

Zooplankton Plankton belonging to the animal kingdom.

v

Table of Contents

ACKNOWLEDGEMENTS......................................................................................................................................... I

ABSTRACT................................................................................................................................................................. II

GLOSSARY............................................................................................................................................................... III

TABLE OF CONTENTS............................................................................................................................................ V

LIST OF FIGURES ................................................................................................................................................. VII

LIST OF TABLES .....................................................................................................................................................IX

1 INTRODUCTION .............................................................................................................................................. 1

2 BACKGROUND................................................................................................................................................. 3

2.1 HOW ACID MINE LAKES FORM...................................................................................................................... 3 2.2 ALKALINITY AND BUFFER CAPACITY ........................................................................................................... 4 2.3 ACIDIC MINE LAKES AND NATURAL LAKES .................................................................................................. 5 2.4 MINE LAKES IN COLLIE................................................................................................................................ 6 2.5 PELAGIC COMMUNITIES AND WATER QUALITY............................................................................................. 7

3 LITERATURE REVIEW .................................................................................................................................. 9

3.1 PHYTOPLANKTON IN ACIDIC MINE LAKES .................................................................................................... 9 Algal biomass: phosphorus, nutrients and inorganic carbon............................................................................. 10

3.2 ZOOPLANKTON IN MINE LAKES .................................................................................................................. 13 3.3 BIODIVERSITY IN MINE LAKES ................................................................................................................... 15

4 METHODS........................................................................................................................................................ 17

4.1 STUDY AREA.............................................................................................................................................. 17 4.2 LAKES........................................................................................................................................................ 18

Blue Waters ........................................................................................................................................................ 19 Chicken Creek .................................................................................................................................................... 19 Stockton .............................................................................................................................................................. 20 WO5B ................................................................................................................................................................. 21 Wellington Dam.................................................................................................................................................. 22 Collie river ......................................................................................................................................................... 22

4.3 QUANTIFYING BIOLOGICAL COMMUNITIES ................................................................................................ 23 4.4 FIELD SAMPLING........................................................................................................................................ 24

Dates and conditions .......................................................................................................................................... 24 Phytoplankton biomass....................................................................................................................................... 25

vi

Biodiversity & Zooplankton abundance ............................................................................................................. 27 Light attenuation ................................................................................................................................................ 28

4.5 LABORATORY ............................................................................................................................................ 28 Phytoplankton biomass....................................................................................................................................... 28 Biodiversity & Zooplankton abundance ............................................................................................................. 30

4.6 CALCULATIONS AND ANALYSES ................................................................................................................ 32 Phytoplankton biomass....................................................................................................................................... 32 Abundance .......................................................................................................................................................... 33 Biodiversity......................................................................................................................................................... 34 Light attenuation ................................................................................................................................................ 34

4.7 SOURCES OF ERROR................................................................................................................................... 35 Phytoplankton biomass....................................................................................................................................... 35 Biodiversity & Zooplankton biomass.................................................................................................................. 36 Light Attenuation................................................................................................................................................ 37

5 RESULTS.......................................................................................................................................................... 38

5.1 CONDITIONS .............................................................................................................................................. 38 5.2 PHYSICAL PARAMETERS............................................................................................................................. 38 5.3 BIOMASS AND ABUNDANCE ....................................................................................................................... 40 5.4 FACTORS AFFECTING BIOMASS BETWEEN LAKES ....................................................................................... 44 5.5 ZOOPLANKTON ABUNDANCE AND OCCURRENCE........................................................................................ 46 5.6 BIODIVERSITY............................................................................................................................................ 48

Species diversity and pH..................................................................................................................................... 51 5.7 TROPHIC STATUS ....................................................................................................................................... 53

6 DISCUSSION.................................................................................................................................................... 54

6.1 PHYTOPLANKTON BIOMASS: VARIATION BETWEEN LAKES......................................................................... 54 6.2 PHYTOPLANKTON BIOMASS AND ZOOPLANKTON GRAZING ........................................................................ 56 6.3 PHYTOPLANKTON BIOMASS: VARIATION WITHIN LAKES ............................................................................ 57 6.4 ZOOPLANKTON: PRESENCE AND ABUNDANCE............................................................................................ 58 6.5 BIODIVERSITY............................................................................................................................................ 59 6.6 BIODIVERSITY AND PH AS WATER QUALITY INDICATORS .......................................................................... 60

7 CONCLUSIONS............................................................................................................................................... 62

8 RECOMMENDATIONS FOR THE FUTURE ............................................................................................. 63

9 REFERENCES ................................................................................................................................................. 65

vii

List of Figures

FIGURE 1. FREQUENCY DISTRIBUTION OF LAKE PH IN THE EAST GERMAN LIGNITE DISTRICTS. AFTER GELLER ET AL. (1998)....4 FIGURE 2. LOCAL RESIDENTS WATER SKIING ON STOCKTON LAKE - AN ACIDIC MINE LAKE NEAR COLLIE, WA. PHOTO: A.

GHADOUANI ...................................................................................................................................................................6 FIGURE 3. AQUACULTURE USING TREATED WATER FROM LAKE WO5H NEAR COLLIE, WA: A CSML RESEARCH PROJECT.

PHOTO: A. GHADOUANI .................................................................................................................................................7 FIGURE 4. ALKALINITY AND H-ACIDITY PRODUCTION IN MINE LAKES. SOLID ARROWS SHOW CARBON FLUXES. BROKEN

ARROWS SHOW ALKALINITY OR H-ACIDITY PRODUCTION. ...............................................................................................8 FIGURE 5. FLUORESCENCE PROFILE OF LAKE FELIXSEE, AUGUST 1996. AFTER LESSMANN AND NIXDORF (2000)..................11 FIGURE 6. PHYTOPLANKTON BIOVOLUME (MAXIMUM BIOMASS) OF DIFFERENT TAXA AND SPECIES IN THREE GROUPS OF MINING

LAKES (GROUPING BASED ON ALGAL ASSEMBLAGES) DURING 1995/96. THESE CORRESPONDED TO GROUPINGS BASED ON

ABIOTIC VARIABLES, MOST NOTABLY PH. GROUP I HAS PH RANGE 2.3 – 3.5; GROUP II, PH 3.0 – 3.9; GROUP III, PH 5.7

– 9.4 (NIXDORF ET AL. 1998A). .....................................................................................................................................12 FIGURE 7. MAXIMUM BIOVOLUME AND MAXIMUM TOTAL PHOSPHOROUS IN 19 LUSATIAN MINE LAKES, PH RANGE 3.3 - 9.4.

AFTER NIXDORF ET AL. (1998A). OUTLIERS WERE REMOVED (LUGTEICH: MAX BIOVOLUME = 16.8 CM3M-3. WALDSEE:

MAX BIOVOLUME = 11.1 CM3M-3). SIMILAR RESULTS WERE FOUND FOR TOTAL NITROGEN AND TIC.............................13 FIGURE 8. OCCURRENCE OF ROTIFERS, CHYDORIDS, OTHER CLADOCERANS AND COPEPODS IN 31 LUSATIAN MINE LAKES

(STEINBERG ET AL. 1998). .............................................................................................................................................14 FIGURE 9. PHYTOPLANKTON SPECIES RICHNESS VS. PH FOR 20 LAKES. THE LINE INDICATES THE TREND AS REVEALED BY

LEAST SQUARES QUADRATIC REGRESSION. AFTER BLOUIN (1989). ...............................................................................16 FIGURE 10. ZOOPLANKTON SPECIES RICHNESS VS. PH FOR 20 LAKES. THE LINE INDICATES THE TREND AS REVEALED BY LEAST

SQUARES QUADRATIC REGRESSION. AFTER BLOUIN (1989)...........................................................................................16 FIGURE 11. THE COLLIE COAL BASIN. THE PREMIER SUB-BASIN LIES TO THE NORTH-EAST AND THE CARDIFF SUB-BASIN TO

THE SOUTH WEST. THE STOCKTON RIDGE LIES IN BETWEEN THE TWO SUB-BASINS. .......................................................18 FIGURE 12. BLUE WATERS. PHOTO: U. SALMON....................................................................................................................19 FIGURE 13. CHICKEN CREEK. PHOTO: U. SALMON. ................................................................................................................19 FIGURE 14. SKI BOAT ON STOCKTON LAKE. PHOTO: A. GHADOUANI .....................................................................................20 FIGURE 15. WO5B WITH NATIVE VEGETATION PLANTED AROUND THE EDGES (FOREGROUND). PHOTO: U. SALMON..............21 FIGURE 16. THE OUTFLOW OF WELLINGTON DAM. PHOTO: U. SALMON. ................................................................................22 FIGURE 17. THE COLLIE RIVER SOUTH BRANCH, DIVERTED TO FLOW AROUND WO5B. PHOTO: U. SALMON...........................22 FIGURE 18. FIELD OFFICER GREG ATTWATER PRIMING ONE PLUNGER OF A VAN DORN BOTTLE. PLUNGERS ARE TRIGGERED BY

SENDING A WEIGHT DOWN THE CONNECTING ROPE TO STRIKE A RELEASE. RUBBER CORD BETWEEN THE PLUNGERS PULLS

THEM CLOSED ON THE TUBING. PHOTO: G. WAKE.........................................................................................................25 FIGURE 19. SEDIMENTATION CHAMBER: (FROM LEFT) BASE, COLUMN AND LID. HEIGHT OF BASE AND COLUMN IS 100MM,

INNER DIAMETER 24MM. PHOTO: AUTHOR....................................................................................................................31 FIGURE 20. PHYSICAL PROFILE OF WO5B IN MARCH, 2004 SHOWING STRATIFICATION AND DISSOLVED OXYGEN PEAK AT THE

THERMOCLINE (SALMON, UNPUBLISHED DATA). ...........................................................................................................40

viii

FIGURE 21. CLOSE UP PHOTOGRAPH OF WHATMAN GF/F FILTER, SHOWING GREEN MATERIAL LEFT AFTER FILTERING

CHLOROPHYLL A FROM ACETONE SOLUTION. PHOTOGRAPH APPROX. 1CM ACROSS. PHOTO: AUTHOR...........................40 FIGURE 22. TEMPERATURE AND PH PROFILE OF CHICKEN CREEK, MARCH 2004. (SALMON, UNPUBLISHED DATA).................41 FIGURE 23. CHRYSOPHYTES FROM AN UNFILTERED SAMPLE FROM CHICKEN CREEK IN JULY. LENGTH OF ORGANISM

APPROXIMATELY 20µM.................................................................................................................................................42 FIGURE 24. PHYTOPLANKTON BIOMASS IN FOUR MINE LAKES, WELLINGTON DAM AND THE COLLIE RIVER IN MARCH AND JULY

OF 2004. SAMPLES WERE TAKEN AT 5M DEPTH IN THE LAKES AND RESERVOIR AND AT THE SURFACE OF THE RIVER.

ERROR BARS SHOW ONE STANDARD DEVIATION. ...........................................................................................................43 FIGURE 25. TWO LUSATIAN MINE LAKES DISPLAYING SEASONAL (PLESSA 111) AND PERMANENT (WALDSEE) DEEP

CHLOROPHYLL MAXIMA (NIXDORF ET AL. 1998A). ........................................................................................................43 FIGURE 26. PHYTOPLANKTON BIOMASS AND TOTAL PHOSPHOROUS IN FOUR MINE LAKES, A RESERVOIR (WEL) AND A RIVER

(COL), IN MARCH (M) AND JULY (J) 2004. TOTAL PHOSPHOROUS IN JULY WAS TAKEN AS A LAKE AVERAGE AS LAKES

WERE FULLY MIXED. WHERE TOTAL PHOSPHOROUS (TOTAL-P) CONCENTRATION AND SOLUBLE REACTIVE PHOSPHOROUS

(SRP) WERE BOTH LESS THAN THE DETECTION LIMIT, AN AVERAGE OF THE DETECTION LIMIT AND ZERO WAS USED.

WHEN SRP WAS MEASURABLE BUT BELOW THE DETECTION LIMIT OF TOTAL-P, THE AVERAGE OF THESE TWO VALUES

WAS USED. TOTAL PHSOPHOROUS WAS TAKEN AS AN AVERAGE IN JULY AS THE LAKES WERE FULLY MIXED. ................45 FIGURE 27. PHYTOPLANKTON BIOMASS AND TOTAL NITROGEN IN FOUR MINE LAKES, A RESERVOIR (WEL) AND A RIVER

(COL), IN MARCH (M) AND JULY (J) 2004. TOTAL NITROGEN IN JULY WAS TAKEN AS A LAKE AVERAGE SINCE LAKES

WERE FULLY MIXED. .....................................................................................................................................................46 FIGURE 28. EXAMPLES OF ZOOPLANKTON SAMPLED FROM COLLIE MINE LAKES. PHOTOS: AUTHOR. ......................................47 FIGURE 29. TOTAL ZOOPLANKTON ABUNDANCE IN FOUR MINE LAKES IN MARCH AND JULY, AND A RESERVOIR (WEL) IN JULY

ONLY. NOTE THE LOGARITHMIC SCALE OF THE DEPENDENT AXIS. .................................................................................47 FIGURE 30. SPECIES RICHNESS IN FOUR MINE LAKES AND A RESERVOIR IN MARCH AND JULY, 2004 (NO DATA COLLECTED FOR

WELLINGTON DAM IN MARCH). ....................................................................................................................................49 FIGURE 31. SHANNON-WEAVER INDEX OF SPECIES DIVERSITY IN FOUR MINE LAKES AND A RESERVOIR IN MARCH AND JULY,

2004 (NO DATA COLLECTED FOR WELLINGTON DAM IN MARCH). .................................................................................49 FIGURE 32. EFFECTIVE NUMBER OF SPECIES IN FOUR MINE LAKES AND A RESERVOIR IN MARCH AND JULY, 2004 (NO DATA

COLLECTED FOR WELLINGTON DAM IN MARCH). EFFECTIVE SPECIES CALCULATED AS THE INVERSE OF SIMPSON'S INDEX

OF SPECIES DIVERSITY...................................................................................................................................................49 FIGURE 33. RANKED RELATIVE SPECIES ABUNDANCE IN WO5B, MARCH AND JULY 2004. .....................................................50 FIGURE 34. UNIDENTIFIED OBJECT OBSERVED IN CHICKEN CREEK..........................................................................................51 FIGURE 35. SPECIES RICHNESS AND PH IN FOUR COLLIE MINE LAKES AND WELLINGTON DAM COMPARED TO THE RESULTS OF

TWO SIMILAR STUDIES ON SEVEN GERMAN MINE LAKES. ...............................................................................................52 FIGURE 36. SPECIES DIVERSITY (SHANNON-WEAVER INDEX) AND PH IN FOUR COLLIE MINE LAKES AND WELLINGTON DAM, AS

COMPARED TO THE RESULTS OF A SIMILAR STUDY ON FIVE GERMAN MINE LAKES. ........................................................52 FIGURE 37. PERCENT CONTRIBUTION OF DIFFERENT PLANKTON GROUPS TO TOTAL BIOVOLUME IN AN ACIDIC MINE LAKE: ML

FELIX (PH 3.4-3.8) WOLLMANN ET AL. 2000). ..............................................................................................................60

ix

List of Tables

TABLE 1. STAGES AND PH THRESHOLDS FOR THE COLONISATION OF THE PELAGIC ZONE BY ZOOPLANKTON AND INSECT

LARVAE. AFTER (NIXDORF ET AL. 1998B) .....................................................................................................................15 TABLE 2. PARAMETERS MEASURED IN MARCH AND JULY, 2004. ............................................................................................24 TABLE 3. WEATHER CONDITIONS AND TIMES OF SAMPLING ON TWO FIELD TRIPS. ...................................................................38 TABLE 4. PHYSICAL PARAMETERS FOR FOUR MINE LAKES, A RESERVOIR AND A RIVER IN MARCH AND JULY, 2004. MINIMA

AND MAXIMA SHOWN FOR ONE PROFILE AT BIOLOGICAL SAMPLE SITE. PHOTIC DEPTH CALCULATED AS DEPTH WHERE

DIFFUSED LIGHT EQUAL TO 1% OF INCIDENT LIGHT. K IS THE DIFFUSE LIGHT EXTINCTION COEFFICIENT. BLANK CELLS

INDICATE NO AVAILABLE DATA. WEL DATA FROM SAMPLE TAKEN IN MAY 2004, COURTESY OF WATER CORPORATION.

.....................................................................................................................................................................................39 TABLE 5. TOTAL PHOSPHOROUS (P) AND NITROGEN (N) IN FOUR MINE LAKES, A RESERVOIR AND A RIVER (SALMON,

UNPUBLISHED DATA). ...................................................................................................................................................44 TABLE 6. THE RESULTS OF CORRELATIONS BETWEEN PHYTOPLANKTON BIOMASS AND A NUMBER OF PHYSICAL AND CHEMICAL

WATER QUALITY PARAMETERS IN THE FOUR MINE LAKES, WELLINGTON DAM AND THE COLLIE RIVER. NOTE THAT, AS

THERE ARE ONLY SIX ECOSYSTEMS, DATA POINTS ARE NOT ENTIRELY INDEPENDENT. ...................................................48 TABLE 7. ZOOPLANKTON PRESENCE IN THE WATER COLUMN OF FOUR LAKES AND A RESERVOIR. ‘Y’ INDICATES FAMILY

OBSERVED. BLANK SPACE INDICATES GROUP NOT OBSERVED. CROSSED SPACE INDICATES LAKE-MONTH NOT TESTED..51 TABLE 8. THE TROPHIC STATUS OF FOUR MINE LAKES, A RIVER AND A RESERVOIR WITH RESPECT TO TOTAL NITROGEN (N),

TOTAL PHOSPHOROUS (P) AND CHLOROPHYLL A ACCORDING TO WETZEL (2001). ........................................................53

1 Introduction

1

1 Introduction

Acidic mine lakes are now an international issue, with hundreds of these lakes in Europe, North

America and Australia. They represent a legal and ethical responsibility and perhaps more

importantly, a resource. In Western Australia, mine lakes are formed in voids created after coal

and other mining operations have ceased. Water fills the void from groundwater or surface

discharge and in many cases these lakes become acidic. There are an estimated 1800 mine voids

containing significant amounts of fresh water in Western Australia (2004). Mine lakes represent

an ethical responsibility on the part of mining companies to leave behind a suitable environment

once production has finished. This is also a legal responsibility – in some cases, bonds are not

returned until adequate rehabilitation has been demonstrated (Bisevac and Majer 1999). These

lakes present an interesting problem in this respect – the original ecosystem is being replaced by

an entirely new one. Thus, mine lakes are not rehabilitated but treated to improve their water

quality.

In areas such as Collie, large deep lakes are scarce and mine lakes represent an important

resource. For the most part, their use as a resource is reliant on their water quality. The ‘water

quality’ of a lake refers to its suitability for a particular purpose. Suggested options for mine

lakes in Western Australia have included drinking water, aquaculture, recreational fishing,

conservation, water sports and use as sacrificial salinity sinks (CSML 2004). Unfortunately,

many mine lakes have poor water quality, especially with respect to acidity and metal

concentrations. Some of the above options may require the lakes to support more advanced

ecosystems than are currently observed, for example recreational fishing or in-situ aquaculture. In

these cases it is the ecological water quality that is of concern. Ecological water quality refers to a

lake’s potential to support desirable ecosystems. In acidic mine lakes, the communities tend to be

low in biodiversity and do not support higher trophic levels, such as fish (Nixdorf et al. 1998b;

Wollmann et al. 2000). This study forms a part of ongoing investigations into the state of

ecological water quality in mine lakes and how best to improve and manage these important

resources.

1 Introduction

2

Acidic mine lakes are regarded by some as ‘swimming pools’ and ‘broken ecosystems’. The first

aim of this study was to assess whether communities existed in these lakes at all. The open waters

of four mine lakes and a nearby reservoir were sampled for plants and animals. These

communities were quantified in terms of basic biological parameters: phytoplankton biomass,

planktonic species diversity and the presence and abundance of major zooplankton groups. The

results were compared to physical and chemical data to investigate the main drivers of these

unique communities. Whereas most of the research on acidic mine lakes has focussed on

extremely acidic lakes (pH < 3.5), the mine lakes of Collie tend to be moderately acidic (pH ~4 -

5.5). Thus, the results for these lakes represent a broadening of current knowledge on mine lake

ecology.

Chapter 2 provides an introduction to acidic mine lakes, how they are formed and what they

represent. Chapter 3 presents a critical discussion of the research undertaken on the biological

communities of acidic mine lakes. Chapter 4 describes the study sites and the methods used to

characterise mine lake communities with respect to phytoplankton biomass, zooplankton group

occurrence, zooplankton abundance and species diversity. The results of the study are presented

in chapter 5 and discussed in chapter 6. Conclusions and recommendations for the future are

addressed in chapters 7 and 8, respectively.

2 Background

3

2 Background

Acidic lakes are found on almost every continent. There are many types of acidic lake and they

are formed in at least three ways: from acid rain, volcanic activity and mining activity. Acid rain

has caused the acidification of many lakes, especially the softwater lakes of North America and

Scandinavia. These lakes are often buffered by aluminium (see below) to a pH of between 4.5

and 5.5 (Geller et al. 1998). This type of lake is referred to as a rain-acidified natural lake.

Volcanic activity has created lake basins in many parts of the world (Cole 1994). The lakes that

form in these depressions can be extremely acidic (pH as low as 0) due to the dissolution of

volcanic gases (Wendt-Potthoff and Koschorreck 2002; Pedrozo et al. 2001; Geller et al. 1998).

These are referred to as naturally acidic lakes. A third way that lakes may become acidic is

through the oxidation of ore bodies after mining operations. Water that passes over or through

these oxidised ores can become acidic, forming acid lakes in the mine void. These are acidic

mine lakes. How these lakes form, why they are acidic, the influence of the organisms in them

and their importance to Collie are explained in the following chapter.

2.1 How acid mine lakes form

Open cut mining operations are often deeper than the water table and water must be pumped out

while mining continues. Once mining operations have finished these pits often fill with water

from aquifers, surface runoff or by deliberate filling from nearby catchments. The host rocks of

many minerals especially those of sulphide ores, high-sulphide coal and gold are in a reduced

state (Castro and Moore 2002). When exposed to atmospheric oxygen, some of these minerals are

oxidised and can produce acidity when brought into contact with water. For example, one of the

most common set of reactions producing acidity in mine lakes is the oxidation of sulphide and

iron in pyrite (FeS2) in the following two reactions (Castro and Moore 2002)

FeS2 + 7/2 O2(aq) + H20 Fe2+ + 2 SO42- + 2H+

Fe2+ + ½ O2(aq) + 5/2 H2O Fe(OH)3 + 2H+

This reaction may be accelerated by bacteria such as Thiobacillus and Ferrobacillus, some of

which operate most efficiently at extremely low pH (Castro and Moore 2002). Thus, as these

disused mine pits fill, they can become acidic. There are a number of processes that alter the

2 Background

4

proton balance in lakes. In order to change the pH one must overcome the buffer capacity of the

lake. The concept of buffer capacity is closely related to that of alkalinity and acidity.

2.2 Alkalinity and buffer capacity

In the most correct sense, the concepts of acidity and alkalinity have little to do with pH

terminology. Whereas pH is a measure of the concentration of H+ ions, acidity (referred to from

here onwards as H-acidity) is a measure of the difference between the equivalent parts of the

anions of strong acids and the cations of strong bases (Wetzel 2001). The term ‘alkalinity’ is, in a

sense, the opposite of H-acidity. Waters with a low pH may have high alkalinity and vice versa.

An operational procedure for determining alkalinity is the acidimetric titration of a water sample

to an equivalence point (Stumm and Morgan 1996). This is known as the acid neutralising

capacity (ANC) and the two terms are often used interchangeably. The opposite is the base

neutralising capacity (BNC) – the ability of the water body to neutralise strong acid – and lakes

with higher BNC or ANC are able to resist changes to their pH.

Lakes are often buffered by a set of solubility equilibrium reactions. The classic example is that

of a carbonate-buffered system, which buffers lakes at pH of 6 to 8.5 (Geller et al. 1998).

However, lakes of lower pH are often buffered by aluminium complexes (pH 4.5 – 5.5) or iron

complexes (pH 2.5 to 4). For example, mining lakes in eastern Germany often either fall into the

iron or carbonate, but not aluminium buffer systems (Figure 1).

pH2 3 4 5 6 7 8 9 10

Freq

uenc

y (%

)

05

1015202530

Fe Al CO3Buffer:

n = 21

Figure 1. Frequency distribution of lake pH in the east German lignite districts. After Geller et al. (1998).

2 Background

5

This buffer system may be controlled by equilibrium solubility. For example, one aspect of the

buffer system in aluminium buffered lakes is the dissolution and precipitation of aluminium

hydroxide:

Al3+ + H2O Al(OH)3(s) + 3H+

Thus, to change the pH in a lake from the range of one buffer system (eg. iron) to the range of

another, one must not only remove the free H+ ions but overcome the buffer capacity – in this

case by precipitating the aluminium hydroxide. It is often more appropriate to refer to the

generation and consumption of alkalinity than protons when discussing alterations to mine lake

chemistry.

2.3 Acidic mine lakes and natural lakes

The buffer systems of acidic mine lakes may be considered their primary distinguishing feature.

A number of other traits also set acidic mine lakes apart from natural lakes. Mine lakes are

relatively young – most were formed after the advent of open-cut mining techniques in the

twentieth century. There may not have been time to develop the same complexity of ecosystem

processes as in natural lakes, which are often hundreds of years old (Wetzel 2001). The lakes did

not form in natural catchments and so the amount of runoff entering the lakes may be small, with

implications for allochthonous carbon input. Due to the low pH metals may be more mobile in

mine lake waters and these lakes tend to have high metal concentrations (Gross 2000). Extremely

acidic lakes may be highly coloured by iron precipitates (Nixdorf et al. 2001). Co-precipitation

with iron (III) may reduce the phosphorous concentration and total dissolved carbon may be very

low due to the low pH (Nixdorf et al. 2001). Geller et al. (1998) propose that acidic mine lakes

compare more closely to volcanic crater lakes than rain-acidified natural lakes, due to their high

ionic concentrations. This may be true for lake chemistry but the communities in mine lakes are

completely different from those of volcanic crater lakes and they can not be considered analogous

(Nixdorf et al. 1998a).

2 Background

6

Figure 2. Local residents water skiing on Stockton lake - an acidic mine lake near Collie, WA. Photo: A. Ghadouani

2.4 Mine lakes in Collie

There are a number of open-cut coal mines in the Collie area that have filled with fresh water and

more will be created from existing mines. These represent a significant water resource for

Collie’s future. Coal has been mined in Collie since the 1890s and remains an essential part of the

local economy (Phillips et al. 2000; Griffin Coal 2004). Mining was undertaken using

underground methods before the first open-cut operation, Stockton, was opened in 1943 (Griffin

Coal 2004). Since that time a number of open-cut mines have been opened and have been

allowed to fill with water from groundwater and runoff. Most of the lakes that have been sampled

so far are acidic. Nevertheless, some mine lakes are already used by local residents for recreation

(Figure 2) and have had freshwater crayfish (‘marron’: Cherax tenuimanus) introduced, which

are used for recreational fishing. There are a number of possible end-use options for these lakes

including aquaculture (Figure 3), recreational fishing, conservation, drinking water and use as

sacrificial salinity sinks (CSML 2004). However, the quality of water in the lakes is not suitable

for these uses as yet. The Centre for Sustainable Mine Lakes (CSML) was established in 2002

with university, state government and industry funding to address the issues surrounding the use

and management of mine lakes in Western Australia. CSML research programs focus on three

main themes (CSML 2004):

• Prediction of water quality in mine lakes with and without remediation

2 Background

7

• Remediation of mine lake water with an emphasis on low pH problems

• Beneficial end uses of mine lakes and mine lake water supplies

Figure 3. Aquaculture using treated water from lake WO5H near Collie, WA: a CSML research project. Photo: A. Ghadouani

2.5 Pelagic communities and water quality

The pelagic communities of mine lakes not only indicate water quality but influence it as well –

they are known to affect the pH, alkalinity, dissolved oxygen, clarity and many other physical

and chemical aspects of lakes (Wetzel 2001; Wendt-Potthoff and Neu 1998). In acidic mine lakes

these communities may be useful for the generation of alkalinity to overcome the buffer capacity.

Fertilisation to stimulate alkalinity-generating processes has been shown to decrease alkalinity

and raise pH in mine lakes and acid mine drainage affected waters (Fyson et al. 1998; George

and Davison 1998). Biologically mediated reactions generally consume alkalinity under oxic

conditions and produce alkalinity under anoxic conditions. Under oxic conditions, nitrate

reduction, photosynthesis (with ammonium uptake) and the respiratory dissimilation of nitrate

produce acidity (George and Davison 1998; Nixdorf et al. 2003). Under anoxic conditions,

nitrate, iron, manganese, and sulphate reduction, along with methanogenesis, produce alkalinity

(George and Davison 1998; Kelly et al. 1982; Mills et al. 1989).

2 Background

8

One useful strategy of primary producers is photosynthesis with nitrate (as opposed to

ammonium) uptake. Primary producers have the potential to affect alkalinity in mine lakes.

Photosynthesis with nitrate assimilation produces alkalinity, according to the equation (Wendt-

Potthoff and Neu 1998; Mills et al. 1989).

106 CO2 +16 NO3- + HPO4

2- + 122 H2O +18 H+ = (C106H263O110N16P) + 138 O2 Alkalinity is increased due to the removal of protons, nitrate ions and phosphate ions. It should be

noted that ammonium uptake is the preferred strategy for algae as it requires less energy (Wetzel

2001). It may be seen from Figure 4 that alkalinity and H-acidity production results from fluxes

of carbon. Pelagic communities have the potential to affect the alkalinity (and thus the pH) of

mine lakes in two ways. Firstly, by the phytoplankton generating alkalinity themselves and

secondly by delivering carbon to the anoxic hypolimnion for bacteria to consume (Figure 4). This

is only one way in which pelagic communities are useful in mine lakes. They can control lake

clarity and accumulate metals such as iron and aluminium (Wetzel 2001; John et al. 2000). The

organisms themselves also serve as water quality indicators (with respect to abundance and

species present) and as a foundation for higher trophic levels (John et al. 2000). These

communities effectively broaden the range of options available for mine lake treatment,

management and utilisation.

Figure 4. Alkalinity and H-acidity production in mine lakes. Solid arrows show carbon fluxes. Broken arrows show alkalinity or H-acidity production.

3 Literature Review

9

3 Literature review

Very little research has been published on moderately acidic mine lakes. Studies on extremely

acidic mine lakes, naturally acidic lakes and lakes in general may give us some perspective. The

most comprehensive range of research on acidic mine lakes was conducted on the coal voids of

Germany and the following review will focus on this set of results. Whilst acidic mine lakes have

no truly analogous natural counterparts, some processes of naturally acidic lakes, and even rain-

acidified natural lakes, may provide useful comparisons. These are also reviewed where

appropriate. The following chapter specifically addresses the non-bacterial, planktonic organisms

of the water column. The phytoplankton expected from acidic environments are described, in

terms of commonly found species and their survival strategies. Influences on the variability of

phytoplankton biomass in and between mine lakes are discussed, with particular reference to

nutrients. Zooplankton occurrence in the lakes is addressed and research on the influence of

acidity on biodiversity is examined.

3.1 Phytoplankton in acidic mine lakes

Some traits of phytoplankton species are more commonly expressed in acidic mine lakes than in

natural lakes. Thus, algal communities (and the trophic levels dependent on them) differ from

those in natural lakes and can not be expected to behave in the same way. The planktonic flora in

Lusatian extremely acidic mine lakes is generally dominated by flagellates belonging to genera of

Chlorophyta (Chlamydomonas), Chrysophyceae (Ochromonas, Chromulina), Cryptophytes

(Cyathomonas), and Euglenophyceae (Lepocinclis, Euglena mutabilis) (Lessmann et al. 2000;

Nixdorf et al. 2001). The most common dominant genera reported are Ochromonas and

Chlamydomonas (Nixdorf et al. 1998a; Wollmann et al. 2000). Extremely acidic mine lakes tend

to be nutrient poor, can display sharp resource gradients and some are strongly coloured by

minerals and humic acids (Lessmann et al. 2000; Nixdorf et al. 2001; Gross 2000).

Phytoplankton in these lakes are expected to be mixotrophic, motile and low resource strategists

(Nixdorf et al. 2003; Tittel et al. 2003; Lessmann et al. 2000; Nixdorf et al. 1998a; Gross 2000).

Gross (2000) points out that habitat generalists are expected, as extremely acidic environments

are rare and remote. Whilst no studies to date have tested for these traits specifically, a number of

authors have shown that the dominant algae in mine lakes fit these criteria. Various chrysophytes,

3 Literature Review

10

cryptophytes and many Ochromonas species are known to be mixotrophic chrysophytes are

particularly adapted to low light (Nixdorf et al. 1998a). Except for two diatom species and

Nanochlorum sp., the phytoplankton taxa found in a study of 14 extremely acidic mine lakes

were all flagellates (Lessmann et al. 2000). Nixdorf et al. (2003) observed a daily migratory

pattern for the mixotrophic nanoflagellate Ochromonas, and some Chlamydomonas species are

known to migrate to build deep chlorophyll maxima (Nixdorf et al. 1998a).

Algal biomass: phosphorus, nutrients and inorganic carbon

There are a number of possible influences on the biomass of these phytoplankton. Variables

found to affect the distribution of algae within mine lakes have included light and nutrient

resources. Lessmann and Nixdorf (2000) attribute the chlorophyll distribution shown in Figure 5

to the development of total inorganic carbon (TIC) gradients during summer stratification. These

authors note the likelihood of deliberate migration to these areas by phytoplankton as the

dominant species are flagellates. Increased phytoplankton biomass at levels below the surface are

common in many lakes worldwide and are known as deep chlorophyll maxima (DCM). They

have traditionally been explained by enhanced nutrient availability at depth. TIC limitation may

be as pressing an issue for algal biomass in moderately acidic lakes (pH ~4 - 5.5) since within

this pH range most of the TIC exists as HCO3- (Wetzel 2001). However, Tittel et al. (2003)

attribute DCM in at least one extremely acidic mine lake (Lake Plessa 111; pH 2.6-3.3) to

spatially selective grazing by mixotrophs. Tittel et al. (2003) argue that the mixotrophs have a

higher threshold for low light and are the most effective grazers in the upper part of the stratified

water column. Thus, they reduce the biomass in the upper levels of the lake and flagellated low

light specialists escape this pressure by migrating to greater depth. This strategy may be

particularly appropriate for mine lakes where motile algae, mixotrophs and low light strategists

may be favoured. It is likely that nutrient and light resources limit phytoplankton within lakes.

3 Literature Review

11

Figure 5. Fluorescence profile of Lake Felixsee, August 1996. After Lessmann and Nixdorf (2000).

The variables influencing spatial distribution within lakes are not necessarily the same as those

influencing the differences between lakes. One obvious possibility for an influential

environmental variable is acidity. However, acidity has not been conclusively shown to affect

algal biomass between lakes. Wollman et al. (2000) report an increase of biomass with pH in

Lusatian acidic mine lakes but only studied three lakes (pH 2.6, 2.8 and 3.6). Kwiatowski and

Ross (1976) found positive correlations between chlorophyll a (chl a) and pH in a study of six

Ontario lakes (cited in Blouin 1989). In a study of 24 Lusatian mine lakes (pH range 2.3 to 9.4)

Nixdorf et al. (1998a) found that while biomass was generally low pH did not limit the maximum

algal abundance (Figure 6). These authors concluded that pH had more influence on algal

community composition than biomass. This result confirmed the findings of Blouin (1989) from

natural lakes in Nova Scotia. In a comprehensive study of 20 lakes with pH ranging from 3 to 8,

Blouin (1989) found that there was no relationship between pH and total algal abundance. In fact,

stepwise regression revealed that pH was a significant predictor of the abundance of only one

algal group – the chlorophytes. This reveals a difficulty in attempting to find controls for the

biomass of all algae in a lake: different species react to environmental pressures in a variety of

ways. The community composition may well influence the results of any such attempt at

generalisation. It is more likely that phytoplankton biomass is limited by nutrient availability than

pH.

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Figure 6. Phytoplankton biovolume (maximum biomass) of different taxa and species in three groups of mining lakes (grouping based on algal assemblages) during 1995/96. These corresponded to groupings based on abiotic variables, most notably pH. Group I has pH range 2.3 – 3.5; Group II, pH 3.0 – 3.9; Group III, pH 5.7 – 9.4 (Nixdorf et al. 1998a).

It has been claimed by some authors that phosphorous and TIC are limiting variables for

phytoplankton communities in mine lakes (eg. Nixdorf et al. 1998a; Beulker et al. 2003).

However, no studies have specifically tested the effect of particular variables (other than acidity)

on broad-scale variation in either biomass or productivity between mine lakes. Some authors

extrapolate from the results of within-lake studies. For example, (Beulker et al. 2003) found that

seasonal succession of algae in Lake Plessa 117, including species composition and abundance,

was closely linked to TIC concentration. Other authors assume that since phosphorous and TIC

concentrations tend to be low then they must be limiting, (eg. Wollmann et al. 2000). Nixdorf et

al. (1998a) present maximum algal biovolume, TIC, total nitrogen and total phosphorous

concentrations for 24 mine lakes during 1995-6, allowing for a test of this assumption.

Manipulation of this data (eg. Figure 7) revealed no correlation between maximum biovolume

I II III

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and any of the three nutrient parameters (r2 < 0.05). The main influences on algal biomass and

production in acidic mine lakes are not clear although it seems likely that they include nutrients

such as phosphorous, nitrogen and inorganic carbon.

Figure 7. Maximum biovolume and maximum total phosphorous in 19 Lusatian mine lakes, pH range 3.3 - 9.4. After Nixdorf et al. (1998a). Outliers were removed (Lugteich: max biovolume = 16.8 cm3m-3. Waldsee: max biovolume = 11.1 cm3m-3). Similar results were found for total nitrogen and TIC.

3.2 Zooplankton in mine lakes

Zooplankton communities have also been studied, albeit sparsely, in acidic mine lakes. As in

natural lakes, the zooplankton are dominated by crustaceans and rotifers, the presence of which

are influenced by pH (Deneke 2000; Wollmann 2000; Wetzel 2001). Whereas in many natural

lakes the top predators are fish, in acidic mine lakes these are absent and corixids (‘water

boatmen’) are often the top predators (Wollmann 2000). Fish do not usually survive in lakes of

pH < 5 (Nixdorf et al. 1998b). To some extent, acidity explains some of the variation in

zooplankton group presence in acidic mine lakes. This was shown to be the case in a study of 31

Lusatian mine lakes by Steinberg et al. (1998) (Figure 8). These authors found rotifers in all lakes

with pH > 2.9. Cladocerans (except for chydorids which were present on some lakes and almost

independent of pH) seemed to have a lower threshold somewhere between pH = 4.5 and 5.9.

Similarly, copepods did not occur in lakes of pH < 3.4. This is in contrast to reports from

naturally acidic lakes in Sweden and Japan, where copepods were found in waters of pH between

3 Literature Review

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2.8 and 3.6 (Vallin 1953 and Uéno 1958, cited in (Deneke 2000). Based on known pH thresholds

for various taxa and observed AML communities, Nixdorf et al. (1998b) suggest ‘stages’ of

AML colonisation by pelagic zooplankton (Table 1), however these should not be viewed as

predictive. Rather, they should provide context for observations of species in moderately acidic

mine lakes.

Figure 8. Occurrence of rotifers, chydorids, other cladocerans and copepods in 31 Lusatian mine lakes (Steinberg et al. 1998).

An important point to note is the number of gaps in the occurrence of zooplankton groups above

their respective thresholds (Figure 8). Despite their ubiquity these groups do not occur in some

lakes within their pH threshold. This is due to the influence of localised variables. An example of

this comes from natural lakes acidified through pollution. Biotic influences, such as predation,

can strongly affect which pelagic species become successful in many recovering natural lakes of

this kind (Wærvågen and Nilssen 2003). Yan et al. (2004) found that pelagic copepod

communities recovered completely after an acidified lake in Sudbury, Canada was neutralised

using lime. However, despite sufficient colonist sources and at least six observed colonisation

attempts, cladocerans had not recovered after 30 years of neutrality. Yan et al. (2004) point to

local variables such as metal toxicity and predation suppressing colonisation. These results show

3 Literature Review

15

that if a diverse mine lake ecosystem supporting higher trophic levels is desired, treatment must

include more than simply raising the pH.

Table 1. Stages and pH thresholds for the colonisation of the pelagic zone by zooplankton and insect larvae. After (Nixdorf et al. 1998b)

Stage Zooplankton groups pH

I Ciliates, rotifers, chironomids, corixids 2-3

II Colonisation by small cladocerans >3

III Colonisation by copepods >4

IV Colonisation by daphnids >5

V ‘Normal’ species assemblage >6

3.3 Biodiversity in mine lakes

The biodiversity of both natural lakes and acidic mine lakes increases with pH. Fewer species are

able to effectively compete in highly acidic environments for several reasons. A number of

studies have considered the influence of pH on species diversity, however not all of these are

relevant to the study at hand. Rain-acidified natural lakes are not an appropriate analogy for mine

lakes in this case. The effects of lower pH on biodiversity are confounded by the fact that the

undamaged lakes tend to have circumneutral pH. Studies on naturally acidic lakes and mine lakes

are more suitable. (Blouin 1989) found a weak but significant increase in algal species number

with pH in naturally acidic lakes up to a pH of about 5.5 (Figure 9) although none of the

separated algal groups displayed any such relationship. Other predictors of species number

included dissolved nitrogen, dissolved organic carbon and colour. Zooplankton species number

also showed a weak relationship to pH, with the highest values occurring between pH values of 5

and 7 (Figure 10). Other predictors included aluminium concentration, alkalinity, colour and

maximum lake depth (Blouin 1989).

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Figure 9. Phytoplankton species richness vs. pH for 20 lakes. The line indicates the trend as revealed by least squares quadratic regression. After Blouin (1989).

Figure 10. Zooplankton species richness vs. pH for 20 lakes. The line indicates the trend as revealed by least squares quadratic regression. After Blouin (1989).

The relationship between species diversity and pH is repeated in Lusatian acidic mine

lakes. Like most extremely acidic waters, the extremely acidic lakes in this area display

lower species diversity than their weakly acidic, pH neutral and alkaline counterparts

(Nixdorf et al. 2001; Wollmann et al. 2000; Deneke 2000). Nixdorf et al. (1998a) report

higher species numbers for phytoplankton in lakes of pH 2.3-3.5 than lakes grouped in

pH ranges of 3-3.9 and 5.7-9.4. This was also shown for zooplankton species over a

much finer pH scale: based on quantitative monthly sampling of the pelagic zone of 21

mine lakes (pH range 2.3 - 3.9) a positive correlation was generated between the number

of zooplankton species and average pH (Deneke 2000). This was despite large

differences in the lake morphometry, hydrology and nutrient conditions (Deneke 2000;

Wollmann et al. 2000). It is likely that species diversity in moderately acidic mine lakes

will increase with pH.

4 Methods

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4 Methods

Sampling of four acidic mine lakes near Collie, WA was undertaken in the early autumn

(March) and mid-winter (July) of 2004. Lakes were stratified in March and fully mixed in

July. Algal biomass, species diversity and zooplankton abundance were measured in the

pelagic zone. Light attenuation was measured to complement other physical data being

collected at the time. A nearby reservoir was sampled for comparison, as was the Collie

river which is used to fill one of the mine lakes. Where possible, biological sampling

coincided with the chemical and physical sampling regimes of other workers.

4.1 Study area

Collie is situated approximately 200 km south-southeast of Perth. It has a Mediterranean

climate with an average rainfall of 950 mm per year. The Collie coal basin is part of the

Collie river catchment and contains large coal deposits in two sub-basins angled north-

west (Figure 11).

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Figure 11. The Collie coal basin. The Premier sub-basin lies to the north-east and the Cardiff sub-basin to the south west. The Stockton ridge lies in between the two sub-basins.

4.2 Lakes

Four mine lakes were chosen for their variety of physical and chemical properties and for

ease of access. The mine lakes were Blue Waters (BW), Chicken Creek (CC), Stockton

(ST) and WO5B. The nearby Wellington dam (WEL) was chosen to represent a ‘natural’

system and to act as a control.

The Collie river was diverted to flow around the WO5B mine while it was operating. The

river is used to fill WO5B when there is sufficient flow. Some samples were taken from

the Collie river diversion (COL) at the point of extraction.

4 Methods

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Blue Waters

Figure 12. Blue Waters. Photo: U. Salmon

Mining operations ceased and filling began in Blue Waters (originally ‘Ewington No. 1’)

in 1959. It is now on privately leased land. The lake is a clear blue colour and surrounded

by vegetation growing up to the edge of white clay cliffs (Figure 12). The cliffs show

evidence of erosion. It has a maximum depth of about 17m and is used for boating and

fishing for freshwater crayfish. Blue Waters appears to receive runoff from overburden

dumps (Phillips et al. 2000) and currently has a pH of around 3.8 (U. Salmon,

unpublished data). No treatment attempts have been made.

Chicken Creek

Figure 13. Chicken Creek. Photo: U. Salmon.

Chicken Creek lies in the Premier sub-basin of the Collie basin (Figure 11). It was

operated as a mine from 1981 to 1997 (Nguyen 2004). It has a surface area of

approximately 40 ha and a maximum depth of 41m, dependent on water levels (Nguyen

4 Methods

20

2004). The steep walls shelter the lake from wind (Figure 13). No remediation attempts

have been made on the overburden around the lake, hence the barren visage. It is

currently managed by the Griffin Coal Mining Company Ltd. In 2003, the pH was

between 2.7 and 3 (Nguyen 2004).

Stockton

Figure 14. Ski boat on Stockton Lake. Photo: A. Ghadouani

Stockton is a disused open cut coal-mine, lying on the Stockton ridge between the Cardiff

and Premier sub-basins (Figure 11). Mining operations ceased in 1957 and the void filled

with water. It is managed by the Department of Conservation and Land Management. It

has a surface area of approximately 15 ha and is a popular recreational lake, used for

swimming, water-skiing and catching marron (Figure 14). Vegetation grows right to the

lake edge. The water is a clear blue colour.

Stockton was reported as having a pH of 4.0 in 1978 and two subsequent efforts were

made by CALM to treat the lake with lime and caustic soda, without success (Phillips et

al. 2000). The pH was 3.1 in 1994 (Phillips et al. 2000) but more recently has risen to

over 4 (U. Salmon, unpublished data).

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WO5B

Figure 15. WO5B with native vegetation planted around the edges (foreground). Photo: U. Salmon

Mining ceased in WO5B, or ‘Kepwari’ as it has recently been renamed, in 1997. The

mine is situated in the Cardiff sub-basin and is larger than Chicken Creek (Figure 11,

Figure 15). The overburden around the edges has been extensively revegetated with

native plants and the lake has been flagged as a recreation resource (SWDC 2001). It is

owned and managed by Wesfarmers Premier Coal.

WO5B has a maximum depth of about 65m and is the focus of much research. A lake

diagnostic system (LDS) was installed in the lake in October 2003 to investigate physical

processes and validate models (Lam 2004). It currently has a pH of between 4.3 and 5

(Salmon, unpublished data).

4 Methods

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Wellington Dam

Figure 16. The outflow of Wellington dam. Photo: U. Salmon.

Wellington dam was originally built on the Collie river, 35km from the Collie townsite,

in 1933 as a source for irrigation on the coastal plain (Mauger et al. 2001). The dam was

raised to its current capacity of 185 GL in 1960 and used for drinking water. Increased

saline input due to clearing in the catchment meant the dam was no longer suitable for

drinking water and was replaced in this capacity by the Harris dam in 1989 (Mauger et al.

2001). It currently has a pH of between 6.5 and 7.5 (Water Corporation, unpublished

data).

Collie river

Figure 17. The Collie river south branch, diverted to flow around WO5B. Photo: U. Salmon.

4 Methods

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The Collie river is the main river system of the Collie basin, running almost 100 km

westward to the Indian Ocean. It was once fresh but due to clearing of the catchment for

agriculture, the salinity is over 1000 mg L-1 (Mauger et al. 2001). Total phosphorus levels

were recorded at over 18 µg L-1 in July of 2004 (Salmon, unpublished data). The south

branch of the river was diverted around the WO5B mine pit during operations (Figure 17)

and is currently used to fill the void when winter flows are sufficient.

4.3 Quantifying biological communities

Biological communities may be quantified in numerous ways, from the fluxes of matter

and energy to the level of biodiversity. Phytoplankton biomass, zooplankton occurrence

and abundance and plankton species diversity were chosen to parameterise mine lake

communities. This was due to their ease of collection and interpretation and their status

as limnological standards.

Phytoplankton are the primary producers of the water column in lakes. They form the

foundation of any pelagic community through the fixation of carbon from the

atmosphere. The biomass of phytoplankton is often quantified using the concentration of

the most common photosynthetic pigment: chlorophyll a (chl a) (Wetzel and Likens

2000).

In many lakes, zooplankton provide a link between the primary producers and higher

trophic levels (Wetzel 2001). Zooplankton are usually larger than phytoplankton and due

to the range of species they are often quantified in terms of their abundance, for example

individuals per unit volume (Wetzel and Likens 2000). As particular zooplankton groups

have different requirements, their presence may give clues as to the state of the aquatic

environment (Deneke 2000).

Biodiversity may be considered at a number of levels, for example, the diversity of

habitats or the diversity of species within a habitat. Species diversity is one of the

simplest and most common ways of measuring biodiversity (Hermy and Cornelis 2000).

4 Methods

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4.4 Field sampling

Dates and conditions

Two sampling expeditions were undertaken in 2004: one in autumn (March 13th to 16th)

and the other in winter (July 27th to 29th). The types of sample taken from each lake are

given in Table 2. Weather conditions at field locations are reported in the Results section.

Autumn

Stockton was sampled on the 13th March; Blue waters on the 14th; WO5B on the 16th. On

the 19th of March, a separate field trip was undertaken to investigate physical properties

of Chicken Creek. Samples for phytoplankton biomass were collected at the same time,

according to the methods described below.

Winter

Chicken Creek was sampled on the 27th of July; WO5B and the Collie river on the 28th;

Stockton, Blue Waters and Wellington dam on the 29th.

Table 2. Parameters measured in March and July, 2004.

March

Lake Phytoplankton

biomass Zooplankton abundance Biodiversity

BW Y Y Y

CC Y N N

ST Y Y Y

WO5B Y Y Y

July

Lake Phytoplankton

biomass Zooplankton abundance Biodiversity

Light attenuation

BW Y Y Y Y

CC Y Y Y Y

ST Y Y Y Y

WO5B Y Y Y Y

WEL Y Y Y N

COL Y N N N

4 Methods

25

Phytoplankton biomass

Prior to the field trip, 1L sample bottles were placed in an acid bath overnight, rinsed

thoroughly with deionised water, left to dry overnight and then rinsed again.

Samples were taken using a van Dorn bottle lowered over the side of a small dinghy. A

van Dorn bottle is a tube of diameter approx. 10cm with plungers at each end (Figure 18).

This was lowered to the required depth (up to 70m) and the plungers triggered to close on

a discrete sample of just over 2L. It was assumed that the van Dorn flushed sufficiently as

it was lowered, so that no contamination from other depths occurred.

Figure 18. Field officer Greg Attwater priming one plunger of a van Dorn bottle. Plungers are triggered by sending a weight down the connecting rope to strike a release. Rubber cord between the plungers pulls them closed on the tubing. Photo: G. Wake.

Two samples from each depth at each sampling position were taken as follows: A 1L

bottle was washed with a small amount of sample, and then filled from the van Dorn

bottle. A second 1L sample bottle was filled from the van Dorn in the same manner, as a

replicate. Bottles were marked accordingly and the GPS coordinates of the sampling

position noted. All samples were taken from a small boat. Samples from Wellington dam

were taken from the dam wall.

4 Methods

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Physical and chemical data were being collected at the same time as the sampling for this

study. In March, these data showed that at least three of the four mine lakes were

stratified. Efforts were made to sample above, at and below the thermocline. In July, all

the lakes and the reservoir were fully mixed and samples were only taken at depths of 5

and 10m.

Where possible, sampling positions were chosen to coincide with chemical and physical

sampling.

Wellington dam was less than 10m deep at the wall and samples were taken at 5m depth

below the surface only.

If sufficient bottles were not available once in the field, bottles were rinsed three times

with deionised water before reuse.

Samples were kept in a covered ice-filled esky in the boat until they could be filtered on

shore.

Samples were filtered directly onto Whatman 42mm GF/F filters using a battery-driven

pump, plastic filter cups and a plastic manifold. Filtration in the field was carried out in

the shade and out of the wind. Volume of sample filtered was measured using a

measuring cylinder and recorded. Where possible, 1L of sample was filtered.

Once the entire sample had been filtered, the pump was turned off quickly and the filter

folded twice, filtrate on the inside, and placed in a clean, marked aluminium foil

envelope. Filters and foil envelopes were only handled with forceps which were rinsed

with deionised water between samples. Manifold cups and measuring cylinders were

rinsed twice with deionised water between samples.

Filters with filtrate were placed in airtight plastic bags and left in a covered ice-filled

esky until a freezer was available to keep them until the return to Perth. They were

4 Methods

27

transported from Collie to the UWA Environmental Research Laboratory (ERL) freezer

in a covered ice-filled esky – a journey of about 2.5 hours.

Biodiversity & Zooplankton abundance

Prior to the field trip, sample jars were rinsed thoroughly with deionised water, left to dry

overnight and then rinsed again.

A zooplankton net, mesh size 25µm, was used to sample the upper part of the water

column for plankton. The net had a detachable sieve at the bottom and was weighted at

the mouth. The net was lowered from the side of a dinghy to a depth of 7m with water

flushing from the exterior to the interior, so as not to collect any organisms on the way

down. The net was then pulled upward slowly to the surface. The sides of the net were

flushed from the exterior to the interior to gather filtrate onto the collecting sieve. The

collecting sieve was then detached and flushed with deionised water into a washed

sample jar. Another identical vertical sweep was taken with the net and the sample placed

into the same sample jar as above.

A small amount of soda water was added to the sample jar to sedate the organisms and

prevent shrinkage when formaldehyde was added. Jars were labelled accordingly. The jar

was transported to the shore in an ice-filled, covered esky. A small amount of

formaldehyde (36%(T) in water) was added to kill and preserve the organisms within 30

minutes of taking the sample (Woelfl and Whitton 2000). Samples were kept in an esky

until transported to the laboratory, where they were kept under cover at room

temperature.

Thus, each sample jar consisted of the all the organisms of size fraction >25µm from two

vertical sweeps of the upper 7m of the pelagic zone at one location. The volume of

sample in each jar differed as the amounts of deionised water, soda water and

formaldehyde were not measured. This volume difference does not affect measures of

abundance but may impact on sampling effort for biodiversity measurements. The

volume of the whole sample was measured in the laboratory before counting (see below).

4 Methods

28

Chicken Creek in July was a deep green colour. To discover what the cause may be, an

unfiltered sample of water was taken from the surface in a sample jar and treated as

above.

Light attenuation

Light attenuation was calculated from irradiance, measured with a Licor LI-193SA

spherical quantum sensor attached to a 2009S lowering frame.

The sensor was calibrated for air and one measurement taken just above the surface. The

sensor was recalibrated for underwater measurement and the frame was lowered with the

spherical sensor down. Measurements were taken just below the surface and at depths of

0, 0.5, 1, 1.5, 2, 2.5, 3, 4, 5, 6, 7 and 8 meters. Most of the attenuation occurs in the upper

part of the water column, hence the higher resolution for depth less than 3m.

Measurements were taken as 15s averages and repeated at each depth. Measurements at

depth are compared to measurements at the surface during analysis (see below), so it is

important that light conditions are constant for the profile. On the only cloudy day of

sampling (see Results section), readings were only taken when cloud cover was the same

as when the first reading was taken.

4.5 Laboratory

Phytoplankton biomass

Chlorophyll a extraction

Samples were kept in a freezer for 22 days or less before chlorophyll extraction. The

recommended chlorophyll a extraction method is described below. The chosen method

was sonication with 90% acetone as a solvent, due to its low toxicity and efficiency for

most types of algae (ERL 2003)

4 Methods

29

Samples were taken from the freezer and kept in a small esky on the bench with a cold

pack. Laboratory light levels were kept to a minimum. One at a time, the filters were

taken from the esky and taken out of the foil envelope with forceps. They were cut up

with scissors and placed in a clean 100x13mm disposable glass vial. 8mL of acetone was

added and the vial covered with parafilm “M” laboratory film to prevent evaporation. The

forceps and scissors were wiped down with acetone before each new sample.

Each sample was then shaken vigorously and placed in a holding rack in a second esky

on the bench top, with a cold pack.

One blank sample was included with each rack of vials. A clean 42mm Whatman GF/F

filter was cut up and placed in a clean 13mL vial and treated as above.

Once the rack was full, it was placed in a Unisonics ultrasonic cleaner, in a cold water

and ice bath to a level below the top of the vials. The rack of vials was covered and

sonicated for 15 minutes at 50Hz.

The rack of vials was then placed in the freezer to steep. Approximately 12 hours later,

each vial was taken out and shaken vigorously before being placed back into the rack in

the freezer for a further 4 to 6 hours.

The TD700 fluorometer was warmed up and calibrated.

Again, the rack of vials was taken out of the freezer and kept in a small esky on the

bench, with a cold pack. Lighting was kept to a minimum. Each glass vial was taken out

and the sample with cut-up filter was filtered through a Whatman 25mm GF/F filter using

a small (50mL) glass manifold. This was to remove the original filter from which chl a

had been extracted overnight. For efficiency, each filter had the remains of the original

42mm filter removed and was used for one more filtration. The glass manifold was rinsed

with acetone before each sample.

4 Methods

30

The filtrate was poured into a clean glass vial and left covered to come to room

temperature (about 1 minute). The vial was wiped down with laboratory tissue to remove

any condensation or marks and placed in the fluorometer. The reading was taken once the

readout on the fluorometer was stable for 5 seconds. This was recorded as Rb, the reading

before acid addition.

If the chl a concentration was higher than the range of the fluorometer then the sample

was diluted by half with 90% acetone and placed back in the fluorometer. The final

concentration was adjusted accordingly.

3 drops of 1M HCl were added to the vial and it was shaken vigorously. The vial was

placed in the fluorometer and after 1 minute a second reading was taken. This was

recorded as Ra, the reading after acid addition.

All equipment was rinsed thoroughly with acetone and left to dry. All glass vials were

placed in a sharps disposal container.

Biodiversity & Zooplankton abundance

Counting

Species abundance was assessed by manually counting sedimented samples. A

sedimentation chamber with clear floor and lid was examined through inverted and

dissecting microscopes.

The sedimentation chamber was washed thoroughly with deionised water and Vaseline

was applied to joins (Figure 19). The chamber was checked for leaks by filling with

deionised water.

The volume in a sample jar was measured using a measuring cylinder. This was gently

stirred and then poured to fill a sedimentation chamber. Four drops of Lugol’s solution

were added to the sedimentation chamber. The absorption of iodine from Lugol’s fixative

stains the phytoplankton and also adds weight, which serves to accelerate sedimentation

4 Methods

31

(Wetzel and Likens 2000). The lid was placed on the chamber and the sample left to

settle for at least 30 hours – the recommended 3 hours for each vertical centimetre

(Wetzel and Likens 2000).

Figure 19. Sedimentation chamber: (from left) base, column and lid. Height of base and column is 100mm, inner diameter 24mm. Photo: Author.

The top section of the chamber was then carefully removed and the lid placed on the

bottom section.

Large organisms ( > ~0.5mm) were counted using a Leica L2 stereoscopic dissecting

microscope at a magnification of 40x (ocular: 4x; objective: 10x) with the sedimentation

chamber on top of graph paper. Individuals were counted over the whole chamber if there

were few.

If there were many individuals, fifteen 1x1mm squares were counted. Individuals were

only counted if touching the left side or upper side of the square. Squares were randomly

selected, with each square allotted a coordinate. Random coordinates were generated on a

Sharp EL-556L Scientific Calculator. The number of individuals counted was then scaled

up to the number in the whole chamber using a factor of the area of the chamber divided

by 15mm2.

4 Methods

32

Small organisms were counted using a Leica DM IRB inverted microscope, at a

magnification of 200x (ocular: 10x; objective: 20x). The diameter of the field of view

was measured using an Olympus objective micrometer calibration slide with 0.01mm

divisions.

15 fields of view were examined with every individual of every species in the field of

view counted. Field of view locations were chosen by moving the stage randomly. As a

new species was identified it was recorded by photograph or by hand. Individuals were

counted if they were touching the left side of the field of view, but not the right side.

Eggs and damaged individuals were not counted.

Filamentous phytoplankton were often propped up above the floor of the sedimentation

chamber due to their elongated structure. After the count of the floor was finished the

plane of view was moved upward as far as possible to count these species. Colonial algae

were counted as number of cells.

The unfiltered sample from the surface Chicken Creek was counted using a magnification

of 630x, as the organisms dominating the sample were very small and numerous. The

technique was the same as above.

4.6 Calculations and analyses

Phytoplankton biomass

Chl a concentration was calculated using the following equation:

[ ] ( ) ( )

−

−

=VvRR

rr

ab1a Chl , [µg.L-1]

where v = volume of extract (i.e. 8 mL)

V = original volume of water filtered

Rb = fluorescence before acidification

Ra = fluorescence after acidification

r = before-to-after acidification ratio of a pure chl a solution

4 Methods

33

r was assumed to be 2.2 (ERL 2003). All chl a results were adjusted by the results for the

blank sample and for dilution if appropriate.

Abundance

Species abundance was calculated as follows:

( )FOV

SCcountSC A

ANN

××=

15

where NSC = total number of individuals in the sedimentation chamber

Ncount = total number counted in 15 fields of view

AFOV = area of field of view

ASC = area of sedimentation chamber floor

The number of individuals in the sample jar (i.e. the total number collected) was

calculated as follows:

SC

SJSC V

VN ×=SJN

where NSJ = total number of individuals in the sample jar

VSJ = volume of sample in the sample jar

VSC = volume of the sedimentation chamber

Thus, the abundance in the lake is calculated as follows:

F

SJ

VN

=Abundance

where VF = total volume filtered

The sedimentation chamber floor diameter was measured as 24mm; the diameter of the

plankton net 255mm; the diameter of the inverted microscope field of view at 200x was

measured at 1mm; the diameter of the inverted microscope field of view at 630x was

measured at 0.36mm; the volume of the sedimentation chamber was 50mL. The volume

4 Methods

34

in the sample jars varied between 103mL (WO5B, July) and 310mL (Wellington dam,

July).

Biodiversity

Diversity consists of both richness (the number of species) and the evenness (the relative

abundance by which species are found in samples). Species abundance, as calculated

above, was converted to three measures of biodiversity: Species richness, the Shannon-

Weaver index and Simpson’s index.

Species richness is one of the most simple diversity indices – simply being the number of

species observed in a sample.

The Shannon-Weaver diversity index (H’) measures both richness and evenness and is

calculated as follows (Shannon and Weaver 1949):

i

s

ii ppH 2

1log' ∑

=

−=

where pi is the proportion of the total abundance made up by species i, and s is the

number of species. Higher number of species and more even distribution give higher

values for H’.

Simpson’s index expresses the probability that two species randomly picked from a

sample will belong to the same species (Simpson 1949):

∑= 2ipP

The effective number of species is simply the inverse of this:

[ ] 12 −

∑= ieff pN and represents the equivalent number of evenly distributed species. More species and a

more even distribution give a higher Neff.

Light attenuation

Irradiance was assumed to decrease logarithmically according to the equation: kz

z eII −= 0 where Iz = irradiance at z metres depth

4 Methods

35

I0 = irradiance at the surface

k = light extinction coefficient

using the above equation, k may be estimated from:

zII

k z

=

0ln

Thus,

zII0ln was plotted against depth and a linear regression performed. An average of

two readings was used for each Iz and I0. Obvious outliers were discarded (for example, if

a reading showed higher irradiance with depth). The gradient of the regression was

recorded as k.

The photic depth was calculated as the depth at which irradiance would be 1% of the

surface reading, given the extinction coefficient extracted from the data. That is, the

photic depth was equal to the natural log of 100 divided by k.

4.7 Sources of Error

Phytoplankton biomass

Sources of error associated with chl a concentration are mostly associated with

degradation of the pigment with excessive heat and light (ERL 2003). Thus, chl a may

have degraded when being transported in 1L bottles from boat to shore (heat, light); when

being filtered on shore (heat, light); whilst being transported between freezers in foil

envelopes in ice-filled eskies (heat); whilst pigment was extracted in acetone in the ERL

(heat, light), or; between extraction and measurement in the fluorometer (heat, light).

Also, filters may have become contaminated from filtration in the field from airborne chl

a.

1L bottles may not have been sufficiently washed between reuse in the field.

4 Methods

36

There may have been contamination of water sample in the van Dorn bottle if it entrained

water from layers above as it was lowered.

In WO5B, samples were taken from near the LDS, to which the boat was attached. This

may have dislodged algae from the mooring ropes and contaminated the sample.

In the laboratory, contamination may have occurred when filtering, as each filter was

used twice. The pump was run until all filtrate had been removed, however.

Any substance that fluoresces in the red region of the spectrum may interfere in the

measurement of chl a and phaeophytin a (ERL 2003)

The low pH of the lake water may have affected the accuracy of chl a measurement, but

this problem is least serious for an acetone solvent, as opposed to methanol or ethanol

(Wetzel and Likens 2000).

Biodiversity & Zooplankton biomass

The plankton net may not have been flushed adequately between sites, thus samples may

have been contaminated with species from other lakes.

On one occasion (WO5B, July) the boat was drifting in the wind as the net was used, so

that the sweep was not vertical but on an angle. Thus, the sample taken was slightly

longer than 7m.

When counting large organisms as a whole under the stereo microscope, squares near the

edge were not whole and thus could not be counted. This may have skewed the

randomised sampling area towards the centre of the sedimentation chamber floor. Large

organisms tend to settle at the edges of these chambers (Wetzel and Likens 2000) and this

may have produced an underestimation of the zooplankton abundance.

4 Methods

37

When counting using the inverted microscope, there may have been insufficient

randomness due to unconscious processes whilst choosing sampling positions.

Species with more than one life cycle may have been mistaken for different species, thus

overestimating the species richness.

Volumes in the sample jars were not standardised before counting. Thus, the searching

effort may have differed from jar to jar, affecting the probability of counting new species

and affecting the species richness (but not abundance).

Light Attenuation

Theoretically, the light extinction coefficient (k) is constant for a given wavelength in

water. In nature, however, underwater irradiance is a composite of many wavelengths

(Wetzel and Likens 2000). Furthermore, the extinction coefficient is a composite of

absorption by water itself, suspended particles and organic compounds (Wetzel and

Likens 2000). Thus, if any of these factors change with depth, the resulting extinction

coefficient may be in error.

5 Results

38

5 Results

5.1 Conditions

For the most part, weather conditions were consistently sunny and mild for both field

trips (Table 3). Samples from Wellington dam were taken at dusk and torchlight was

required to assist with plankton samples, possibly introducing some error with respect to

organisms attracted to light.

Table 3. Weather conditions and times of sampling on two field trips.

March Lake Weather Samples taken13th Stockton Sunny, clear, ~30º Midday14th Blue Waters Cloudy, 20-25º Midday15th WO5B Sunny then cloudy, wind After midday

July Lake Weather Samples taken27th Chicken Creek Sunny, clear, calm, warm Midday28th WO5B Sunny, clear, calm, warm After midday29th Stockton Sunny, clear, light breeze, cool ~10am29th Blue Waters Variable clouds, light breeze, warm After midday29th Wellington dam Partly cloudy, cool Early evening (6pm)

5.2 Physical parameters

The mine lakes differed considerably in their physical characteristics, including pH

(Table 4). Chicken Creek may be considered ‘extremely acidic’ with a pH 2.4 – 2.8.

Stockton and Blue Waters displayed some similar physical characteristics, with pH ~ 3.8

– 4.4 and may be considered ‘moderately acidic’. WO5B may also be considered

‘moderately acidic’, with a pH ranging from 4.3 to 5.

Physical profiles of three of the mine lakes were taken in March, showing that these were

stratified (eg. Figure 20). Dissolved oxygen in WO5B was at around 90% saturation in

the epilimnion and 60% in the hypolimnion with a peak of 100% at the thermocline

(Figure 20). Interestingly, there was also a slight peak in pH at this depth (Figure 20). Chl

a concentration at 21m was equal to that at 5m depth.

5 Results

39

Table 4. Physical parameters for four mine lakes, a reservoir and a river in March and July, 2004. Minima and maxima shown for one profile at biological sample site. Photic depth calculated as depth where diffused light equal to 1% of incident light. k is the diffuse light extinction coefficient. Blank cells indicate no available data. WEL data from sample taken in May 2004, courtesy of Water Corporation.

min max min max

BW MarchJuly Mixed 12.7 13 3.8 3.9 20.2 0.228

CC March Strat 14.5 22 2.4 2.6July Mixed 13.1 14 2.7 2.9 11.8 0.390

ST March Strat 14.5 24 4.3 4.4July Mixed 13.4 13 3.9 4.4 17.1 0.269

WO5B March Strat 13.6 23 4.3 4.6July Mixed 13.5 14 4.7 5 12.5 0.367

WEL MarchMay Mixed 7.4 7.4

COL MarchJuly n/a 8.5 8.5 6.5 6.5

k [m-1]Temp [ºC] pHStratified/ mixed

Photic depth [m]

Whilst oxygen concentration was decreased in the lower strata, none of the three lakes

physically profiled in March (Chicken Creek, Stockton and WO5B) displayed anoxic

hypolimnia.

In July, both Blue Waters and Stockton were clear and blue, whereas WO5B and Chicken

Creek were green in colour. The latter two lakes had higher light attenuation coefficients,

giving shallower photic depths (Table 4). Both WO5B and Chicken Creek were a distinct

green colour and it was originally attributed to high phytoplankton biomass. However,

when water was filtered from Chicken Creek to measure chl a, green material remained

on the filter after acetone extraction of the living pigment (Figure 21). Furthermore, when

unfiltered water from the surface of Chicken Creek was sedimented and examined under

a microscope, irregular material that did not stain with Lugol’s solution was observed.

These observations imply that some of the colour in Chicken Creek may have been

mineral in nature, rather than biological.

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Figure 20. Physical profile of WO5B in March, 2004 showing stratification and dissolved oxygen peak at the thermocline (Salmon, unpublished data).

Figure 21. Close up photograph of Whatman GF/F filter, showing green material left after filtering chlorophyll a from acetone solution. Photograph approx. 1cm across. Photo: Author

5.3 Biomass and abundance

There was considerable variability in phytoplankton biomass, with chlorophyll a

concentrations varying within and between lakes by an order of magnitude. Chl a also

varied between seasons by a factor of 2. Chicken Creek and WO5B had the highest

phytoplankton biomass in both March and July (Figure 24). In both lakes, the July

5 Results

41

biomass was more than double that of March, excepting for the high result for Chicken

Creek at 15m in March. There was a slight reduction in phytoplankton biomass in

between March and July in Blue Waters (~0.2 to

~0.1µg.L-1) and in Stockton (~0.5 to ~0.2µg.L-1).

Figure 22. Temperature and pH profile of Chicken Creek, March 2004. (Salmon, unpublished data).

In March, samples were taken from a number of

depths in the stratified lakes. A high concentration of

pigment was found at 15m depth in Chicken Creek.

This represents a sample taken from below the

thermocline (Figure 22). Such relatively high

concentrations of pigment below the surface layer are

known as ‘deep chlorophyll maxima’. The peak in

dissolved oxygen displayed in WO5B is known as a

metalimnetic oxygen maximum (MOM). These are

almost always caused by algal populations whose rate

of production is faster than their rate of sinking

(Wetzel 2001). Wetzel (2001) points out that the

depth at which this occurs is directly correlated to

transparency of the water, with most MOM occurring

at 3-10m. Thus, WO5B may have been relatively

transparent at this time. Lakes were fully mixed in

July and it is likely that Wellington dam was fully

mixed at the dam wall.

In Stockton, Chicken Creek (upper 10m only) and

WO5B phytoplankton biomass was significantly

higher in July than in March (p < 0.05, 5 & 10m depths, one-tailed paired t-tests). The

phytoplankton biomass in the March hypolimnion of Chicken Creek was the highest

recorded in this study (Figure 26). Phytoplankton biomass in Blue Waters was

significantly lower in July than March.

5 Results

42

In July, when phytoplankton biomass was compared to the Collie river and Wellington

dam, both Chicken Creek and WO5B

showed far higher results than the ‘natural’

systems, whereas Stockton and Blue

Waters had lower phytoplankton biomass

(Figure 24). Chicken Creek was

dominated by a chrysophyte species

(Figure 23) at an abundance of 3.6 x 106

individuals L-1. This is within the range

observed in Lake Plessa 117, an extremely

acidic mine lake in Lusatia, which had an

abundance of Ochromonas sp.

(Chrysophyceae) of between 1 x 106 and 9

x 106 individuals L-1 (Beulker et al. 2003).

Figure 23. Chrysophytes from an unfiltered sample from Chicken Creek in July. Length of organism approximately 20µm.

The phytoplankton biomass found in Collie mine lakes is similar to results reported from

Lusatian mine lakes. Nixdorf et al. (1998a) found chl a concentrations of less than 5

µg.L-1 in the epilimnion of four acidic mine lakes generally but maxima of up to 523

µg.L-1 were also observed. Lessmann and Nixdorf (2000) found chlorophyll a

concentrations were usually less than 5 µg.L-1 in one mine lake (Plessa 117, pH ~3) but

ranged up to 26 µg.L-1.

5 Results

43

Figure 24. Phytoplankton biomass in four mine lakes, Wellington dam and the Collie river in March and July of 2004. Samples were taken at 5m depth in the lakes and reservoir and at the surface of the river. Error bars show one standard deviation.

Figure 25. Two Lusatian mine lakes displaying seasonal (Plessa 111) and permanent (Waldsee) deep chlorophyll maxima (Nixdorf et al. 1998a).

5 Results

44

Phytoplankton biovolumes and chl a concentrations showed maxima in winter (under

ice), autumn and during summer stagnation in the hypolimnion of Lusatian mine lakes

(Lessmann and Nixdorf 2000). This was shown to be dependent on inorganic carbon

supply. Deep chlorophyll maxima have been observed in at least three acidic mine lakes

in Lusatia, both permanent and seasonal (Figure 25).

5.4 Factors affecting biomass between lakes

As expected, pH was not found to affect phytoplankton biomass or zooplankton

abundance. It may be seen from Figure 24 that the mine lakes with the lowest and highest

pH values (Chicken Creek and WO5B, respectively) have the highest phytoplankton

biomass. Furthermore, Wellington dam has the highest pH of all yet has a lower

phytoplankton biomass than these two mine lakes. By linear regression, pH defines less

than 1% of the variation in chl a and the regression was not significant (p>0.1, n = 23).

Not all data points were independent as they were drawn from only six ecosystems.

(Nixdorf et al. 1998a) found that, whilst phytoplankton biomass is generally low in acidic

mine lakes, the standing crop is not restricted by low pH: The two lakes with highest

maximum phytoplankton biomass had pH 2.6-2.8 and pH 3.0-3.4, far higher than the

biomass in lakes of pH > 5.7.

Whilst biological samples were being taken, chemical data was being collected at more

than one site in each lake. The phosphorous concentrations observed from this data are

similar to results from oligotrophic or ultraoligotrophic natural lakes (Wetzel 2001). The

same may be said for total nitrogen levels, except in WO5B, which had very high total

nitrogen (Table 5).

Table 5. Total phosphorous (P) and nitrogen (N) in four mine lakes, a reservoir and a river (Salmon, unpublished data).

March July March JulyChicken Creek ? 10-11 ? 370 - 440

Blue Waters <5 <5 200 260 - 310Stockton <5 <5 <50 -170 130 - 160

WO5B <5 <5 - 9 1400 - 1500 1200 - 1500Wellington dam ? <5 ? 440 - 1500

Collie river ? 18.5 ? 1400

Total P [µg.L-1] Total N [µg.L-1]

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45

Phytoplankton biomass was compared to total phosphorous concentration (Figure 26).

Boulton and Brock (1999) point out that total phosphorous is often the most appropriate

measure of phosphorous in an ecosystem due to the rapid, dynamic cycling of the

nutrient. Of the lakes and reservoir, two groups may be seen – those of low total

phosphorous and low biomass and those of high total phosphorous and high biomass

(Figure 26). Interestingly, WO5B appears in both groups: in the low biomass group in

March and the high biomass group in July. The extra phosphorous in WO5B may be

partly explained by runoff from the overburden: one week before these samples were

taken the first rains of winter occurred. The phosphorous content of the overburden

runoff was tested and found to be very high (U. Salmon, pers. comm.). The Collie river,

whilst having the highest phosphorous loading had only a moderate sestonic algal

biomass.

Total Phosphorous (µg P.L-1)

0 2 4 6 8 10 12 14 16 18 20

Chl a

(µg

.L-1

)

0

1

2

3

4

5 BW (M)ST(M)WO5B (M)BW (J)CC (J)ST (J)WO5B (J)WEL (J)COL (J)

Figure 26. Phytoplankton biomass and total phosphorous in four mine lakes, a reservoir (WEL) and a river (COL), in March (M) and July (J) 2004. Total phosphorous in July was taken as a lake average as lakes were fully mixed. Where total phosphorous (total-P) concentration and soluble reactive phosphorous (SRP) were both less than the detection limit, an average of the detection limit and zero was used. When SRP was measurable but below the detection limit of total-P, the average of these two values was used. Total phsophorous was taken as an average in July as the lakes were fully mixed.

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A weaker pattern is shown for total nitrogen loading (Figure 27). The Collie river and

WO5B in March show high nitrogen loading yet only a moderate phytoplankton biomass.

The trend seen here seems entirely driven by the high nitrogen loading and high July

phytoplankton biomass in WO5B (Figure 27). Furthermore, whereas WO5B has the same

amount of total nitrogen in both March and July, the biomass is clearly greater in the

latter month (Table 8).

Total Nitrogen (µg N.L-1)

0 200 400 600 800 1000 1200 1400 1600

Chl a

(µg

.L-1)

0

1

2

3

4

5 BW (M)ST(M)WO5B (M)BW (J)CC (J)ST (J)WO5B (J)WEL (J)COL (J)

Figure 27. Phytoplankton biomass and total nitrogen in four mine lakes, a reservoir (WEL) and a river (COL), in March (M) and July (J) 2004. Total nitrogen in July was taken as a lake average since lakes were fully mixed.

5.5 Zooplankton abundance and occurrence

Phytoplankton biomass may also be related to zooplankton abundance. The systems with

the lowest phytoplankton biomass – Stockton, Blue Waters and Wellington dam (Figure

24) - also had the highest zooplankton abundance (Figure 29). These animals consisted

primarily of calanoid crustaceans but in WO5B only rotifers were found (Table 7). In

Blue Waters, both calanoids and small cladocerans (~0.5mm) were observed in July.

Cladoceran abundance was 0.01 individuals per m3. This was far outweighed by the

abundance of calanoids at 0.45 individuals per m3 at the same time. Despite the

5 Results

47

similarities in parameters such as pH, nitrogen and phosphorous concentrations and

phytoplankton biomass in Blue Waters and in Stockton, there was an order of magnitude

difference in zooplankton abundance between the lakes. No zooplankton were found in

the pelagic zone of Chicken Creek, however aquatic insects were numerous around the

edges, as was the case in WO5B.

Figure 28. Examples of zooplankton sampled from Collie mine lakes. Photos: Author.

0.001

0.01

0.1

1

10

100

BW CC ST WO5B WEL

Indi

vidu

als

m-3

March

July

Figure 29. Total zooplankton abundance in four mine lakes in March and July, and a reservoir (WEL) in July only. Note the logarithmic scale of the dependent axis.

5 Results

48

It should be noted that through linear regression, phytoplankton biomass showed

statistically significant correlation to a number of physical and chemical water quality

parameters other than total nitrogen and total phosphorous (Table 6). This is probably due

to the low number of sites and the resulting dependent data points.

Table 6. The results of correlations between phytoplankton biomass and a number of physical and chemical water quality parameters in the four mine lakes, Wellington dam and the Collie river. Note that, as there are only six ecosystems, data points are not entirely independent.

r2 p-value n

Conductivity 0.48 < 0.0005 22NO3+NO2 0.35 < 0.005 27

Ammonia 0.32 < 0.005 27Chloride 0.30 < 0.005 27

Dissolved Oxygen (% Sat) 0.29 < 0.01 22Salinity 0.33 < 0.05 22

SO4 0.23 < 0.05 27Silicate 0.23 < 0.05 27

5.6 Biodiversity

Species richness was over twice as high in Wellington dam in July than any of the mine

lakes at any time, save Stockton in March (Figure 30). Richness in Chicken Creek was

far lower than the other mine lakes, all three of which gave similar results (Figure 30).

These patterns were reflected in two measures of species diversity incorporating evenness

as well as richness: The Shannon-Weaver index (Figure 31) and Simpson’s index

(reported here as the inverse – the effective number of species, Neff) (Figure 32). The

drop in species richness in Stockton from 23 to 15 species between March and July is

carried through into both the latter indices. There were a number of differences in lake

physics and chemistry between these dates, including a slight drop in the pH in the top

10m of this lake, from 4.3 to 3.9 between March and July (Table 4). Furthermore, the

temperature at the surface dropped from 24 to 13.5 ºC and the lake was fully mixed.

5 Results

49

05

1015202530354045

BW CC ST WO5B WEL

num

ber o

f spe

cies

March

July

Figure 30. Species richness in four mine lakes and a reservoir in March and July, 2004 (No data collected for Wellington dam in March).

00.5

11.5

22.5

33.5

44.5

BW CC ST WO5B WEL

H'

March

July

Figure 31. Shannon-Weaver index of species diversity in four mine lakes and a reservoir in March and July, 2004 (no data collected for Wellington dam in March).

0

2

4

6

8

10

12

BW CC ST WO5B WEL

Effe

ctiv

e sp

ecie

s

March

July

Figure 32. Effective number of species in four mine lakes and a reservoir in March and July, 2004 (no data collected for Wellington dam in March). Effective species calculated as the inverse of Simpson's index of species diversity.

5 Results

50

One pattern not picked up by species richness or the Shannon-Weaver index is the

opposite trend in WO5B: an increase in diversity between March and July from 3 to 8

effective species. Whilst the richness changes little (from 12 to 13 species) the number of

species comprising over 10% of the sample (‘dominant species’) rises from 3 to 4 (Figure

33). Lower ranked species are given more weight, which can be seen as a shift to the

right in the ranked abundance figure below (Figure 33). These trends combine to increase

the number of effective species. Higher diversity was also exhibited in a shift in the

dominant species: in March the dominant species were all filamentous, whilst in July,

dominance was shared between a filamentous phytoplankton species, two unidentified

species and a diatom. There was a slight increase in pH in this lake of 0.4 pH units

between March and July, there was a decrease in the temperature from 24 to 13ºC, there

was an increase in the phosphorous concentration and the lake became fully mixed in

July, any or all of which may explain the shift in species composition (Table 4, Table 5).

Figure 33. Ranked relative species abundance in WO5B, March and July 2004.

Counts also revealed which lakes contained different zooplankton types (Table 7). Blue

Waters, Stockton and Wellington dam all contained calanoid populations. Blue Waters

and Stockton also contained rotifers, but no rotifers were observed in Wellington

dam.Rotifers were the only zooplankton observed in the pelagic zone of WO5B. Aquatic

insects were observed around the edges. No zooplankton were observed in Chicken

March

0

0.1

0.2

0.3

0.4

0.5

0.6

1 2 3 4 5 6 7 8 9 10 11 12

Rank

Prop

ortio

n of

sam

ple

(Pi)

July

0

0.1

0.2

0.3

0.4

0.5

0.6

1 2 3 4 5 6 7 8 9 10 11 12 13

Rank

5 Results

51

Creek at all, however aquatic insects were also observed around the edges and an object

closely resembling a calanoid egg was sampled in the pelagic zone (Figure 34).

Figure 34. Unidentified object observed in Chicken Creek.

Table 7. Zooplankton presence in the water column of four lakes and a reservoir. ‘Y’ indicates family observed. Blank space indicates group not observed. Crossed space indicates lake-month not tested.

March July March July March July March July March JulyCalanoid Y Y Y Y Y

Rotifer Y Y Y Y YCladoceran Y

WELBW CC ST WO5B

Species diversity and pH

A relationship was found between pH and species richness, H’ and Neff. This reflects the

findings of both Wollmann et al. (2000) and Lessmann and Nixdorf (2000) who

examined diversity in five German acidic mine lakes. All three sets of results are plotted

in Figure 35 and Figure 36. It should be noted that the values reported for species

richness by Lessmann and Nixdorf (2000) and Wollmann et al. (2000) are higher than

those of this study. This is probably due to a difference in sampling methods – whilst the

study at hand involved standardised sampling effort for internal comparative purposes,

both Wollmann et al. (2000) and Lessmann and Nixdorf (2000) accessed whole-lake

studies. A much higher sampling effort was expended in these cases, greatly increasing

the chances of finding more species and inflating measures of richness. This difference in

methods may also explain the shallower gradient of trend in the Collie data for H’ (Figure

5 Results

52

36). Nonetheless, it may be seen that the trend reported for German mine lakes is

repeated in Collie.

pH

2 3 4 5 6 7 8 9

num

ber

of s

peci

es

0

20

40

60

80

100

120 ST(M)WO5B (M)BW (J)CC (J) ST (J)WO5B (J) WEL (J) Lessmann & Nixdorf 2000Wollmannet al. 2000

Figure 35. Species richness and pH in four Collie mine lakes and Wellington dam compared to the results of two similar studies on seven German mine lakes.

pH

2 3 4 5 6 7 8 9

H'

0

1

2

3

4

5 ST(M)WO5B (M)BW (J)CC (J)ST (J)WO5B (J)WEL (J) vs H' Lessmann &Nixdorf (2000)

Figure 36. Species diversity (Shannon-Weaver index) and pH in four Collie mine lakes and Wellington dam, as compared to the results of a similar study on five German mine lakes.

5 Results

53

5.7 Trophic Status

Of the four mine lakes, Blue Waters and Stockton may be considered ultraoligotrophic

whilst WO5B and Chicken Creek may be considered oligotrophic – similar to the nearby

Collie river and Wellington dam ecosystems (Table 8). Whilst the March chlorophyll

maximum of 4.3 µg L-1 in Chicken Creek is comparable to mesotrophic systems

elsewhere (Wetzel 2001), this was only one measure from one depth and position.

Table 8. The trophic status of four mine lakes, a river and a reservoir with respect to total nitrogen (N), total phosphorous (P) and chlorophyll a according to Wetzel (2001).

Chl a Total P Total NBW U U/O U UltraoligotrophicCC O O O OligotrophicST U U/O U Ultraoligotrophic

WO5B O U/O Eu Oligotrophic, high NWEL O U/O O OligotrophicCOL U/O O Meso Oligotrophic, high N

6 Discussion

54

6 Discussion

The observations from biological sampling reveal a number of potentially important

findings for these mine lakes and their processes: 1. Pelagic communities were found in

the mine lakes, consisting of primary producers and consumers from the three main

zooplankton groups. The mine lakes may be considered oligo- or ultraoligotrophic and

both phytoplankton biomass and zooplankton abundance were comparable to nearby

natural systems. 2. Phytoplankton biomass varied consistently between lakes.

Furthermore, evidence of seasonal succession and the observation of a deep chlorophyll

maximum show that phytoplankton biomass in the lakes varies over time and space

within lakes. It appears that variation in phytoplankton biomass is not influenced by pH

but by nutrient limitation, as may be the case in extremely acidic mine lakes. Grazing by

zooplankton may also play a role. 3. The overburden around some mine lakes may be a

significant source of nutrients for mine lake communities. 4. The occurrence of major

zooplankton groups may be influenced by pH but local variables must also be considered.

5. Biodiversity increases with pH in Collie’s mine lakes.

6.1 Phytoplankton biomass: variation between lakes

This sub-section addresses whether the mine lakes of the Collie area behave in a similar

manner to extremely acidic mine lakes and natural lakes in other parts of the world, with

respect to phytoplankton biomass. In a study of 27 mine lakes, Nixdorf et al. (1998a)

found that all were populated by algae, so it is no surprise that pelagic communities are

present in Collie’s acidic mine lakes.

No relationship was found between pH and algal biomass. This confirms the findings of

Nixdorf et al. (1998a) that, whilst algal biomass is generally low in acidic mine lakes, the

standing crop is not restricted by low pH. Most studies of natural lakes show the same

result. For example, in a study of twenty natural lakes in Nova Scotia (pH 3 – 8), Blouin

(1989) found that algal abundance was not affected by pH and that there was only a very

weak negative relationship between chl a and pH.

6 Discussion

55

The presentation of this data begs the question: why would we expect pH to affect

biomass? Though it may be counter-intuitive, many species are able to thrive at a range

of pH. Gross (2000) points out influences of low pH on algae and other organisms

including a high proton gradient across the cell membrane, higher osmotic pressure due

to proton concentration, weakening of hydrogen bonds in cell walls and the increased

mobility of toxic metals. Organisms require mechanisms to deal with these environmental

pressures and extra energy may be spent on them, rather than on growth. However,

evidence has shown that some phytoplankton are adapted to production at a broad range

of pH and that other variables, such as soluble reactive phosphorous, total dissolved

nitrogen and dissolved organic carbon are far better predictors of abundance (Blouin

1989). A strong relationship between plankton abundance and total phosphorous may

also mask the effects of pH (Blouin 1989), though this has not been reported for acidic

mine lakes.

In this study, the mine lakes with highest algal biomass also had the highest total

phosphorous and total nitrogen. This is not only similar to the findings for other mine

lakes but for lakes worldwide – the classic paradigm for a freshwater aquatic ecosystem

is phosphorous limitation. In most freshwater aquatic systems, phosphorous is the

‘limiting’ nutrient for both growth and yield (Boulton and Brock 1999). Less often it is

nitrogen (Boulton and Brock 1999). Nixdorf et al (1998a, p.324) found that the “trophic

potential” of acidic mine lakes is likely controlled by phosphorous and nitrogen

concentrations but do not specify whether they refer to productivity or biomass. The

seasonal variation in WO5B of biomass with phosphorous provides some evidence for

this (see below). However, Lessmann and Nixdorf (2000) found that phytoplanktonic

succession in extremely acidic mine lakes followed inorganic carbon supply. This

parameter was not tested consistently at the time of the present study. If the biomass of

algal communities in mine lakes is phosphorous or nitrogen limited then this is one way

in which acid mine lakes behave like natural lakes.

6 Discussion

56

6.2 Phytoplankton biomass and zooplankton grazing

Nutrients are not the only possible limiting variable for algal biomass. Zooplankton

abundance may be limiting algal biomass in Collie mine lakes and Wellington dam. In

this study, the lakes with the highest zooplankton abundance had the lowest algal

biomass. This may indicate ‘top-down’ (as opposed to ‘bottom-up’) control of algal

biomass. In a review of 363 north temperate lakes, Mazumder (1994) found that chl a

increased with phosphorous concentration at a much slower rate in lakes with less

pressure from grazers. Furthermore, these systems displayed lower algal biomass overall.

In other words, grazing accounted for much of the variation in algal biomass. In the

southern hemisphere, a study of 97 Argentine lakes revealed that mean zooplankton size

was a significant predictor of residual variance in the total phosphorous-chl a relationship

(Quiros 1990 cited in matveev and Matveeva 1997). The pelagic zones of Collie’s mine

lakes appear to have no planktovores: when present, calanoid or rotifer grazers are the top

trophic level. It is possible that grazers are suppressing algal biomass in Stockton and

Blue Waters.

It has been proposed that the process of herbivorous zooplankton suppressing

phytoplankton biomass is not applicable to Australian lakes. Australian lakes often lack

efficient cladoceran grazers – the zooplankton are usually less efficient calanoids and

rotifers (Boon et al. 1994). This pattern was observed in Collie’s mine lakes.

Furthermore, the density of zooplankton in Stockton and Blue Waters was very low - less

than 26 individuals per m3. Matveev and Matveeva (1997) found that in two Australian

reservoirs, zooplankton community structure (rather than total biomass) is important for

predicting variation in phytoplankton biomass: whilst large cladocerans may have

negative impacts, small copepods may have positive impacts. In the present study,

cladocerans were only found in one lake: Blue Waters. They were small (~0.5mm) and at

a low density (0.011 individuals per m3). Thus, it seems less likely that grazing by large

zooplankton (crustaceans and rotifers) suppresses algal biomass in Collie’s mine lakes.

The influence of other grazers can not be ruled out. Packroff (2000) found that the

abundance of at least one mine lake species – the dinoflagellate Gymnodinium sp. – has

been found to be affected by small zooplankton, especially ciliates.

6 Discussion

57

6.3 Phytoplankton biomass: variation within lakes

A deep chlorophyll maximum was found below the thermocline in Chicken Creek in

March. There are a number of possible explanations. Deep chlorophyll maxima (DCM)

are known from nutrient poor waters, such as clearwater lakes. The phenomenon

occurred in at least four lakes in Lusatia, with pH ranging from 2.6 to 4.3 (Nixdorf et al.

1998a). DCM have traditionally been explained by higher nutrient availability at depth

(Tittel et al. 2003). Other explanations, for example depth-selective grazing or excessive

surface light levels, are available (Tittel et al. 2003; Beulker et al. 2003). Unfortunately,

no chemical or light intensity data from Chicken Creek is available from March, 2004.

Phytoplankton biomass was significantly higher in mid-winter than early autumn in two

of the four mine lakes. In Chicken Creek, the phytoplankton biomass in the upper 10m

increased between seasons but remained lower than the hypolimnion of that lake when

stratified. There are a number of possible explanations for these results. In the case of

Chicken Creek, winter mixis may simply have redistributed the high algal biomass

observed in the March hypolimnion. Algal growth may also have occurred. While algal

biomass effectively doubled in Stockton, and halved in Blue Waters, there were few

chemical changes in these lakes. If resources are higher in the hypolimnion when lakes

are stratified, then mixing may make them available to algae nearer the surface and at

more productive light conditions. However, chemical samples from one lake (WO5B)

showed that neither total phosphorous nor total nitrogen were higher in the hypolimnion

than the epilimnion (Salmon, unpublished data). Nonetheless, phosphorous in WO5B was

generally higher in July.

The higher phosphorous in WO5B in July, with a concomitant increase in chl a, is an

interesting result. There are at least two explanations. The increased nutrients may be a

product of phosphorous-laden runoff from the overburden after the first rain of winter.

Nixdorf et al. (1998a) report an increase in production due to overburden runoff in the

only moderately acidic (pH 3.6-3.9) mine lake in Lusatia. They attribute the change to the

input of organic carbon and subsequent production of dissolved inorganic carbon. If the

algae are nutrient-limited, nearby overburden dumps may be an important source for

6 Discussion

58

mine lake communities. Secondly, WO5B is filled from the nearby Collie river when the

river levels are sufficiently high. Filling was occurring while samples were taken in July.

The river was found to have total phosphorous and total nitrogen concentrations of 18.5

µg.L-1 and 1400 µg.L-1, respectively (Salmon, unpublished data). Thus, the river may also

be a significant source of nutrients for this lake.

All the mine lakes may be classified as oligotrophic or ultraoligotrophic with respect to

biomass and nutrients, although WO5B has a relatively high level of total nitrogen. The

applicability of this conventional type of categorisation has been brought into question,

for extremely acidic lakes at least, due to their acidity and apparent inability to develop

stable trophic conditions (Nixdorf et al. 1998a). It remains unclear whether this is an

appropriate classification system for moderately acidic lakes.

6.4 Zooplankton: presence and abundance

The lakes varied in both zooplankton abundance and the species present. pH may partly

explain the presence or abundance of zooplankton species as particular species only

thrive within a particular range of pH. In a study of 31 Lusatian mine lakes, Steinberg et

al. (1998) found that significantly more zooplankton groups could be expected from

circumneutral mine lakes as opposed to extremely acidic mine lakes. One pattern was that

no rotifers or crustaceans were found in lakes of pH less than 2.9, as was the result for

Chicken Creek (pH ~2.8). The pH in Chicken Creek may be inhibiting zooplankton

abundance or even colonisation.

The presence and abundance of zooplankton in the other mine lakes are less simple to

explain. The variance in zooplankton abundance may be an example of localised

variables affecting the success of particular groups. The only lake (including Wellington

dam) in which all three major zooplankton groups (cladocerans, rotifers and copepods)

were observed was Blue Waters. Despite a relatively high pH, rotifers were the only

group observed in WO5B. In their study of 31 mine lakes, Steinberg et al. (1998) found

rotifers in all lakes of pH > 2.9. Surprisingly, these animals were not observed in Blue

Waters nor Wellington dam in July. This may reflect the limited sampling regime: only

6 Discussion

59

one position was sampled per lake per field trip and only a part of that sample was

analysed. Alternatively, there is any number of proximal reasons why a zooplankton

group may not be observed. For example, colonisation events may simply not have

occurred: WO5B and Chicken Creek were only seven year old lakes when sampling was

undertaken for this study. The importance of local variables was noted by Yan et al.

(2003) when following the recovery of an acidified natural lake in North America. They

observed the recovery of copepod but not cladoceran populations in the 30 years after the

lake was neutralised. This is in spite of at least six colonisation attempts. They found that

this was due to the greater sensitivity of the Cladocera to metal toxicity and to the

presence in the lake of a planktivorous fish species. Thus, variables specific to a lake may

inhibit certain groups from successfully colonising. This is in line with the findings of

Steinberg et al. (1998), who found that cladocerans and copepods sometimes did not

occur despite lake pH being within their threshold.

6.5 Biodiversity

One interesting result was the change in species diversity with season in Stockton and

WO5B. This may be due to seasonal succession or changes in water quality. Despite an

increased phytoplankton biomass and steady zooplankton abundance, species richness in

Stockton dropped from 23 to 15 species between March and July. The opposite occurred

in WO5B: the number of effective species rose from 3 to 8. This latter result is simply

due to a shift in species composition as the July conditions favoured species otherwise

less abundant in the epilimnion. The decrease in species richness in Stockton may be due

to a slight drop in pH, a drop in temperature or the abundance of some species becoming

very low due to seasonal succession. Large seasonal changes in relative abundance of

phytoplankton have been observed in Lusatian acidic mine lakes (eg. Figure 37)

(Lessmann and Nixdorf 2000; Wollmann et al. 2000). Details of planktonic succession

have been correlated with many water quality parameters, such as light, temperature,

nutrients and grazing (Wetzel 2001). Wetzel (2001) points out that although correlations

may be useful guides for future study, they do not reveal the causal mechanisms of these

compositional changes. Such mechanisms may only be discovered through intensive

studies.

6 Discussion

60

Figure 37. Percent contribution of different plankton groups to total biovolume in an acidic mine lake: ML Felix (pH 3.4-3.8) Wollmann et al. 2000).

The results of this study agree with those of other mine lakes and natural lakes around the

world: species diversity increases with pH in acidic lakes. Whilst this trend has been

observed in extremely acidic (pH < 3.5) mine lakes, it has not been previously shown to

continue into moderately acidic (pH 3.5 – 5.5) mine lakes. There are a number of

selective pressures against organisms operating at low pH, outlined above. This means

that fewer organisms can effectively compete in acidic environments, although acidic

lakes do not necessarily exhibit lower production or biomass.

6.6 Biodiversity and pH as water quality indicators

The concept of water quality is linked to the intended use of a water resource, be it the

support of an ecosystem or the provision of water for irrigation. pH is a common water

quality indicator in mine lakes and they are often classified by this parameter alone. A

major goal of acidic mine lake treatment is to raise the pH. The relationship between pH

and biodiversity prompted Wollmann et al. (2000, p.11) to label pH ‘the most important

limiting factor for the establishment of more diverse colonisation and as a consequence

for more complex food webs’. Steinberg et al. (1998), however, claim that this view is

due to a lack of detailed ecological understanding and that raising pH is insufficient to

meet all ‘ecological demands’. Acidity is still an appropriate indicator for a number of

end-uses proposed for mine lakes. In Western Australia these include irrigation, potable

6 Discussion

61

water sources, ex-situ aquaculture and water sports. For these end-uses, indicators of

water quality for (or using) the biota may be less useful than chemical or physical data.

Biological and physical/chemical parameters are interrelated in aquatic environments. It

is important to understand the processes by which the biota affect physical parameters.

One example is the impact of lake clarity on recreational and aesthetic values. Clarity is

often affected by biological processes and has proven to be a major water quality

parameter in lakes used for aesthetic purposes. For example, the decline in clarity of Lake

Tahoe in California has been (and will be) the focus of huge research efforts and

expensive management plans (Schuster and Grismer 2004). The lack of clarity observed

in July in Chicken Creek and WO5B may be of concern to managers if recreational uses

are planned for these lakes. Whereas in WO5B this may have been due to an algal bloom,

in Chicken Creek the lack of clarity may be partly mineral in nature. Another obvious

example is the effect of the lake biology on alkalinity and pH. Many biological processes,

including anoxic microbial redox reactions and photosynthesis with nitrate assimilation,

affect the buffer capacity and pH of mine lakes (Davison et al. 1995). Understanding

these processes is critical to treating and managing these lakes.

A number of end-uses for mine lakes benefit from a broader range of water quality

indicators. End-use options including conservation, in-situ aquaculture and recreational

fishing will benefit from ecological water quality indicators such as biodiversity,

biomass, trophic state and food web characteristics. Conservation of native species has

been proposed as one use for mine lakes (CSML 2004). Indicators of ecological water

quality are important if a population of high-value species is to be supported. Mine lake

ecosystems may also be harnessed by in-situ aquaculture or for recreational fishing. This

is already occurring in an ad hoc capacity with the introduction of marron into some

lakes. If the benefits of these options are to be fully realised, ecological water quality

indicators must be incorporated into mine lake monitoring and treatment.

7 Conclusions

62

7 Conclusions

The first aim of this study was to determine whether functioning ecosystems were present

in the mine lakes of the Collie region. Pelagic communities were observed in all the lakes

investigated. The mine lakes could be classified as oligo- or ultraoligotrophic based on

their algal biomass and phosphorus levels. These levels are comparable to nearby natural

systems and to many lakes worldwide. Each of the major zooplankton groups: rotifers,

copepods and calanoids were present in some lakes and evidence of seasonal succession

of algae was observed.

The second aim of the study was to investigate the main drivers of moderately acidic

mine lake ecosystems. The ecological processes in these lakes may be similar to those of

extremely acidic mine lakes or natural lakes in general. Whilst species diversity was

lower than a nearby natural system, it was found to increase with pH. It is likely that

variation in biomass of the primary producers may be influenced by nutrients or

zooplankton grazing. The most likely variables are inorganic carbon and phosphorous.

Interestingly, the overburden around some mine lakes may not just be a source of acidity

but of nutrients as well.

The communities and processes investigated in this study represent an extension to the

range of possibilities for treatment and management of these lakes. Whilst our

understanding remains poor there is potential for these ecosystems to improve water

quality and support higher trophic levels. The results of this study confirm the need for

the treatment and management of mine lakes as ecosystems rather than simply

‘swimming pools’.

8 Recommendations for the Future

63

8 Recommendations for the Future

One limitation of this study was that of scale. Only four mine lakes and two ‘natural’

ecosystems were investigated in this study and any trends described must be treated with

caution. It is recommended that simple biological, physical and chemical data be taken

from a wider range of mine lakes in the Collie area. A wider sample of lakes may clarify

the trends proposed in this study and provide baseline data for ungauged systems.

It is only through intensive investigation that the ecological processes in these lakes may

be elucidated. It is recommended that regular sampling of at least one lake be undertaken

to assess the drivers of seasonal changes to the community. An investigation into the

variables influencing lake clarity is also recommended. This is an important water quality

parameter for recreational use.

The role of the benthic and littoral communities should be addressed. Little is known

about the interactions between the benthic and pelagic zones of acid lakes (for example

zooplankton recruitment) (Koschorreck and Tittel 2002). Benthic processes may

represent the most significant effects of biota on lake chemistry (Koschorreck and Tittel

2002; Bell and Weithoff 2003).

The influence of non-microbial organisms on the chemical and physical properties of

acidic lakes remains unclear. It is likely that these communities may be harnessed to

generate alkalinity and overcome the buffer capacity of acidic mine lakes (Davison et al.

1995; George and Davison 1998; Fyson et al. 1998). The alkalinity generating capacity

of aquatic organisms is closely linked to the carbon cycle (Figure 4). To date, no

investigations have quantified the generation of alkalinity by pelagic organisms in-situ

(however, see George and Davison 1998). The standard methods of quantifying carbon

fluxes may provide an indirect means of estimating this source of alkalinity. Furthermore,

as nutrient addition may be an appropriate treatment strategy for moderately acidic mine

lakes (Fyson et al. 1998) it is recommended that this approach be investigated for acidic

mine lakes in Collie.

8 Recommendations for the Future

64

Indicators of ecological water quality include measures from the lake community, for

example species abundance, species occurrence and biodiversity. It is recommended that

ecological water quality indicators be defined for mine lakes in Western Australia. These

should be simple to collect and repeatable. Indicators should be defined by considering

proposed end uses for mine lakes. As pointed out by Steinberg et al. (1998), raising the

pH of mining lakes is necessary but not sufficient in itself to satisfy all ecological

demands.

9 References

65

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Bisevac, L. and Majer, J. D. 1999, 'An evaluation of invertebrates for use as Success Indicators for minesite rehabilitation', in The Other 99%. The Conservation and Biodiversity of Invertebrates. Transactions of the Royal Zoological Society of New South Wales, eds W. Ponder and D. Lunney, Mosman, pp. 46-49.

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George, D. G. and Davison, W. 1998, 'Managing the pH of an acid lake by adding phosphate fertiliser', in Acidic Mining Lakes: Acid Mine Dranage, Limnology and Reclamation, eds W. Geller, H. Klapper and W. Salomons, Springer, Berlin, pp. 365-384.

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