Healthy HeadWaters
Coal Seam Gas Water Feasibility Study
Stream ecosystem health response
to coal seam gas water release
Biological monitoring guidelines
Science Delivery Division
Department of Science, Information Technology, Innovation and the Arts
Prepared for the Department of Natural Resources and Mines
This document presents outcomes of Activity 4 (Stream ecosystem health response to coal
seam gas water release) of the Healthy HeadWaters Coal Seam Gas Water Feasibility Study.
The Healthy HeadWaters Coal Seam Gas Water Feasibility Study is analysing the
opportunities for, and the risks and practicability of, using coal seam gas water to address
water sustainability and adjustment issues in the Queensland section of the Murray-Darling
Basin.
The study is being funded with $5 million from the Commonwealth Government, with
support from the Queensland Government, as part of the Healthy HeadWaters Program,
which is Queensland’s priority project funded through the Commonwealth Government’s
Water for the Future initiative. The study is being managed by the Queensland Department of
Natural Resources and Mines (DNRM).
This report was prepared by the Science Delivery Division, Department of Science,
Information Technology, Innovation and the Arts (DSITIA) for the State of Queensland
(DNRM). This report was originally developed by the Department of Environment and
Resource Management in 2011 and has subsequently been reviewed and released as a final
version by DSITIA in 2012.
Disclaimers
This document was prepared exclusively for the State of Queensland (Department of Natural
Resources and Mines) and is not to be relied upon by any other person. DSITIA has made
every effort to ensure that the information provided is accurate but errors and omissions can
occur and circumstances can change from the time that the document was prepared.
Therefore, except for any liability that cannot be excluded by law, DSITIA excludes any
liability for loss or damage, direct or indirect, from any person relying (directly or indirectly)
on opinions, forecasts, conclusions, recommendations or other information in this report or
document.
© State of Queensland 2012.
The State gives no warranty in relation to the contents of this report (including accuracy,
reliability, completeness, currency or suitability) and accepts no liability (including without
limitation, liability in negligence) for any loss, damage or costs (including consequential
damage) relating to any use of the contents of this report.
Citation
Takahashi E, McGregor G & Rogers S. 2011. Stream ecosystem health response to coal seam
gas water release: Biological monitoring guidelines. Brisbane: Queensland Department of
Natural Resources and Mines.
Acknowledgements
The authors would like to acknowledge the contributions of the Healthy HeadWaters Coal
Seam Gas Water Feasibility Study team for their support and all reviewers of this report. We
also wish to thank Dean Holloway and Sara Clifford for their assistance with some of the
report’s graphics.
About this report
This is one of a series of reports produced through Activity 4 (Stream Ecosystem Health Response to Coal Seam
Gas Water Release) of the Healthy HeadWaters Coal Seam Gas Water Feasibility Study. This report reflects the
state of knowledge and policy at that time it was written. A full list of reports from Activity 4 is provided below.
Reports from Activity 4 of the Healthy HeadWaters Coal Seam Gas Water Feasibility Study
•
McGregor G, Marshall J & Takahashi E. 2011. Stream ecosystem health response to coal
seam gas water release: Guidelines for managing flow regimes. Brisbane: Department of
Science, Information Technology, Innovation and the Arts, Queensland Government.
•
Rogers S, McGregor G, Takahashi E, Shaw M & McNeil V. 2011. Stream ecosystem
health response to coal seam gas water release: Salinity guidelines. Brisbane: Department
of Science, Information Technology, Innovation and the Arts, Queensland Government.
Shaw M. 2010. Stream ecosystem health response to coal seam gas water release: Hazard
characterisation. Brisbane: Department of Science, Information Technology, Innovation
and the Arts, Queensland Government.
•
•
•
•
Takahashi E, McGregor G & Rogers S. 2011. Stream ecosystem health response to coal
seam gas water release: Direct toxicity assessment. Brisbane: Department of Science,
Information Technology, Innovation and the Arts, Queensland Government.
Takahashi E, McGregor G & Rogers S. 2011. Stream ecosystem health response to coal
seam gas water release: Biological monitoring guidelines. Brisbane: Department of
Science, Information Technology, Innovation and the Arts, Queensland Government.
(This report)
Takahashi E, Rogers S, McGregor G & Shaw M. 2011. Stream ecosystem health response
to coal seam gas water release: Decision support system. Brisbane: Department of Science,
Information Technology, Innovation and the Arts, Queensland Government.
The Healthy HeadWaters Coal Seam Gas Water Feasibility Study is analysing the opportunities for, and the risks
and practicability of, using coal seam gas water to address water sustainability and adjustment issues in the
Queensland section of the Murray–Darling Basin.
The study is being funded with $5 million from the Commonwealth Government and support from the Queensland
Government as part of the Healthy HeadWaters Program, which is Queensland’s priority project funded through
the Commonwealth Government’s Water for the Future initiative.
i
Summary
The volume of coal seam gas (CSG) water is increasing with growing CSG operations. Under the current
regulatory framework biological monitoring needs to be addressed, but there are no specific monitoring guidelines
that relate to the disposal of CSG water.
This guideline presents a biological monitoring framework designed to identify indicators for the response of
aquatic ecosystems to the disposal of CSG water. It includes conceptual models that highlight the stressors
associated with CSG operations and sensitive aspects of the stream ecology, as well as information collated from
the literature to assist CSG operators who may be required through environmental authority conditions to assess
stream ecosystem responses to CSG water releases.
Generic biological indicators are provided; however, indicators need to be selected for each specific site. Therefore,
further investigation is highly recommended for each of the release sites.
The recommendations from this report are to:
1.
Identify key stressors from CSG operations
2.
Conduct a literature review to produce a list of organisms, ecosystem processes and key places in the areas
that are sensitive to the stressors
3.
Produce conceptual models to outline how the key biological indicators are affected by stressors
4.
Design a monitoring program to incorporate the appropriate duration, frequency, location and sampling size to
capture the effects the CSG operation will have on the selected biological indicators
5.
Conduct monitoring, collect data and analyse the potential CSG-related impacts on the ecosystem
6.
7.
Re-adjust the monitoring design if required
Use the information for future evaluation and planning of licence conditions.
ii
Contents
About this report .................................................................................................................................................. i
Summary .............................................................................................................................................................. ii
Contents .............................................................................................................................................................. iii
List of figures ..................................................................................................................................................... iv
List of tables ....................................................................................................................................................... iv
Definitions and abbreviations ............................................................................................................................ v
1
Background .......................................................................................................................... 1
2
Introduction .......................................................................................................................... 2
3
2.1
Regulatory framework for monitoring and reporting ............................................................................ 2
2.2
Existing monitoring frameworks ............................................................................................................. 2
2.3
Adaptive monitoring framework.............................................................................................................. 2
2.4
What are biological indicators? .............................................................................................................. 3
2.5
Selecting biological indicators ................................................................................................................ 3
Sampling designs ................................................................................................................ 5
3.1
Why monitor .............................................................................................................................................. 5
3.2
What to monitor ........................................................................................................................................ 6
3.2.1 Applying PSR approach to CSG scenarios .................................................................................... 10
4
3.3
Where to monitor .................................................................................................................................... 12
3.4
When to monitor ..................................................................................................................................... 13
3.5
Data analysis ........................................................................................................................................... 15
Management framework .................................................................................................... 17
4.1
5
Recommendations .................................................................................................................................. 18
References ......................................................................................................................... 19
Appendix A
Multivariate techniques.............................................................................. 22
Ordination .......................................................................................................................................................... 22
Correlation of attributes with ordinations ...................................................................................................... 22
ANOSIM .............................................................................................................................................................. 22
SIMPER .............................................................................................................................................................. 22
BIOENV .............................................................................................................................................................. 23
iii
List of figures
Figure 1. Conceptual model of the ecological effects of CSG-related water entering the ephemeral
stream during the dry and wet phase. .................................................................................... 9
Figure 2. Potential sampling sites for each of the three possible discharge scenarios for CSG water in
the absence of data prior to CSG water discharge. .............................................................. 13
Figure 3. BACI design incorporates before and after effects, allowing measurement of the natural
variation* .............................................................................................................................. 14
Figure 4. How the biological monitoring feeds into the regulatory framework .......................................... 17
List of tables
Table 1. Definitions of aquatic ecosystem condition (QWQG 2009) .......................................................... 5
Table 2. Example of low flow dependent indicators for QMDC and Fitzroy Basin* .................................... 6
Table 3. Applying PSR approach to CSG scenarios ............................................................................... 11
Table 4. Limits of acceptable changes for waters at different levels of protection (adapted from QWQG
2009). ................................................................................................................................... 15
iv
Definitions and abbreviations
Aquatic Conservation Assessments
(ACA)
The product of applying the AquaBAMM assessment method that is used for selecting ecological
conditions of the area.
ANZECC/ARMCANZ guidelines
Australian and New Zealand Environment and Conservation Council/Agriculture and Resource
Management Council of Australia and New Zealand: Guidelines for fresh and marine water
quality.
Aquatic Biodiversity Assessment
and Mapping Method
(AquaBAMM)
A method developed to assess conservation values of wetlands in Queensland. It is a
comprehensive method that uses available data, including data resulting from expert opinion, to
identify relative wetland conservation/ecological values within a specified study area.
Aquatic ecosystem
An ecosystem located in a body of water. Freshwater aquatic ecosystem includes lakes, ponds,
rivers, streams and wetlands.
Aquifer
A geological formation containing or conducting groundwater
Associated water
Underground water taken or interfered with, if the taking or interference happens during the
course of, or results from, the carrying out of another authorised activity under a petroleum
authority, such as a petroleum well, and includes waters also known as produced formation water.
The term includes all contaminants suspended or dissolved within the water
(EPA, 2007).
Biological indicators
Species used to monitor the health of an environment or ecosystem
Coal seam gas (CSG)
Natural gas, consisting primarily of methane, that collects in underground coal seams by bonding
to the surface of coal particles. The coal seams are generally filled with water, and it is the
pressure of the water that keeps the gas as a thin film on the surface of the coal.
Conceptual model
Concise and visually stimulating illustrations that use symbols or drawings to depict the
important features, processes and management challenges in a particular environment. This is
accomplished using the most current knowledge or understanding of that particular environment
and is presented in a way that is easy to understand.
CSG water
Water that has been extracted from coal seams in order to release coal seam gas.
DERM
Department of Environment and Resource Management
Direct toxicity assessment (DTA)
Assessment aimed at quantifying the potential toxicity of a sample by exposing a range of test
specimens to the sample
Effect size
In statistics, an effect size is a measure of the strength of the relationship between two variables in
a statistical population, or a sample-based estimate of that quantity. It is the allowable change due
to the impact or the size of the difference between the null and the alternative hypothesis.
Electrical conductivity (EC)
Estimate of the amount of total dissolved salts (TDS) or the total amount of dissolved ions in the
water. It is measured in micro siemens per meter (µS/m).
Environmental Authority (EA)
Environmental Authority is essentially a permit to conduct environmentally relevant activities
and may include specific conditions regarding the management and operation of these activities.
(Coal seam gas projects are considered petroleum activities—refer to section 309(a) of the
Environmental Protection Act 1994.).
Environmental relevant activity (ERA)
An activity listed under the Environmental Protection Act 1994 (and associated Environmental
Protection Regulation 2008) that has the potential to cause environmental harm and is regulated
by the Department of Environment and Resource Management, or devolved to local government.
Ephemeral streams
Streams that are either rarely inundated or only inundated for a very short period of time.
Food web
Representations of the predator–prey relationships between species within an ecosystem or
habitat
Guideline
A numerical concentration limit or descriptive statement recommended for the support and
maintenance of a designated water use (ANZECC/ARMCANZ, 2000). It can be determined using
either biological effects data for a contaminant (toxicity guidelines) or background water quality
data for a local area of interest (referential guidelines).
Hydrology
The study of the movement, distribution and quality of water for surface water or groundwater
Macrophyte
An aquatic plant large enough to be seen without magnification that grows in or near water and is
either emergent, submergent or floating.
v
Macroinvertebrate
An invertebrate animal (animal without a backbone) large enough to be seen without
magnification.
Mixing zone
An area of a watercourse where pollutants from a point source discharge are mixed with cleaner
water. The mixing zone is an area where the higher concentration is diluted to legal limits for
water quality.
Nephelometric Turbidity Units (NTU)
Units of turbidity from a calibrated nephelometer (instrument for measuring suspended
particulates)
Power analysis
It is the likelihood of achieving statistical significance with a given sample size. Power can be
defined as the probability of finding a real difference. It can also be used to determine the sample
size required to detect an effect size with a given degree of confidence.
Pressure stressor response (PSR)
An approach which is based on three components: human pressures, physicochemical and
biological stressors, and the ecological responses.
QMDB
Queensland Murray–Darling Basin
QWQG
Queensland Water Quality Guidelines (2009)
Reverse osmosis (RO)
Reverse osmosis is a filtration method which pumps a solvent from a region of high solute
concentration through a semipermeable membrane to a region of low solute concentration by
applying a pressure in excess of the osmotic pressure.
Riffle
A short, relatively shallow and coarse-bedded length of stream over which the stream flows at
lower velocity and higher turbulence than it normally does in comparison to a pool. Riffles are
usually caused by an increase in a stream bed's slope or an obstruction in the water.
RO permeate
Solvent and other dissolved components that pass through the membrane.
Streams
Riverine wetlands – wetlands and deepwater habitats within a channel: The channels are naturally
or artificially created, periodically or continuously contain moving water, or form a connecting
link between two bodies of standing water (Queensland Wetlands Program,
<www.epa.qld.gov.au/wetlandinfo>).
Threshold of concern (ToC)
Hypothesis of the limits of acceptable change in the structure, composition and function of an
ecosystem, following exposure to a known stressor (such as alteration to the flow regime).
Represents scientifically described endpoints or ecosystem limits, derived using the full extent of
current knowledge.
Type II error
A statistical term used in the context of hypothesis testing. Type II error occurs when one accepts
a null hypothesis when it is actually false.
Water Resource Plan (WRP)
Subordinate legislation under the Water Act 2000 which establishes a framework to share water
between human consumptive needs and environmental values. Water resource plans are prepared
for each major catchment area in Queensland <www.derm.qld.gov.au/wrp/index.html>.
vi
Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
1
Background
Coal seam gas (CSG) is a natural gas consisting primarily of methane adsorbed onto coal. CSG is extracted by
dewatering coal seams to reduce the pressure keeping the gas in place. This process results in significant quantities
of associated water, which is typically saline, being brought to the surface. Queensland CSG production is
increasing rapidly, with further dramatic increases predicted as CSG producers seek new markets for their product
by establishing a liquefied natural gas industry. The Queensland Government's CSG Water Management Policy
states that the preferred options for managing CSG water are either injection into aquifers or beneficial use (DERM
2010a).
There may be situations where both the demand for treated CSG water for beneficial uses and the potential for
injection will fall short of extraction rates in some areas, resulting in surplus volumes requiring alternative
management. Disposal into surface waters is not considered to be a preferred option under the CSG Water
Management Policy. However, there may be some cases where this is the only viable alternative. In such cases a
rigorous environmental assessment would be required as part of a CSG Water Management Plan prior to any
release of CSG water being authorised under an environmental authority. The environmental authority (EA) will
stipulate flow and water quality criteria that must be met to protect the defined ecosystem values and water quality
objectives, as well as stringent monitoring requirements.
CSG resources in Queensland are located primarily in the Surat and Bowen basins—geological structures that
underlie sections of the Queensland Murray–Darling Basin (QMDB). The QMDB, which is located in the southwest of the state, consists of the Border Rivers, Moonie, Condamine–Balonne, Warrego, Paroo and Bulloo drainage
basins. The majority of Queensland’s CSG resources underlie the Condamine–Balonne drainage basin in the
QMDB and the Fitzroy Drainage Basin located to the north of the QMDB.
Rivers in the QMDB support nationally significant wetlands that fall into the Level 1 or high ecological value
classification of the Australian and New Zealand Guidelines for Fresh and Marine Water Quality
(ANZECC/ARMCANZ guidelines) (ANZECC/ARMCANZ 2000). Surface waters in the region are also used for
irrigation and human and livestock drinking water supplies. Locally relevant guidelines are therefore required to
adequately assess any potential impacts associated with the discharge of raw or treated CSG water into surface
streams. Many water courses in the QMDB exist as networks of ephemeral channels and waterholes that experience
extended no-flow periods followed by episodic high magnitude flows and flooding that are typically associated
with summer sub-monsoonal rainfalls. Long-term high-volume continuous releases of CSG water have the
potential to alter important natural hydrological cycles in aquatic ecosystems adapted to these conditions.
Consequently, guidelines relating to hydrological modification (i.e. timing, magnitude, rate of rise and fall, etc.) of
surface waters due to the discharge of CSG water are also required to minimise the risk of potentially adverse
environmental impacts in the QMDB.
Under the Water for the Future Program, the Commonwealth Government allocated $5 million towards a feasibility
study to examine the use of CSG water in addressing water sustainability and adjustment issues in the QMDB. The
Coal Seam Gas Water Feasibility Study analyses the opportunities for, and the risks and practicability of, using
CSG water to assist in achieving the long-term goals in the QMDB of transitioning irrigation communities to lower
long-term water availability and securing viability of ecological assets.
Activity 4 of the Coal Seam Gas Water Feasibility Study relates to securing the viability of ecological assets by
investigating stream ecosystem health responses to CSG water releases. The CSG operations are likely to cause
stress to the stream ecosystem. Biological indicators are useful in measuring the effects caused by such stressors.
This report focuses on providing guidance in selection of the biological indicators to assess the impacts from CSG
operations. It also emphasises the importance of applying the results from such studies into the regulatory
framework. The two main objectives for this task are:
•
to review the existing information in order to provide methods for selecting the potential biological
indicators that can be used for monitoring the response of stream ecosystems to CSG water discharge
•
to provide guidance on a monitoring framework for CSG water, using biological indicators.
1
Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
2
Introduction
2.1
Regulatory framework for monitoring and reporting
Under the Queensland Environmental Protection Act 1994 (EP Act) monitoring is required for certain
Environmental Relevant Activities (ERAs) including CSG operations. Most activities regulated under the EP Act
and the Water Act 2000 have focused on discharging contaminated water or on taking the water from the system.
CSG water is a novel contaminant as the treated water is very clear and in the QMDB it is normally released to
ephemeral systems. Currently, there are no specific industry-based guidelines for biological monitoring and
reporting; hence, such guidelines are required for establishing appropriate monitoring regimes for CSG operations.
2.2
Existing monitoring frameworks
There are a number of relevant monitoring frameworks, including the ANZECC/ARMCANZ guidelines framework
and the Queensland Integrated Waterways Monitoring Framework (Water Quality and Accounting 2010). The
Queensland Water Quality Guidelines (QWQG) 2009 and the Monitoring and Sampling Manual (2009), developed
under the Environmental Protection (Water) Policy 2009, also provide generic monitoring guidelines.
The proposed guideline outlined in this document addresses the specific issues of CSG-related water discharge. It is
in accordance with the Queensland Integrated Waterways Monitoring Framework and the department's Aquatic
Ecosystem Conceptual Framework that uses the Pressure Stressor Response (PSR) approach, which is based on
conceptual modelling of Queensland waterways (WPE 2010). This PSR approach is based on three components:
human pressures, chemical–physical and biological stressors, and the ecological responses. This guideline is also
consistent with the methodologies for sampling design from QWQG (2009), Monitoring and Sampling Manual
(2009) and ANZECC/ARMCANZ Guidelines (2000).
2.3
Adaptive monitoring framework
The key to successful environmental management is to incorporate the information from the ecological risk
management into the planning and decision-making stage (US EPA 2003; Barnthouse 2008). The interactions
between managers, policy makers and researchers are facilitated by a decision support system (Rogers & Biggs
1999). This consists of a) operations such as setting goals, defining management actions, and measuring and
implementing goals; b) predictions of consequences of management actions; and c) system response which
monitors and measures goal attainment (Rogers & Biggs 1999). It is critical to have a circular management
framework that incorporates new information and adapts available information accordingly, then bases policy
decisions on such information. Adaptive management is a structured process of making decisions when faced with
uncertainty (Holling 1978). It provides for learning and acquiring additional information, and adapting new
management processes. There are three components of adaptive management that when used for environmental
practices allow for improvements in designing and managing programs (Rout et al. 2009; Stankey et al. 2005):
•
test assumptions by designing a monitoring framework using existing knowledge
•
adapt changes in response to new information obtained through the monitoring framework
•
learn from the previous errors.
It is important to design a monitoring framework around clearly defined objectives (Biggs & Kilroy 2000) which
can feed into the adaptive management framework. This can be achieved by using a causal mechanism to plan
monitoring for specific ecosystem resources. Such monitoring is required to evaluate responses to disturbances, to
provide baseline conditions of the system, and to detect any changes in ecosystem structure and function
(Lindenmayer & Likens 2009). The use of causal analysis to systematically identify the probable cause of
environmental problems has been recommended by the United States Environmental Protection Agency (US EPA
2011). Causal analysis seeks to identify and understand the reasons why things occur. This process links the
sources of stressors to the biological effects of the stressors (US EPA 2011). Monitoring is a tool to better
understand the relation between the stressors and the biological effects.
2
Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
The linkage between science and environmental decision making is the key for successful monitoring and
management (Barnthouse 2008; Lindenmayer & Likens 2010). Effective management incorporates a monitoring
program and cross-checks the accuracy of the initial assumptions. This will then provide feedback for evaluating
the policy outcome (Rogers & Biggs 1999). There are six key points in developing effective monitoring which can
feed into the adaptive management framework (Karr & Chu 1999; Lindenmayer & Likens 2010).
•
build conceptual models of how the ecosystem works to understand the biological signals that need to be
measured before random sampling
•
select measurable attributes that provide reliable and relevant signals
•
•
develop sampling protocols and designs that ensure biological attributes are measured
devise analytical procedures to extract and understand relevant patterns in the data
•
ensure that monitoring goals have management relevance
•
communicate results and collaborate with managers and policy makers.
A regulatory framework to facilitate adaptive management in relation to CSG water management has been put in
place by recent amendments to the EP Act. This framework requires CSG companies to propose measurable
criteria with which the success of the CSG water management measures will be assessed. The companies will then
be required to report on these measurable criteria annually. If it becomes apparent that the measures in place are not
meeting required outcomes, DERM can use this as a trigger to amend the conditions of the environmental authority
to drive improved management methods. Appropriate monitoring of potential impacts of CSG water discharges to
waterways will be critical in enabling this process to happen.
2.4
What are biological indicators?
Biological indicators are biological attributes whose function, population or status can be used to determine
ecosystem or environmental integrity (ANZECC/ARMCANZ 2000). They are used to document any divergences
from expected baseline conditions associated with known impacts (Karr & Chu 1999). For example, organisms are
monitored for changes in biochemical, physical and behavioural attributes that may indicate a problem within their
ecosystems. There are different types of indicators as listed below (Karr & Chu 1999):
•
individual level, which measures effects on individual species such as fish imbalance or deformity
•
population level, which measures taxa richness and the absence/presence of certain taxa
•
assemblage level, which measures altered trophic structure such as a shift from specialist to generalist or a
shift from seasonal spawners to all-year spawners
•
landscape level, which measures spatial heterogeneity, vegetation cover/types and habitat types such as
riffles and waterholes.
2.5
Selecting biological indicators
A general guide for selecting biological indicators can be found in Section 8.1 of ANZECC/ARMCANZ
guidelines. When selecting biological indicators, it is important to know which potential stressors may affect the
organisms or the ecosystem. Once the stressors are identified, those indicators that are sensitive to specific stressors
can be selected (US EPA 2011).
Individual organisms or populations may be selected as indicators. If selecting individuals as indicators, growth
rate, size, age of maturity and fecundity can be assessed. If populations are selected, attributes such as density, age
structure and sex ratio can be assessed (Hellawell 1986). Population may be more useful for environmental impacts
that affect growth and development of the group (Hellawell 1986).
Composition of communities of organisms or a particular taxonomic group can also be used as an indicator of
environmental quality (Hellawell 1986). In order to understand how to interpret a community response, it is
necessary to consider how communities are organised (Hellawell 1986). This will provide information about
interaction within the community between different taxa and the food web framework of the ecosystem. As the
3
Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
environment changes, the alteration in community structure can be detected. For example, functional changes in a
community can be seen between unstressed and stressed communities.
Organisms that may be potentially useful for understanding community interaction or population trends include
fish, macroinvertebrates and algae (Hellawell 1986). Fish populations and communities can respond actively to
changes in water quality, but are also strongly influenced by changes in hydrology and physical habitat structure
(Davies & Nelson 1994; Gehrke 1992; Harris 1995; Hellawell 1986). Fish are near the top of the food chain, which
allows them to act as indicators for both producer and consumer communities (Harris 1995). Additionally, due to
their longevity and their ability to move, they can act as indicators over large temporal and spatial scales.
Abundance of native as well as exotic fish taxa can be used for monitoring degraded water quality (Kennard et al.
2005).
Macroinvertebrates are used for biological monitoring worldwide because they are found in most habitats, are
limited in mobility and easy to sample. Additionally, their habitats have a diversity of organisms which ensures a
wide range of sensitivities to changes in both water quality and habitats (Hellawell 1986).
Macro and micro algae are also useful bioindicators. Their growth and production can be measured to assess their
biomass, which is a good indication of the nutrient and light availability (Schmid et al. 1998).
Reich et al. (2010) found that altering flow regime and converting ephemeral to perennial streams in Victoria
affected both the fish and the aquatic macroinvertebrate compositions, thus favouring certain taxa that are not
dominant in the unregulated sites. These studies have indicated that if a stable balance of species in an ecosystem is
changed, this may prevent certain species from maintaining their population abundance and, in the absence of
competition, other species may become more abundant (Hellawell 1986).
The long-term impacts can also be obtained by comparing the historical data from the area to the post-release data
(Humphrey 1990). This is also applicable to CSG water and, for the purpose of conducting long-term impact
studies, it highlights the importance of obtaining baseline information prior to the discharge of any CSG water. As
there is very little baseline information, wet season surveys should be included in the model conditions.
4
Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
3
Sampling designs
The fundamental component of the sampling design is the question setting (Lindenmayer & Likens 2009). The
questions will define the objectives of the monitoring and the direction of the sampling design. For example, in the
current study the key question will be how to assess the impact of CSG water on ecosystem health. This question
can be divided into further questions based on specific stressors caused by release of CSG water to the watercourse.
It is important to produce a sampling design and to have an understanding of the biological stressors and indicators
before conducting random sampling (Karr & Chu 1999). It is also important to have conceptualisation of the river
system, with adequate sampling design outlining sampling size and analytical processes (Karr & Chu 1999). The
DERM Monitoring and Sampling Manual (DERM 2009a) comprehensively covers general methodologies for using
bio-indicators and sampling design. It states that the objectives should be clearly defined to set the sampling design.
The key points to consider are listed below (DERM 2009a; Downes et al. 2002; Karr & Chu 1999).
•
why: objectives of sampling
•
•
what: sampling material
when: temporal scale and frequency of sampling
•
where: spatial boundaries of sampling
•
how: data analysis.
3.1
Why monitor
The purpose of monitoring is to assess ecological integrity and ecosystem health. Ecological health here is referred
to as the ‘capacity to support and maintain a balanced integrated adaptive biological system having a full range of
expected natural procedure’ (Downes et al. 2002). The aim of this guideline is to outline a monitoring framework to
understand and measure the changes to the ecosystem caused by CSG water being discharged. To do so, the levels
of acceptable changes or threshold of concern (ToC) need to be set; hence, the detection of valued ecosystem
components and processes known as the ‘level of protection’ is required. The ANZECC/ARMCANZ (2000)
guidelines establish a framework for categorising the ecosystem conditions into such levels of protection (Table 1).
Ecosystem conditions have been assigned for some parts of Queensland under the Schedule 1 documents of the
Environmental Protection (Water) Policy 2009; however, for other areas where the level of protection has not been
determined, the aquatic biodiversity assessment and mapping method (AquaBAMM) can be used to set the levels
of protection (DERM 2010c). The sites can be selected based on criteria from the Aquatic Conservation
Assessments (ACA) adapted from the AquaBAMM (Clayton et al. 2008). Once the levels of protection are set,
monitoring can be used to assess the ecological health for the specific level of protection.
Table 1. Definitions of aquatic ecosystem condition (QWQG 2009)
Ecosystem
condition
Definition
Level 1
High ecological/
conservation value
‘These are effectively unmodified or other highly valued systems, typically (but not always) occurring in
national parks, conservation reserves or in remote and/or inaccessible locations. While there are no aquatic
eco- systems in Australia and New Zealand that are entirely without some human influence, the ecological
integrity of high ecological/conservation value systems is regarded as intact.’ (ANZECC/ARMCANZ 2000; p.
3.1–10)
Level 2
Slightly to moderately
disturbed
‘Ecosystems in which aquatic biological diversity may have been adversely affected to a relatively small but
measurable degree by human activity. The biological communities remain in a healthy condition and
ecosystem integrity is largely retained. Typically, freshwater systems would have slightly to moderately
cleared catchments and/or reasonably intact riparian vegetation; marine systems would have largely intact
habitats and associated biological communities. Slightly to moderately disturbed systems could include rural
streams receiving run-off from land disturbed to varying degrees by grazing or pastoralism, or marine
ecosystems lying immediately adjacent to metropolitan areas.’ (ANZECC/ARMCANZ 2000; p. 3.1–10)
5
Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
Level 3
Highly disturbed
3.2
‘These are measurably degraded ecosystems of lower ecological value. Examples … would be some ports and
sections of harbours serving coastal cities, urban streams receiving road and stormwater run-off, or rural
streams receiving run-off from intensive horticulture. The third ecosystem condition recognises that degraded
aquatic ecosystems still retain, or after rehabilitation may have, ecological or conservation values, but it may
not be feasible to return them to slightly to moderately disturbed condition.’ (ANZECC/ARMCANZ 2000; p.
3.1–10)
What to monitor
It is important to select indicators that are sensitive to be used for measuring potential environmental harm (DERM
2009a). Biological indicators can be used to monitor the effects of discharging CSG water into streams. There are
several sources of information that can assist in selecting the relevant indicators for specific release sites. Each
indicator needs to be applicable to the specific monitoring site, and it is therefore highly recommended that further
specific studies are taken for selection of indicators relevant to the receiving water system.
The asset selection reports for regional Water Resource Plans (WRPs), which are part of the Environmental Flow
Assessment Program (EFAP), are useful for selecting the appropriate flow dependent indicators for specific basins
(DERM 1999). The Fitzroy WRP Review T3 report also provides details on the flow dependent ecological assets in
the Fitzroy Basin that are at high risk (Cockayne et al. 2009).
Individual industry reports highlight the existing state-of-the-environment and list organisms that are present in the
operational area (AECOM 2009; Coffey Natural Systems Pty Ltd 2009; RPS 2010; URS Australia Pty Ltd 2009).
Additionally, as stated in the Nature Conservation Act 1992, basic information on the status of the organisms (such
as their vulnerability) can also be used in selecting the indicators. It is recommended that monitoring is conducted
to assess the existing populations of all listed aquatic and semi-aquatic species.
Examples of low flow dependent indicators are listed in Table 2. Exotic species have also been included in this
table as potential flow-related indicators, as they have been identified in the QMDB and are flexible to changes
because they can adapt to an altered environment (Kennard et al. 2005). Presence and absence of exotic fish, as
well as the ratio of exotic to native species of fish, can be used for comparison (Kennard et al. 2005).
For analysis of bioindicators such as macroinvertebrates, fish and algae, the methods from the Ecosystem Health
Monitoring Program (EHMP 2010) and Queensland Australian River Assessment System (AusRivAS) (Conrick &
Cockayne 2000) are recommended, depending on the species richness and data availability. There are three main
analyses that can be used for calculation of fish abundance (EHMP 2010). The first is to use the percentage of
native species expected. This refers to the number of native fish species observed to occur at a site, where the
expected number of fish is predicted by a model. The second method is to use the ratio of observed to expected
native fish, and the third is to use the proportion of exotic fish.
Other biological indicators may include macro and micro algae. Information on primary production reflects the
change in light availability and the ambient nutrient loads of the streams. RO permeate is known to have low
turbidity, which can, along with available nutrients in the water, lead to algal blooms. Therefore, monitoring of
algal biomass may be critical in some areas. Measuring chlorophyll a is related to productivity, which can provide a
good indication of the algal biomass in the water (EHMP 2010; Schmid et al. 1998). The oxygen produced and
used during photosynthesis and respiration, measured by a respirometer, can be used to calculate the productivity in
a system (Biggs & Kilroy 2000). Productivity can be measured based on oxygen evolved during photosynthesis and
oxygen consumed by algae during respiration (Biggs & Kilroy 2000). Another method of measuring productivity is
to use benthic metabolism (EHMP 2010). This refers to the rate of respiration and primary production occurring at
and below the sediment–water interface.
Table 2. Example of low flow dependent indicators for QMDC and Fitzroy Basin*
QMDC
Common name
Notes
Ecological asset indicators – fauna
Ambassis agassizii
Olive perchlet
Seasonal spawning. Found in pools (0.5 m) and low flow (0.05 m/s); stable
low-flows for successful recruitment; water temperature rise required to
stimulate spawning and slow rate of water level change required for larval
growth and survival
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Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
Leiopotherapon unicolor
Spangled perch
Could be affected by increase in flow velocity
Maccullochella peelii peelii
Murray cod
Vulnerable
Macquaria ambigua ambigua
Golden perch
Still shallow water required for spawning ; supplementary flow could
reduce larvae and food source
Melanotaenia fluviatilis
Murray rainbow fish
Juveniles require slow flow
Mogurnda adspersa
Purple-spotted gudgeon
Found in slow moving or still waters of rivers and creeks; historic decline
in MDB; listed as endangered in NSW. Flow magnitude whereby eggs and
larvae are not washed downstream would be required to improve stock
survival
Retropinna semoni
Australian smelt
Flow magnitude whereby eggs and larvae are not washed downstream
would be required to improve stock survival
Tandanus tandanus
Freshwater catfish
A sharp change in rate and rise of hydrograph may affect spawning
migrations
Litoria wilcoxii
Stony creek frog
Stable flow required
Ecological asset indicators – flora
Eucalyptus camaldulensis
River red gum
Potential impact from waterlogging
Acacia chinchillensis
Chinchilla wattle
Potential impact from waterlogging
Fitzroy
Common name
Notes
Ecological asset indicators – fauna
Ambassis agassizii
Olive perchlet
Seasonal spawning. Found in pools (0.5 m) and low flow (0.0 5m/s); stable
low-flows for successful recruitment; water temperature rise required to
stimulate spawning and slow rate of water level change required for larval
growth and survival
Amniataba percoides
Barred grunter
Increase in flow velocity could affect spawning recruitment. Spawning not
dependant on flooding, so spawn prior to break of the wet season;
competitive advantage over species that rely on flow
Anguilla obscura and
A. reinhardtii
Short- and longfinned eel
Downstream migration could be affected if the rate of rise and fall in the
hydrograph changes sharply as a result alteration to the flow regime
Hephaestus fuliginosus
Sooty grunter
Require flow; rely on riffles
Macquaria ambigua oriens
Golden perch
Still shallow water required for spawning; supplementary flow could
reduce larvae and food source
Melandaenia splendida splendida
Eastern rainbow fish
Juveniles require low flow (0.05 m/s) and egg development require water
level fluctuations (<5 cm)
Mogurnda adspersa
Purple-spotted gudgeon
Found in slow moving or still waters of rivers and creeks; historic decline
in MDB; listed as endangered in NSW. Flow magnitude whereby eggs and
larvae are not washed downstream would be required to improve stock
survival
Neosilurus hyrtlii
Hyrtle's tandan
Spawning in riffles
Tandanus tandanus
Freshwater catfish
Sudden changes in flow may cause nest abandonment
Rheodytes leukops
Fitzroy River turtle
Require riffles as habitat
Ecological asset indicators – flora
Acacia
Wattle
Too much water can cause waterlogging
Eucalyptus coolabah
Eucalypt
Too much water can cause waterlogging
Ecological asset indicators – habitat
Riffles as habitat
Exotic species
Carassius auratus
Critical habitat requiring low flow
Common name
Goldfish
Notes
Exotic species
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Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
Cyprinus carpio
Carp
Exotic species
Gambusia hobrooki
Mosquitofish
Exotic species
Eichhornia spp.
Water hyacinth
Exotic species
Salvinia spp.
Floating fern
Exotic species
*Adapted from EFAP and Shaw 2010. These indicators are potential indicators and are only to be used as a guide as site-specific indicators will need to be selected.
A direct toxicity assessment (DTA) has identified macroinvertebrates to be most sensitive to the alteration of EC
and ionic composition (Takahashi et al. 2011). Site-specific DTA should be conducted to select indicators suited
for each site and the water quality of the CSG water being released. Macroinvertebrates are useful as biological
indicators as they show the highest sensitivity to alteration of EC and ionic composition (Takahashi et al. 2011).
Recent studies in Queensland and Italy showed that dry river beds represent habitat for a unique invertebrate
assemblage for terrestrial invertebrates (Steward et al. 2011). It would be important to acknowledge these unique
assemblages. The presence and absence of macroinvertebrate taxa in baseline monitoring can be compared to that
of the CSG water discharge sites. Taxa richness is frequently used for macroinvertebrate-based assessments
(EHMP 2010; Marshall & Choy 1999). The EHMP monitoring program uses PET richness. PET stands for the
three key families of macroinvertebrates: Plecoptera (stoneflies), Ephemeroptera (mayflies) and Trichoptera
(caddisflies). The taxa richness or PET richness analysis can be used to compare the CSG impacted sites to those of
the control sites. Along with the taxa richness, Stream Invertebrate Grade Number–Average Level (SIGNAL)
scores can be used. SIGNAL is based on the average sensitivity to disturbance of aquatic macroinvertebrate taxa
present in a sample based on a pre-determined set of sensitivity grades (Chessman 2003; EHMP 2010).
Freshwater crustaceans, such as Macrobrachium spp. are another useful indicator for alteration to calcium (Ca)
level (Zalizniak et al. 2009). These organisms are known to depend on Ca availability in the streams. Morphometric
and meristic parameters can be used to compare the population of such crustaceans in the impacts sites to those of
the control sites (Munasinghe & Thushari 2010).
Fish, macroinvertebrates and algae are not always present in ephemeral systems, so indicators that could be
monitored for sites that are consistently dry include riparian conditions and habitat characteristics such as riffles, all
of which support biological indicators. Alternatively, the nearest permanent receiving waters could be monitored
for shifts in community structure (Marshall & Choy 1999).
As CSG operations and the amount of treated CSG water proposed to be discharged into streams increase, it is
important to establish a well-designed, long-term ecological monitoring program for operators. Previous
investigations have identified the potential hazards and stressors posed by CSG water to the aquatic ecosystem in
terms of contaminants and alteration to water quality and quantity (DERM 2010b; Shaw 2010; Takahashi et al.
2011). Conceptual models have been developed to better understand the stressors related to CSG operations (Figure
1). These models were developed based on the Aquatic Ecosystem Framework which uses the PSR analysis (WPE
2010). As the models show, predictions can be made regarding the potential water quantity and quality changes or
stressors to the ecosystem resulting from discharging CSG water to streams. These stressors include:
•
alteration to hydrology leading to decrease in dry spells
•
alteration to hydrology leading to constant flow and decrease in seasonality
•
decrease in electrical conductivity (EC)
•
increase in transparency of the water
•
alteration in ionic composition of the water
•
cumulative toxicological impacts from contaminants.
Possible effects of altering the flow regime by having constant flow and decreasing the seasonality have been
shown to affect the stability of the aquatic ecosystem (McGregor et al. In prep.). Additionally, alteration of water
quality parameters, such as the EC, turbidity, alkalinity and the ionic composition, are likely to have negative
impacts on aquatic ecosystems (Rogers et al. 2011; Takahashi et al. 2011). Biological indicators can be used as
monitoring tools to measure these impacts.
8
Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
Figure 1. Conceptual model of the ecological effects of CSG-related water entering the ephemeral stream
during the dry and wet phase.
9
Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
3.2.1
Applying PSR approach to CSG scenarios
The use of a PSR approach is recommended for biological indicator selection and assessment. In a PSR conceptual
framework, indicators are selected according to the known stressors and the likelihood of each of the stressors. The
consequences of the responses can then be categorised for risk analysis (WPE 2010).
The PSR approach has been applied to each of the identified stressors from the CSG operations (Table 3). The
pressure for the case of CSG operations is the release of CSG water into the surface water. The potential stressors
and responses for CSG water are listed below and are highlighted in the conceptual models (Figure 1). It is
recommended that a similar approach to PSR be taken and data be collected accordingly for each discharge site.
Both the stressors and responses need to be measured.
It is also important to understand the means by which something happens, not just that it happens; hence, levels of
stressors also need to be measured (Karr & Chu 1999). The potential stressors related to CSG water discharge are
listed in Table 3.
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Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
Table 3. Applying PSR approach to CSG scenarios
Stressors
Alteration to hydrology leading
to decrease in dry spells
Constant release of water
leading to constant flow and
decrease in seasonal flow
Measurement of
stressor
•
•
•
Decrease in EC due to constant
RO water being released
Alteration to ionic composition
due to constant RO water being
released
•
•
Days of ‘no flow’
compared to previous
100 + years
Days of ‘no flow’
compared to previous
100 + years
Potential responses
•
Ionic concentrations
and composition of
water
− Na
− Mg
− Ca
− Cl
− SO4
− HCO3
− K
Decrease in turbidity leading to
increase in water transparency,
which leads to higher light
penetration
•
Turbidity of water
•
Light penetration of
water
Cumulative toxicological
impacts from contaminants
•
Toxicant
•
macroinvertebrates
•
macrophytes
•
fish species (see
Table 2)
•
Increase in connectivity may encourage
some fish species to migrate into the streams
where they couldn’t previously and
potentially increase invasive species.
•
Increase in aquatic macrophytes in
permanent pools and backwaters
•
Decrease in species that rely on seasonal
cues for breeding.
•
macroinvertebrates
•
macrophytes
Increase in invasive species due to decreases
in native species and alterations to the food
web and the ecosystem
•
flow dependent fish
(Table 2)
•
exotic species of fish
(Table 2)
•
riffles
•
macroinvertebrates
•
macrobrachium
•
exotic species of fish
(Table 2)
•
macroinvertebrates
•
algae
•
macrobrachium
•
aquatic plants
•
Seasonal variations in
flows
EC level in water
Decrease in ephemerality: e.g. some
macroinvertebrates that inhabit the
ephemeral streams may not be able to
survive in the altered system.
Possible
indicators#
•
Decrease in macroinvertebrates that are
sensitive to low EC, especially below
thresholds of 120 µS/cm.
•
Increase in invasive species due to decreases
in native species and food web and
ecosystem alterations
•
Increase in tolerant family groups which will
dominate the community structure
•
Decrease in PET richness and SIGNAL
scores following disturbance
•
Decrease in species of macroinvertebrates,
fish and algae may be sensitive to such a
change as altered ionic composition is known
to be toxic to certain species.
•
Decrease in macroinvertebrates that rely on
Ca for their physiology as RO permeate can
strip Ca from its surroundings.
•
Increase in invasive species and number due
to decreases in native species and alterations
to the food web and the ecosystem
•
Increase in algal blooms especially if the
nutrient levels are high
•
algae
•
primary production
Increase in acute and chronic toxicity on
various sensitive organisms
•
fish
•
algae
•
aquatic plants
•
macroinvertebrates
•
crustaceans
•
#The pressure in this case is the release of CSG water into the surface water. The Monitoring and Sampling Manual (2009) is recommended for sampling indicators.
11
Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
3.3
Where to monitor
Location of sampling is also critical and needs to represent the releasing water and the receiving environment. The
sampling locations should include both upstream and downstream locations, as well as the point of release
(Hellawell 1986). It is important to select the sampling site that allows for comparisons between sites.
There are three types of sampling sites: reference, control and test sites (Downes et al. 2002; Marshall & Choy
1999). Reference sites provide background information on the natural state of streams and the natural variation
between upstream and downstream sites. It provides an indication of what the environment will look like without
human disturbances (Downes et al. 2002). Natural variations within streams (up and downstream from impact site)
can occur (Karr & Chu 1999). This variation can be quantified by placement of adequate reference sites at a similar
spatial scale in adjacent streams. Adjustment can be made either by using such reference sites or be based on
analysis of existing DERM data. Upstream sites or sites on an alternative stream can be used as reference sites
(DERM 2009b). It would be ideal to have monitoring data from both upstream and alternative stream sites;
however, a relevant parallel stream may not be available. Hence, selection of reference sites would be sitedependent (Figure 2).
A reference site should be an unpolluted reach outside, but in the proximity of, the CSG scheme. It should have
physical, chemical and biological properties approximating those that occurred in pre-development. Such a pristine
site may be difficult to find; hence, it is more important to look over a wide area than to select a degraded local site
(Karr & Chu 1999) where minimal impacts of anthropogenic disturbances will be detected. The minimum criteria
are listed below (Marshall & Choy 1999). If there are difficulties in finding sites that fully comply with these
criteria, it may be necessary to use lesser quality or best available sites (QWQG 2009):
•
no intensive agriculture within 20 km upstream
•
no major extractive industry within 20 km upstream
•
no major urban area within 20 km upstream
•
no significant point source waste water discharge within 20 km upstream
•
•
no dam or major weir within 20 km upstream
seasonal flow regime not greatly altered
•
riparian zone of natural appearance
•
riparian zone and banks not excessively eroded beyond natural levels or significantly damaged by stock
•
stream channel not affected by major geomorphological change
•
physical and geomorphological properties to be comparable to those of potential test sites.
Control sites are upstream of the impact site used to set the monitoring objectives. These control sites represent the
ecological health of streams prior to CSG water discharge. Control sites must approximate the selected test sites on
broad geophysical characteristics such as stream order and altitude. Selected sites are designed to be as similar to
the impact sites as possible (Downes et al. 2002), including the effects from sources of contaminants other than the
CSG water. Comparison of health of control sites with that of reference sites will indicate how degraded the
starting point is for that specific stream. It would account for natural spatial variation in the control zone. The level
of replication permits analyses of differences between test and control sites. Repeated sampling of the same site
will not achieve these aims due to problems associated with pseudo-replication and confusion of temporal versus
spatial variation (Marshall & Choy 1999).
Test sites are within the impacted area and those downstream of the scheme. These are subject to impacts of the
activities. Comparison of index values at test sites with those at control sites provides the mean for identifying and
assessing impact-related changes. A thorough desktop review of existing knowledge of the water quality of the
CSG scheme should be conducted. Sources of impact should be located and potential test sites chosen up- and
downstream of each impact site. Additional potential test sites should be located downstream of the entire CSG
area at increasing distances. Pilot studies should be carried out at all these sites together with the reference and
control sites. This intensive pilot survey will identify key sites within the system that will become test sites for the
program. Conducting pilot surveys will identify optimum locations for sites.
12
Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
The downstream test sites are useful in understanding how far the impact of CSG water has been felt. This can then
be used to determine the size of the mixing zones. Further site-specific studies are required to identify the distance
between the discharge site and the downstream site. To determine the downstream distance, an investigation of how
far downstream the effects of CSG water are detected needs to be established for both flow and non-flow periods.
This can be conducted either by running pilot studies or by running hydrological models. The procedural guidelines
for waste water discharge to Queensland waters states that one must quantitatively assess the impacts on the
receiving waters (EPA 2008). In circumstances where simple modelling indicates that the water quality exceeds the
objectives for the receiving waters, more detailed predictive mixing zone models will be required (EPA 2008).
2. Discharge to tributary which travels
down and enters the main channel
Parallel
stream reference sites
Upstream
control site
Downstream
test sites
Discharge
test site
Downstream
test sites
nel
l chan
paralle
Discharge
test site
Reference sites
Reference sites
l
hanne
main c
Upstream
control site
3. Discharge to main channel
Upstream
control site
Discharge
test site
l
hanne
main c
Discharge to tributary
l
hanne
main c
1.
Downstream
test sites
Figure 2. Potential sampling sites for each of the three possible discharge scenarios for CSG water in the
absence of data prior to CSG water discharge.
There are options for reference sites depending on the availability. Number of downstream test sites will vary
depending on how far downstream the impact of CSG water has travelled.
3.4
When to monitor
Another issue to consider is the temporal scale, which refers to the length of time over which a system is to be
observed. It is highly recommended that the Before-After-Control-Impact (BACI) is adapted into the sampling
design to assess the impact of the CSG water discharge (Downes et al. 2002; Lindenmayer & Likens 2010). For
any impact assessment monitoring it is critical to distinguish between changes caused by the activity of interest (i.e.
CSG water) or by natural variation. Therefore, it is important to understand the natural variation to know the state
of the environment before the activity took place. BACI design incorporates pre-discharge data and can hence
discriminate the natural variation (Figure 3). Comparison between the reference sites from before and after the
discharge can provide information on the natural variation. Similarly, comparison between the control sites from
before and after the discharge can provide information on natural variation. Comparison between before and after
the discharge at the impact sites can provide information on the natural variation and the alteration caused by the
discharge. Similarly, comparison between the control and the impact sites after the discharge can provide
information on the natural variation and those caused by the discharge. Using the above information, the true
effects due to the discharge can be extracted. However, if the monitoring is only conducted after the CSG water has
been discharged into streams, uncertainty will be introduced and data will be compromised (Downes et al. 2002).
For this reason, monitoring for background information is critical in understanding the true impact of the
contaminants. In most cases, however, the CSG water has already been released and hence a degree of uncertainty
in the results has already been introduced. Furthermore, to obtain adequate background and reference data, the
duration of the sampling design should be over several seasonal variations. Hence, a minimum of two years of
sampling data is recommended (ANZECC/ARMCANZ 2000).
13
Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
Before discharge
Reference R
After discharge
Control C
Reference r
Control c
Impact I
Impact i
Control
R vs r
C vs c
Control
(I vs i) – (C vs c) = impact
(c vs i) – (C vs I) = impact
natural variation
Without the “before discharge” data
r vs c = natural variation
c vs i = impact but with uncertainty
I vs i = natural + impact
C vs I = natural variation
c vs i = natural variation + impact
Figure 3. BACI design incorporates before and after effects, allowing measurement of the natural variation*
*Without the information on the ecosystem before the discharge, uncertainty will be introduced to the measurement of impact.
Sampling also needs to be conducted within a narrow period of time and flow conditions, such as within a month of
each other (Downes et al. 2002; Marshall & Choy 1999). Additionally, continuous sampling is desirable to obtain
long-term monitoring data for the life of the CSG operations. Such long-term monitoring data is useful in detecting
trends and cumulative impacts of the operations (Lindenmayer & Likens 2010).
In the case of environmental incidents, sampling should take place immediately after the event (DERM 2009a). It is
also important to sample before and after the discharge of CSG water, whether it be an authorised, emergency or
unplanned discharge (DERM 2009a).
Sampling frequency should reflect the objectives and the variation of the system and be sufficient to satisfy the
program objective (DERM 2009a). Assessment of test sites is based on deviations from mean index values at
reference and control sites. Until more appropriate measures can be determined, the limit of acceptable changes or
ToC should be based on the level of protection set for the area of interest (ANZECC/ARMCANZ 2000). The
frequency of sampling needs to be sufficient to detect these deviations from the reference sites (Table 4).
14
Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
Table 4. Limits of acceptable changes for waters at different levels of protection (adapted from QWQG
2009).
Level of protection
Limitation of acceptable change
High ecological value systems
No change to natural systems
Slightly/moderately disturbed systems
Deviation of no more than 20th and 80th percentiles
Highly disturbed systems
Deviation of no more than 10th and 90th percentiles
If, for example, the aim of the project is to assess the daily variation in water temperature, the sampling may be
required to be taken twice daily. If on the other hand the aim is to detect seasonal variation alone, then minimal byannual sampling is required (DERM 2009a). If there are strong seasonal variations at the sites, each season needs to
be considered. Most of the ecological assets selected in Table 2 are dependent on seasonal flow variation. It is
therefore critical to conduct sampling at the beginning and the end of each of the dry and wet phases (Humphrey et
al. 1990). Queensland AusRivAS protocols also recommend sampling either side of the wet season in spring and
autumn (AusRivAS 2001).
3.5
Data analysis
There are different analytical or statistical models that can be used to detect effects from CSG water and it is
important to understand and apply the appropriate models. The structure of such models should be determined at
the planning stage of the sampling design. In many cases, there is insufficient data and it is critical to recognise that
uncertainties will exist and one would need to compromise and interpret the data accordingly (Downes et al. 2002).
Analysis allows for testing specific hypotheses set at the beginning of the sampling design (Downes et al. 2002).
For example, analysis provides evidence to the hypothesis that CSG water impacts the ecosystem of interest.
Analysis relies on the association between the stressors and responses, and such associations are strengthened with
the replication of data (Downes et al. 2002). It is preferable to define the effect size on a conceptual basis and use
power analysis to estimate replicates needed to detect it. Power is a measure of detecting an effect of the defined
size with the prescribed confidence if it truly exists. It is used to determine how big the sampling size should be.
The larger the sample size, the less likely that the Type II error or the false negative assumption will be made
(Downes et al. 2002). Hence the probability that the test will make a false negative decision by falsely accepting
the null hypothesis is dependent on the sample size.
For this reason, the number of samples taken is critical. Multiple samples are required to allow for calculating the
means and confidence intervals and for conducting statistical analysis. The monitoring program should be
specifically designed to provide strong statistical analysis (Lindenmayer & Likens 2010). Replication requirements
should be determined by the power analysis and, if necessary, a pilot study can be used to confirm the required
number.
Other levels of uncertainty exist due to sampling and measuring errors, temporal changes and natural variations. It
is recommended that the BACI design is used and the results from pre and post CSG water disposal be compared
where possible. As discussed previously, the lack of pre-discharge data introduces uncertainty to inferences made
from the data, and any decisions resulting from the analysis should be considered as probabilistic (Downes et al.
2002). To calculate whether an impact has occurred where there is no pre-impact data, multiple control sites can be
used (Marshall & Choy 1999). This approach assumes that the biological indicators would have behaved similarly
at the impact and reference sites in the absence of an impact.
Appropriate analytical methods vary depending on the design of the program. Some examples of statistical models
are listed in this guideline; however, there are other applicable models. Both linear and non-linear models can be
used. In the linear model, the relationship between a predicted variable and the response variable is linear,
expressed as a straight line or polynomial or curvilinear (Downes et al. 2002). In a non-linear system, the
relationship cannot be depicted by a straight line. There are two key linear models which are commonly used for
univariate analysis: the regression model and analysis of variance (ANOVA) model. The regression model can be
used to determine the strength of the relationship between the stressor and the response or between the control and
the impact sites. ANOVA is a statistical method for making simultaneous comparison between two or more means.
It determines whether a significant relation exists between variables.
15
Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
All tests require ‘before’ and ‘after’ and ‘control’ data for strong inferential power. Multivariate approaches still
have inferential power for ‘before’ as well as ‘after’ data. They are used for comparing multivariate responses such
as ionic composition or community responses. Analytical models such as the multivariate analysis of variance
(MANOVA) can test for multivariate relationships. Other useful multivariate techniques are listed in Appendix A.
16
Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
4
Management framework
The usefulness of effective monitoring comes from communication between the scientist and the management units
(Lindenmayer & Likens 2010). The stressors and indicators should be selected in accordance with the management
goals (Rogers & Biggs 1999). The monitoring of CSG operations should be question-driven and based on the sitespecific stressors shown in the conceptual model (Figure 1). The information obtained from the biological
monitoring should then feed into the regulatory framework (Figure 4), as monitoring should not be an end in itself
but rather the means for achieving management goals (Rogers & Biggs 1999).
Figure 4. How the biological monitoring feeds into the regulatory framework
The hypothesis of effect size or threshold of concern outlined in the conditions of an approval can be formed from
the available information. Monitoring can test this hypothesis based on the effect size to ensure that the regulations
are protecting the aquatic environment.
Currently, EAs are used for regulating any ERAs. The assumptions of impacts are determined by measuring
approval assessments against the relevant legislations. These assumptions are underpinned by the conditions of the
EAs, and biological monitoring will be useful in checking and adjusting those conditions if unforseen
environmental impacts occur (Figure 4). This cycle is not limited to just the biological monitoring, but can also
apply to any other water quality monitoring programs.
17
Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
4.1
Recommendations
The recommendations for biological monitoring of CSG water are as follows:
1.
Identify key stressors from CSG water disposal, such as the ones listed in this document
decrease in dry spells
constant flow and decrease in seasonality
decrease in EC
increase in transparency of the water
changes in ionic composition of the water
cumulative toxicological impacts from contaminants
2.
3.
Conduct a literature review to produce a list of organisms, ecosystem processes or key places in the area that
have the potential to be impacted by the stressors. Refer to the sources listed in this report
Produce conceptual models to outline how the key biological indicators are affected by stressors
4.
Design monitoring programs to incorporate appropriate duration, frequency and timing to capture the effects
the CSG operation will have on the selected biological indicators
5.
Conduct monitoring, collect data and analyse the potential CSG-related impacts on the ecosystem
6.
7.
Re-adjust the monitoring design if required
Provide the information to DERM for future evaluation and planning of the licence conditions.
18
Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
5
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Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
Appendix A
Multivariate techniques
1
Ordination
The technique of ordination aims to accurately summarise complex multivariate relationships between objects in a
small number of dimensions that are relatively easy to understand and interpret. For example, objects are sampling
sites and their multivariate attributes are the presence or absence of each taxon of macroinvertebrate. One
ordination technique commonly applied to such datasets is multidimensional scaling (MDS). This expresses the
multidimensional difference matrix calculated between all pairs of objects graphically in a small number of
dimensions (preferably 2 or 3). The preferred difference measure for ecological data is the Bray-Curtis index. On
resulting ordination plots, the distance between objects represents the difference in their faunal composition. A
stress statistic indicates the accuracy of the representation. Stress values of 0.2 or less are considered acceptable.
The use of the PRIMER software package is recommended for ordination.
Correlation of attributes with ordinations
The PCC routine within the PATN software package calculates linear correlation vectors between attributes of the
objects ordinated and the resulting ordination. The attributes can be the faunal data or any other attributes
associated with the objects. It is often useful to use environmental attributes of the sampling sites. The procedure
output indicates the direction and strength of correlation vectors. Significance of the correlations can be determined
by Monte Carlo simulations. The direction of strong and significant environmental correlations can aid in the
interpretation of patterns in the distribution of objects on the ordination. Correlation with the fauna helps to identify
taxa which are important associates with patterns.
ANOSIM
Analysis of similarity (ANOSIM) is a multivariate test for differences between predetermined groups of objects.
This test can be performed to identify significant differences between reference, test and monitoring site groups.
This should be followed by SIMPER analysis to identify which taxa are responsible for differences between groups
of sites. Absences or additions of taxa to monitoring sites should then be related to their sensitivities, flow
preferences and substrate preferences. Ordinations and subsequent correlations with environmental variables
(hydrological, chemical and physical) and the occurrences of taxa should then be performed to assist with the
separation of the effects of different impact types.
The technique utilises the Bray-Curtis dissimilarity between objects and compares the mean difference within a
group to the mean difference between groups. The significance of this is tested by a randomisation procedure. The
power of the test is related to the degree of replication within groups.
Groups of six or more objects are recommended. This technique could be used, for example, to test the hypothesis
that there is a difference between the fauna of target sites and test sites. ANOSIM is a routine of the PRIMER
software package.
SIMPER
Similarity percentages (SIMPER) are calculated where ANOSIM has identified a significant difference between
groups of objects. The technique allows the identification of attributes contributing most to the difference. This is
used to identify taxa contributing to differences between groups of sites. SIMPER is a routine of the PRIMER
software package.
1
From Marshall and Choy 1999
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Stream ecosystem health response to coal seam gas water release: Biological monitoring guidelines
BIOENV
BIOENV is a routine within the PRIMER software package that selects a subset of all available environmental
variables, which in effect produces a difference matrix between sites that is maximally correlated with the BrayCurtis difference matrix between sites based on their fauna. The routine is akin to multiple Mantels tests between
the matrices. The strength of the correlation indicates the robustness of the relationship. The subset of
environmental variables can be considered to be closely associated with faunal composition of sites if the
correlation coefficient is high.
23