Modelling water-related
ecological responses to
coal seam gas extraction
and coal mining
This report was commissioned by the Department of the Environment on the
advice of the Independent Expert Scientific Committee on Coal Seam Gas and
Large Coal Mining Development (IESC).
January 2015
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Copyright
© Copyright Commonwealth of Australia, 2015.
Modelling water-related ecological responses to coal seam gas extraction and coal mining is licensed
by the Commonwealth of Australia for use under a Creative Commons By Attribution 3.0 Australia
licence with the exception of the Coat of Arms of the Commonwealth of Australia, the logo of the
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This report should be attributed as ‘Commonwealth of Australia 2015, Modelling water-related
ecological responses to coal seam gas extraction and coal mining, prepared by Auricht Projects and
the Commonwealth Scientific and Industrial Research Organisation (CSIRO) for the Department of the
Environment, Commonwealth of Australia’.
The Commonwealth of Australia has made all reasonable efforts to identify content supplied by third
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Enquiries concerning reproduction and rights should be addressed to:
Department of the Environment, Public Affairs
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Acknowledgements
This report was commissioned by the Department of the Environment on the advice of the
Independent Expert Scientific Committee on Coal Seam Gas and Large Coal Mining Development
(IESC).
The report was prepared by Auricht Projects (Christopher Auricht and Sarah Imgraben) with input from
Adjunct Professor Andrew Boulton (University of New England), Dr Justine Murray (CSIRO),
Dr Carmel Pollino (CSIRO) and Dr Moya Tomlinson (Office of Water Science, Department of the
Environment).
The report was peer reviewed by Dr Martin Andersen (University of New South Wales), Professor
Angela Arthington (Griffith University), Dr Bruce Chessman (ecological consultant), Dr Alexander Herr
(CSIRO), Professor Ray Froend (Edith Cowan University) and Dr Anthony O’Grady (Ecology Lead,
Bioregional Assessments Programme). Dr Jennifer Firn (Queensland University of Technology)
reviewed
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Table 4.1 and Table 4.2 on Melaleuca irbyana and Dr Keith Walker reviewed the silver perch case
study.
Disclaimer
The views and opinions expressed in this publication are those of the authors and do not necessarily
reflect those of the Australian Government or the Minister for the Environment or the IESC.
While reasonable efforts have been made to ensure that the contents of this publication are factually
correct, the Commonwealth and IESC do not accept responsibility for the accuracy or completeness of
the contents, and shall not be liable for any loss or damage that may be occasioned directly or
indirectly through the use of, or reliance on, the contents of this publication.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Contents
Summary ................................................................................................................................................. v
Abbreviations .......................................................................................................................................... vii
Glossary...................................................................................................................................................ix
1 Introduction ......................................................................................................................................... 1
1.1
Project context............................................................................................................................ 1
1.2
Purpose and outline of this report .............................................................................................. 1
1.3 Limitations in current assessment of water-related ecological responses to coal seam gas
extraction and coal mining .................................................................................................................. 3
1.4
Potential water-related stressors associated with coal seam gas extraction and coal mining .. 4
1.5
Expected project outcomes ........................................................................................................ 6
2 Using models to predict water-related ecological responses to coal seam gas extraction and coal
mining ................................................................................................................................................. 7
2.1
Using ecological conceptual models to represent complex ecosystems ................................... 7
2.2
Two broad types of conceptual models ..................................................................................... 8
2.3
Addressing issues of scale and uncertainty in ecological conceptual models ......................... 10
2.4
A framework for assessing vulnerability coal seam gas extraction and coal mining activities 12
3 Project methodology ......................................................................................................................... 17
3.1
Overview .................................................................................................................................. 17
3.2
Control and stressor models .................................................................................................... 19
3.3
Expert workshop assessment of some worked examples of ecological conceptual models ... 21
4 Results: case study and worked examples ...................................................................................... 23
4.1
Ecological conceptual models for Purga Nature Reserve ....................................................... 23
4.2
Bayesian network session ........................................................................................................ 37
4.3
Gunnedah Basin case study: conceptual model for silver perch ............................................. 41
5 Discussion ........................................................................................................................................ 50
5.1 The role of ecological modelling in assessment of proposals for coal seam gas extraction and
coal mining ........................................................................................................................................ 50
5.2
Ecological conceptual models in coal seam gas extraction and coal mining proposals .......... 51
5.3 Challenges in generating ecological conceptual models for proposals for coal seam gas
extraction and coal mining ................................................................................................................ 52
5.4
Feasibility of the proposed approach as a desktop exercise ................................................... 54
5.5
Bayesian networks within an EIS application ........................................................................... 55
5.6
Conclusion................................................................................................................................ 56
6 References ....................................................................................................................................... 57
Appendix A - Case study: conceptual model for Silver Perch ............................................................... 62
Appendix B - Bayesian network models ................................................................................................ 66
Appendix C - Workshop agenda ........................................................................................................... 74
Appendix D - Workshop participants ..................................................................................................... 78
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Appendix E - Abstracts of presentations ............................................................................................... 80
Appendix F - Case study: Purga Nature Reserve ................................................................................. 88
Tables
Table 4.1 Narrative table to accompany the control model ................................................................... 25
Table 4.2 Narrative table to accompany the stressor model ................................................................. 30
Table 4.3 Scenario construction for the Purga Nature Reserve, with type of stressor and
frequency of occurrence ................................................................................................... 38
Table 4.4 Narrative table listing drivers, stressors, water-related ecological effects and
hypothesised ecological effects on silver perch (SP) ....................................................... 43
Figures
Figure 1.1 Hydrological stressors from coal seam gas extraction and coal mining ................................ 5
Figure 2.1 An integrated framework to assess the vulnerability of species to climate change ............. 13
Figure 2.2 Sensitivity assessment ......................................................................................................... 14
Figure 2.3 Conceptual model for brook trout ......................................................................................... 16
Figure 3.1 Flow-chart of ecological conceptual model development .................................................... 17
Figure 4.1 Location of Purga Nature Reserve ....................................................................................... 23
Figure 4.2 Box-and-arrow diagram of the control model for Melaleuca irbyana ................................... 29
Figure 4.3 Purga Nature Reserve (wet phase)...................................................................................... 32
Figure 4.4 Purga Nature Reserve (dry phase) ...................................................................................... 33
Figure 4.5 Conceptual model of a coastal and subcoastal floodplain tree swamp (Melaleuca
and Eucalyptus spp.) ........................................................................................................ 34
Figure 4.6 Box-and-arrow diagram of the stressor model for Melaleuca irbyana ................................. 35
Figure 4.7 Landscape setting of Purga Nature Reserve ....................................................................... 36
Figure 4.8 Influence diagram developed in the workshop showing interactions between
hydrological stressors and endpoints................................................................................ 39
Figure 4.9 Example of a small Bayesian network ................................................................................. 40
Figure 4.10 Conceptual model for silver perch...................................................................................... 42
Figure 4.11 Conceptual model of how fish are influenced by aspects of the riparian zone .................. 49
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Summary
Ecological conceptual models are rarely used in Environmental Impact Statements (EISs) for
coal seam gas extraction and coal mining proposals in Australia. In contrast, hydrological
and hydrogeological conceptual models are well-established tools for identifying and
assessing potential impacts of development projects. There is a need to integrate current
hydrological and hydrogeological conceptual models with ecological ones to provide a
complete picture of the likely water-related ecological impacts of coal seam gas extraction
and coal mining. These combined models should then be used in EISs to support statements
of likely ecological responses to coal seam gas extraction and coal mining, and to illustrate
mechanisms by which proposed mitigation strategies would operate to reduce potential
impacts.
This report presents the findings of a project exploring an approach to ecological conceptual
modelling aimed at improving the assessment of water-related ecological impacts of coal
seam gas extraction and coal mining. The approach, presented as a series of consecutive
steps and illustrated with worked examples, could assist those preparing and reviewing EISs
to construct ecological conceptual models and associated narrative tables that specify
hypothesised responses, and document supporting evidence. By using this approach,
assumptions about ecological impacts in assessment of development proposals are made
explicit, response pathways are identified and illustrate interactive and cumulative effects,
and there is a transparent and consistent framework for design of monitoring programmes to
test the implicit hypotheses.
The approach to ecological conceptual modelling in this report follows that described by
Gross (2003) for constructing ‘control’ and ‘stressor’ models, except for the modification that
the ‘control’ model includes not only natural drivers and stressors but also anthropogenic
ones not related to coal seam gas extraction and coal mining. Thus, the ‘control’ model
conceptualises ecosystem components and interactions within the area of project impact
before coal seam gas extraction and coal mining, whereas the ‘stressor’ model includes the
hypothesised ecological responses to drivers and stressors associated only with such
activities. Comparing the ecological conceptual models of the ‘before’ and ‘after’ states
illustrates hypothesised ecological responses to coal seam gas extraction and coal mining.
Pictorial conceptual models, influence diagrams and a Bayesian network were developed as
a ‘proof-of-concept’ trial, and refined during an expert workshop that was informed by a field
visit to a case study area. Pictorial conceptual models showing the components and
processes in an area of interest help to make response pathways explicit. Models illustrating
components, processes and responses developed at a hierarchy of spatial scales
(e.g. groundwater-fed pools in river reaches nested in catchments) aim to portray spatial and
temporal variability in ecological responses in an EIS. The temporal scale should take into
account the time lags in hydrological and ecological responses to stressors such as
groundwater extraction, which may extend for decades.
Careful consideration of spatial and temporal scales is only one of the challenges in the
assessment of ecological responses in EISs. Other challenges include gaps in data and
site-specific knowledge, constraints in extrapolating short-term measurements to predict
long-term responses, difficulty in demonstrating or quantifying causality, and the need to
consider likely effects of stressors on various life-history stages as vulnerabilities may differ
between recruitment/seedling establishment and adult stages.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
A key conclusion of the report is that the approaches to modelling and conceptualisation of
hydrology and hydrogeology currently used in EISs should be extended to incorporate
ecological components to produce ecohydrological models capable of illustrating likely
water-related ecological responses to coal seam gas extraction and coal mining. Given that
nearly all stressors interact, they should not be treated independently when assessing likely
responses. Despite the challenges, the approach outlined in this report seeks to provide
proponents of development proposals with the tools to better portray and understand the
hydrology-ecology relationships in areas of planned coal seam gas extraction and coal
mining, and to clearly articulate hypothesised stressor and response pathways, supported by
reference to scientific and other credible literature. However, it is important to note that
compiling conceptual models and the supporting narrative tables is not the final step. The
purpose is to provide a transparent rationale, referenced to the scientific literature, for the
ecological responses and proposed mitigation actions and monitoring strategies identified in
an EIS.
Application of the proposed approach is expected to:

enhance capability in the resources industries to identify and predict the water-related
impacts of coal seam gas extraction and coal mining, through uptake of the approach to
ecological conceptual modelling and integration of the ecological modelling approach
with hydrological and hydrogeological modelling and conceptualisation

improve identification and understanding of the potential water-related ecological
responses to coal seam gas extraction and coal mining in Australia, achieved through
assisting the Independent Expert Scientific Committee on Coal Seam Gas and Large
Coal Mining Development (IESC) in its evaluation of EIS documentation for coal seam
gas and coal mining proposals and provision of advice to regulators

provide a framework for ecological conceptual modelling that could be drawn upon in the
bioregional assessments.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Abbreviations
General
abbreviations
Description
ANAE
Australian National Aquatic Ecosystem
As
Symbol for arsenic
BA
Bioregional Assessment
BN
Bayesian network
BOD
Biological oxygen demand
CPT
Conditional probability table
CSG
Coal seam gas
CSGCM
Coal seam gas and coal mining
CSIRO
Commonwealth Scientific and Industrial Research Organisation
DDT
Dichlorodiphenyltrichloroethane
DO
Dissolved oxygen
ECD
Ecological Character Descriptions
EHNV
Epizootic Haematopoietic Necrosis Virus
EIS
Environmental Impact Statement
EM
Expectation Maximisation
EPBC Act
Environment Protection and Biodiversity Conservation Act 1999
Fe
Symbol for iron
GDE
Groundwater dependent ecosystem
Govt
Government
IESC
Independent Expert Scientific Committee on Coal Seam Gas and Large Coal
Mining Development
IPCC
Intergovernmental Panel on Climate Change
MI
Melaleuca irbyana
Mn
Symbol for manganese
NSW
New South Wales
OWS
Office of Water Science
PVA
Population viability analysis
Qld
Queensland
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
General
abbreviations
Description
RCI
River Condition Index
RO
Reverse osmosis
SP
Silver perch
TDS
Total dissolved solids
US
United States
WAIT
Water Asset Information Tool
y
Year
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Glossary
Term
Description
Bioregion
As defined in the bioregional assessment methodology (Barrett et al.
2013):
‘…the land area that constitutes a geographic location within which are
collected and analysed data and information relating to potential impacts
of coal seam gas or coal mining developments on receptors identified for
key water-dependent assets’
Bioregional
assessments
A bioregional assessment (BA) is a scientific analysis of the ecology,
hydrology, geology and hydrogeology of a bioregion, with explicit
assessment of the potential direct, indirect and cumulative impacts of coal
seam gas and coal mining development on water resources. The central
purpose of BAs is to analyse the impacts and risks associated with
changes to water-dependent assets that arise in response to current and
future pathways of coal seam gas and coal mining development
(Barrett et al. 2013)
Baseflow
The groundwater contribution to stream flow (Fetter 2001)
Coal seam gas
development
Any activity involving coal seam gas extraction that has, or is likely to
have, a significant impact on water resources either in its own right or
when considered with other developments, whether past, present or
reasonably foreseeable (IESC 2014)
Conceptual model
A conceptual model is ‘…a descriptive and/or schematic hydrological,
hydrogeological and ecological representation of the site showing the
stores, flows and uses of water, which illustrates the geological
formations, water resources and water-related assets, and provides the
basis for developing water and salt balances’ (IESC 2014).
Ecological conceptual models show linkages among drivers, stressors,
processes and components to represent known and hypothesised
ecological responses to one or more stressors; a powerful way to
communicate complex interactions among processes and components
deemed important in an ecosystem with defined bounds and scope
(after Gross 2003)
Confidence
A qualitative estimate of the quality of evidence and agreement among
sources about a given situation, statement or hypothesis. This approach,
used by the IPCC (2013) in efforts to predict the effects of future climate
change, is used in this report as a surrogate partial measure of the
uncertainty associated with support for hypothesised ecological responses
to coal seam gas and coal mining development. However, ‘confidence’ is
not the same as ‘uncertainty’, and these two terms should not be used
interchangeably
Control conceptual
model
A model that represents key processes, interactions and feedbacks
(Gross 2003). In the context of this project, we define the control
conceptual model as representing key processes, interactions and
feedbacks in response to natural and anthropogenic activities not related
to coal seam gas and coal extraction. This definition differs from the one
by Gross (2003) that explicitly excludes stressors
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Term
Description
Drivers
The major external driving forces that have large-scale influences on
natural systems. Drivers can be natural or anthropogenic forces
(Jean et al. 2005)
Ecological endpoints
Ecological endpoints are a selected subset of the physical, chemical and
biological elements and processes of natural systems that are selected to
represent the overall health or condition of the system, known or
hypothesised effects of stressors, or elements that have important human
values (adapted from Gross 2003)
Ecosystem
Organisms and the non-living environment, all interacting as a unit
Groundwater
Water occurring in the saturated zone and the capillary fringe
Groundwater
dependent ecosystem
(GDE)
Natural ecosystems which require access to groundwater on a permanent
or intermittent basis to meet all or some of their water requirements so as
to maintain their communities of plants and animals, ecological processes
and ecosystem services (Richardson et al. 2011). The broad types of
GDE are (Eamus et al. 2006):



ecosystems dependent on surface expression of groundwater
ecosystems dependent on subsurface presence of groundwater
subterranean ecosystems
Hyporheic
Associated with the saturated sediments below and alongside rivers and
streams where surface water and groundwater exchange
Spring
A natural discharge of water from the ground (modified from
Barrett et al. 2013)
Stressors
Physical, chemical, or biological perturbations to a system that are either
foreign to that system or natural to the system but applied at an excessive
or deficient level. Stressors cause significant changes in the ecological
components, patterns and processes in natural systems (Gross 2003)
Stressor conceptual
model
A model that represents relationships among stressors (or drivers),
ecosystem components and effects (Jean et al. 2005). In the context of
this project, we define the stressor conceptual model as representing the
relationships between coal seam gas and coal mining-related stressors
and their ecological effects. The control and stressor models are
combined to conceptualise water-related ecological responses to natural
and anthropogenic (including coal seam gas extraction and coal mining)
drivers and stressors
Stygofauna
Aquatic fauna living in groundwater
Uncertainty
A partial or total lack of understanding or knowledge of an event, its
consequence, or its likelihood (modified from Barrett et al. 2013). This
definition is derived from the Standards Australia and New Zealand Risk
Management Guidelines (AS/NZS ISO 31000:2009)
Water-related asset
‘A defined value or public benefit with a dependence on surface or
groundwater, including water dependent ecosystems (as defined by the
Water Act 2007 (Cwth)), drinking water, public health, recreation and
amenity, Indigenous and cultural values, fisheries, tourism, navigation,
agriculture and industry values’ (IESC 2014)
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Term
Description
Water resources
Defined by the Water Act 2007 (Cwth) as:
‘…surface water or groundwater; or a watercourse, lake, wetland or
aquifer (whether or not it currently has water in it); and includes all aspects
of the water resource, including water, organisms, and other components
and ecosystems that contribute to the physical state and environmental
value of the resource’ (IESC 2014)
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
1 Introduction
1.1 Project context
In 2012, the Australian Government established an Independent Expert Scientific Committee
on Coal Seam Gas and Large Coal Mining Development (IESC) to provide scientific advice
to government regulators on the impacts that coal seam gas (CSG) extraction and large coal
mining development may have on Australia’s water resources. The IESC is supported by the
Office of Water Science (OWS) within the Australian Government Department of the
Environment.
The OWS conducts research areas under three priority themes:

hydrology: changes in dynamics and aquifer interconnectivity

ecosystems and water: environmental tolerances, responses and mitigation

chemicals: water-related risks to environmental health.
Monitoring, assessment and evaluation of cumulative impacts is a cross-cutting theme
across the three priority themes.
This project, modelling water-related ecological responses to coal seam gas extraction and
coal mining, provides the theoretical basis for subsequent projects within the second theme
above, Ecosystems and water (hereafter referred to as the ‘Ecology theme’). The aim of this
project was to explore the development of tools specifically to assess the water-related
ecological impacts of coal seam gas extraction and coal mining projects in Australia.
The IESC also provides advice to the Australian Government on bioregional assessments
(BA). In this context, a bioregional assessment is a collation of baseline information on the
ecology, hydrology, geology and hydrogeology of a designated region, termed a bioregion,
with explicit assessment of the potential direct, indirect and cumulative impacts of coal seam
gas extraction and coal mining on water resources. Bioregional assessments and other
research aim to improve the knowledge base regarding the potential water-related impacts of
coal seam gas extraction and coal mining.
The Bioregional Assessment Programme targets regions with significant coal deposits.
Assessments are currently being undertaken in 13 subregions within six bioregions across
central and eastern Australia, including the Clarence-Moreton Basin.
As part of the bioregional assessments, the direct, indirect and cumulative impacts on
receptors representing ecological, economic and socio-cultural water-dependent assets will
be reported. This Ecology theme project and an expert-panel workshop (section 3.3)
explored approaches for developing ecological conceptual models that portray likely
ecological water-related responses to coal seam gas extraction and coal mining, providing a
framework that could be drawn upon by related work, such as the bioregional assessments.
1.2 Purpose and outline of this report
The purpose of this project was to examine how ecological conceptual models could be used
to improve current methods of assessment of the water-related ecological impacts of coal
seam gas extraction and coal mining. Specifically, the project aimed to find the most feasible
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
approach for developing ecological conceptual models to support this assessment process,
trial the approach as a ‘proof-of-concept’ using a case study in the Clarence-Moreton Basin,
and discuss the models and results with scientific experts at a facilitated workshop that
included a field visit to the case study area.
This report begins (Chapter 1) with a brief description of the project’s context with the current
BAs, followed by a review of the limitations of the present approach to assessing
water-related ecological responses to coal seam gas extraction and coal mining in Australia
and some examples of ecological assumptions derived from recent EISs. The potential
water-related stressors associated with coal seam gas extraction and coal mining are briefly
reviewed, supported by a diagram showing how they likely interact with each other. This
chapter concludes with a list of the expected project outcomes.
Chapter 2 gives some theoretical background to the approach taken in this project.
Ecological conceptual models are defined and their advantages and uses are listed, followed
by a description of ‘control’ and ‘stressor’ models (Gross 2003) and their combination in the
current project to represent likely water-related ecological responses to coal seam gas
extraction and coal mining. This chapter concludes with a brief review of the issues
associated with scale and uncertainty in ecological conceptual models; both are major
considerations in using these models to assess water-related ecological responses.
Chapter 3 outlines the project methods, describing the seven-step approach to deriving an
ecological conceptual model that combines ‘control’ and ‘stressor’ models to illustrate the
likely pathways by which one or more stressors associated with coal seam gas extraction
and coal mining would affect specific ecosystems, habitats, species populations or life history
stages at different scales. It also lists the main types of information needed to compile the
models and accompanying narrative tables. The methods used in the case studies (including
a test of the Bayesian Network (hereafter BN) approach) are presented, along with a brief
summary of the expert workshop procedure.
Chapter 4 describes the results of the case studies, and presents the control and stressor
ecological conceptual models for the ‘wet’ and’ dry’ phases of the Swamp Tea-tree
(Melaleuca irbyana) population in the Purga Nature Reserve in the Bremer River catchment,
Clarence-Moreton bioregion. The full narrative tables for the control and stressor models are
provided. This chapter also presents a BN derived at the workshop to predict likely
responses of the Swamp Tea-tree population in the Purga Nature Reserve to hypothesised
water-related stressors of coal mining. This derivation was done to test the feasibility of the
BN approach for indicating potentially important mechanism(s) by which the stressors elicit
ecological responses (i.e. a ‘proof-of-concept’).
Chapter 5 begins by discussing the roles of ecological modelling in assessing water-related
ecological responses to coal seam gas extraction and coal mining, recommending that the
approaches to modelling and conceptualisation of hydrology and hydrogeology currently
used in EISs be extended to incorporate ecological components to produce ecohydrological
models capable of predicting likely water-related ecological responses to coal seam gas and
coal mining development. As virtually all models rely on a conceptual framework, the rest of
the discussion focuses on ecological conceptual modelling, especially the benefits and
challenges involved in deriving the conceptual models. After discussing the ‘lessons learned’
from the various case studies and the BN analysis, this chapter concludes by listing the
principal specific questions that should be addressed by future ecological conceptual
modelling, including the approach proposed in this project.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
1.3 Limitations in current assessment of water-related
ecological responses to coal seam gas extraction and
coal mining
The assessment of water-related ecological impacts of development proposals for coal seam
gas extraction and coal mining is challenged by our incomplete understanding of ecological
responses to hydrological alteration, particularly interactive and cumulative effects at multiple
spatial and temporal scales. Currently, analysis of ecological impacts in development
assessments is largely qualitative, disregards ecological processes, is poorly integrated with
hydrogeological conceptualisation and hydrological modelling, and lacks robust and
transparent consideration of multi-stressor impacts and cumulative effects.
There is a pressing need to improve the sophistication of ecological assessment by
improving the capacity to predict ecological responses, incorporating consideration of
ecosystem processes such as nutrient cycling and organic matter decomposition (Bernhardt
& Palmer 2011), and better integrating ecological and hydrogeological conceptualisation and
modelling. These predictions (hypotheses) need to be clearly stated and their assumptions
validated with explicit reference to relevant scientific literature, empirical data and other
credible evidence.
Environmental assessment documentation for coal seam gas extraction and coal mining
projects in Australia reveals a number of assumptions regarding water-related ecological
impacts. These assumptions are seldom supported by data or a scientific rationale.
Examples include:

Vegetation in the study area is drought-tolerant and has low physiological sensitivity to
water availability (i.e. is resistant to hydrological change).

Instream fauna is tolerant of turbidity, poor water quality and flow variability, and
therefore will be unaffected by any hydrological impacts of coal seam gas or coal mining.

The ecology of the area is already impacted by clearing and grazing so any further
impacts will be insignificant.

Brigalow is relatively tolerant of periodic inundation, so impacts of subsidence are
considered minimal.

Ponds created by subsidence will provide enhanced habitat for aquatic species.

There are no cumulative impacts of subsidence, groundwater drawdown and loss of
stream flow.

Groundwater in the study area is too deep to be accessed by vegetation.

There is no surface water-groundwater interaction in the project area.

There is limited connectivity between the coal seams and the source aquifers for springs,
and therefore there will be no significant impact on the springs.

Impacts on springs can be mitigated by piping water to the spring.

Groundwater contribution to flow in non-perennial rivers is ecologically insignificant.

Fracturing of stream beds may lead to drainage of overlying pools, loss of aquatic
habitat and associated biota and loss of connectivity between pools. Such losses would
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
not be important in non-perennial drainage lines, as aquatic habitat would be present
only during flow events and for a short time thereafter.
This project explores the use of ecological models in making these assumptions explicit,
identifying causal pathways (i.e. how a stressor might elicit an ecological response),
investigating interactive and cumulative effects and providing a framework for testing the
implicit hypotheses. To put these hypotheses into an appropriate context, a logical starting
place is the preparation of credible ecological conceptual models, tailored to appropriate
scales of space and time, to complement the current hydrological and hydrogeological
conceptual models in many EISs. By integrating these three forms of conceptual models,
robust hypotheses can be derived about the likely water-related ecological responses to one
or more impacts of coal seam gas extraction and coal mining. These, in turn, could lead to
the development of quantitative decision-support tools that would enable more transparent
and defensible decisions and facilitate ecologically sustainable water management
(Arthington et al. 2010).
1.4 Potential water-related stressors associated with coal
seam gas extraction and coal mining
Activities associated with coal seam gas extraction and coal mining typically lead to a range
of water-related stressors. Most of these stressors interact and their effects can seldom be
separated. Indeed, assessing individual effects is inappropriate because it is the collective
suite of effects and their interactions (Figure 1.1) that are responsible for water-related
ecological changes caused by coal seam gas extraction and coal mining.
The two principal types of stressors are those associated with water regime and those with
water quality, and these also interact. Surface water regime, as presented in Figure 1.1,
refers to where, when and how much water is present. In standing waters, this regime would
include water levels, extent and permanence, whereas in running waters, discharge
characteristics (volume, seasonal pattern, variability) and velocity are also relevant aspects
of the water regime. Groundwater regime includes water table fluctuations and groundwater
flux, pressure, and residence time. Water quality is defined here as the physical and
chemical features of either surface water or groundwater, affecting ecological processes, the
distribution of biota and human uses. Stressors that alter surface water and groundwater
regimes result from activities that directly remove or add water (e.g. water extraction for
mining, disposal of co-produced water) or activities that indirectly affect water regimes by
impounding stream flow and altering catchment and floodplain runoff, infiltration and
recharge (Figure 1.1). Stressors that alter surface water and groundwater quality arise from
direct contamination (e.g. runoff from stockpiled mine waste) or activities that indirectly affect
water quality such as when barriers alter water regimes in rivers.
Some stressors (e.g. those that alter water quality) may be caused by multiple activities, and
these are likely to have cumulative effects that interact in a complex way. Some stressors
give rise to a cascade of related stressors. For example, groundwater drawdown may reduce
water availability for deep-rooted and riparian vegetation, change surface water-groundwater
connectivity regimes and baseflow volumes leading to increased duration and spatial extent
of cease-to-flow periods, change extent and quality of habitat for stygofauna and hyporheic
fauna, change environmental conditions that support biogeochemical processes in the
hyporheic zone and in aquifers, and reduce spring discharge. The key point here is that most
ecological responses to water-related stressors result from cumulative effects of a suite of
interacting stressors rather than from a single stressor. Ecological conceptual models strive
to portray this complex cumulative interaction as simply as possible – seldom an easy task.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Note. Interactions among stressors (bold black type) from coal seam gas extraction and coal mining (blue type);
dashed lines represent possible linkages.
Figure 1.1 Hydrological stressors from coal seam gas extraction and coal mining
Furthermore, responses to hydrological stressors are likely to interact over space and time,
including beyond the period of coal seam gas extraction or coal mining. Factors such as the
flow regime, shapes of the channels and drainage networks, and the effects of stressors
unrelated to coal seam gas extraction and coal mining (e.g. agriculture or urbanisation) are
likely to determine the ecological responses at different points along a river (cf. McCluney et
al. 2014) whereas seasonal factors and temporal changes in land use may govern ecological
responses at different points in time.
The cumulative effects of some stressors, such as those from the discharge of mine-affected
water or co-produced water from coal seam gas operations, may ameliorate with increasing
distance from the point of discharge if the system has the capacity to assimilate the impacts
through dilution by inflows from unaffected tributaries (Dunlop et al. 2013). Perceptions of the
extent and severity of these ecological responses and their cumulative interactions are also
strongly influenced by the physical scale of the modelling (discussed in Chapter 2).
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
1.5 Expected project outcomes
The expected outcomes from the project were:

enhance capability in the resources industries to identify and predict the water-related
impacts of coal seam gas extraction and coal mining, through uptake of the approach to
ecological conceptual modelling and integration of the ecological modelling approach
with hydrological and hydrogeological modelling and conceptualisation

improve identification and understanding of the potential water-related ecological
responses to coal seam gas extraction and coal mining in Australia, achieved through
assisting the IESC in its evaluation of EIS documentation for coal seam gas and coal
mining proposals and provision of advice to regulators

provide a framework for ecological conceptual modelling that could be drawn on in the
bioregional assessments.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
2 Using models to predict
water-related ecological responses
to coal seam gas extraction and coal
mining
2.1 Using ecological conceptual models to represent complex
ecosystems
Natural ecosystems are incredibly complex, comprising numerous components and
interactions that constantly change at multiple temporal and spatial scales. For most
ecosystems, understanding of responses to natural and anthropogenic disturbances is
limited. However, it is acknowledged that these responses are often unexpected ‘ecological
surprises’ (Gordon et al. 2008), especially when multiple interacting stressors are involved.
Many ecological responses to stressors in ecosystems are nonlinear, frequently resulting in
dramatic and rapid changes in species abundances or community composition or even
switches between alternative states (Scheffer & van Nes 2007). These changes may be
irreversible (e.g. for some aquatic ecosystems in salinised parts of the Western Australian
Wheatbelt [Davis et al. 2010]), extinguishing natural biodiversity and producing ecosystems
that no longer deliver desired goods and services.
To predict the risk of irreversible changes and undesirable outcomes in response to human
activities, ecological models are commonly used (Lindenmayer et al. 2010). Ecological
models range from verbal descriptions and pictorial graphics to mathematical descriptions
and computer-aided models that seek to quantify outcomes and their probability (Jean et al.
2005). This report uses verbal descriptions and pictorial graphics as a means of
conceptualising interactions among drivers, stressors, components and processes in an
ecosystem, and refers to these as ‘ecological conceptual models’.
There is seldom time to determine experimentally the responses of natural ecosystems to
different types of disturbances, especially in assessment of likely environmental impacts of a
given development such as coal seam gas extraction or coal mining in which stressors and
responses may occur over large spatial scales that are difficult or impossible to replicate
experimentally. Therefore, models need to be based on the best available science (Ryder et
al. 2010) to help identify likely important pathways of cause and effect, how these would be
influenced by activities associated with coal seam gas extraction and coal mining, and what
might be the water-related ecological responses. These models aim to integrate hydrological
and hydrogeological models (e.g. Wondzell et al. 2010; Gondwe et al. 2010), predict and
compare likely outcomes from various management actions and enhance communication
between scientists and representatives of resource-extracting industries (Westgate et al.
2013). Ecological conceptual models are especially powerful for this last goal.
The many advantages of using ecological conceptual models in ecosystem science and
environmental monitoring (Lindenmayer & Likens 2010) include:

specifying the scope and scales of the system of interest

illustrating the main components, processes and interactions at a given scope and scale
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Modelling water-related ecological responses to coal seam gas extraction and coal mining

generating explicit hypotheses about particular interactions and outcomes

integrating input from different experts into a formalised shared understanding

facilitating rapid communication among scientists, managers and the public about the
complexity of the diverse ecosystem components, interactions and responses to multiple
stressors

revealing likely responses to one or more stressors so that potential management
strategies to minimise impacts can be devised.
Ecological conceptual models are universally used as an essential component of effective
environmental science, monitoring and impact assessment (Noon 2003; Jean et al. 2005;
Harwell et al. 2010). Without a proper scientific framework based on one or more reliable
conceptual models, predictions lack credibility or consistency and costly errors result
(Lindenmayer & Likens 2010). Therefore, an excellent investment of time at the start of any
project is to develop conceptual models using expert advice, relevant scientific literature and
other credible information (Chapter 3), making successive refinements as more information
and understanding is achieved by monitoring and research (Westgate et al. 2013).
Many ecological conceptual models of complex ecosystems are used to explore and portray
how the interactions among different components of the ecosystems influence some
particular component or process of interest. In this context, the component or process is an
‘ecological endpoint’ of the ecological conceptual model (Section 2.2) and might be selected
because it represents the overall health or condition of the system, known or hypothesised
effects of stressors or elements that have important human values (Gross 2003). This
definition and uses of ‘ecological endpoint’ closely resembles those of ‘ecological indicators’,
and many of the desirable attributes are the same: they must be easily measured, be
sensitive to relevant stressors, respond to these stressors in a predictable manner and have
a known response to natural disturbances and anthropogenic stressors (Cairns et al. 1993;
Dale & Beyeler 2001).
Consequently, literature from the research discipline exploring the uses and constraints of
ecological indicators is a valuable source of information and examples when selecting
appropriate ecological endpoints for use in ecological conceptual modelling. A good starting
place is the review by Niemi and McDonald (2004) about the use of ecological indicators.
This review deals explicitly with the importance of clearly stated objectives, the recognition of
spatial and temporal scales, assessments of statistical variability, precision and accuracy,
and establishing linkages with specific stressors.
2.2 Two broad types of conceptual models
Gross (2003) recognises two fundamentally different types of conceptual models: control
models and stressor models. He defines a control model as a:
‘…conceptualism of the actual controls, feedback, and interactions responsible for
system dynamics…’ (p.6).
This is probably what most ecologists would think of as a typical ecological conceptual
model. He defines a stressor model as one:
‘…designed to articulate the relationships between stressors, ecosystem components,
effects, and (sometimes) indicators…’ (p.7).
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Stressor models typically contain only a subset of system components and aim to illustrate
sources of stress and the ecological responses of some attribute(s) of interest. These models
are based on known or hypothesised ecological relationships, often derived from control
models (Gross 2003).
The two types of models are distinguished from each other because they have different
goals. Control models portray the most complete and accurate picture of the ecosystem
components and interactions whereas stressor models illustrate direct linkages between
stressors, ecological responses and ecological endpoints.
A key goal of this project was to support the IESC in providing advice on water-related
ecological responses to coal seam gas extraction and coal mining (Chapter 1). This goal led
to an important modification of the approach described by Gross (2003). As the intention was
to portray potential ecological responses to coal seam gas extraction and coal mining in
landscapes that are often already modified by other human activities, control and stressor
models had to be combined so that the ‘control’ model included natural and anthropogenic
drivers and stressors not related to coal seam gas extraction and coal mining. This model
represents the state before extraction and mining. The ‘stressor’ model incorporates the
hypothesised drivers and stressors associated with coal seam gas extraction and coal
mining, and the resulting potential ecological responses. Comparing the ecological
conceptual models of the ‘before’ and ‘after’ states illustrates hypothesised ecological
responses to coal seam gas extraction and coal mining at a given spatial and temporal scale.
The two types of models and the approach to conceptual modelling described by Gross
(2003) were adopted for this project because this method is currently used by many other
major Australian programs in natural resource management (e.g. Ramsar site ecological
descriptions [Butcher & Hale 2010]) and has underpinned the management of national parks
in the US for over a decade (Gross 2003; Jean et al. 2005).
Another advantage of this approach arises when trying to weigh the benefits and
environmental costs of allowing a development to proceed (i.e. setting the two types of
conceptual models in the context of society’s values). One way of representing these values
is to consider them in terms of ‘ecosystem services’. Ecosystem services are the benefits
that people derive from the components and processes of natural ecosystems (Millennium
Ecosystem Assessment 2005), including pollination of crops, water filtration in river beds,
and atmospheric oxygenation by plants. All ecosystem management, including the
management of water-related ecological responses to coal seam gas extraction and coal
mining, should explicitly address ecosystem services as well as intrinsic values such as
biodiversity (Dudgeon 2014).
In a recent paper, Keble et al. (2013) argue that ecological conceptual models should
explicitly identify relevant ecosystem services. This approach would help shift the perspective
from a narrow one, looking at the impacts of single issues and often short-term economic
gains, to a broader one that considers longer-term social benefits by optimising provision and
protection of diverse ecosystem services. These authors describe the application of this
ecosystem-service perspective to ecological conceptual modelling of the Florida Keys and
Dry Tortugas ecosystem. This approach may be relevant for application of ecological
conceptual models to coal seam gas extraction and coal mining. Although this report
describes the components and processes in the conceptual models in ecological terms,
many of these could also be communicated as ecosystem services.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
2.3 Addressing issues of scale and uncertainty in ecological
conceptual models
Two issues deserve special discussion because they influence all conceptual models and
their development and interpretation. The first of these is scale: how to choose appropriate
scales in space and time, how to represent temporal changes (or differences in time scales)
on a two-dimensional spatial conceptual model, and how to integrate models representing
ecosystems and their components and processes at different scales.
In developing ecological conceptual models, choice of scale is dictated by the goals of the
model and the bounds of the system (Gross 2003). For example, if a model aims to
represent the ecological processes that influence the persistence of a species population, the
spatial and temporal scales at which these processes operate are relevant. Drivers and
processes affecting the species population operate at different spatial and temporal scales.
Further, a particular driver or process will often occur at multiple spatial and temporal scales,
with its influence varying accordingly. For example, drivers such as climate and landform
may operate at the landscape scale down to the scale of microhabitats. Although the spatial
bounds might be specified in an EIS as the mine site and an area of groundwater drawdown,
it is likely that stressors and processes (e.g. species dispersal or recruitment) affecting the
relevant ecological endpoints operate at broader landscape scales.
This wide range of spatial and temporal scales means it is unlikely that a single conceptual
model could ever capture their full span, obliging the modeller to decide on one or more
scales of space and time that best represent the main ecological pathways and responses in
the context of the goal of the model and the bounds of the system being examined.
Potentially, two models could be developed: a broad-scale one (landscape to catchment) that
includes longer-term processes (decades to centuries) and a series of nested ones at finer
spatial and temporal scales that focus on particular locations (e.g. a spring complex, riparian
zone or river reach) at seasonal to annual scales. As an example of this approach, Ogden et
al. (2005) present a ‘total system’ ecological conceptual model of the Everglades,
supplemented by a series of ‘regional’ conceptual models such as that of the southern marl
prairies (Davis et al. 2005) within the Everglades. In this approach, the accompanying
narratives describing each ecological interaction (Chapter 3) are crucial because they specify
the spatial and temporal scales of effect and response.
Two-dimensional pictorial models are good for showing static, spatial arrangements of
ecosystem components but are unable to effectively illustrate temporal trends in a simple
way. One solution is to generate several pictorial models to represent the system at different
times (e.g. wet season compared with dry season; immediately after an impact compared
with a decade later). Another solution might be to supplement the two-dimensional
conceptual models with accompanying plots of expected changes in the state of a variable
over time. A third, better suited for computer presentations, could employ animations to show
changes over time. Incorporation of multiple spatial and temporal scales in two-dimensional
ecological conceptual models should complement the spatial and temporal scales of
hydrological and hydrogeological conceptual models currently presented in many EISs.
Consideration should also be given to integrating models describing ecosystems and their
components and processes at different scales so that they capture the interactions among
these scales. One challenge is matching hydrological and hydrogeological conceptual
models, which are usually presented at the regional or landscape scale, with ecological
models where some of the processes may be operating at much finer scales (e.g. fish
feeding on macroinvertebrates in a river pool). Another challenge is adequately representing
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
the effects of stressors that operate at multiple interacting scales. For example, alteration of
flow regime by adding co-produced water to a usually dry streambed may have particle-scale
effects on biofilm dynamics, reach-scale effects on algal productivity and aquatic invertebrate
population sizes, and catchment-scale influences on channel shape and form, potentially
assisting dispersal of invasive fishes. All of these effects potentially interact.
The second issue to consider is uncertainty: its definition, sources in ecological modelling
and implications for deriving hypotheses from ecological conceptual models. Uncertainty is
defined following the Standards Australia and New Zealand Risk Management Guidelines
(AS/NZS ISO 31000:2009) as:
‘…the state, even partial, of deficiency of information related to understanding or
knowledge of an event, its consequence, or likelihood’.
This definition was chosen because it accords with the risk-based assessment approach
endorsed by the IESC and is adopted in the bioregional assessment methodology (Barrett et
al. 2013).
Estimates of causes and relative magnitudes of uncertainty are especially important because
the bioregional assessments include risk analyses (Component 4, Barrett et al. 2013). These
risk analyses combine information from the BA’s risk register (prepared for each bioregion)
with the likelihood of an event occurring and an understanding of the uncertainties
associated with the impacts. Ecological conceptual models can inform this process by:
1. portraying the predicted pathways of ecological effects and responses resulting from
particular events
2. indicating the degree of uncertainty associated with these predictions, as explained in
more detail below.
Inevitably, every modelling effort is plagued by uncertainty (Tartakovsky 2013). In ecological
conceptual models, uncertainty has multiple causes ranging from poorly understood
interactions of nonlinear responses that generate ‘ecological surprises’ (Gordon et al. 2008)
through to the unknown effects of different scales of impact and response, the often limited
availability of data, and inherent uncertainty surrounding all assumptions underpinning all
modelling approaches (Lindenmayer & Likens 2010; Westgate et al. 2013). Panels of experts
are often used when ecological conceptual models are being developed and potentially
introduce further uncertainty as motivational and/or cognitive bias in their input; a rich
literature describes these issues and approaches to address them (reviewed in Krueger et al.
2012).
Therefore, every prediction from a model must involve rigorous uncertainty quantification
(Tartakovsky 2013). This process involves estimates of the effects of structural uncertainty
(uncertainty about the validity of a particular model) and parametric uncertainty (uncertainty
about the parameters and driving forces in a model). These two sources are sometimes
termed epistemic uncertainty because they can be reduced by collecting more data in
contrast to irreducible uncertainty, which arises from ‘inherently random phenomena’
(Tartakovsky 2013), exemplified by uncertainty resulting from the interactions of many
ecological processes. In the current project, both sources of uncertainty are relevant and, in
the absence of further data, there is heavy reliance on expert input and robust ecological
conceptual models that record the supporting science and specify the sources and relative
magnitudes of uncertainty.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Irreducible uncertainty is especially prevalent in ecological modelling, and like that
encountered in efforts to predict the effects of future climate change (IPCC 2013), arises
from uncertainty about starting conditions, response pathways and model approximations.
The IPCC expressed this uncertainty in a qualitative manner, based on the extent of
agreement between evidence from different sources (low, medium and high) and the quality
and consistency of this evidence (limited, medium and robust). Combining the agreement
and quality of the evidence resulted in five grades of confidence (used here as a partial
surrogate for uncertainty):
1. Very low: low agreement, limited evidence
2. Low: low agreement, medium evidence; medium agreement, limited evidence
3. Medium: low agreement, robust evidence; medium agreement, medium evidence; high
agreement, limited evidence
4. High: high agreement, medium evidence; medium agreement, robust evidence
5. Very high: high agreement, robust evidence.
A similar approach to that of the IPCC (2013) could be used to qualitatively estimate
irreducible uncertainty in ecological models, accepting that experts will differ in their
judgements within these categories of agreement and evidence quality. An example of this
application is illustrated in a narrative table accompanying an ecological conceptual model
for silver perch (Appendix A).
2.4 A framework for assessing vulnerability coal seam gas
extraction and coal mining activities
Several frameworks have been proposed for assessing vulnerability of species to climate
change, especially where uncertainty is high about what species, habitats and ecosystem are
most vulnerable, what aspects of species’ ecological and evolutionary biology determine their
vulnerability, and how this information can be used to minimise the potential impacts. The
framework by Williams et al. (2008) is especially appealing because it integrates insights
from the disciplines of ecology, physiology and genetics into assessing which ecological
traits dictate vulnerability of a given species or group of taxa.
Vulnerability, defined as the susceptibility of a system to a negative impact (Smith et al.
2000), is the outcome of the extrinsic factors that determine exposure to a stressor and the
intrinsic factors that govern sensitivity to it (i.e. ecological traits). Williams et al. (2008) portray
exposure at two scales in their framework (regional and microhabitat; orange boxes in Figure
2.1), and then go on to show how these features of exposure interact with changes in habitat
(induced by external drivers) and ecology (e.g. habitat use; pale yellow box in Figure 2.1) as
one component of vulnerability. The other component, species sensitivity, arises from
adaptive capacity and resilience (bright yellow boxes in Figure 2.1) and resistance that, in
turn, arise from aspects of the species’ ecology, physiology and genetics.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Source: Williams et al. (2008). See main text for details.
Figure 2.1 An integrated framework to assess the vulnerability of species to climate change
Once an estimate of vulnerability is derived and appropriate pathways of exposure and
ecological response portrayed in the ecological conceptual models, management strategies
can be recommended that would reduce or remove actual or potential impacts of coal seam
gas extraction and coal mining. The framework by Williams et al. (2008) also includes
feedbacks (blue box in Figure 2.1) whereby changes in ecological interactions and
ecosystem processes caused by existing anthropogenic stressors potentially feed back into
the knowledge of species’ ecology, physiology and genetics.
Walker (2010) modified this framework in a project assessing vulnerability of species in the
South Australian River Murray corridor to climate change. He grouped and simplified some of
the features of the model by Williams et al. (2008) and used this framework to identify
ecological, physiological and genetic traits that an expert panel could consider to address 12
propositions (hypotheses) about the extent to which the regional population of a given
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
species might tolerate climate change. Degree of impact was presented on a qualitative
scale (minor, moderate or major, with a fourth option of ‘unknown’) and colour-coded in
‘RAG’ format (
Source: Walker (2010).
Figure 2.2). Thus, for the 12 propositions for 10 very different species in the study area, a
range of sensitivities could be portrayed, and summed for an overall indication of sensitivity (
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Source: Walker (2010).
Figure 2.2), which also could be adjusted for null assessments.
Source: Walker (2010).
Figure 2.2 Sensitivity assessment
Provisional assessments of sensitivity for 10 selected species of flora and fauna from the
River Murray study area (Table 4.3 in Walker 2010). In response to the question “To what
extent does this trait constrain the ability of the regional population of this species to
withstand exposure to climate change?”, experts’ responses (null: unknown,
1: minor, 2: moderate, 3: major) have been colour coded in “RAG” format for easy reference
(Red 3, Amber 2, Green 1, null blank). Initial outcomes (the numbers within each category)
are shown in the three right-hand columns, and imply the hardyhead is most sensitive and
the yabbie is the least.
This framework and sensitivity-scoring approach may be useful in assessing water-related
ecological responses by various species to coal seam gas extraction and coal mining
development, although Walker (2010) warns that choice of the traits and wording of the
propositions must be careful. Perhaps this approach would be most useful where a number
of species are to be considered in the EIS for a given area and some effort is being made to
determine which ones are most vulnerable and therefore deserve most attention. It will also
reveal where information is lacking as well as where groups of species may share parallel
responses and, hence, some redundancy in selection of species to model in more detail.
A further example of ecological conceptual modelling is an examination of impacts of
hydraulic fracturing on eastern brook trout (Salvelinus fontinalis) in the Marcellus Shale
region of the eastern US (Figure 2.3). The approach used was a causal conceptual model,
wherein life-cycle components of the trout were used as the endpoints. This ecological
conceptual model portrays how different stages of the life cycle of the trout vary in their
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
vulnerability to different stressors associated with hydraulic fracturing, and emphasises the
complexity of assigning vulnerabilities at the species level when multiple life stages are
involved. Unfortunately, this information is seldom available for species that are likely to be
affected, especially for their juvenile stages (e.g. seedlings, larvae) which tend to be the most
sensitive to most stressors.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Conceptual model of relationships between drilling and hydraulic fracturing activities and the life cycle of eastern brook trout (Salvelinus fontinalis). UIC = Underground Injection
Control; TDS = total dissolved solids. Source: Weltman-Fahs and Taylor (2013).
Figure 2.3 Conceptual model for brook trout
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
3 Project methodology
3.1 Overview
Activities associated with coal seam gas extraction and coal mining occur or are predicted in
environments ranging from arid and semi-arid inland areas to temperate or subtropical
regions near the coast (hence, the range of bioregions described in section 1.1). These
environments have diverse geology, soils, topography, hydrogeology, surface drainage, land
use, vegetation cover, and communities of plants and animals. To identify water-related
ecological impacts arising from coal seam gas extraction and coal mining in these different
areas, ecological conceptual models are needed that include the appropriate drivers,
stressors, components and processes for the linked terrestrial and aquatic ecosystems in
each area.
This section describes seven steps (Figure 3.1) in developing an ecological conceptual
model, which were followed during the expert workshop. These steps may be a useful
sequence for similar models when preparing an EIS. The first two steps in the process are to
agree on the goals of the conceptual models and specify the bounds of the system of interest
(Figure 3.1). These are related issues because the goals dictate the selection of the bounds
(spatial and temporal scales) of the conceptual model (discussed in section 2.3).
Figure 3.1 Flow-chart of ecological conceptual model development
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
These steps in ecological conceptual model development were followed in this project
(following recommendations by Gross (2003) with modifications described in section 2.2).
Although the final output is the full model including stressors associated with coal seam gas
extraction and coal mining, comparison with the control model excluding these (created in
Step 4) helps to predict ecological effects of coal seam gas extraction and coal mining.
Feedback loops exist among most steps in this process because successive refinements of
the conceptual models will occur as more information becomes available. The resulting
conceptual models can then be further analysed using approaches such as BNs.
The third step (Figure 3.1) is to identify the drivers, stressors, ecological effects and likely
ecological endpoints. A logical starting place is to compile a table of likely drivers and
stressors associated with natural and anthropogenic perturbations, the latter both related and
unrelated to coal seam gas and coal extraction. Although attention focuses on coal seam gas
extraction and coal mining, their ecological effects have to be predicted in the context of
natural and pre-existing anthropogenic factors as well. This table can then be used as a
checklist to ensure that region-specific conceptual models include all the principal drivers and
stressors. Later, this table can be extended as a ‘narrative table’ to include explicit reference
to relevant literature on water-related ecological responses to these different stressors and
drivers, especially where region-specific information exists (e.g. that from BAs or new
research in the area), and is expanded in the fourth step when the control model is
constructed.
The fourth step is drawing up a control model to portray the main interactions among relevant
ecosystem components in a given area at a given temporal scale. Only the main interactions
and components should be selected so that the control model is tractable (Gross 2003); it is
too cumbersome to show every possible interaction and component. The result is a pictorial
representation of the main drivers, stressors, processes, components and interactions
(including feedbacks) of the linked terrestrial and aquatic ecosystems in that area, except
drivers, stressors and responses associated with coal seam gas extraction and coal mining.
Typically, this pictorial representation is either a ‘box-and-arrow’ diagram (also termed an
‘influence diagram’, see Appendix B) or an illustration that represents the landscape using
cross-sectional diagrams and icons. On the ‘box-and-arrow’ diagram, the boxes represent
drivers, stressors and ecosystem components and the arrows portray pathways of influence.
The ‘box-and-arrow’ diagram is unable to convey information about, for example, the relative
locations of stressors and water-related assets at a given site. In contrast, the landscape
illustration is able to show geographic proximity of stressors and assets, and uses icons to
represent drivers, stressors and ecosystem components.
Although arrows can be included in a landscape illustration to show movements of water,
materials such as sediments or nutrients, and biota, it is seldom possible to portray the
pathways of ecological responses to one or more stressors as clearly as on the box-andarrow diagram. Further, the box-and-arrow diagram is a more useful starting point for BNs
than the landscape diagrams (section 4.2). As each graphic has its own advantages and
strengths in illustrating different aspects of the ecological responses, both are often
presented. These diagrams are supplemented by a matching narrative table (often presented
as a legend at the bottom of the landscape figure) that states the hypothesised or known
ecological responses to a given stressor. Where possible, relevant scientific and other
credible literature is cited in support of each hypothesis or statement.
It will seldom be possible to present adequate detail for all the components in a single model
(Ogden et al. 2005). Therefore, nested within this general control model are likely to be
submodels dealing with specific ecosystems (e.g. springs, riparian zones), whose linkages
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
are shown in the broader-scale general model. The general control model and the specific
control submodels nested within it serve to illustrate broad assumptions about how drivers
unrelated to coal seam gas extraction and coal mining influence ecosystem components,
processes and interactions. Where possible, these control models would indicate the
magnitude and direction of the effects.
The fifth step is to identify anthropogenic stressors associated with coal seam gas extraction
and coal mining (Figure 3.1). Again, this step should be accompanied by a detailed narrative,
with reference to relevant literature, that describes the stressors and likely spatial and
temporal scales. Linking this stressor model (and narrative) to the control model developed in
Step 4 is the sixth step and yields the final conceptual model of hypothesised water-related
ecological responses to coal seam gas extraction and coal mining. This conceptual model
and its associated narrative table(s) are used in Step 7 to predict the likely ecological
outcomes of interacting stressors (Figure 3.1).
The combination of these two models, along with their associated narratives, now allows the
generation of hypotheses about likely water-related ecological responses to different
scenarios of coal seam gas extraction and coal mining in a given biophysical region. Of
course, these hypotheses will include varying degrees of uncertainty (section 2.3) because
ecosystems are dynamic and ecosystem components usually interact in nonlinear ways and
change over time.
3.2 Control and stressor models
One of the main goals of this project was to trial the process of constructing one or more
ecological conceptual models, following the steps described in section 3.1. The intention was
a ‘proof-of-concept’ to ascertain whether it was feasible to produce useful control and
stressor models based on information from sources that would be accessible to proponents
seeking to assess the likely water-related ecological responses to coal seam gas extraction
and coal mining at a given site. This information would include relevant details from the
bioregional assessments (Chapter 1) as well as the site- and region-specific information
identified by the IESC (2014) Information Guidelines:

geological information, including names and descriptions of formations with
accompanying data on surface and subsurface geology (e.g. as cross-sectional
diagrams) and information on structures (e.g. faults, strata of high hydraulic conductivity)
that may affect movement and connectivity of water, especially flow, recharge and
discharge of groundwater

hydrogeological information on hydraulic features (e.g. hydraulic conductivity and
storage characteristics) of each hydrogeological unit, the varying depths to these units
(including standing water levels or potentiometric heads) and their hydrochemical
features, and the likely recharge and discharge pathways and volumes for each unit,
especially those likely to be affected by the proposed development

geomorphological information (e.g. drainage patterns, channel features, floodplain
development) matched with relevant information on the hydrological regime
(e.g. temporal trends in stream flow and/or water levels, flood regimes and areas
inundated at a range of flows exceeding bankfull discharge), sediment regime
(e.g. turbidity and sources of sediment production and deposition), and geochemical
features and processes that would affect water quality (e.g. alkalinity, salinity, ionic
page 20
Modelling water-related ecological responses to coal seam gas extraction and coal mining
composition, and concentrations of organic chemicals, radionuclides and other
potentially harmful materials)

hydrological information not covered above, including timing, volumes and directions of
surface water-groundwater exchanges, connectivity among aquifers, and connectivity
with sea water

information on the water resources of the site and surrounding region, including aspects
of the water balance (e.g. seasonal and annual variations in precipitation,
evapotranspiration, surface water permanence and exchange with groundwater) and
other relevant hydrological and hydrogeological data, including water quality for surface
and groundwater (e.g. alkalinity, salinity, ionic composition, and concentrations of
organic chemicals, radionuclides and other potentially harmful materials)

information on the water-related assets (e.g. surface waters, springs and other
groundwater dependent ecosystems) of the site and surrounding region, including data
from surveys of relevant habitats and their biota, especially details of their reliance on
surface water and groundwater resources, and the associated ecological processes

information about the natural and pre-existing anthropogenic drivers and stressors at the
site and in the surrounding region (to be used for the control model) and about the
drivers and stressors likely to be associated with coal seam gas extraction and coal
mining (to be used for the stressor model).
Obviously, the amount and quality of this information largely dictate the level of detail that
can be provided by the resulting ecological conceptual models and, in turn, their
appropriateness for judging the likely water-related responses to coal seam gas extraction
and coal mining at a particular site. It is also preferable to use relevant scientific and other
credible literature to support the assumptions made when attributing various ecological
responses to the drivers and stressors presented in the ecological conceptual model. Finally,
it is likely that there will be one or more field surveys, conducted according to relevant
protocols, to gather site-specific data on environmental conditions and the biota, and to
inspect the water resources and their catchments or recharge zones to infer likely influences
from natural and pre-existing anthropogenic drivers and stressors on relevant ecosystem
components and processes. All of these activities generate information that can be used in
the construction of the control and stressor versions of the ecological conceptual models and
their accompanying narratives for the site and surrounding region.
A hypothetical case study from the Clarence-Moreton bioregion was used to determine the
feasibility of constructing several ecological conceptual models at varying spatial scales with
sources of information described above that were readily available. A field site was visited
during the expert workshop (section 3.3) but without ecological sampling. The primary goal of
this part of the project was a ‘proof-of-concept’ to derive some ecological conceptual models
in as complete a form as possible and to discuss the ‘lessons learned’ during the process. In
an ecological assessment as part of EIS, ecological conceptual models must be informed by
site-specific data collected at appropriate temporal and spatial scales.
As a case study in developing a species-specific ecological conceptual model, the
EPBC-listed silver perch (Bidyanus bidyanus) in a section of the Mooki River (Gunnedah
Basin) was chosen (Appendix A). A desktop survey of relevant literature was used to
generate a narrative table that listed hypotheses about inferred ecological responses by
silver perch to various stressors. For each hypothesis, the table also presented qualitative
estimates of evidence, agreement and confidence (following the IPCC 2013 approach
page 21
Modelling water-related ecological responses to coal seam gas extraction and coal mining
described in section 2.3) as a surrogate means of expressing uncertainty. The narrative table
was used to help generate an influence diagram portraying the main natural and
anthropogenic drivers and stressors (and their interactions) likely to affect the persistence of
silver perch populations in a section of the Mooki River. The validity of the table and
hypotheses was subsequently confirmed by an independent expert to test whether reliable
narrative tables could be derived from desktop surveys of the literature.
3.3 Expert workshop assessment of some worked examples
of ecological conceptual models
One major aim of this project was to trial the development of ecological conceptual models at
varying spatial scales and to supplement the current hydrogeological conceptual models,
using the sources of information described in section 3.2 that were readily available. To do
this in as realistic a way as possible, Auricht Projects was commissioned to generate several
ecological conceptual models to represent hypothesised ecological responses to plausible
coal mining scenarios in the Clarence-Moreton Basin as a case study.
The case study focused on the potential ecological responses to coal mining of the Swamp
Tea-tree (Melaleuca irbyana) population in Purga Nature Reserve in the Bremer River
catchment, south-east Queensland. This species was chosen because “Swamp Tea-tree
(Melaleuca irbyana) Forest of South-east Queensland” is listed as a Critically Endangered
Ecological Community under the Environment Protection and Biodiversity Conservation Act
1999 (Commonwealth) and as an Endangered Regional Ecosystem under the Vegetation
Management Act 1999 (Queensland). The case-study site was appropriate because coal
mining has occurred in the area, the Swamp Tea-Tree Forest is of conservation interest,
there was very little data on the region and the species of interest (i.e. a realistic situation),
and the field visit observations helped generate the influence diagram used for exploring the
potential for applying Bayesian modelling (section 4.2).
Two ecological conceptual models illustrated the likely effects of coal mining on the Swamp
Tea-tree Forest at two phases of its water regime: the ‘wet phase’ when the wetland is
inundated and aquatic processes would be expected to be at their peak, and the ‘dry phase’
when surface water is absent. A third ecological conceptual model drawn at the landscape
scale revealed the geographic context of the Purga Nature Reserve in the Bremer River
catchment.
The next step was to validate the veracity and usefulness of these ecological conceptual
models in representing the likely water-related ecological responses to potential coal seam
gas extraction and coal mining. This was done during a three-day workshop with scientists
with expertise across hydrology, hydrogeology, biogeochemistry, freshwater ecology,
groundwater dependent ecosystems and water resource management. Expert advice and
information was sought about:

the suitability of the conceptual framework of the project, such as the use of the control
and modified stressor models (section 3.2)

current understanding of hydrology-ecology relationships at several spatial and temporal
scales for selected taxa or communities in habitats likely to be affected by coal seam gas
extraction and coal mining

the scientific accuracy and usefulness of the conceptual ecological models

the appropriateness of applying BNs (Appendix B) to supplement the use of the
conceptual ecological models.
page 22
Modelling water-related ecological responses to coal seam gas extraction and coal mining
The workshop agenda is given in Appendix C, brief biographies of participants in Appendix
D, and abstracts of presentations in Appendix E. Details of the case study area (Purga
Nature Reserve) are given in Appendix F. Discussion in the field presented by experts with
different backgrounds generated valuable insights into the types of information needed when
compiling conceptual ecological models. These insights, rather than determining specific
features of Purga Nature Reserve, were the focus of this exercise.
Attention in the workshop focused on the Purga Nature Reserve case study and the trial
application of the BN. The veracity and usefulness of the conceptual ecological model for the
silver perch example from the Gunnedah Basin was also assessed at the workshop and,
later, by an independent expert (Dr Keith Walker) familiar with the relevant ecological
literature on this species. The conceptual models and accompanying text were also reviewed
by four technical advisors with expertise in hydrogeology (Dr Martin Andersen), aquatic
ecology (Dr Bruce Chessman), plant ecology (Prof. Ray Froend) and landscape ecology
(Dr Alexander Herr), and by a theoretical and applied ecologist (Dr Jennifer Firn) with a
research interest in M. irbyana, Dr Anthony O’ Grady (ecology lead, BAs) and
Prof. Angela Arthington, the IESC ecologist.
page 23
Modelling water-related ecological responses to coal seam gas extraction and coal mining
4 Results: case study and worked
examples
4.1 Ecological conceptual models for Purga Nature Reserve
As the intention of this project was to provide a ‘proof-of-concept’ of the process for
developing ecological conceptual models by following the seven steps in Figure 3.1, the
results for each step are presented in sequence. The primary goal of the ecological
conceptual model for this case study (Step 1) was to portray the main drivers, stressors and
pathways of likely water-related ecological effects of coal mining affecting the principal
ecosystem components that support the persistence of the Swamp Tea-tree Melaleuca
irbyana population in the Purga Nature Reserve. The horizontal bounds in space of the
ecological conceptual model (Step 2) were set as the episodically filled basin and fringing
margins of the wetland within the 140-hectare Purga Nature Reserve (Figure 4.1).
A field visit to the site indicated that the basin of this wetland lacks any distinct edge defined
by either geomorphology (e.g. bank or sediment strand-line) or bordering semi-aquatic
vegetation (e.g. a fringe of reeds or rushes). This is common for many shallow seasonally
filled wetlands but does not prevent development of ecological conceptual models. The
aquatic-terrestrial transition zone is an important ecological component and must be included
in all models of aquatic ecosystems. Although the wetland may be perched above the
regional water table, the swamp tea-trees were hypothesised to access groundwater
occasionally, so the vertical spatial bound of the ecological conceptual model was set to
encompass the likely annual range of groundwater fluctuation below the wetland and its
fringing vegetation.
Dotted line encloses approximate bounds of target population of Swamp Tea-tree. Data sources: World_Imagery
- Source: Esri, DigitalGlobe, GeoEye, i-cubed, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AEX,
Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community.
Figure 4.1 Location of Purga Nature Reserve
page 24
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Natural and anthropogenic drivers and stressors not associated with coal mining were
identified (Step 3) and are listed in the first two columns of Table 4.3. This step also entailed
listing the key ecosystem components that could have a bearing on the persistence of the
Melaleuca irbyana population, the ecological endpoint. These ecosystem components are
presented in the third column of Table 4.31, along with their hypothesised ecological effects
(Step 4). Information to support these hypotheses was derived from published and ‘grey’
literature (Table 4.3), discussion among experts during the field site visit and scientific advice
from Prof. Ray Froend and Dr Jennifer Firn.
Step 4 also entailed drawing up a box-and-arrow diagram (Figure 4.2) to illustrate the
ecological interactions among the relevant ecosystem components in the wetland and their
hypothesised ecological responses to the drivers and stressors presented in Table 4.3. This
diagram was used as the basis for the two control models illustrating the ‘inundated’ and ‘dry’
phases of the seasonal wetland (upper panels of Error! Reference source not found. and
Error! Reference source not found.). A conceptual model developed by the Queensland
Department of Environment and Heritage Protection (DEHP) for coastal and subcoastal
floodplain tree swamps with Melaleuca and Eucalyptus species (Figure 4.5) was also drawn
upon to assist the conceptualisation of the components and processes of Purga Nature
Reserve. This model has been verified and reviewed by experts, and therefore is a robust
starting point. It is not, however, specific to a site or species (i.e. Melaleuca irbyana), and so
not all information is directly transferable (refer to section 2.3).
The main natural drivers affecting the persistence of the Melaleuca irbyana population at the
study site are hypothesised to be climatic, hydrological and hydrogeological,
geomorphological (landform) and geological ones affecting stressors such as fire regime,
water and nutrient availability, and soil pH and salinity (
page 25
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Table 4.1, Error! Reference source not found. to Figure 4.5). Anthropogenic drivers and
stressors not associated with coal mining include the effects of historical and current land
clearance, weed invasion and grazing by non-native animals (
page 26
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Table 4.1, Figure 4.2 to Error! Reference source not found.).
page 27
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Table 4.1 Narrative table to accompany the control model
This table lists the natural and anthropogenic drivers and stressors (excluding those associated with coal mining)
and their hypothesised ecological effects on the persistence of the Melaleuca irbyana (MI) population in Purga
Nature Reserve. Numbers in the third column correspond to arrows in the box-and-arrow diagram (Figure 4.2).
Climate change, although an important stressor, is not included in this table or in the conceptual ecological
models. References relevant to each hypothesis are included (where available), along with qualitative estimates
of evidence, agreement and confidence (following the IPCC 2013 approach presented in section 2.3).
E = evidence, A = agreement, C = confidence.
Driver
Stressor
Hypothesised ecological effects on
the persistence of the
Melaleuca irbyana (MI) population
References
Climate
Maximum air
temperature
1. Sustained high air temperatures
probably stress adult MI and kill
seedlings.
Dept. of the
Environment
(2014)
Fire frequency
and intensity
2. MI plants are likely killed by frequent
burning and/or very hot fires (and
recruitment is probably especially
vulnerable because very frequent fire
inhibits regeneration).
Dept. of the
Environment
(2014)
1
1
1
Low humidity
and high
evaporation
(including
wind)
3. MI populations cannot persist if
evaporative losses (accelerated by low
humidity, high air temperatures and
warm winds) are too high for too long
(exact limits unknown but seedlings
are likely to be high vulnerable).
Logan City
Council (n.d.)
1
1
1
Amount and
timing of
annual rainfall
4. MI population persistence probably
requires a ‘window’ of inundation that
occurs at the right time of the year and
is long enough to supply the species’
needs but not so long that it kills MI
plants or enables competitors to
succeed.
DEHP (2013)
1
1
1
Exposure to
solar radiation
5. MI, especially seedlings, are likely to
be harmed by excessive exposure to
solar radiation (e.g. edge effects of
fragmentation; loss of overstorey
shading).
Logan City
Council (n.d.)
1
1
1
Soil fertility
6. MI plants tolerate low-nutrient soils,
potentially giving them a competitive
advantage over other species.
Dept. of the
Environment
(2005)
1
1
1
Soil pH
7. MI plants grow on vertosols that are
alkaline, potentially giving them a
competitive advantage over other
species.
Dr Jenn Firn,
pers. comm.
1
Soil salinity
8. It is likely that excessive and/or
sustained soil salinity impairs the
species’ population persistence
(although most Melaleuca species are
quite salt-tolerant).
DEHP (2013)
1
Landform
and geology
page 28
E
A
C
1
1
1
1
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Driver
Hydrology
Agriculture
bordering
the Nature
Reserve
Stressor
Hypothesised ecological effects on
the persistence of the
Melaleuca irbyana (MI) population
References
E
A
C
Soil features
(e.g. heavy,
grey, cracking
clays)
9. MI plants require seasonally
cracking, grey clay soils that are
heavy, coarse, and poorly drained,
which likely gives them a competitive
advantage over many other species.
Dept. of the
Environment
(2005)
1
1
1
Cracking
characteristics
10. The cracking characteristics of the
soils alter microtopography, may
provide microclimates for germination
of MI, and also trap organic matter and
other nutrients that support the plants’
growth.
1
Topography
11. Basin shape, drainage and
microtopography likely create
important microclimates for
germination and persistence of the MI
population, especially in terms of water
regime and inundation.
1
Groundwater
quality
(including
salinity)
12. As MI plants are thought to have a
deep root system and may have ‘some
reliance on groundwater supplies’
(Logan fact sheet), poor groundwater
quality (including high salinity) may
impair MI population persistence.
Logan City
Council (n.d.)
1
1
1
Seasonal
water table
fluctuations
13. As MI plants are thought to have a
deep root system and may have ‘some
reliance on groundwater supplies’
(Logan fact sheet), excessive or
sustained groundwater drawdown may
impair the species’ population
persistence.
Logan City
Council (n.d.)
1
1
1
Flow regime of
Purga Creek
14. Assuming overbank flows from
Purga Creek are relevant to the
wetland and the MI forest, altered flow
regimes may change inundation
patterns, impairing MI population
persistence.
1
Extraction of
groundwater
15. Groundwater extraction for
agricultural use may lower the water
table, reducing access by MI plant
roots and impairing the species’
population persistence.
1
page 29
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Driver
Historical
and current
land
clearance
Stressor
Hypothesised ecological effects on
the persistence of the
Melaleuca irbyana (MI) population
References
Extraction of
surface water
16. Surface water extraction from the
Bremer River and Purga Creek for
agricultural use may reduce the very
occasional flooding from the river into
the wetland, altering the inundation
regime and impairing MI population
persistence.
1
Increased
contamination
risk from
chemicals
17. Agricultural chemicals carried in
runoff are likely to impair MI population
persistence.
1
Increased
edge effects
from land
clearance,
tracks, fence
lines, etc. for
agriculture.
18. Land clearance for agriculture
around the Nature Reserve causes
edge effects:
Altered rates
of
sedimentation
19. Sedimentation from agricultural
runoff may smother seedlings,
impairing MI population persistence.
Increased risk
of agricultural
weed invasion
20. Exotic pasture grasses and other
weeds are likely to invade from
agricultural areas, restricting
germination and competing with MI
seedlings for resources such as water,
nutrients and space (also may alter
fuel loads, affecting local fire regimes).
Dept. of the
Environment
(2005)
1
1
1
Fragmentation
by land
clearance
(including for
fire breaks and
tracks)
21. Fragmentation of populations
causes edge effects (see below) and
leads to loss of genetic diversity
because of disruption to natural gene
flow, impairing long-term population
persistence of MI.
Dept. of the
Environment
(2005)
1
1
1
Altered rates
of
sedimentation
22. Sedimentation may smother
seedlings and erosion may expose
roots, impairing MI population
persistence.
1
Removal of
native plant
cover
23. Clearing plant cover alters rainfall
interception and infiltration patterns,
affecting runoff and soil moisture,
impairing MI population persistence.
1
Logan City
Council (n.d.)
E
1
A
1
C
1
“MI communities are likely to be
negatively impacted by edge effects
such as weed invasion, increase in
wind and evaporation, and changes to
solar radiation and temperature
changes”.
page 30
1
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Driver
Tourism and
other human
activities in
Purga
Nature
Reserve
Stressor
Hypothesised ecological effects on
the persistence of the
Melaleuca irbyana (MI) population
Increased
salinity in saltprone areas
24. Salinity (‘secondary salinity’) in
salt-prone areas may rise through
evapoconcentration in seasonal
wetlands, impairing MI persistence.
1
Increased risk
of weed
invasion
25. Clearance increases the risk of
spread of invasive plants (weeds) that
compete with MI, especially seedlings.
1
Altered rates
of
sedimentation
26. Sedimentation from altered runoff
and cleared pathways and carparks
may smother seedlings, impairing MI
population persistence.
1
Tourist
pressure
27. Compaction by vehicles and
trampling likely alter local surface
water and rainfall runoff and infiltration
patterns, potentially impairing MI
population persistence.
1
Infrastructure
28. Construction and maintenance of
tourist facilities such as boardwalks,
carparks and pathways may fragment
MI populations, accentuate problems
associated with edge effects, and alter
runoff and infiltration patterns.
1
Illegal wood
collection
29. Removal of dead timber illegally
from the Nature Reserve reduces
stocks of organic matter and nutrients,
affecting natural decomposition
processes and altering carbon cycling
in a way that may impair MI population
persistence.
1
Illegal rubbish
disposal
30. Dumping of household or industrial
rubbish illegally in or near the Nature
Reserve may physically impair MI
seedling establishment and growth,
poison adult and young MI plants, and
alter runoff and infiltration patterns,
potentially impairing MI population
persistence.
1
Grazing by
non-native
animals
31. Non-native animals such as
rabbits, hares and other vertebrates
impair MI population persistence (and
probably recruitment) by grazing,
especially on new growth and
seedlings.
(e.g.
boardwalks,
pathways)
page 31
References
Dept. of the
Environment
(2005)
E
1
A
1
C
1
Modelling water-related ecological responses to coal seam gas extraction and coal mining
This diagram shows the hypothesised ecological effects on the persistence of the Swamp Tea-tree Melaleuca irbyana population in Purga
the natural and anthropogenic drivers and stressors (excluding coal mining) listed in
page 32
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Table 4.1. Numbered arrows refer to specific hypotheses in
page 33
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Table 4.1.
Figure 4.2 Box-and-arrow diagram of the control model for Melaleuca irbyana
page 34
Modelling water-related ecological responses to coal seam gas extraction and coal mining
The fifth step was to list the water-related stressors associated with a scenario of coal mining
near Purga Nature Reserve, and the results of this step are given in Table 4.2. As before, the
hypothesised effects of these stressors were also tabulated (together with relevant
references) and used to generate the stressor models shown in the lower panels of Error!
Reference source not found. and Error! Reference source not found.. Combining the
stressor and control models (Step 6), resulted in the box-and-arrow diagram in Figure 4.6,
ultimately used for the assessment of the BN approach. The principal stressors associated
with the scenario of coal mining near Purga Nature Reserve were hypothesised to be
alterations to overland flow, runoff and inundation regimes of the wetland, topographic
changes through subsidence, and weed invasion.
Table 4.2 Narrative table to accompany the stressor model
This table lists the drivers and stressors associated with coal mining (the case study scenario) and their
hypothesised water-related ecological effects on the persistence of the Melaleuca irbyana (MI) population in
Purga Nature Reserve. Numbers in the third column correspond to arrows in the box-and-arrow diagram (Figure
4.6). References relevant to each hypothesis are included, along with qualitative estimates of evidence,
agreement and confidence (following the IPCC 2013 approach presented in section 2.3). E = evidence,
A = agreement, C = confidence.
Driver
Stressor
Hypothesised water-related ecological
effects on the persistence of
Melaleuca irbyana (MI)
References
E
A
C
Coal
mining
Groundwater
drawdown
32. As MI plants appear to have a deep root
system and may have ‘some reliance on
groundwater supplies’ (Logan fact sheet),
excessive or sustained groundwater
drawdown may impair the species’
persistence.
Logan City
Council (n.d.)
1
1
1
Altered
ground water
quality
33. Changes in pH and concentrations of
nutrients and salt of subsurface water may
impair MI population persistence either
physiologically, by favouring competitors, or in
both ways.
1
Subsidenceinduced
topographic
change
34. Topographic changes caused by
subsidence may alter floodplain inundation
and/or overland flow, in turn altering the
‘window’ of inundation, either reducing it to
being insufficient to supply the species’ needs
or increasing it so that it kills MI plants or
enables competitors to succeed.
1
Altered
surface water
quality
35. Changes in pH and concentrations of
nutrients and salt of surface (and infiltrated)
water may impair MI population persistence
either physiologically, by favouring
competitors, or in both ways.
1
Altered
floodplain
inundation
and/or
overland flow
36. Increased or decreased floodplain
inundation and/or overland flow may alter the
‘window’ of inundation, either reducing it to
being insufficient to supply the species’ needs
or increasing it so that it kills MI plants or
page 35
DEHP (2013)
1
1
1
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Driver
Stressor
Hypothesised water-related ecological
effects on the persistence of
Melaleuca irbyana (MI)
References
E
A
C
DEHP (2013)
1
1
1
enables competitors to succeed.
Altered flow
regime in
Purga Creek
37. Changes in the flow regime of Purga
Creek that either increase or reduce river
water inputs to the wetland may alter the
‘window’ of inundation, either reducing it to
being insufficient to supply the species’ needs
or increasing it so that it kills MI plants or
enables competitors to succeed.
Altered rates
of
sedimentation
38. Increased sedimentation rates may
smother seedlings whereas erosion may
expose roots, impairing MI population
persistence.
1
Increased
spread of
exotic
species
39. Weed invasion, especially of pasture
grasses, may restrict germination and weeds
may compete with MI seedlings for resources
such as water, nutrients and space (also may
alter fuel loads, affecting local fire regimes).
1
40. Non-native animals such as rabbits, hares
and other vertebrates impair MI population
persistence (and probably recruitment) by
grazing, especially on new growth and
seedlings.
1
page 36
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Figure 4.3 Purga Nature Reserve (wet phase)
page 37
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Figure 4.4 Purga Nature Reserve (dry phase)
page 38
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Source WetlandInfo, Department of Environment and Heritage Protection, Queensland, Accessed 12th July 2014,
<http://wetlandinfo.ehp.qld.gov.au/wetlands/ecology/aquatic-ecosystems-natural/palustrine/floodplain-treeswamp/flora.html>. For explanation of symbols, see website.
Figure 4.5 Conceptual model of a coastal and subcoastal floodplain tree swamp (Melaleuca and
Eucalyptus spp.)
page 39
Modelling water-related ecological responses to coal seam gas extraction and coal mining
This diagram shows the combined control and stressor model showing the hypothesised water-related ecological effects on the persistence of
Melaleuca irbyana population in Purga Nature Reserve influenced by the drivers and stressors listed in
page 40
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Table 4.1 and Table 4.2. Numbered arrows refer to specific hypotheses in
page 41
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Table 4.1 and Table 4.2.
Figure 4.6 Box-and-arrow diagram of the stressor model for Melaleuca irbyana
page 42
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Figure 4.7 Landscape setting of Purga Nature Reserve
page 43
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Step 7, the final one, uses the narrative tables (
page 44
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Table 4.1 and Table 4.2) and the ecological conceptual models (Figure 4.2 to Error!
Reference source not found., Figure 4.6) to predict and compare likely ecological
responses to coal mining in the study area. In this example, the responses to stressors by
the Melaleuca irbyana population in the Purga Nature Reserve are likely to vary between the
seasonal wetting and drying phases. For example, a fire occurring during the inundated
phase will probably be less intense than one that occurs during the dry phase when there is
likely to be a large fuel load of dry organic matter, resulting in a hotter fire potentially intense
enough to kill Melaleuca irbyana seedlings and impair regeneration. Consequently, dryseason fires are likely to be more harmful to the persistence of the Melaleuca irbyana
population. Temporal factors such as these seasonal differences are important aspects to
include in ecological conceptual models, often requiring multiple-panel diagrams such as
Error! Reference source not found. to Figure 4.5.
Another important aspect of understanding the water-related ecological effects of natural and
anthropogenic drivers is knowledge about the landscape context of the Melaleuca irbyana
population in the Purga Nature Reserve. This information is not captured by the
box-and-arrow diagrams or the site-scale models. Figure 4.7 presents a catchment-scale
pictorial ecological conceptual model that portrays the geographic proximity of the various
natural and anthropogenic drivers and stressors potentially affecting Melaleuca irbyana
population persistence in the Purga Nature Reserve within the Bremer River sub-catchment.
This diagram shows how the wetland is likely to be more influenced by flooding in Purga
Creek than by the main stem of the Bremer River which receives inputs such as sediments
and excessive nutrients from abattoirs, irrigated pastures and croplands (Figure 4.7).
Therefore, water quality in the Bremer River is unlikely to be relevant to conditions in the
wetland or Melaleuca irbyana population persistence in Purga Nature Reserve.
4.2 Bayesian network session
This section describes the outcomes from the facilitated workshop session held in July 2014.
The purpose was to trial the development of a Bayesian network as a potential method for
use in EISs. Bayesian networks are causal networks with predictive capabilities that can be
used to explore knowledge gaps and potential stressors, their interactions, and their
strengths in influencing an endpoint. Note, all outcomes are hypothetical, with the purpose
being to demonstrate potential of the approach rather than developing a robust model.
Endpoint
The Bayesian network was focused on the Swamp Tea-tree (Melaleuca irbyana) population
at the Purga Nature Reserve. Two endpoints were identified:

persistence of the Melaleuca irbyana population (where persistence may be measured
by adult reproduction and seedling establishment and maturation)

composition of the overall vegetation community, representing potential for change to a
more terrestrial vegetation type.
The intended outcome was a model that can be used to better explore the role of hydrology
in supporting ecological values, and the potential interactions of coal mining stressors on the
system. The outcome was not intended to be a modelling tool to inform management.
Scale
The spatial scale for the model is the Purga Nature Reserve, and the timeframe considers
20 years of mining operation.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Scenario of change
The workshop constructed a scenario from expert opinion of how nine stressors could affect
the Swamp Tea-tree (Melaleuca irbyana) Forest at the Purga Nature Reserve (Table 4.3).
Table 4.3 Scenario construction for the Purga Nature Reserve, with type of stressor and frequency of
occurrence
Stressor
Scenario construction
Stressor type and
frequency
1. Groundwater drawdown
1 metre
Chronic
2. Change in river flow regime
- assume groundwater
connectivity, mine de-watering
Decreased duration of baseflows,
increased magnitude and duration of high
flows, altered timing, linked to Stressor 9
Event: multiple/season
3. Altered floodplain
inundation
Increased duration of drying, reduced
frequency of inundation events, altered
timing, linked to Stressor 9
Event: 1 in 10 years
3. Altered rainfall runoff
patterns (overland flow) - local
inundation
Increased duration of inundation after
rainfall
Event: wet season
4. Altered sedimentation from
river
Increased deposition in very wet years,
levee failure
Event but cumulative 1 in 30 years
5. Subsidence-induced
topographic change
Localised, 10s of cm
Chronic
6. Altered surface water
quality
Changes to cation concentrations - soil
structures, organic acids - pH, inorganics,
organics
Cumulative, event and
cumulative, event spills
7. Altered groundwater quality
As above
Cumulative, event and
cumulative, event spills
8. Spread of exotic species
Weed invasion, drying sediments terrestrialisation
Chronic, chronic
9. In-stream barriers,
diversions and levees
Decrease in hydrological connectivity
Chronic
Influence diagram
An influence diagram was used to explore the interactions between stressors and endpoints
(Figure 4.8). Potential pathways and major knowledge gaps were explored as part of model
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
development. The influence diagram was informed by available information on the species
listing and coal resource development, and a site visit. Note that the influence diagram is
largely hypothetical, and is for demonstration purposes only.
The experts acknowledged a potential for increased groundwater drawdown, leading to
increased local subsidence from the loss of groundwater pressure, and the regime of
overbank flows inundating the Reserve (information on the precise nature of the
hydrogeology was not available at the workshop; the scenario explored is speculative only).
The scenario focussed on local subsidence that may influence localised floodplain
inundation, potential for groundwater drawdown, increased interception of rainfall runoff,
extraction and disposal of surface water, presence of infrastructure (levees and barriers), and
potential interactions with climate and pests.
Note - this influence diagram is for demonstration purposes only.
Figure 4.8 Influence diagram developed in the workshop showing interactions between hydrological
stressors and endpoints
In terms of impacts, de-watering of the aquifer could lead to disposal of water into the local
stream, which would decrease the duration of low-flow periods. An increased interception of
rainfall and associated runoff from the mining development could lead to a decrease in
overland flow. This decrease would lead to changes in aspects of floodplain inundation
(drying) such as decreased duration of inundation, decreased frequency of inundation and
decreased extent of inundation. These changes would affect soil water storage, which would
affect the Melaleuca species’ population persistence through changes in both adult survival
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
and recruitment opportunities. For example, high rainfall extremes, levee failure and
construction of impervious surfaces might also lead to increased sedimentation.
Cumulative changes in hydrological processes could enhance the habitat for those native
species that have increased tolerance for drier conditions as well as exotic species. These
hydrological changes could influence the structure and composition of the swamp
community.
Bayesian network
To demonstrate how a causal model can be framed in a Bayesian network, a model was
developed in the workshop on the basis of the influence diagram (Figure 4.8), but not
populated. Note that the states of the variables when populated would capture a control
model versus a stressor model.
A small Bayesian network example (Figure 4.9) was presented to allow workshop
participants to gain an understanding of how a Bayesian network is developed, populated
and used for running some scenario analyses. The participants were able to follow the
Bayesian network example with a temporary set of chosen probabilities as an example of
how the wet and dry phases could be captured within the model. For example, increased
groundwater drawdown and increased temperature would stress most components of the
vegetation community, under especially dry conditions, potentially leading to the elimination
of some species.
This example focuses on impacts of groundwater drawdown on a Melaleuca community in wet and dry phases.
Note this model is for demonstration purposes only to depict how a BN would predict an outcome. The underlying
probabilities are only an example. Thus the model does not portray a realistic scenario of a system.
Figure 4.9 Example of a small Bayesian network
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
The next steps for this model would be to:

carefully define a specific, measurable ecological endpoint (e.g. recruitment, adult
survival)

define states for all nodes

determine whether data and/or expert knowledge are available to populate the model or
any of its component variables. The Bayesian network can consist entirely of empirical
evidence or expert opinion or can be a mixture of these or other knowledge sources (e.g.
output from another model).

do an initial parameterisation by populating the conditional probability tables from a
range of inputs, including expert knowledge and field and simulated data.
4.3 Gunnedah Basin case study: conceptual model for silver
perch
Silver perch (Bidyanus bidyanus) was chosen as a case-study example to explore the
process of developing a species-specific ecological conceptual model and its accompanying
narrative table for a given species at a specific location. The chosen endpoint was the
persistence of a silver perch population in the section of the Mooki River at its confluence
with Quirindi Creek. A desktop survey of relevant literature (Appendix A) was used to
generate the control conceptual model of the natural drivers and stressors and
anthropogenic ones not related to coal seam gas extraction and coal mining (upper part of
Figure 4.10), and its supporting narrative table (Table 4.3). This table lists specific
hypotheses for various inferred pathways in the ecological conceptual model, and presents
qualitative estimates of evidence, agreement and confidence (following the IPCC (2013)
approach described in section 2.3) as a surrogate of uncertainty.
Hypothetically assuming that longwall mining for coal might occur in the vicinity, a list of likely
drivers and stressors associated with this form of coal mining was added to the narrative
table, and expected ecological responses were hypothesised. This process guided the next
step of adding the stressor model (lower part of Figure 4.10) to produce an influence diagram
representing the main water-related ecological responses of silver perch to natural and
anthropogenic drivers and stressors at this site. Not all stressors listed in Table 4.3 turned
out to be relevant to the study area (e.g. presence of instream barriers) or pertinent to silver
perch (e.g. effects of legal fishing because in NSW, fishing for this species is illegal) so they
were omitted from the final ecological conceptual model. The ecological endpoint for this
ecological conceptual model could be a commonly measured characteristic of silver perch
such as physical condition or abundance (Figure 4.10).
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Conceptual model of hypothesised water-related ecological effects of natural drivers (yellow boxes) and
anthropogenic drivers excluding those associated with longwall mining (green boxes) drivers on abundance of
silver perch [upper part of figure, control conceptual model] and to the coal-mining-associated driver of longwall
mining [grey box, lower part of the figure, stressor conceptual model] in the Mooki River at the Quirindi Creek
confluence. Red text denotes stressors listed in Table 4.3. Thick arrows indicate the major pathways of ecological
effects. The dashed lower box encloses groups of the predicted principal determinants of silver perch population
size in the case study area. Note that some processes (e.g. sedimentation) are listed several times on the
diagram for simplicity of representation; different stressors affecting these processes would interact.
Figure 4.10 Conceptual model for silver perch
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Table 4.4 Narrative table listing drivers, stressors, water-related ecological effects and hypothesised ecological effects on silver perch (SP)
Relevant references and qualitative estimates of evidence, agreement and confidence (following the IPCC (2013) approach presented in section 2.3) are included.
E = evidence, A = agreement, C = confidence.
Driver
Stressor
Water-related
effects
Hypothesised ecological effects on SP
References
E
A
C
Climate
Air
temperature
Water
temperature
Extremes of water temperature over 38C are
harmful to SP.
NSW DPI (2006)
2
1
2
Spawning will occur when water temperatures
exceed 23C (21.6C in Thurstan & Rowland 1995).
Lake (1967a);
Frawley et al. (2011)
3
2
4
Warm water temperatures (20-25C) promote
invertebrate secondary production, increasing food
resources for SP.
Boulton et al. (2014)
and references therein
2
2
3
Landform and
geology
Rainfall
(volume and
timing)
Runoff and
river flow
Prolonged low-flow and cease-to-flow conditions,
especially during periods of normal migration (OctApr), likely reduce SP dispersal and recruitment.
Mallen-Cooper et al.
(1995)
2
1
2
Rainfall
variability
Variability in
runoff and river
flow regime
Natural variability in flow regime favours native fish
species such as SP, especially for migration and
spawning.
DSE (2005);
NSW DPI (2006) and
references therein
3
3
5
Topography
River channel
morphology
Flat topography and lowland meandering rivers and
floodplains provide suitable habitat for SP.
Cadwallader &
Backhouse (1983);
Rowland (1995);
NSW DPI (2006)
3
3
5
Soil features
Turbidity
SP can tolerate naturally high turbidity.
NSW DPI (2006);
McNeil et al. (2013)
2
2
4
Soil features
Dissolved
nutrient
concentrations
Background concentrations of nutrients entering the
waterway under normal conditions (i.e. pre-clearing
and fertilisation) would not affect SP (e.g. via algal
blooms) except when natural peaks in nutrient
NSW DPI (2006)
1
1
1
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Driver
Stressor
Water-related
effects
Hypothesised ecological effects on SP
References
E
A
C
Boulton et al. (2014)
and references therein
2
2
3
1
1
1
concentrations promote invertebrate secondary
production, increasing food resources for SP.
‘Hydrogeology’
Agricultural land
use (for beef
cattle, dryland
cropping)
Soil fertility
Allochthonous
organic matter
(OM)
production
Fertile catchments (and riparian zones) favour
inputs of allochthonous OM, which support prey of
SP (food web link). Allochthonous OM as leaf litter
and wood from trees in the riparian zone provide
habitat for SP prey.
Soil pH
Water pH
The pH of river water under natural conditions is
unlikely to fall outside of tolerances of SP adults.
There may be sub-lethal effects on SP eggs and/or
larvae.
Soil salinity
Water salinity
The salinity of river water under natural conditions is
unlikely to exceed adult SP tolerances (salinity LC50
= 16 g/L).
McNeil et al. (2013)
2
1
2
Groundwater
regime
Baseflow
Natural variability in flow regime favours native fish
species such as SP, especially for migration and
spawning.
DSE (2005);
NSW DPI (2006) and
references therein
3
3
5
Groundwater
salinity
Water salinity
The salinity of river water under natural conditions is
unlikely to exceed adult SP tolerances (salinity LC50
= 16 g/L).
McNeil et al. (2013)
2
1
2
Native
vegetation
clearance
(incl. riparian
zone veg.)
Sedimentation
Excessive fine sediments may smother eggs and
prey of SP.
Clunie & Koehn (2001)
2
2
3
Allochthonous
OM inputs*
Removal of native vegetation from catchment and
riparian zone may alter the quantity and quality of
allochthonous detritus entering the river, potentially
Boulton et al. (2014)
and references therein
1
1
1
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Driver
Stressor
Water-related
effects
Hypothesised ecological effects on SP
References
E
A
C
constraining food supply and habitat for SP prey.
Agricultural
chemicals
Water
extraction
from river
Shading*
Unshaded water may be warmer but given high
natural turbidity and tolerance of adult SP to high
water temperature, effects may be minimal.
Boulton et al. (2014)
and references therein
1
1
1
Reduced
instream wood*
Reduced inputs of instream wood may reduce
habitat for SP prey.
DSE (2005)
1
2
2
Reduced bank
stability*
Removing riparian zone vegetation may cause
banks to slump, resulting in impacts of
sedimentation on SP (see earlier).
Clunie & Koehn (2001)
2
2
3
Secondary
salinity
Unless salinisation is severe, river salinity is unlikely
to exceed adult SP tolerances (salinity LC50 =
16 g/L).
McNeil et al. (2013)
2
1
2
Inputs of
pesticides and
herbicides
Inputs of agricultural chemicals are unlikely to
directly harm SP but may reduce invertebrate prey
populations.
Sunderam et al. (1992)
2
2
3
Inputs of
fertilisers
Inputs of nutrients entering the waterway from poorly
managed fertilisation would not affect SP (e.g. via
algal blooms) except when natural peaks in nutrient
concentrations promote invertebrate secondary
production, increasing food resources for SP.
NSW DPI (2006)
1
1
1
Reduced river
flow
Prolonged low-flow and cease-to-flow conditions,
especially during periods of migration (Oct-Apr),
likely reduce SP dispersal and recruitment.
Mallen-Cooper et al.
(1995)
2
1
2
Altered flow
regime
Natural variability in flow regime favours native fish
species such as SP (especially for migration and
spawning) over species of exotic fishes that cannot
tolerate wide variation in flow regime.
DSE (2005);
NSW DPI (2006) and
references therein
3
3
5
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Driver
Instream
barriers (incl.
weirs, road
crossings)
Translocation of
species by
humans
Stressor
Water-related
effects
Hypothesised ecological effects on SP
References
E
A
C
Altered flooding
regime
Spawning of SP appears to be related to flooding.
However, it does not seem essential (Mallen-Cooper
& Stuart 2003) for spawning success of SP.
DSE (2005)
2
2
3
Removal of
water
Pumping from weir pools may remove SP eggs and
larvae.
Gilligan & Schiller
(2003)
1
2
2
Groundwater
extraction
Reduced river
flow
Prolonged low-flow and cease-to-flow conditions,
especially during periods of normal migration
(Oct-Apr), likely reduce SP dispersal and
recruitment.
Mallen-Cooper et al.
(1995)
2
1
2
Physical
barrier
Altered flow
regime
Natural variability in flow regime favours native fish
species such as SP, especially for migration and
spawning.
DSE (2005);
NSW DPI (2006) and
references therein
3
3
5
Altered flooding
regime
Spawning of SP appears to be related to flooding.
However, it does not seem essential (Mallen-Cooper
& Stuart 2003) for spawning success of SP.
DSE (2005)
2
2
3
Impede
instream
movement of
biota
Impeding normal migration likely reduces SP
dispersal and recruitment. Barriers may also cause
physical injury and/or mortality to drifting eggs and
larvae of SP.
Mallen-Cooper et al.
(1995);
Clunie & Koehn (2001)
2
2
3
Alter sediment
regime
As SP prefer sandy beaches, sediment supply to
restore beaches downstream may be impaired by
barriers that retain the sediments.
J. Koehn, unpubl. data
cited in DSE (2005)
2
1
2
Clog waterways
Dense infestations of translocated water plants such
as Typha may constrain waterways and restrict fish
migration.
Boulton et al. (2014)
and references therein
1
1
1
Exotic water
plants
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Driver
Stressor
Exotic fishes
Water-related
effects
Hypothesised ecological effects on SP
References
E
A
C
Compete with
native water
plants
Although native water plants provide habitat for the
prey of SP, and SP have been found in stands of
Phragmites, it is likely exotic plants could play
equivalent roles as habitat. Therefore, this is likely
not a serious threat to SP.
Cadwallader (1979)
1
1
1
Carp
Carp may threaten SP through competition for food
resources and by increasing sedimentation through
their feeding habits.
DSE (2005)
1
2
2
Gambusia
Gambusia are not considered a major threat to SP.
NSW DPI (2006)
1
2
2
Fish diseases
Exotic fishes such as carp and gambusia may be
major vectors transmitting diseases to SP. Epizootic
Haematopoietic Necrosis Virus (EHNV) is a
particular concern in NSW (NSW DPI 2006).
Langdon (1989);
Glazebrook (1995);
Whittington et al.
(1995); Dove et al.
(1997)
2
2
3
Fishing
Commercial
and
recreational
fishing
Removal of SP
As commercial and recreational fishing for SP are
illegal, this stressor is unlikely to be serious. Illegal
fishing may deplete SP stocks at low flows or in
remnant pools.
NSW DPI (2006)
2
2
3
Longwall mining
Subsidence
Reduced
surface runoff
to river
Prolonged low-flow and cease-to-flow conditions,
especially during periods of normal migration (OctApr), likely reduce SP dispersal and recruitment.
Mallen-Cooper et al.
(1995)
2
1
2
Altered flow
regime
Natural variability in flow regime favours native fish
species such as SP, especially for migration and
spawning.
DSE (2005);
NSW DPI (2006) and
references therein
3
3
5
Altered flooding
regime
Spawning of SP appears to be related to flooding.
However, it does not seem essential (Mallen-Cooper
& Stuart 2003) for spawning success of SP.
DSE (2005)
2
2
3
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Driver
Stressor
Water-related
effects
Hypothesised ecological effects on SP
References
E
A
C
Groundwater
drawdown
Reduced
baseflow
Prolonged low-flow and cease-to-flow conditions,
especially during periods of normal migration (OctApr), likely reduce SP dispersal and recruitment.
Mallen-Cooper et al.
(1995)
2
1
2
Spoil piles
and
processing
Input of
toxicants
Toxicants associated with mine waste may be sublethal to SP eggs, larvae and adults.
1
1
1
Salinity
Unless excessive, inputs of salt from mining activity
into the river is unlikely to exceed SP tolerances
(salinity LC50 = 16 g/L).
2
1
2
Acidification
Unless excessive (<4) or pulsed, decreases in pH
from acidic runoff from mining activity into the river
are unlikely to fall outside of tolerances of SP adults.
There may be sub-lethal effects on SP eggs and/or
larvae.
1
1
1
Sedimentation
Excessive fine sediments may smother eggs and
prey of SP.
2
2
3
Land
clearance for
infrastructure
McNeil et al. (2013)
Clunie & Koehn (2001)
*A conceptual model from Pusey and Arthington (2003) of how fish are influenced by aspects of the riparian zone, reproduced in Figure 4.11, summarises many of the
‘ecological effects’ associated with the riparian zone described above.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Source: Pusey and Arthington (2003).
Figure 4.11 Conceptual model of how fish are influenced by aspects of the riparian zone
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
5 Discussion
5.1 The role of ecological modelling in assessment of
proposals for coal seam gas extraction and coal mining
An important goal of this project was to evaluate the roles ecological modelling might play in
the assessment of proposals for coal seam gas extraction and coal mining. Currently,
hydrological and hydrogeological models are accepted (and recommended [IESC 2014]) as
valuable tools in EISs for indicating potential changes in movements, volumes and quality of
the water resources of a region in response to coal seam gas extraction and coal mining.
However, ecological modelling has been far less commonly used in EISs in Australia to
predict how particular taxa, assemblages or ecosystem processes (e.g. carbon cycling) might
respond to proposed coal seam gas extraction and coal mining. Instead, there tends to be a
heavy reliance on statements that are often unsupported by scientific evidence and are
ambiguous or only partly true (Section 1.4).
Our project suggests that ecological modelling of water-related responses to coal seam gas
extraction and coal mining is as important as the widely accepted hydrological and
hydrogeological modelling, and plays similar roles. These roles include:
1. explicit specification of the scales and bounds of the system of interest
2. description and representation of the main drivers, stressors, components and
interactions at one or more given scales
3. generation of testable hypotheses about particular interactions and outcomes in
response to particular drivers
4. demonstration (and sometimes quantification) of likely response pathways to one or more
stressors so that potential management strategies to minimise impacts may be identified.
All of these roles clearly fit within the brief of an EIS to assess likely water-related ecological
responses to coal seam gas extraction and coal mining.
The benefits of these roles in the ecological modelling of water-related responses to coal
seam gas extraction and coal mining are likely to be maximised when ecological models are
used in tandem with hydrological and hydrogeological models. Indeed, given the focus on
water-related responses, an effective model requires a tight association of hydrology,
hydrogeology and ecology. This association is a fundamental tenet of the subdiscipline of
ecohydrology and hydroecology (Hannah et al. 2004). The approaches to modelling and
conceptualisation of hydrology and hydrogeology currently used in EISs could be extended
to incorporate ecological components to produce ecohydrological models capable of
predicting likely water-related ecological responses to coal seam gas extraction and coal
mining.
There is a diverse array of ecological modelling approaches and an equally broad suite of
models, ranging from verbal qualitative ones to complex mathematical models whose
algorithms have heavy computational requirements (Lester & Fairweather 2008). All of these
ecological models are attempts to represent reality in a simplified form to different degrees,
largely depending on the questions being addressed in the study, the amount and type of
data available, and the goals of the modelling approach.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Although ecological models are diverse, virtually all begin with some form of conceptual
model, often represented as a diagram, to help communicate the elements and linkages in
the model (Chapter 2). Therefore, this project focused on ecological conceptual models,
seeking an approach that would be feasible and useful for proponents of EISs of
water-related responses to coal seam gas extraction and coal mining in Australia. In addition
to providing the basis for subsequent and more sophisticated ecological and ecohydrological
modelling, these ecological conceptual models enable integration of input from different
experts into a formalised shared understanding and facilitate communication among
scientists and to managers and the public about the complexity of the diverse ecosystem
components, interactions and responses to multiple stressors (Lindenmayer & Likens 2010,
Chapter 2).
5.2 Ecological conceptual models in coal seam gas extraction
and coal mining proposals
After consideration of Australian and overseas literature on ecological conceptual modelling,
this project adopted a conceptual framework of control and stressor models with
accompanying narrative tables (Gross 2003, Section 2.2) to help proponents of development
proposals to identify and assess water-related ecological impacts. At the workshop held as
part of the project, experts from diverse scientific backgrounds agreed that this conceptual
framework was an appropriate and powerful one. Other advantages are that the framework is
currently used by other Australian programmes in natural resource management such as
ecological descriptions of Ramsar sites (e.g. Butcher & Hale 2010), and it acknowledges that
most ecosystems are already modified by anthropogenic activities prior to coal seam gas
extraction and coal mining, but still have significant ecological values and functions (as
expressed in the control model).
Existing pictorial conceptual models for ecosystem types or broader landscapes (e.g. the
suite of models on WetlandInfo) are a useful starting point for drawing up ecological
conceptual models that identify components and processes that may be present in the area
of interest. These pictorial representations of ecological conceptual models are often
illustrations that represent the landscape as cross-sectional diagrams and icons. They are
especially useful in showing geographic proximity of stressors and water-associated assets.
To complement these pictorial representations, box-and-arrow diagrams can be drawn up
where the boxes represent drivers, stressors and ecosystem components and the arrows
portray pathways of influence. The main strength of the ‘box-and-arrow’ diagram is its
capacity to show potential pathways of impacts from stressors, and interactions among
stressors.
One of the main conclusions from this project and discussion at the expert workshop was the
importance of highlighting the fact that nearly all stressors interact and should not be treated
independently when assessing likely water-related ecological responses. For this reason, a
box-and-arrow diagram was constructed of the principal stressors likely to be associated with
coal seam gas extraction and coal mining (Figure 1.1) to illustrate the main interactions that
might occur. This generic diagram may assist those preparing EISs to generate ecological
conceptual models for specific situations, and encourages recognition of the cumulative and
interactive effects of changes to water regime and water quality arising from different
activities associated with coal seam gas extraction and coal mining.
Another of the major outputs of this project was to suggest a series of consecutive steps that
would assist those preparing EISs to construct control and stressor conceptual models
(Figure 3.1, Chapter 3). This was fundamental to the ‘proof-of-concept’ approach of this
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
project and was used to derive several ecological conceptual models as examples. During
the derivation of these models, particular attention was paid to identifying where challenges
might arise and suggesting potential solutions to some of the problems. Of course, each
situation will pose its own context-specific challenges, but the ‘proof-of-concept’ approach in
this project aimed to address the major constraints and challenges.
5.3 Challenges in generating ecological conceptual models
for proposals for coal seam gas extraction and coal
mining
Perhaps the greatest challenge in generating ecological conceptual models to portray likely
responses to coal seam gas extraction and coal mining is the lack of empirical data,
especially at the site level. Furthermore, there is a severe lack of ecological data on habitat
and other requirements of most Australian fauna and flora, even common taxa such as river
red gums (Eucalyptus camaldulensis) or threatened species such as silver perch (Bidyanus
bidyanus). This lack of data means that many of the pathways and ecological responses
portrayed in ecological conceptual models of likely responses to coal seam gas extraction
and coal mining must be expressed as hypotheses.
These hypotheses will usually be based on ecological knowledge derived from the scientific
literature, other credible literature, expert opinion and/or field observations. Typically, the
predictions will be extrapolated from data and observations of related taxa or environments
considered to be reasonable surrogates for the specific situation. Although not ideal, it is the
best option available. However, it is crucial that each hypothesis is accompanied by an
explanation of the source of the information underpinning the prediction (e.g. references to
scientific literature or web sites, acknowledgement of expert input) along with some indication
of the confidence in the supporting evidence (Section 2.3). Narrative tables supporting the
development of the control and stressor models should present each hypothesis, the
supporting evidence, some indication of the agreement among multiple sources of evidence,
and the overall confidence in the reliability and validity of the evidence.
One of the major constraints to assessing the ecological predictions and claims made in
many current EISs is the lack of evidence presented to support them (Section 1.3). This
prevents independent judgement of their likely veracity and risks over- or under-estimating
the severity of a potential stressor’s impact on a given ecological endpoint. The proposed
conceptual framework helps to remedy this problem. For example, a statement equating
non-perennial streams with low ecological value and absence of baseflow would need to be
supported by credible hydrogeological and ecological conceptual models, including
box-and-arrow diagrams with accompanying narrative tables containing references to the
scientific and other literature.
Another challenge lies in setting the hydrological and hydrogeological frameworks at
appropriate scales for the ecological conceptual model. Mismatch of scales among
disciplines is a common constraint to environmental research seeking to integrate findings
from diverse knowledge structure such as ecology, hydrology and geomorphology (Benda et
al. 2002) and is usually resolved by exploring interactions at several different scales of time
and space. Although ecological conceptual models can be built from an existing generic
model, an understanding of the specific context is vital. Ideally, there will be stream flow and
groundwater level data collected at appropriate intervals and locations, and a
hydrogeological conceptualisation showing stratigraphy and the permeability of geological
formations, areas of recharge and discharge, connectivity between surface and groundwater,
groundwater depths and flow directions. Years to decades of stream gauge and groundwater
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
level data are needed for the construction of a site water balance, whereas long-term (years
to decades) monitoring data are required to capture recruitment and population dynamics of
long-lived taxa such as fish, turtles and trees. Again, in the absence of suitable data and
robust hydrogeological conceptualisation, the surface and groundwater hydrology and its
ecological relationships must be hypothesised. This is especially true because it is perilous to
extrapolate short-term measurements to predict long-term impacts, especially when tipping
points and thresholds cause nonlinear responses over time (Section 2.1).
Yet another challenge lies in demonstrating or quantifying causality, especially when the only
data available are correlative. Again, this is a common constraint in most environmental
research (Downes et al. 2002). Two problems arise. The first is that, strictly speaking,
causality can only be demonstrated by experimental manipulation of a stressor and
statistically robust comparison of the ecological responses in the presence and absence of
the stressor. Such manipulation is seldom possible when scales are broad, as in most
situations requiring EISs for assessment of likely responses to coal seam gas extraction and
coal mining. The second problem arises because environmental stressors almost never act
alone (Section 5.2) and ecological responses arise from the complex interplay of multiple
drivers and stressors. Consequently, all ecological conceptual models must be treated as
‘best guesses’ and the pathways and responses are inferred from correlative data or
hypotheses rather than from robust experiments that will always generate the same
ecological outcomes. The sensitivity assessment approach of Walker (2010) (see
Source: Walker (2010).
Figure 2.2) may be applied here.
A final challenge is unique to ecological conceptual models that deal with plant and animal
species, almost all of which have life cycles that involve multiple stages whose requirements
vary. For example, many aquatic insects have terrestrial adults but entirely aquatic larvae.
Vegetation along the riparian zone will have life stages from seed to seedling to juvenile to
adult, all varying in their vulnerability to suites of stressors. Often, a particular life stage will
have a very specific requirement that, if not met at the right time or in the right amount, will
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
lead to local extinction of the species. Robust ecological conceptual models must be able to
portray these different life stages’ requirements, usually expressed as hypotheses because
there is seldom adequate ecological data. Unfortunately, the paucity of data is usually most
severe for the juvenile stages of many Australian plants and animals, which are often the
most vulnerable.
The case study of the Purga Nature Reserve (Chapters 3 and 4) illustrated some of these
challenges: determining suitable spatial and temporal scales for the models, severe lack of
adequate data, constraints in extrapolating short-term measurements to predict long-term
impacts, difficulty in demonstrating or quantifying causality, and the need to consider likely
effects of stressors on various life-history stages because vulnerabilities may differ between
recruitment/seedling establishment, growth and adult stages. Other challenges included the
need for expert multidisciplinary expertise and local knowledge as well as the substantial
time required for development, review and revision of models. This final challenge is
ongoing, and all ecological conceptual models are likely to need continual iterative
refinement as new data and information are gathered. This acquisition of new data means
that carefully designed monitoring programs are essential (Downes et al. 2002) and points to
another role of ecological conceptual models as a guide to choosing suitable attributes of
various ecosystem components to measure.
Even the output from the ecological conceptual models can be challenging. The inevitable
complexity of box-and-arrow diagrams that seek to illustrate control and stressor models is
often off-putting to non-ecologists. However, although these diagrams may seem
overwhelming, this complexity reflects the real world. Making the pathways and interactions
explicit in these models is an attempt to clarify natural complexity and make it more tractable.
5.4 Feasibility of the proposed approach as a desktop
exercise
One major goal of the project was to explore the feasibility of an approach that might be used
by a person preparing an EIS, by compiling data from a desktop study to provide support for
specific hypotheses about the potential water-related effects of coal seam gas extraction and
coal mining on a given ecological endpoint. The endpoint chosen was the persistence of a
population of a relatively well-known fish species (silver perch, Bidyanus bidyanus) in a
section of the Mooki River in the Gunnedah Basin (Section 4.3). The intention was to see
whether it was feasible for an ecologist to rapidly derive (1) a useful table of hypothesised
responses, supported with explicit reference to relevant literature and (2) a simple
box-and-arrow conceptual model that might be refined for use in a BN.
A number of challenges emerged, many of which have been discussed above. These
included:

difficulties in distinguishing drivers and stressors in some instances

lack of data on the species’ field ecology (even for such a well-known species)

lack of data on interactions among response pathways, especially at multiple scales

uncertainty about appropriate spatial and temporal scales of response pathways

difficulty in wording of hypotheses (largely limited by lack of data), resulting in ambiguity

likely inconsistency in assigning criteria of evidence and agreement because of
unfamiliarity with relevant literature
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Modelling water-related ecological responses to coal seam gas extraction and coal mining

challenges in drawing a pictorial conceptual model that presented the main pathways
and interactions (including cumulative effects of multiple stressors) yet was not overly
simplistic and did not omit crucial linkages or lump variables that were best considered
separately.
The entire exercise took approximately 12 hours, entailing compilation of relevant literature
(Appendix A) in a form that could be used in the narrative tables, drafting up the tables and
box-and-arrow diagrams for the combined control and stressor models, and refining them to
focus on the major hypothesised pathways of water-related ecological responses to coal
seam gas extraction and coal mining. Because there was a richer literature than for the
Swamp Tea-tree example (Section 4.1), there was more resolution in the assessment of the
evidence, agreement and confidence in the supporting references for each hypothesis (Table
4.4).
Despite the challenges listed above, an independent expert was satisfied that the narrative
table did not have any serious errors or omissions and that the hypotheses looked
reasonable. Further, the workshop participants confirmed that the approach provided an
effective method to collate data from a desktop study in a structured way that could then be
presented as a narrative table and matching ecological conceptual model. Although there is
scope for refinement, the process appears transparent, logical, consistent and feasible for
those involved in preparing EISs. Particular strengths of the approach include the ease of
illustrating and communicating the complex interactions among multiple stressors and the
use of narrative tables that refer directly to relevant supporting literature for each hypothesis.
5.5 Bayesian networks within an EIS application
Another goal of this project was to explore the feasibility of using Bayesian networks, derived
from the box-and-arrow ecological conceptual models, as a means of modelling ecological
responses to different scenarios of stressors and their magnitudes. A Bayesian network
populated with conditional probabilities provides a predictive framework for understanding
the relationships among a set of contributing variables. In this case, the Bayesian network
could be developed to determine the environmental impacts or risks of coal seam gas
extraction or coal mining to ecosystems in their current states. Most stressors affect a system
in a variety of ways, and the Bayesian network is able to model the interactions among the
different stressors and the various components of a system. It is also able to reveal some of
the knowledge gaps that could be targeted for research or referral to a suitable expert. The
predictive capabilities of Bayesian networks make them particularly useful in EISs.
Bayesian networks have been used in EISs but their inclusion is rare (Marcot et al. 2001;
Perdicoúlis & Glasson 2006). Liu et al. (2012) used a Bayesian network to assist in an EIS to
determine the survival of a waterbird species and predict the adverse ecological effects of
proposed development. They found the probabilistic relationships useful in predicting
population survival status with different scenarios.
EISs have three main principles: transparency, integration and being systematic (Perdicoulis
& Glasson 2006). Bayesian networks support transparency by identifying the factors (nodes)
that are important for the EIS and identifying the potential causal links between the nodes.
They are also an efficient tool for integrating varying aspects that need to be assessed within
an EIS, and can model the interrelationships of social, economic, biophysical and ecological
aspects. Bayesian networks are systematic because they are able to incorporate all relevant
data/information in a logical causal framework that can reveal knowledge gaps, alternative
pathways and the impacts of proposed activities (Perdicoulis & Glasson 2006). Bayesian
networks are also easily updated when new data or information becomes available.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
There are some benefits to using the BN approach. Using the BN within the workshop helped
the experts to focus on the aspects of flow regime and groundwater/surface water
connectivity that are particularly relevant for a given scenario, where inputs could be from
other sources such as outputs from hydrological and hydrogeological models. It also helps
identify knowledge gaps. The BN and conceptual modelling approaches could be combined
to identify which impact pathways are likely to be of importance; this could be through lack of
knowledge or with a strong evidence base. The EIS process could focus on these prioritised
pathways. The BN could also be developed with probabilities set and asking those preparing
EISs to provide the data (e.g. data collected specifically to reduce knowledge gaps) to feed
into it. The BN articulates links between environmental inputs and outcomes, which can
identify and drive data needs. Finally, the BN allows the predictive exploration of cumulative
impacts of multiple stressors, as the conditional probability tables under each node require a
probability for each potential combination of states from each parent node feeding into each
child node.
However, there are some limitations to the BN approach. One of the major limitations is that
it has limited capacity to deal with temporal aspects. It also cannot have feedback loops so
systems requiring feedback cannot be modelled. For example, some predator-prey cyclic
relationships would be difficult to model as fluctuations in predator populations are often
similar to those of their main prey after a time lag. Many approaches exist, but having the
data and knowledge to apply them is important. As is the case with most statistical
approaches, there is a need for a good information base to populate the models
(i.e. stressor/threat/outcome/effect) especially as the complexity of the model increases. BNs
based on expert knowledge may also generate spurious outcomes due to potential biases in
available expert knowledge or knowledge gaps. Collecting or finding data sources to
substantiate the expert knowledge, where possible, is advised. For example, there were
many components of the case-study BN that the group was unsure about. Quantitative
modelling may also help to focus on the drivers and important factors. If ample data are
available, a statistical or mechanistic model may be more appropriate. Complex systems
may not be represented adequately within a BN and parameterisation of these models may
be difficult. As continuous data cannot be represented, discretising them into relevant states
or thresholds may result in over-simplification of the system.
Bayesian networks would not be suitable in all situations in an EIS. The choice needs to be
made on a case-by-case basis. The choice of model needs to be fit-for-purpose. There are
strong advocates for the use of Bayesian networks as well as strong adversaries who
challenge their use. Nearly all statistical models have some limitations to their applications.
Provided users are aware of the Bayesian network’s limitations and can still model their
systems effectively (does the model make sense ecologically?) and some model validation
occurs, it remains a suitable method to be considered for enhancing the reporting of the
potential impacts associated with coal seam gas extraction and coal mining on the current
environment.
5.6 Conclusion
This project was a ‘proof-of-concept’ of an approach to identifying water-related ecological
impacts of coal seam gas extraction and coal mining in Australia. Despite the challenges of
knowledge gaps, context dependency and issues of spatial and temporal scale, the approach
is likely to provide proponents with the tools to better understand the hydrology-ecology
relationships in development areas, and articulate stressor and response pathways. The
approaches to modelling and conceptualisation of hydrology and hydrogeology currently
used in EISs could be extended to incorporate ecological components to produce
ecohydrological models capable of illustrating likely water-related ecological responses to
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
coal seam gas extraction and coal mining. These models, supported by references to the
scientific literature, could provide a transparent rationale for the ecological responses and
proposed mitigation action and monitoring strategies identified in an EIS.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
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Tartakovsky, DM 2013. 'Assessment and management of risk in subsurface hydrology: A review and
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consortium to the SA MDBNRM Board in 2009. Walker, pers. comm. 11 July 2014.
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Hydrological Processes, vol. 24, pp. 686-694.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Appendix A - Case study: conceptual
model for Silver Perch
Species: Silver Perch Bidyanus bidyanus (Terapontidae)
EPBC Act status:
Critically endangered and on the Qld Govt WetlandInfo indicator list (Silver perch – Bidyanus
bidyanus, WetlandInfo, Department of Environment and Heritage Protection, Queensland,
<http://wetlandinfo.ehp.qld.gov.au/wetlands/ecology/components/species/?bidyanusbidyanus>).
Listed as Vulnerable under the Fisheries Management Act (NSW).
Conceptual model goal:
Identify main factors affecting attributes of Silver Perch (SP) such as body condition and
population size in a section of the Mooki River with [full conceptual model] and without
[control conceptual model] water-related impacts from coal seam gas and coal mining
development.
Bounds:
Spatial scale: Mooki River at confluence with Quirindi Ck [includes consideration of migration
movements].
Temporal scale: persistence into foreseeable future (30-y project life, 50 y).
Species’ biology notes:
Mainly from DSE (2005), NSW DPI (2006), McNeil et al. (2013) and references therein.
General:
This species has been well studied in culture conditions but its natural ecology is poorly
known (NSW DPI 2006). They appear long-lived (aging studies up to 27 y (Mallen-Cooper &
Stuart 2003)). In wild, are sexually mature at 3 to 5 y (based on gonad examinations
(Mallen-Cooper et al. 1995).
Natural range:
Includes most of the Murray-Darling drainage division, excluding the cool, higher altitude
upper reaches of streams on the western side of the Great Dividing Range (Merrick 1996). In
NSW, SP now absent from most of their natural range (NSW DPI 2006) and now there are
very few self-sustaining populations (DSE 2005; NSW DPI 2006).
Habitats:
Include rivers and large streams, as well as lakes and impoundments. Occurs in cooler,
clearer, upper reaches of the Murray-Darling River system on the eastern side of the Great
Dividing Range with gravel beds and rocky substrates, and in the turbid, slow-flowing rivers
in the west and north (Rowland 1995). Merrick and Schmida (1984) noted they prefer fast
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
flowing waters, particularly where there are rapids and races. However, in NSW, the most
significant natural population occurs in a lowland river where rapids are rare (NSW DPI
2006). In Victoria, SP preferred open waters over those that were heavily de-snagged
(Cadwallader & Backhouse 1983) and surveys in the Murray River in June 1996 found them
mainly from open waters off sandy beaches (J. Koehn, unpubl. data cited in DSE 2005). In
Seven Creeks, Victoria, Cadwallader (1979) recorded SP where cover was provided by
debris and occasional stands of Phragmites and where the water was very turbid.
Spawning:
Generally occurs in spring and summer when water levels increase and water temperatures
rise above 23C (Lake 1967b) but has been observed at 21.6C (Thurstan & Rowland 1995).
Spawning may occur in flooded backwaters of low-gradient streams (Lake 1967d) as well as
in impoundments (Hogan 1995), provided an increase in both water level and temperature
occur. It is clear from these requirements that alterations to natural flooding and water
temperature regimes have the capacity to seriously affect the spawning behaviour and
potential spawning success of SP (DSE 2005). However, spawning can occur in non-flood
years; Mallen-Cooper and Stuart (2003) found strong age classes matched times when flows
remained largely within the channel.
Fecundity varies with fish size: up to around 500 000 eggs have been recorded from a 1.8 kg
female, but approximately 300 000 eggs is more typical. Eggs are pelagic and drift
downstream with the current; in still water, however, they will settle to the bottom
(Cadwallader & Backhouse 1983). There is no apparent parental care of eggs following
spawning (Lake 1967c). Eggs hatch rapidly (within 28 to 31 hours at temperatures of
24 to 27C), and juveniles are free swimming by 5 days and commence feeding at
4 to 6 days (Lake 1967a; Guo et al. 1993).
Migration:
Is entirely in freshwater, usually after water temperatures increase above 20C. A wide
variety of ages undergoes upstream migration (sometimes over extensive distances).
Immature fish move upstream from October to April, while mature fish move upstream over a
shorter period from November to February (Mallen-Cooper et al. 1995). Increased migration
has also been observed after increases in flow (Clunie & Koehn 2001). The upstream
migration of juvenile SP is thought to be for one or more of the following strategies: to
optimise feeding, to enhance colonisation, or to compensate for the downstream drift of
pelagic eggs and larvae (Mallen-Cooper et al. 1995). The pelagic nature of SP eggs and
larvae (they drift downstream for 12 to 15 days) may be partly responsible for the upstream
migration of mature Silver Perch prior to spawning (Mallen-Cooper et al. 1995). Barriers to
migration are believed to adversely affect these strategies.
Water temperature tolerance:
Water temperature tolerance is 2 to 38C in lab conditions, but growth and feeding are
optimal at 23 to 28C (NSW DPI 2006). Other relevant water quality tolerances (from
McNeil et al. 2013) are: conductivity (LC50 16 g/L); DO (> 2 mg/L), turbidity (‘high’),
cease-to-flow conditions (‘high’).
Diet:
Includes zooplankton (major component, NSW DPI 2006), crustaceans, aquatic insects and
algae; the proportion of algae in the diet increases with age (Clunie & Koehn 2001). Adult SP
are omnivorous. Larvae are obligate planktivores (McNeil et al. 2013).
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Threats (non- coal seam gas and non-coal mining):
Instream barriers can prevent upstream migrations and alter flow and temperature regimes,
affecting spawning success and the survival of eggs and juveniles (Koehn & Morrison 1990).
Cold water pollution (from low level outlets on dams) may lead to localised extinctions
downstream of large dams if water consistently fails to reach temperatures required for
spawning (23C). Upstream migration (triggered at temperatures above 20C)
(Mallen-Cooper et al. 1995) may also be affected, as may metabolic functioning and growth,
feeding, maturation and food availability (Clunie & Koehn 2001; Ryan et al. 2004). Barriers to
migration may limit or prevent adults and juveniles accessing upstream habitats, and
consequently prevent their dispersal and access to feeding areas and their ability to
compensate for downstream drift of eggs and larvae, resulting in the local extinction of SP in
affected stretches of river. Furthermore, eggs and larvae may settle out in the low flow areas
immediately above barriers, subjecting them to conditions that threaten their survival.
Barriers may also cause physical injury and/or mortality to drifting eggs and larvae (Clunie &
Koehn 2001).
River regulation and water abstraction may affect spawning success because spawning is
at least partially initiated by rises in water level. Adults move upstream prior to spawning, and
adult movement patterns may also be affected. River regulation and abstraction may also
alter both the quality and availability of floodplain habitats such as backwaters and billabongs
in which SP have been recorded (Clunie & Koehn 2001). The recruitment of SP may be more
localised and opportunistic than previously believed, and fish may spawn both during
inchannel flows and during large floods (Clunie, pers comm. cited in DSE 2005). The NSW
DPI (2006) report has a detailed discussion of specific aspects of likely effects of altered
flows.
Water diversions and pumping from weir pools may remove eggs and larvae (Gilligan &
Schiller 2003).
Competition for food from introduced cyprinids and predation by Redfin (Perca fluviatilis)
may also represent a threat. While the exact impact of Carp (Cyprinus carpio) on SP is not
clear, perceived problems include competition for food resources and increased
sedimentation due to the feeding habits of Carp (DSE 2005). Gambusia are not considered a
major threat to SP (NSW DPI 2006).
Sedimentation: Deposited sediments may be detrimental to eggs and larvae of SP,
particularly in still-water and depositional habitats such as backwaters, floodplains and weir
pools. If depositional events occur when SP spawn and eggs and larvae settle in still waters,
reproductive success may be reduced. Deposited sediment may reduce gas exchange and
inhibit development of eggs, larvae and juveniles (Clunie & Koehn 2001). Sedimentation may
also affect the abundance of food items such as phytoplankton, zooplankton and insects
associated with aquatic macrophytes (Clunie & Koehn 2001). It is not known whether high
suspended sediment levels affect respiration or feeding in SP (DSE 2005).
Instream habitat losses: Although the significance of aquatic vegetation as a habitat
component for SP is unknown, it is possible that aquatic vegetation provides nursery habitat
for juveniles. Aquatic vegetation also supports assemblages of aquatic insects which are in
turn a food source for SP (Clunie & Koehn 2001). The significance of woody debris as a
habitat component (including habitat markers, refuges from high water velocity, protection
from predators, or nursery sites for larvae and juveniles) for SP is unknown (DSE 2005).
However, many food items of SP (e.g. chironomid larvae and small crustaceans) are found
on woody debris (DSE 2005).
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Salinity: SP appear quite tolerant of high salinity levels, although (like most fish species)
early life history stages are the most sensitive (DSE 2005). The effects of sub-lethal levels of
salinity on SP (including stress which may make them more susceptible to infections) are
unknown, as are the effects of elevated salinity levels on food sources such as invertebrates,
algae and macrophytes. Impacts on habitat complexity and quality are also largely unknown
(DSE 2005).
Low dissolved oxygen concentrations are considered responsible for at least two
recorded fish kills associated with sedimentation (NSW DPI 2006).
Agricultural chemicals: Residues of DDT and endosulfan have been recorded from fish
flesh in some rivers. In toxicity tests, SP were found to be one of the least sensitive species
to endosulfan (Sunderam et al. 1992). Exposure to endosulfan and chlorpyrifos reduced the
critical upper lethal water temperature of SP (Patra et al. 2007 cited in McNeil et al. 2013).
Degradation and destruction of riparian vegetation: The specific impacts of these
processes on SP have not been determined. Generally accepted adverse effects on instream
habitat include loss of shading, loss of organic inputs, increased runoff, increased erosion,
streambank slumping and sedimentation (DSE 2005). Such changes may have affected SP
in relation to food sources, water quality and breeding success.
Disease: Very little is known about the prevalence of diseases in SP. However, three
diseases and one parasite have been identified as potential threats. These are: Epizootic
Haematopoietic Necrosis Virus (EHNV) to which SP has been found to be highly susceptible;
Viral Encephalopathy and Retinopathy which has been demonstrated to cause mortalities of
SP in trials; Goldfish Ulcer Disease; and Asian Fish Tapeworm (Langdon 1989; Glazebrook
1995; Whittington et al. 1995; Dove et al. 1997). Native fish are generally believed to become
infected with these diseases following contact with introduced fish species (which act as
vectors). EHNV is a particular concern in NSW as it seems to be widespread (NSW DPI
2006).
Angling pressure: Unknown. Bag limit of 5, size limit of >250 mm (DSE 2005) and fishing
permitted only in stocked waters in NSW. SP in NSW rivers have been totally protected from
angling since 1998. Commercial fishing in NSW for the species has collapsed and a total ban
has been in place since 2001 (NSW DPI 2006).
Algal and cyanobacterial blooms: It is not known whether algal and cyanobacterial blooms
have played a significant role in the decline of SP, or whether associated water quality
problems have had less obvious, sub-lethal effects.
Five key threatening processes (listed in NSW DPI 2006): Degradation of riparian zone
vegetation, removal of woody debris, introduction of fish outside their normal range, instream
barriers and alteration of flow regime.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Appendix B - Bayesian network models
This appendix gives a brief overview of BNs and the process for building one.
Bayesian networks (also referred to as Bayesian decision networks or Bayesian belief
networks) are model-based decision-support tools that are ideal for environments where
considerable uncertainty exists, and for diverse problems of varying size and complexity,
where disparate issues require consideration. Their graphical model structure depicts the
causal or correlative relationships between key factors and final outcomes. They provide
clarity by making causal assumptions explicit and are often used to model relationships not
easily expressed in mathematical notation.
A Bayesian network is represented as a directional graph of connected variables (henceforth
called nodes), wherein directed connections from terminal (parent) nodes to a child node
indicate that the parent node is having a direct influence on the child node. The BN uses
conditional probability distributions under each child node to define dependencies between
the interacting parent nodes and their associated categories (henceforth called states) within
the nodes (Murray et al. 2014). Probabilities, which describe the strength of relationships
between variables, can be defined from: empirical data (observed data, monitoring data,
etc.), input data from other models, other ‘parent’ models, expert knowledge or a combination
of these sources. A conditional probability distribution (often defined as a conditional
probability table) is used to describe the relative likelihood of the state of a child node,
conditional on every possible combination of states in the parent(s).
Bayesian models are particularly useful for rapidly reviewing alternative scenarios of system
change, including change in response to management actions. Consultation through
workshops and via one-on-one meetings is an integral part of building a BN. Workshops can
assist in developing or refining model structure, identifying and refining model inputs, and
reviewing model outputs.
Bayesian probabilities
Bayesian probability interprets probability as "a measure of a state of knowledge", rather
than as a frequency (as in frequentist statistics). The Bayesian interpretation of probability is
seen as an extension of logic that enables reasoning with uncertain statements. To evaluate
the probability of a hypothesis, a prior probability (which can also be uninformative or ‘flat’) is
used that can be updated with new relevant data.
BNs use the network structure, combined with the junction tree algorithm, to calculate how
probable certain events are, and how these probabilities can change given subsequent
observations, or predict change given external interventions. A prior (unconditional)
probability represents the likelihood that an input node will be in a particular state; a
conditional probability calculates the likelihood of the state of a child node given the states of
input parent nodes affecting it; and a posterior probability calculates the likelihood that a
node will be in a particular state, given the input parent nodes, the conditional probabilities,
and the rules governing how the probabilities combine. The network is solved when nodes
have been updated using Bayes’ Theorem:
𝑃(𝐴|𝐵) =
𝑃(𝐵|𝐴)𝑃(𝐴)
𝑃(𝐵)
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Where P(A) is the prior distribution of parameter A. After collection of data B, P(A|B)
represents the posterior (new) distribution of A given the new knowledge (B). P(B|A) is the
likelihood function that links A and B.
BNs use the network structure to calculate the probability certain events will occur, and how
these probabilities will change given subsequent observations or a set of external
(management) interventions. Probabilities can be updated as new information becomes
available, using Bayes’ Theorem. Being probabilistic, BNs readily incorporate uncertain
information, with uncertainties being reflected in the conditional probabilities defined for
linkages. When analysing risk, communication of the sources and magnitudes of
uncertainties is essential. Uncertainty sources can include imperfect understanding or
incomplete knowledge of the state of a system, randomness in the mechanisms governing
the behaviour of the system, or a combination of these factors.
Major limitations of the approach are the:

need to express conditional probabilities as discrete nodes with categorical states

inability to incorporate feedbacks or loops in models

difficulties in eliciting expert knowledge in complex models

potential for introduction of expert bias.
Table B1 shows the strengths and weaknesses of Bayesian networks (Hart & Pollino 2009).
Table B1 An overview of strengths and weaknesses of Bayesian networks
Criteria
BNs
Dynamic systems (loops)
Poor
Continuous distributions
Poor
Imprecise probabilities: Exact inference *
Poor
Transparency
Poor/Good
Multiple stressors
Good
Communication tool
Good
Integration tool: Across disciplines, data and knowledge
Good
Adaptive management: Model updating
Good
Scenario analysis: What if?
Good
* Exact inference refers to probabilities not being bounded (as in Bayesian statistical approaches).
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Constructing Bayesian networks
To construct BNs, the following steps are followed:
1. Selection of endpoint/s
2. Development of influence diagram (a box-and-arrow conceptual model)
3. Creation of model structure from influence diagram
4. Discretisation of nodes (assigning states) and clarification of definitions for nodes and
states
5. Specification of probabilities
6. Parameterisation of parent nodes using data (optional)
7. Compilation of model
8. Model evaluation
9. Identification of knowledge gaps and priority risks
10. Alternative scenario analysis (optional)
Selection of endpoints
An endpoint is the output of the model being developed and investigated. It can be in the
form of an endpoint that can be measured at one level of organisation (e.g. population birth
rate and mortality of an individual) that could be incorporated in a model predicting effects on
an endpoint at another organisational level (e.g. availability of habitat for a species in a
stream). Endpoints need to be ecologically relevant, ideally representative of how the
ecosystem is structured and functions, and sensitive enough to respond to the stressors
within the ecosystem (Landis et al. 2005). Points of consideration in this study include
assessing the relevant scale, the availability of suitable expertise, and the overarching
objectives of the project.
In a previous study (Jacobs SKM 2014) exploring direct and indirect impacts of coal seam
gas development on peat swamps, the endpoint selected was change in the EPBC-listed
community ‘Temperate Highland Peat Swamps on Sandstone’ (Figure B1). In this case there
was one endpoint. This model was then adapted to explore the impacts on individual
species.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Source: Jacobs SKM (2014).
Figure B1 Example of an endpoint with direct and indirect effects from the impacts of longwall coal
mining on peat swamps
Development of the influence diagram
The next step is to develop the influence diagram leading to the endpoints. In the context of
this study, an influence diagram is a series of working hypotheses connected together by
arrows to indicate relationships (i.e. the box-and-arrow diagram described in section 3.1). It
generally portrays how an ecological system functions in its current state as well as the
potential effects of stressors on the ecosystem (Landis et al. 2005). Figure 2.3 is a good
example of the detail that can go into an influence diagram. It captures the ecological
components that are important for persistence of eastern brook trout populations and adds
the ecological effects of drilling activities for hydraulic fracturing.
Creation of model structure from an influence diagram
The next step is to develop a causal structure in a BN format (based on the influence
diagram), with relevant nodes (variables) and dependencies. Important criteria for inclusion
of variables in BNs are that the variable is either: (a) manageable, (b) predictable, or (c)
observable at the scale of the management problem. This structure can be derived from
conceptual models developed during a ‘problem formulation’ phase. See
<http://www.cs.ubc.ca/~murphyk/Software/bnsoft.html> for a listing and comparison of BN
software.
Discretisation of nodes (assigning states) and clarification of
definitions for nodes and states
States can be qualitative or quantitative, categorical (e.g. absent vs. present; 0 vs. 1) or
discrete (continuous data can be represented as a set of discrete intervals), where numerical
ranges are assigned (e.g. 0 to 3, 3 to 10). Nodes can be discretised according to guidelines,
existing classifications or percentiles of data. The number of states is unlimited but as it
increases, so does the number of probabilities to be estimated. Nodes and states need to be
clearly defined to facilitate interpretation of the network.
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Specification of probabilities
After defining node states, the strengths of relationships between nodes need to be
described. A probability distribution is required to describe the relative likelihood of the state
of each child variable, conditional on every possible combination of parent variables. This
relationship is defined with a conditional probability table (CPT). If a node has no parents, it
can be described probabilistically by a marginal probability distribution.
Figure B2 shows how CPTs work within a simple BN, where nodes A and B (parent nodes)
represent the causal factors of node C (child node). This example was created with the
programming shell Netica (http://www.norsys.com).
Figure B2 Bayesian network – simple example.
All nodes are discretely binomial, with the states defined as either true or false but the
probability distributions unspecified. The parent nodes A and B can be defined by marginal
probabilities, but the state probabilities for the child node C are conditional on how the states
of A and B combine.
The entries in a CPT can be ‘parameterised’ by a range of methods, including directly
observed data (monitoring, research), probabilistic or empirical equations, results from model
simulations, elicitation from expert knowledge, or any combination of these methods. The
methodology used to parameterise variables and the sources of information for each variable
are documented for each model. In Figure B3, direct expert elicitation is used.
Figure B3 Conditional probability table
Elicitation often takes the form of scenarios, which are described as they appear in the table
(e.g. given A is true and B is true, what is the probability that C is true (here 100%)?) The
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
elicitation process can represent probabilities as bounds to capture uncertainties in
knowledge. The method used for probability generation must be rigorously documented,
including any assumptions and limitations.
When the probability distributions of each node have been defined, the network can be
‘compiled’ or ‘solved’. After evaluation tests, the BN is complete and can be used for
scenario analysis.
Parameterisation of parent nodes with data (optional)
The quality of knowledge (Table B2) can also vary and this has implications on the
robustness of an assessment. Probabilistic relations can be specified from data (organised
as case files). Data sources can be entered into the network as a series of ‘cases’. Cases
can represent data collected during a monitoring program or as part of a research study.
Data can be used to specify probability distributions, via learning algorithms in Netica
(e.g. the Expectation Maximisation or EM algorithm).
Table B2 Narrative quality ranking for different inputs to the risk analysis BN
Rank
Statistical
analyses
Processbased model
Database
Literature
Expert
High
High
calibration
with data
(≥95%)
Comprehensive
validation using
independent
data set
Large samples,
multiple sites and
times
Published in
peer reviewed
forum
Multiple
experts – high
consensus
Moderately
well
calibrated
with data
(90 to <95%)
Some
validation using
independent
data set
Limited sampling
Non-peer
reviewed
publication
Multiple
experts –
partial
consensus
Poor
calibration
with data
(<90%)
No validation
presented
Small sample, single
site and time
Unreviewed
publication
Single expert
Medium
Low
Best practice design
and collection
methods
Accepted design
and collection
methods
Poor design and
collection methods
Source: Modified from Bowden (2004).
Compilation of model
Once the network is set up with its nodes, states and probabilities, it can be complied by the
associated software. Figure B4 shows an example with the Netica software.
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Figure B4 Netica software
Screenshot of Netica software showing the button (signifying a lightning bolt in this case)
used to compile the network. Note, child node belief bars will be grey until the network has
been compiled.
When a network is compiled, the software usually builds a junction tree (an internal structure
that the program uses for belief updating) using a minimum-weight search for a good
elimination order from the Bayes net.
Model evaluation
An important aspect in building a BN is evaluation. Evaluation of a BN requires assessing the
model behaviour to determine whether the model is representative of the system. To
evaluate the quantitative performance of the model, three types of evaluation methods are
discussed: sensitivity analysis, data-based evaluation and independent evaluation of model
outputs using expert evaluations.
Running a sensitivity analysis enables the modeller to determine how much a finding at one
node could change the belief at another. It determines the effect of each parent node on the
child node. Sensitivity analysis determines how ‘sensitive’ a model is to changes in model
parameters. By measuring the uncertainty in the model, emphasis can be placed on
parameters with enough sensitivity to affect model behaviour significantly when parameter
values are changed. Netica does this by determining the entropy reduction (variance
reduction), which is the expected reduction in uncertainty of the node being queried due to
information being given at the parent node. Hence, if information is supplied about the state
of a parent node, this may reduce the maximum range of values possible in the distribution of
the child node and reduce its uncertainty and variance within the distribution (Norsys
Software Corp 2009). Sensitivity analysis can be used to determine whether the model is
behaving as expected by checking whether predictors considered important by experts are
also important in the model.
Because BNs can incorporate information from various sources, it is also possible to
evaluate them via a combination of statistical data and domain expert evaluation. Further,
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Bayesian methods can be used to test expert predictions against empirical data, assess
expert bias, and provide a framework for the efficient accumulation and use of evidence.
Where empirical data are not available, model evaluation will be limited. Therefore the
acquisition of empirical data, collected via adaptive management processes, should be seen
as a crucial component of model evaluation.
Where possible, evaluation tests should be quantitative; however, this is not always possible.
In cases where large data sets are not available (especially common in complex systems
such as ecological and biological systems), model review by an independent domain expert
(e.g. an expert not engaged in constructing the model) can be used.
Identification of knowledge gaps and priority risks
Once the structure of the model, and the relationships used to drive it, are established, the
key knowledge gaps in understanding and priority risks can be identified. To do this,
sensitivity analysis can be used. When a model does not perform as expected, the cause
may be a knowledge gap around the parent and child node interaction relationship,
highlighting the uncertainty around the effect a parent node has on a child node.
Alternative scenario analysis (optional)
To determine how probabilities change in response to external interventions (such as
management actions) it is possible to enter evidence (by assigning a fixed distribution to the
parameter of interest). Thus, the original function is assigned a new function that specifies a
value, with other variables being kept the same.
The updated model represents the system’s behaviour under the intervention and can be
solved (through the propagation of probabilities) for the other variables to determine the net
effect of the specified intervention. The effect of the scenario can be examined by its effect
on other nodes, as illustrated in Figure B5. A scenario node can be used, which represents
scenario options as variable states.
(a) without a scenario intervention and (b) with an intervention scenario, where B = true.
Figure B5 Bayesian network with scenario intervention
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Appendix C - Workshop agenda
Time
Day and activities
Presenter
Monday 21 July
From 2pm
Check-in to Binna Burra Lodge
5–5.45 pm
Welcome function
5.45–5.55
Welcome to workshop
Anthony Swirepik
5.55–6.05
House keeping
Chris Auricht/Sarah Imgraben
6.05–6.35
CSG and coal mining in Australia – ecological impacts
Moya Tomlinson/Angela Arthington
6.35–6.45
Introduction to conceptual models and their use in the workshop
Chris Auricht/Sarah Imgraben
6.45–7.00
Introduction to Bayesian Networks
Carmel Pollino/Justine Murray
7.00
Dinner
Tuesday 22 July
From 7am
Breakfast
8.00–8.30
Objectives of the workshop and housekeeping
Chris Auricht
8.30–8.50
Natural flow regimes and hydrological responses to coal seam gas and large coal mining
development
Mark Kennard
8.50–9.10
The River Condition Index Impact Assessment Tool Project – Implementing the NSW
Aquifer Interference Policy
Julie-Anne Harty
9.10–9.40
Riparian and hyporheic zone processes – water quality effects of water balance changes
Martin Andersen
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Time
Day and activities
Presenter
9.40–10 am
TBA
Mike Ronan
10–10.30
Morning tea
10.30–10.50
Phreatophyte response to groundwater drawdown
Ray Froend
10.50–11.10
Potential impacts of CSGCM on palustrine and lacustrine aquatic ecosystems: another nail
in the coffin?
Rhonda Butcher
11.10–11.40
Water-related ecological responses of stygofauna to groundwater drawdown
Stefan Eberhard
11.40–12.10
Local scale conceptualisation of springs in the Surat Basin
Steve Flook
12.10–1pm
Lunch
1.00–1.20
CSG and coal mining in Australia – hydrological impacts
Matthias Raiber
1.20–1.40
Stream ecosystem health response to coal seam gas water release: hazards and
responses
Glenn McGregor
1.40–2.00
Fish and population persistence
Nick Bond
2.00–2.20
Aquatic invertebrates in dryland rivers: likely effects of CSG and coal mining
Fran Sheldon
2.20–2.40
Water-related ecological responses to CSG and coal mining: the hyporheic zone
Andrew Boulton
2.40–3.00
Australian freshwater turtles: diversity, ecology and potential responses to CSG and coal
mining development
Bruce Chessman
3.00–3.20
Afternoon tea
3.20–3.40
Assessment of risk in the Bioregional Assessment Programme
Simon Barry
3.40–4.00
Assessing the Potential Impacts of CSG extraction on GDEs in Eastern Victoria
Steve Wickson
4.00–4.30
An evidence-based approach to demonstrate causal relationships between anthropogenic
Will Clements
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Time
Day and activities
Presenter
stressors and macroinvertebrate community responses
4.30–5.00
Wrap up of day, refer to conceptual models
6.30
Dinner
Chris Auricht/Sarah Imgraben
Wednesday 23 July
From 7am
Breakfast
8.00–8.30
Preamble
8.30
Leave for field trip site
Visit various locations, discuss conceptual models
Justine Murray
12.00–1pm
Lunch at Purga Nature Reserve
1.00–4.00
Visit various locations, discuss conceptual models
6.30
BBQ Dinner
8.00–9.00
Presentation by local Guide
Start time
Thursday 24 July
From 7am
Breakfast
8.00–8.10am
Recap on day 2, overnight thoughts, plan for day 3
Chris Auricht
8.10–8.55
Presentation and discussion of updated conceptual models
Sarah Imgraben
8.55–10.00
Facilitated BN population, discuss case study
Carmel Pollino
10.00–10.30
Morning tea
10.30–12.00
Continued facilitated BN population, discuss case study
Justine Murray
Presenter
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Carmel Pollino
Modelling water-related ecological responses to coal seam gas extraction and coal mining
Time
Day and activities
Presenter
12.00–1pm
Lunch
1.00–3.00
Continued facilitated BN population, discuss case study
3.00–3.20
Afternoon tea
3.20–5.00
Review/discussion of approaches (conceptual models and BNs)
Chris Auricht/Sarah Imgraben
Chris Auricht/Justine Murray
Wrap up
6.30
Dinner
Friday 25 July
From 7am
Breakfast and check-out
8.30–8.40
Recap on day 3, overnight thoughts, plan for day 4
Chris Auricht
8.40–12 pm
Review hypotheses and consider interactions, application of case study to the other
regions, prioritise research needs and key questions (scoping future Ecology projects)
Chris Auricht
12–12.30
Summing up and conclusion of workshop
Chris Auricht
12.30–1.30
Lunch and attendees depart
1.30–3.30
Technical Advisors - Meeting 2
Facilitators, Tech advisors, OWS
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Appendix D - Workshop participants
Surname
First name
Title
Affiliation
Discipline
Andersen
Martin
Dr
University of New South Wales
Hydrogeology
Arthington
Angela
Prof.
IESC
Ecology
Auricht
Chris
Mr
Auricht Projects
Facilitator
Barry
Simon
Dr
CSIRO
BA/risk
Bond
Nick
Dr
Griffith University
Aquatic ecology
Boulton
Andrew
Prof.
University of New England
Ecology
Butcher
Rhonda
Dr
Water’s Edge Consulting
Wetlands
Chessman
Bruce
Dr
Independent consultant
Aquatic ecology
Clements
Will
Prof.
Colorado State University
Coal mining and toxicology
Eberhard
Stefan
Dr
Subterranean Ecology
Groundwater ecology
Flook
Steven
Mr
Department of Natural Resources and Mines, Qld
Spring conceptual modelling
Froend
Ray
Prof.
Edith Cowan University
Groundwater dependent vegetation
Harty
Julie-Anne
Dr
NSW Office of Water
Herpetology
Herr
Alexander
Dr
CSIRO
Ecology
Imgraben
Sarah
Ms
Auricht Projects
Facilitator
Kennard
Mark
Dr
Griffith University
Flow regime classification BNs
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Surname
First name
Title
Affiliation
Discipline
McGregor
Glenn
Dr
Department of Science, Information Technology, Innovation
and the Arts, Queensland
Stream ecosystem response
Murray
Justine
Dr
CSIRO
BA Ecology lead CLM
Pollino
Carmel
Dr
CSIRO
Ecological modelling
Raiber
Matthias
Dr
CSIRO
Hydrogeology and modelling
Ronan
Mike
Mr
Dept. of Environment and Heritage Protection
Wetland conceptual modelling
Swirepik
Anthony
Mr
OWS, Department of the Environment
Research
Tomlinson
Moya
Dr
OWS, Department of the Environment
Ecology
Wickson
Steven
Dr
Dept. of Environment and Primary Industries, Victoria
Invertebrates
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Appendix E - Abstracts of
presentations
Andersen, Martin
Connected surface water groundwater systems – potential effects of water
management on water quality and streambed ecology
Water management in Australia’s arid and semi-arid regions has largely focused on
managing the quantities of water in an environment of drought and increasing water demand
from irrigated agriculture. The increase in groundwater resource development has changed
groundwater flow paths in riparian zones on a massive scale. In catchments with large
groundwater abstraction, streams and rivers are transitioning from overall gaining to losing
conditions. This has implications for biogeochemical reactions and the transport of dissolved
constituents between riparian aquifers and rivers. The shallow riparian groundwater zone
(here including the hyporheic zone) often has an abundance of fresh reactive organic matter
either from recent sedimentation or via infiltration of dissolved and particulate organic matter
from the river. The oxidative demand of this organic matter from microbial metabolism drives
reduced redox conditions of the groundwater, which is therefore depleted in oxygen and
contains high concentrations of dissolved reduced species. A gaining scenario is able to
confine these species to the riparian groundwater zone, from where they would eventually be
discharged to the river, diluted in the surface water flow, re-oxidised, and for some species
(e.g. Fe(III), Mn(IV) and As(V)) precipitated as oxides. In contrast, in the losing scenario
caused by groundwater abstraction, the reduced water would instead migrate towards
potential abstraction bores and have no further interaction with the stream. Considerable
questions and uncertainty remains about these processes and their effect on ecology and the
fate of contaminants. In this talk the potential effects of changing flow paths on
biogeochemical reactions and ecology is discussed.
Barry, Simon
Assessment of Risk in the Bioregional Assessment Programme
The Bioregional Assessment Programme is designed to estimate the cumulative impacts of
coal seam gas and coal mining developments on ecological, cultural and economic assets in
selected bioregions. This process needs to be implemented in a range of situations,
sometimes in large regions containing thousands of assets. This talk will provide an overview
of the implementation of this methodology. It will outline the role of conceptual modelling and
the proposed methodology for assessing impact. It will discuss the challenges, and some of
the solutions to doing these assessments.
Bond, Nick
Spatially explicit modelling of fish population persistence in intermittent rivers
In many dryland rivers fish habitat primarily consists of isolated waterholes, which contract
during the dry period, but are replenished and connected by seasonal wet-season flows.
Waterhole persistence is governed by these reconnections, and multi-year droughts can
reduce the number of waterholes that persist in the landscape. Sedimentation and water
resource development also pose increasing threats to overall levels of waterhole persistence
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
in many dryland rivers. Because fish can move among waterholes while rivers are
connected, population dynamics and persistence over multiple generations at the river-scale
can depend strongly on the spatial dynamics of recruitment, mortality and dispersal. Spatial
population models and population viability analysis (PVA) provide a valuable approach for
quantifying potential long-term population trends under different hydro-climatic conditions.
Here I outline the general demographic modelling framework, and present two case studies
of modelling fish population persistence; one for Carp Gudgeon (Hypseleotris spp.)
populations persisting in small headwater streams, and another for Golden Perch (Macquaria
ambigua) populations in a large lowland river. Both case studies highlight the potential
sensitivity of populations (in terms of extinction risk) to changing hydrologic regimes.
Extinction debt can also makes these risks. I also hope to highlight the role of numerical
models in conceptualising problems, assembling existing ideas and data, guiding empirical
data collection, and aiding dialogue within interdisciplinary teams.
Boulton, Andrew
Water-related ecological responses to CSGCM: the hyporheic zone
The hyporheic zone is the saturated sediments below and alongside streams and rivers
where surface water exchanges with shallow groundwater. This exchange of water creates
redox gradients within the hyporheic zone along which occur many biogeochemical
processes, crucial to stream ecosystem function (e.g. nutrient cycling, organic matter
decomposition), often mediated by microbial assemblages. Aquatic invertebrates also inhabit
this zone which apparently can serve as a refuge from flooding and drying at the surface.
Groundwater drawdown associated with CSGCM is likely to alter the strength and/or
direction of hydraulic exchange, affecting redox gradients within the hyporheic zone.
Biogeochemical processes and hyporheic invertebrate assemblages may also be affected by
alterations of natural surface flow regimes and sedimentation arising from direct extractions
of surface water, additions of co-produced water (into the surface stream or shallow
aquifers), altered run-off from modified catchments, poorly controlled sediment inputs and the
effects of subsidence. These responses are largely hypothetical as there are few empirical
data on the effects of CSGCM on the hyporheic zone. Nonetheless, except in bedrockcontrolled streams or where hydrological exchange in the hyporheic zone is minimal (e.g.
fine sediments or ‘perched’ beds), there are likely to be adverse effects on fauna and
ecological processes in the hyporheic zone resulting from several activities associated with
CSGCM, with flow-on consequences for riverine ecosystem function downstream.
Butcher, Rhonda
Potential impacts of CSGCM on palustrine and lacustrine aquatic ecosystems: another
nail in the coffin?
Classification, mapping and conceptual modelling of palustrine and lacustrine ecosystems
have progressed considerably in the past 10 years. Our level of understanding of the impacts
of threatening processes has advanced as well, although we still have significant knowledge
gaps, including how altered water regimes influence groundwater surface water interactions.
Impacts from altered water regimes vary according to ecosystem type, degree of hydrological
connectivity and landscape context. A number of simple matrices of hydrological stressors
against CSGCM stressors will be presented showing strength of impacts of each
combination for four different wetland types. Knowledge gaps will also be identified.
Combinations which have a strong or known impact, ecological effects can be detailed using
the terminology adopted from the Ramsar Rolling Review. Combined this represents a
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
simple way of presenting our level of understanding of the impacts of CSGCM stressors on
hydrological regime and associated ecological effects for palustrine and lacustrine wetlands.
A complete set of these models could be developed for palustrine and lacustrine systems
using the ANAE as the basis for a typology. Stressor models developed either for Ecological
Character Descriptions (ECD) or as part of the Ramsar Rolling Review are presented as
further examples of the range of models available which could be adapted to reflect CSGCM
impacts. Lessons learnt from preparing ECD and developing cause-effect models for
identifying thresholds of change at Ramsar sites, include the importance of standard
terminology and consistency when applying classifications of ecosystem type, threats,
stressors, ecological effects and endpoints.
Chessman, Bruce
Australian freshwater turtles: diversity, ecology and potential responses to coal seam
gas and coal mining development
Freshwater turtles are often neglected in environmental management but can be an
important ecosystem component. Australia has about 23 species, most of which are
endemic, and they occur throughout the continent with the exception of Tasmania, the Alps
and much of the arid zone. They probably make substantial contributions to ecosystem
processes because they can reach high biomass densities and collectively consume a great
variety of foods including algae, water plants, fallen fruits of terrestrial plants, aquatic and
terrestrial invertebrates and fish. Some species can survive for months in a dormant state
buried in soil or dry wetland sediments, but they cannot feed out of water. Their life histories
are characterised by low rates of egg and hatchling survival, slow maturation and great adult
longevity. Consequently they have low intrinsic rates of population increase and are
vulnerable to reductions in adult survival. Very little is known about their susceptibility to gas
extraction or coal mining. Areas of potential concern include loss of aquatic habitat as a
result of upstream impoundment of diversion, changes in runoff, or subsidence, which could
expose turtles to in situ predation or induce hazardous overland movements. A further
concern is possible contamination of surface waters with sediment, which could reduce food
availability, and with chemicals that could have toxic effects or accumulate in turtle tissue. In
addition, coal seam gas extraction and coal mining activities might flood or damage turtle
nesting areas or block overland migration.
Clements, William H.
An evidence-based approach to demonstrate causal relationships between
anthropogenic stressors and macroinvertebrate community responses
Water resource managers today are challenged to demonstrate causal relationships between
changes in water quality and measures of biological integrity such as species richness or
community composition. However, because most biological assessments rely exclusively on
observational data, these causal inferences are often weak. Using data from field surveys of
benthic macroinvertebrates, we tested the hypothesis that contaminants associated with
mining operations were directly responsible for changes in benthic community structure. We
first examined relationships between macroinvertebrate community structure and metal
concentrations in >300 Colorado streams. Results showed consistent and predictable
alterations in community composition along a gradient of metal contamination. These data
were supplemented by a set of 24 stream microcosm experiments that established
concentration-response relationships and allowed us to estimate community-level LC20
values. Additional evidence for a causal relationship between metals and macroinvertebrate
responses was provided by a long-term (24 year) “natural” experiment in which we
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
documented macroinvertebrate responses to the removal of heavy metals. Although these
data showed significant improvements in water quality and macroinvertebrates over time,
communities remained impaired when metal concentrations exceeded the community-level
EC20 values. Finally, to investigate plausibility and coherence of these results, we examined
mechanisms responsible for differences in sensitivity among species. Overall, these
investigations provided strong evidence that metals associated with historical mining
operations were the primary stressors responsible for changes in macroinvertebrate
communities.
Eberhard, Stefan
Water-related ecological responses of stygofauna to groundwater drawdown
Groundwater is the critical habitat for stygofauna. Most stygofauna are invertebrates,
predominantly crustaceans, but annelids, molluscs, water mites and fish also inhabit
groundwater. Stygofauna occur across Australia in most types of aquifer (alluvial,
fractured-rock, karstic) where there is a habitable porosity, oxygenated groundwater and
nutrient inputs. Stygofauna are most diverse and abundant in shallow aquifers near to the
ground surface where nutrient availability is greatest, however they may also occur in some
deep aquifers when conditions are suitable.
Many species of stygofauna are exclusively dependent on groundwater and have restricted
distribution ranges, so they are vulnerable to water-related impacts including groundwater
drawdown and changes to water quality. While stygofauna evidently possess some degree of
adaptive capacity and resilience to these stressors, there is little quantitative data or
understanding of ecological responses, thresholds and endpoints. Laboratory studies
simulating groundwater drawdown indicate varying responses and sensitivity among different
taxa.
A case study in a karst aquifer in southwest Western Australia documented the changes in
an endangered stygofauna community caused by groundwater decline driven by climatic
drying and other anthropogenic stressors. The retrospective approach (from a healthy
ecosystem to near extinction) characterised and defined the ecological condition, thresholds
and end-points. This model may have applicability to CSGCM in eastern Australia after
taking account of regional and site-specific characteristics.
Flook, Steve
Local scale conceptualisation of springs in the Surat Basin
In the Surat and Southern Bowen basins, groundwater dependent wetlands are
predominantly discrete natural features within the broader regional groundwater flow system.
Surface water, local groundwater flow systems, local structural features and land
management practices affect the susceptibility of a wetland to a change in groundwater
pressure. Understanding the natural variability and the interactions between these influences
is required to inform research on thresholds to change for species dependent on these
environments.
A sound hydrogeological understanding of the wetland system is also required to adequately
assess impacts associated with coal, petroleum and gas activities. This knowledge provides
the foundation for the assessment of the 'likelihood' component when assessing risks to
these systems.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
From a groundwater perspective, this requires an understanding of the wetland water
balance. For each wetland, the components which influence wetland condition and the
interactions between these components need to be identified. This is required to understand
variability and therefore susceptibility to a change in any one of the wetland components.
Identifying and understanding these influences is paramount to ensure effective monitoring
and management of groundwater impacts. For further information see
http://www.dnrm.qld.gov.au/ogia/research/spring-knowledge-project
Froend, Ray
Phreatophyte response to groundwater drawdown
The notion of groundwater-dependent vegetation (phreatophytes) implies groundwater is an
important contributor (but not the only one) to the maintenance of the hydrological regime
supporting the vegetation. Furthermore, a change in the quantity or quality of groundwater
will impact on the state and condition of the vegetation. The nature of dependence on
groundwater relative to other sources of water is important in differentiating these responses
to changes in groundwater availability. Different species assemblages will develop and
become characteristic of the predominant hydrological regime. Catastrophic (and largely
irreversible) changes in the availability of groundwater, such as the exacerbation of droughtinduced drawdown by groundwater abstraction, has resulted in widespread mortality of
groundwater dependent vegetation (e.g. the Swan Coastal Plain) and local extinction of
sensitive species. There are also examples of phreatophytic vegetation demonstrating some
resilience however this is precluded where drawdown is persistent and at a high rate. The
evidence that vegetation will shift to an alternative ecohydrological state (defined by
composition and abundance) in accordance with changes in depth to water table, is
substantial. At sites subject to lower rates of groundwater drawdown (9cm year-1), shifts in
floristic characteristics of each community was represented by a change in species
abundance, i.e. reduction in density rather than species turnover. In contrast, where rapid
hydrological change occurred (50cmyear−1), species turnover was more pronounced with
increased representation of facultative, xerophytic species and loss of drought-sensitive
obligate species. The impacts of CSGCM on groundwater dependent vegetation will
correspond with the magnitude, rate and persistence of water table decline provided the
transition is not interrupted by other disturbances or further hydrological change. Impacts on
vegetation composition and productivity will influence associated fauna however less is
known about the extent of direct and indirect consequences of phreatophyte decline on other
biota.
Harty, Julie-Anne
The River Condition Index Impact Assessment Tool Project – Implementing the NSW
Aquifer Interference Policy
Under the NSW Water Management Act 2000, and the Aquifer Interference Policy (‘the
Policy’), proponents of activities that are likely to have an impact on the water table, pressure
or quality (including impacts on dependent ecosystems), are required to document the
impacts at the planning and approvals stage of a project. This may include consideration of
whether the condition of the nearest waterway to the aquifer interference activity will be
reduced (as measured by the River Condition Index (RCI)). The RCI is a state-wide,
spatially-expressed riverine condition index which incorporates data on riparian vegetation,
geomorphology, hydrology, biodiversity and catchment disturbance. It was created to align
water sharing and catchment management planning in NSW. The aim of the River Condition
Index Impact Assessment Tool Project is to modify the RCI into a site-specific, cumulative
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
impact assessment tool for aquifer interference activities. The project includes describing the
direct, indirect and cumulative ecological impacts from longwall coal mining, open cut coal
mining, coal seam gas exploration and production, and sand and gravel mining on surface
water; creating conceptual models to define pressure-response relationships; identifying
thresholds/ecosystem resilience; dealing with knowledge gaps and uncertainty, and working
with empirical evidence and expert knowledge to create a web-based, spatially-expressed
impact assessment tool. The project commenced in March 2014, is due for delivery in
March 2016, and is currently in the literature review and scenario testing/’proof-of-concept’
phase.
Kennard, Mark J
Natural flow regimes and hydrological responses to coal seam gas and large coal
mining development
Flow regimes are a key driver of aquatic ecosystem structure and function. Changes to the
natural spatial and temporal patterns of river flows due to human activities can have
important consequences for the long term sustainability of aquatic ecosystems and the
goods, services and long-term benefits they provide for people. This presentation reviews the
key ecologically important components of the natural flow regime (i.e. magnitude, timing,
frequency, duration, and variability of flows), and characterises natural flow regime types in
each of the Bioregional Areas currently being assessed for coal seam gas and large coal
mining development (coal seam gas and coal mining development). Potential hydrological
consequences of coal seam gas and coal mining are identified at a range of spatio-temporal
scales and evaluated with reference to other potentially interacting hydro-ecological stressors
(e.g. water infrastructure development, land use, climate change). This information provides
an initial hydrologic foundation for predicting ecological responses to coal seam gas and coal
mining.
McGregor, Glenn
Stream ecosystem health response to coal seam gas water release: hazards and
responses
Production of coal seam gas in Queensland is likely to result in significant quantities of
co-produced reverse osmosis (RO) treated water being discharged to surface water streams.
In areas where these discharges are likely to occur, streams exist as networks of ephemeral
channels and waterholes that experience extended no-flow periods followed by episodic high
magnitude flows and flooding associated with summer sub-monsoonal rainfalls. Long-term
continuous release of coal seam gas water has the potential to interrupt ecological cycles in
aquatic ecosystems adapted to these conditions. Studies aimed at characterising the
hazards and potential ecosystem responses to coal seam gas water releases in the
Queensland Murray-Darling Basin (QMDB) identified a number critical hazards associated
with these releases:

alteration to the hydrological regime (loss of intermittency and seasonality)

decreased electrical conductivity

altered ionic composition

increased water transparency

river bank instability and erosion

cumulative toxicological impacts from contaminants.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
A Pressure-Stressor-Response framework provides a useful approach to predict likely
interactions and ecosystem outcomes at the individual stressor level; however current
approaches to integrate multi-stressor effects at the site, sub-catchment, and catchment
scales are limited due to a paucity of empirical data. For example, co-produced RO treated
water has been shown to flocculate suspended colloids at dilution thresholds between
10-50%. Proximate responses include fine sediment deposition and habitat loss, gill
clogging, increase in BOD, altered light climate leading to changes in food webs from
allochthonous to autochthonous production. Changes in photochemical processes may also
alter rates of contaminant decay, whereas increased water column transparency may modify
predator-prey interactions. These effects will interact in varying ways based on the hydrogeological setting, and cascading interactions are likely to occur with other coal seam gas
water disposals at the sub-catchment and catchment scale. These interactions pose
substantial conceptual and practical challenges to model effects at a range of spatial and
temporal scales.
Raiber, Matthias and Rassam, David
Coal seam gas and coal mining in Australia – hydrological impacts
Coal seam gas and coal mining activities can potentially have significant impacts on
hydrological systems, as these activities require de-pressurisation of the coal bearing
formation (for coal seam gas development) or de-watering (for coal mining).
In the Clarence-Moreton bioregion (the eastwards-draining part of the Clarence-Moreton
Basin), five major alluvial systems (Lockyer Valley, Bremer/Warrill, Logan/Albert in Qld and
Richmond River and Clarence River catchments in NSW) host important alluvial groundwater
and surface water resources that are intensively used for irrigation. In addition, these
catchments host significant assets such as groundwater-dependent ecosystems (e.g. springs
and wetlands). In order to predict the potential impacts of de-pressurisation associated with
coal seam gas extraction or de-watering for coal mining from the Walloon Coal Measures
(major target of coal seam gas exploration and coal mining in the Clarence-Moreton
bioregion), an accurate understanding of the links between different components of the
hydrological system is essential.
In this presentation, we will show examples how sedimentary bedrock aquifers, alluvial
aquifers and streams or wetlands are connected in the Clarence-Moreton bioregion. In
addition, this presentation will highlight how baseflow, which sustains flow in many streams
and wetlands, varies spatially and temporally and how climatic variability together with
geological and geomorphologic factors are primary controls of this variability.
Sheldon, Fran
Aquatic invertebrates in dryland rivers: likely effects of coal seam gas and coal mining
development
Much of Australia is semi-arid or arid, drained by ephemeral streams and dryland rivers
whose flow regimes are notoriously variable and unpredictable. These variable flow regimes
drive the ‘boom-and-bust’ ecology of dryland rivers: ‘boom’ periods of immense productivity
across vast inundated floodplains are followed by ‘bust’ periods when much of the water
disappears, except in a few refugial waterholes. Pivotal to the foodwebs of all inundated
floodplains and dwindling waterholes are aquatic invertebrates, feeding on detritus, algal
biofilms or phytoplankton and serving as prey for fish and birds. These invertebrates survive
the ‘bust’ period as resting stages in the sediments (‘stayers’) or remain in persistent
waterholes as ‘permanent refugials’ while others disperse by flight even when waterholes are
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
disconnected (‘movers’) or make use of remnant or intermittent channel connections
(‘networkers’). Although flooding is important, the inter-flood periods of low or zero flow are
just as vital because these promote invertebrate biodiversity across a mosaic of isolated
waterbodies. Therefore, alterations of hydrological regime that include longer periods of flow
(e.g. where co-produced water from coal seam gas extraction enters normally dry channels)
may be as disruptive as water extraction from channels and remnant water holes. Many
current or planned coal seam gas and coal mining activities occur in dryland parts of
Australia. Likely effects of these activities on aquatic invertebrates in dryland rivers include:
changes to water quality, reduced variability of flow regimes, reduced groundwater inputs
that sustain waterhole refuges, altered inputs of detritus and nutrients, sedimentation that fills
refugial waterholes, and changed patterns of surface runoff (e.g. from subsidence or road
construction in a low-relief catchment).
Tomlinson, Moya
This presentation will provide background and rationale for the workshop through a summary
of the current ‘state of play’ of analysis of ecological impacts in development assessment
documentation. There is a need to integrate hydrogeological and ecological
conceptualisation , make assumptions about ecological responses and impacts explicit, and
support these ‘hypotheses’ with explicit reference to relevant scientific literature, empirical
data and other credible evidence.
Wickson, Steve
Assessing the Potential Impacts of coal seam gas extraction on GDEs in Eastern
Victoria
Victoria’s existing gas energy demands are projected to double by 2030 and Victoria’s
existing reserves are expected to be depleted by this time. There may be potential for
unconventional gas, which includes coal seam gas, to replace or supplement Victoria’s
declining conventional gas supply. The Gippsland region has significant coal measures and
is attracting the most interest for development, making it a priority for the Commonwealth
Bioregional Assessment Programme. The first phase of the bioregional assessments is to be
delivered in two parts (A & B). Part A delivered on the core requirements of the IESC and the
Department of the Environment, producing the Water Asset Information Tool (WAIT). The
projects within Part B were developed to be compatible with the Bioregional Assessment
methodology to ensure the most effective use of the project outputs for the Gippsland basin
Bioregional Assessment. The Bioregional Assessment Programme complements work
currently being undertaken by DEPI and has allowed projects to be accelerated in the
Gippsland region. DEPI is currently undertaking the following projects:

Improving knowledge of water-dependent assets and receptors through conceptual
modelling.

Improving certainty of existing baseflow studies.

Quantification of groundwater contribution and dependence of groundwater resources
for wetlands.

Gippsland GDE Prioritisation Framework.
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Modelling water-related ecological responses to coal seam gas extraction and coal mining
Appendix F - Case study: Purga Nature
Reserve
Purga Nature Reserve (140 hectares) on Purga Creek in the Bremer River catchment west of
Brisbane in South East Queensland was selected as a case study. The vegetation
community present at Purga Nature Reserve is Swamp Tea-tree (Melaleuca irbyana) Forest
of South-east Queensland, which is listed as a Critically Endangered Ecological Community
under the Environment Protection and Biodiversity Conservation Act 1999 (Cwlth) and as an
Endangered Regional Ecosystem under the Vegetation Management Act 1999 (Qld).
Swamp Tea-trees (Melaleuca irbyana) usually occur in thickets about 8 to 12 m high
underneath an open canopy of eucalypt trees. Typical eucalypt species in this community
include Narrow-leaved Ironbark (Eucalyptus crebra), Silver-leaved Ironbark
(E. melanophloia), Grey Box (E. moluccana) or in the case of Purga Nature Reserve, the
Blue Gum (E. tereticornis). The understorey is sparse and comprises grasses, sedges and
herbs with few shrubs and vines present (DEH 2005).
The Swamp Tea-tree Forest is restricted to Quaternary alluvial plains and Cainozoic and
Mesozoic sediments, and occurs on level ground to slightly elevated areas on alluvial plains,
and on the sides, saddles and tops of low rolling hills in areas with impeded drainage. The
Swamp Tea-tree Forest grows on clay soils which drain slowly and often become
waterlogged after heavy rains, resulting in the appearance of numerous temporary ponds
(DEH 2005). These soils have been described as brown to dark grey, heavy, coarse
structured cracking clays that are low in nutrients. The subsoils are dark grey to dark brown,
highly erosive and highly saline, strongly sodic, and dominated by magnesium (Dept. of the
Environment 2014).
The community does not generally grow along water courses or within permanent
swamps/wetlands, but is commonly associated with areas that experience periods of
inundation of 3 to 6 months for several weeks after summer rainfall as a result of perched
water tables, in locations where runoff flows overland rather than in distinct drainage lines.
The average annual rainfall in areas where Swamp Tea-tree Forest occurs is 853 to 924 mm
(Dept. of the Environment 2014). Areas that are inundated for longer periods becoming
dominated by grass, sedge, and herb wetlands (DEHP 2013).
Historically, the Swamp Tea-tree Forest has been extensively cleared for improved pastures
and rural residential development (Boulton et al. 1998), and also to expand coal mining.
Eucalyptus trees associated with the ecological community have been logged for a century
or more, and this logging has led to absence or wide spacing of eucalypts in the
Swamp Tea-tree Forest.
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