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Applying modern ecological methods for monitoring and modelling mine rehabilitation success A M Lechner1, S Arnold2, N B McCaffrey3,4, A Gordon5, P D Erskine6, M J Gillespie7 and D R Mulligan8 ABSTRACT Mine rehabilitation monitoring programs where the post-mining land use goal is native ecosystems, are often conducted to address regulatory compliance requirements. In some cases these monitoring programs do not have the suﬃcient statistical power or understanding of ecological systems to assess, quantitatively, whether the rehabilitation targets or progress towards those closure targets have been met. Monitoring programs commonly rely on analogue sites or premining baselines to assess whether rehabilitation sites are ecologically similar and thus successfully rehabilitated. Fundamental to any monitoring program is testing the sampling design to ensure that it can detect these differences, which requires adequate sample sizes (that is, statistical power). Additionally, a good understanding of the ecological system is required to ensure that differences detected are ecologically significant. Modelling ecosystem properties is crucial to providing a quantitative understanding of how ecosystems change over time and space, in response to natural environmental variations. Within the ecological literature, guidelines for robust statistical monitoring designs and modelling methods exist but are not routinely being used in the mining industry. Examples of applied ecological modelling methods include: the use of simulation modelling to assess the impacts of urban growth on threatened vegetation communities; species distribution models and multispecies prioritisation tools aimed to support biodiversity decision-making; and the use of least-cost path analysis to assess landscape connectivity across regional landscapes. This paper is a revised version of Lechner et al (2012a) and uses simple examples to discuss: • problems with current monitoring methods • importance of robust statistical techniques • how modelling can assist rehabilitation assessment and guide monitoring programs. The aim of this paper is to describe monitoring and modelling techniques that could be potentially used for mine site monitoring and rehabilitation completion assessment. These methods may be considered by mining companies as an extra expense that provides little practical benefit, and the domain of academia. However, engaging these methods may be more cost-effective in the long-run than conducting untargeted monitoring schemes that give no confidence to regulators on whether rehabilitation criteria have been met nor feedback to mining companies on the effectiveness of their rehabilitation methods. Well-focused monitoring is essential for continual improvement and such improvement is a normal process over the life of a mine (with many mines operating over several decades) to ensure that monitoring informs the next cycle of rehabilitation and tracks towards rehabilitation completion targets.

INTRODUCTION Mining practices are under pressure from community concerns of environmental harm and increasing regulatory requirements to minimise environmental harm (Environment Australia, 2002; Lamb, Erskine and Fletcher, 2015). An important consideration during the life of the mine is addressing the effects of mine operations on natural ecosystems, either as a result of mining 1. 2. 3. 4. 5. 6. 7. 8.

Assistant Professor, School of Environmental and Geographical Sciences, University of Nottingham Malaysia Campus, Semenyih 43500, Malaysia. Email: [email protected] Research Fellow, Centre for Water in the Minerals Industry, Sustainable Minerals Institute, The University of Queensland, St Lucia Qld 4072. Email: [email protected] Honorary Fellow, Centre for Mined Land Rehabilitation, Sustainable Minerals Institute, The University of Queensland, Brisbane Qld 4072. Email: [email protected] Senior Ecologist, WSP | Parsons Brinckerhoff, Natural Resource Management Group, Southbank Vic 3006. Senior Research Fellow, School of Global Studies, Social Science and Planning, RMIT University, Melbourne Vic 3000. Email: [email protected] Senior Research Fellow, Centre for Mined Land Rehabilitation, Sustainable Minerals Institute, The University of Queensland, Brisbane Qld 4072. Email: [email protected] Senior Research Oﬃcer, Centre for Mined Land Rehabilitation, Sustainable Minerals Institute, The University of Queensland, Brisbane Qld 4072. Email: [email protected] FAusIMM, Director, Centre for Mined Land Rehabilitation, Sustainable Minerals Institute, The University of Queensland, Brisbane Qld 4072. Email: [email protected]

affecting surrounding or above-ground ecological communities, or during the rehabilitation process post-mining (Audet et al, 2013; Arnold et al, 2013). According to the Berlin Guidelines 1991 (revised 2000), both government and mining companies need to prioritise environmental management during the licensing process and during the development and implementation of environmental management systems (United Nations, 2002). The sound design of a monitoring scheme is an important feature of any environmental management system. Well-designed monitoring programs are required to minimise the impacts of mining on ecological communities and to ensure that the rehabilitation of these communities is successful. Recent reviews of environmental regulation by the Queensland Audit Office (Queensland Audit Office, 2013) and the Australian National Audit Office (Australian National Audit Office, 2014) revealed that there is a need for improvement in environmental monitoring. However, there are few protocols to help guide the design and implementation of specific monitoring programs to maximise the ability to detect changes (Glenn et al, 2014; McCaffrey et al, 2014a). One exception to this, is the research and monitoring that has taken place over many decades ensuring successful rehabilitation of bauxite mining operations in Western Australia (see Koch and Hobbs, 2007). As the type of monitoring design used can have a profound effect on management decisions, it is important to understand how reliable and useful the monitoring data is for informing management. Monitoring programs commonly used in mining rely on analogue sites or premining baselines describing ecosystems or landscapes (henceforth described as reference sites) to assess

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impacts and the level of rehabilitation success. For rehabilitation, the ecological properties of the ecosystem within the reference site serve as a model for assessing outcomes and a target to guide management and is considered one of the fundamental principles of ecological restoration in the ‘National standards for the practice of ecological restoration in Australia’ (McDonald, Jonson and Dixon, 2016). In both cases, differences between impacted or rehabilitated sites and reference sites are evaluated through assessing differences in ecological indicators such as species diversity or abundance (Society for Ecological Restoration (SER) International Science and Policy Working Group, 2004). In the case of mine rehabilitation, if the reference and the rehabilitation site are similar, rehabilitation is considered to have been achieved successfully. In the case of monitoring for mine impacts, if the ecological metrics in the impacted site diverge from reference data, the site is considered to be impacted. In practice, however, there are a whole range of other factors that affect these assessments beyond differences in data alone.

This paper discusses how monitoring methods for assessing differences between reference and impacted/rehabilitation sites could incorporate power analysis methods and ecological modelling to improve the robustness of long-term monitoring. There is a need to move beyond minimal monitoring for compliance (that is, only to satisfy regulatory requirements) to designing effective ecological monitoring programs for mine rehabilitation which, if successfully undertaken and incorporated in site management, improve the likelihood of companies obtaining successful sign-off for their rehabilitation. This process is illustrated in three steps. First, an introduction to power analysis methods that can be used for assessing the statistical robustness of monitoring programs is provided. Next the importance of good ecological knowledge to inform what size of difference between reference and monitoring sites need to be measured (that is, effect sizes) is discussed. Finally, it is suggested that by using monitoring data in experimental analyses and with modelling a greater understanding of ecological systems can be achieved and monitoring programs further refined. Monitoring data can be used as inputs into ecological models for the purposes of predicting future ecosystem condition (Arnold, Thornton and Baumgartl, 2012). These predictive models are rarely applied in the mining context, although in other sectors, modern ecological methods are being applied successfully (Arnold et al, 2014a).

SET-UP OF MONITORING PROGRAM

Monitoring design and methods There is burgeoning guidance and literature on what is considered responsible mine closure and rehabilitation. However, the discipline of rehabilitation is an evolving field with some debate on whether formal science is more effective than trial and error based approaches for achieving effective rehabilitation (Cabin, 2007; Giardina et al, 2007). Regardless of the approach, some form of monitoring and evaluation is essential to better understand and guide rehabilitation practices. Without progressive evaluation of rehabilitation efforts, there is the risk of reducing the credibility of the science and practice of mine rehabilitation. Furthermore, knowledge of the success and cost of progressive rehabilitation is necessary to inform financial assurance and also enable lease relinquishment. Unfortunately monitoring does not always inform management decisions (Reid, Hazell and Gibbons, 2013) and in some circumstances the benefits of collecting the data do not always outweigh the costs of acquiring it (McDonald-Madden et al, 2010). Compounding this, is limited reporting or data that is publicly available on mine site rehabilitation and monitoring (Mudd, 2009). Therefore, a well-designed monitoring program tailored to a site should not only inform decisions about managing

rehabilitation, but the benefits should outweigh the cost of undertaking the exercise in the first place.

At the planning stage, and before monitoring is commenced, the statistical robustness of monitoring programs should be assessed through the use of a power analysis (Fairweather, 1991; Legg and Nagy, 2006) (Figure 1: step 1). A key prerequisite for a power analysis is good ecological knowledge to inform effect sizes – the difference between reference and monitoring sites (Figure 1: step 1). When using monitoring data with experimental analyses, a greater understanding of ecological systems can be derived and the effect size further refined (Figure 1: steps 2–3). Effective monitoring requires a commitment to undertake systematic and reliable measurements that are sufficiently comprehensive and precise. Monitoring must be able to detect changes in conditions due to rehabilitation efforts as distinct from those due to natural environmental variation, and any follow-up management actions when required (Barker, 2001). This distinction can only be achieved if the monitoring program is carefully conceived with a rigorous design, as was emphasised in a survey of mine site environmental managers in Western Australia regarding the adequacy of mine rehabilitation monitoring practices (Thompson and Thompson, 2004).

Characteristics of an effective monitoring program

An effective rehabilitation monitoring program should draw on guidelines described in the existing monitoring literature (Elzinga et al, 2001; Green, 1979; Legg and Nagy, 2006; Lindenmayer and Likens, 2010; Yoccoz, Nichols and Boulinier, 2001), which include: •• identify clear, unambiguous monitoring and rehabilitation objectives •• identify suitable reference sites to allow at least broad comparisons with rehabilitated areas •• select sampling units and methods appropriate to the system (for example with appropriate stratification of soil types or vegetation) •• establish adequate spatial and temporal coverage to address the objectives •• establish sufficient replication to enable statistical analysis of results at an acceptable power with predetermined effects •• avoid or minimise bias when selecting the monitoring locations (for example by randomisation of replicate selection within the sampling design) •• use pilot testing to evaluate the effectiveness of the sampling design for the site conditions, such as in McCaffrey et al (2014a)

FIG 1 – Improved, more robust ecological monitoring requires a better understanding of the ecological system to inform the monitoring design at the set-up stage. Monitoring data can be used in ecological models to further refine the monitoring analysis.

APPLYING MODERN ECOLOGICAL METHODS FOR MONITORING AND MODELLING MINE REHABILITATION SUCCESS

•• use training and testing to ensure that the methods are repeatable and comparable over time and between different observers •• maintain quality control to ensure that the data enable statistical analysis and inference.

Pilot testing can be used to evaluate the effectiveness of a sampling design for measuring the impacts of mining. This can lead to the creation of new survey and detection methods (McCaffrey et al, 2014b). Testing observer variations is a further strategy applied to ensure consistency that the methods are repeatable and comparable over time (Blick et al, 2013; McCaffrey et al, 2014a).

Power analysis

Vegetation communities vary considerably, both spatially and temporally, and may exhibit superficial improvement in their condition that mask long-term trends that may remain undetected if an inappropriate sampling design is used. Measurement accuracy and sampling intensity will determine whether real improvements or declines in vegetation condition can be identified from natural variation. The early identification of trends in long-term condition can allow for targeted management activities and the potential to address negative environmental impacts and/or inappropriate management methods (Van Gorp and Erskine, 2011). A monitoring program that can detect changes in ecosystem condition is critical to assess the success of rehabilitation or detect negative impacts of mining. However, in some cases there is no assessment of the statistical robustness of the sampling design and insufficient power in the monitoring program to detect impacts or assess whether rehabilitation and reference sites deviate significantly.

Fundamental to any monitoring program is to ensure that the sampling design is capable of detecting significant change (Fairweather, 1991; Field et al, 2007; Legg and Nagy, 2006). The probability of detecting changes, known as the statistical power, depends on a number of factors, namely the statistical significance of the test (α), the effect size and the sample size. Generally, as sample size increases so does the power. Power analysis is key to the design and planning phase of any monitoring program as it enables an estimate of the appropriate sample size required to detect an impact of a specific effect size (Fairweather, 1991; Steidl, Hayes and Schauber, 1997). Conducting a power analysis before monitoring commences will ensure costeffectiveness through either too little sampling, resulting in a monitoring program that cannot detect meaningful differences, or too much sampling, where time and resources are wasted.

Power is the probability that a statistical test will reject a null hypothesis when the null hypothesis (H0) is actually false (Figure 2). This is the probability of not committing a type II error (1 – probability of finding a difference that does not exist (β); Table 1). In terms of monitoring for impacts or ecosystem rehabilitation, H0 can be considered to be ‘no difference in ecological indicators between rehabilitation and reference site’ and the alternative hypothesis (Ha) can be considered to be ‘difference in ecological indicators between impacted/ rehabilitation and reference site’. Power can be calculated with the following equation: Power ∝ (ES × α × √ n)/σ

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Formally, the effect size can be considered as the difference between two sample populations. The specific form of the equation depends on the statistical method used (for example analysis of variance (ANOVA), t-test). It can be solved for power, sample size, or effect size (Fairweather, 1991).

The knowledge of statistical power is crucial for assessing whether detected differences found are the result of a lack of statistical power or the fact that there are no biologicallysignificant differences (that is, H0 is correct). Incorrectly accepting H0 is especially an issue where the assessment of rehabilitation success is a focus of the monitoring. In these kinds of studies, an outcome of no significant difference between rehabilitation and reference sites (that is, accepting H0) indicates that rehabilitation is successful and thus a trigger to halt further monitoring.

A key input parameter required for power analysis calculations is the estimation of sample population standard deviation (σ). This may be estimated from previous studies in other geographic regions or other taxa. Alternatively, estimates can be made through pilot studies (Steidl, Hayes and Schauber, 1997). The determination of standard deviation for power analyses is an important phase in the development of any monitoring program.

There are numerous software options for power analyses. These range from free statistical software such as Russ Lenth’s Applet (http://www.stat.uiowa.edu/~rlenth/Power/), Real Statistics Excel add-in (http://www.real-statistics.com/), G*Power 3 (http://www.gpower.hhu.de/en.htm), through to commercial power analysis software such as Power and Precision (http://www.power-analysis.com/) or PASS (http://www.ncss. com/pass.html) and modules within common statistical software packages such as Minitab®.

Power analysis example

In a hypothetical simplified mine example (Figure 2), a t-test is used to compare impacted vegetation sites with undisturbed native vegetation on reference sites (see comment box). T-tests can be used to compare the difference between two means in relation to their standard deviation. In this case, the impact of mining is assessed through a decline in the ecological indicator, species diversity. The ecological knowledge of the system suggests that a decline in species diversity of greater than five is a critical value (effect size). A pilot study conducted to derive the standard deviation of species diversity resulted in σ = 6. Both regulators and the mining company decided that a power of 0.8 and a significance level of 0.5 was a suitable risk. Using the σ derived from the pilot study, a power analysis was conducted to find that a sample size of 19 is required to be able to detect a minimum decline in species diversity of 5 (Figure 3).

(1)

where: ES is the effect size α is the significance level and thus the type I error rate (probability of incorrectly rejecting Ha) n is the sample size σ is the sample population standard deviation (Di Stefano, 2003)

FIG 2 – Hypothetical pilot study used for rehabilitation scenario with rehabilitation areas in blue and native undisturbed vegetation areas in green. At each site location, species diversity is measured and the standard deviation (σ) is calculated. This type/arrangement of pocket mining layout example would also rarely exist (see discussion in the box text).

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FIG 3 – Example of a power curve for a one-sided two sample t-test (with mean of specific value), where significance level (α) = 0.05 and standard deviation of the sample population (σ) = 6. (A) Curve describing the relationship between power and detectable differences; (B) power as a function of sample size. For a power of 0.8, and with a minimum difference of 4.5 (one-tailed), the required sample size is 19.

Pseudoreplication and rehabilitation/impact monitoring design An important consideration for the design of monitoring programs is addressing the sampling design and its relationship between spatial distribution of impacts or rehabilitation sites. In many cases, mine impacts can be considered point source as there is only a single sample point at which impacts occur. Common sampling designs in ecology rely on a uniform impact across sites comparing multiple treatment (impacted) sites versus multiple control (reference) sites (Figure 2). Differences are measured in terms of mean values between treatment versus control sites with respect to the standard deviation of the two sample populations. Such sampling designs may be inappropriate in the mining context where there may be single impacted sites and multiple non-impacted sites such as in the case of a hard rock mine (Figure 4). This kind of sample design results in pseudoreplication, whereby the experimental units are not independent. It is important to ensure that replicates are dispersed, usually through some sort of randomisation (Platt and Rapoza, 2008). In the case of a hard rock mine there is often only a single replication of the rehabilitation site in the tailings dam or the waste rock dump and there may be multiple reference sites found in the surrounding vegetation (Figure 4). In this case, in order to have true replication there would need to be multiple tailings dams and multiple reference sites. In this example, replication within the tailings dam would be considered pseudoreplication as all the rehabilitation replicates are not spatially independent, that is the rehabilitation sampling points in the dam occur together (clustered) and have a higher probability of being closer to another rehabilitation sampling point than a reference sampling point. Any replication, however, is important if trying to assess the condition of the whole site (that is, what is the species diversity for the whole site?).

FIG 4 – Example of a pseudoreplicated monitoring design. All the rehabilitated sites are spatially autocorrelated (that is, they are clustered) and thus not independent. A key assumption for most statistical tests is the independence of sample data.

REFINING A MONITORING PROGRAM An important consideration for any monitoring program is knowing what is a biologically-meaningful effect size, that is, what size of difference between reference data and impact/ rehabilitation data is important. For example, when is a change in an ecological indicator great enough to have a critical effect on the ecological condition of a site that may result in endangered fauna species (dependent on that habitat) becoming locally extinct? Often an important indicator of negative ecological impact on vegetation communities is assessing whether there is a change to the community that is potentially irreversible (for example Lindenmayer et al, 2011). Thus, it is critical to know whether variability over time and between plots is natural or

part of a negative trend to a potentially irreversible change in condition, in the case of impacts, or a positive trend in the case of rehabilitation. What needs to be measured and the size of differences between monitoring and reference data should be the first questions asked of regulators and scientists engaged in monitoring. This question should first be asked at the set-up stage, but also as monitoring continues.

Crucial to setting detectable effect sizes is good ecological knowledge to drive the selection of this value. The selection of effect sizes for statistical tests is best determined by the ecology and should not be amended to satisfy statistical design (that is, by increasing effect size a smaller sample size is required) (Di Stefano, 2003; Steidl, Hayes and Schauber, 1997). There is no


simple way of determining an acceptable level of power and thus the appropriate effect size to be measured (Di Stefano, 2003). Cohen’s (1988) classic text on power analysis describes a rule of thumb assessment of effect size, where effect size is: where:

0.5

(treatment mean – control mean)/ standard deviation of control group

is a trivial effect is a small effect is a moderate effect is a large effect

However, choosing an effect size based on a thorough understanding of the ecology of a site (Thomas and Juanes, 1996) is better than using a rule of thumb. The use of ecological models to guide the selection of this value is seen as the appropriate and favoured approach.

Once monitoring has begun, further refinement of the monitoring program should take place. An example of this is the review of monitoring the effects of underground coal mining on upland swamp communities to test both the effectiveness of standard sampling techniques (Tierney, Fletcher and Erskine, 2015) and the effect of power of a new survey design (Johns et al, 2015). A key task is to assess the confidence in the outcome of an analysis in terms of type I and type II errors. In the case of type I, this can be assessed with significance values resulting from statistical tests. In the case of type II errors, one might be tempted to conduct a post-hoc assessment of power to assess what differences are detectable, although this is an inappropriate use of power analysis and using confidence intervals is the better alternative (Hoenig and Heisey, 2001; Steidl, Hayes and Schauber, 1997).

Understanding ecological systems

Commonly, the scientific requirements imposed on a company by regulators include surveying what was present premining and identifying risks and threats to these ecological communities. Monitoring is then required to ensure that there is no, or minimal, impact on ecosystems potentially impacted by mining. Finally there is a requirement for monitoring to ensure that rehabilitation ecosystems, or managed offset areas, are proceeding on the right trajectory. The differences between reference and monitored rehabilitated ecological communities are rarely defined in terms of effect size; a necessary requirement for setting up a monitoring program. In order to determine appropriate sampling programs, in terms of suitable sampling designs and effect sizes, a thorough knowledge of the ecological system being investigated is a prerequisite. Many Australian ecological systems in mining areas are not particularly well understood and thus the impact of mining on, and rehabilitation practices for, a particular system may be unknown.

Ecology suffers from a lack of quantitative theory, making it difficult to identify criteria and strict thresholds (that is, effect sizes) desired by regulators and policy for assessing rehabilitation success. Theory in ecology tends to be implicit or verbal, such as rules of thumb based on sound, real-world observations, thus lacking the rigour and precision of theory that is mathematicallybased (Turner, 2006; Vermaat et al, 2005; Wiens, 2002). Ecology tends to focus on specific ecological phenomena, deriving conclusions that may be value laden and specific to a location (Vermaat et al, 2005; Wiens, 2002). Therefore, in most cases, to assign meaningful effect sizes or deciding on the appropriate ecological indicators for monitoring, research on the specific ecological system is a prerequisite. Research is often conducted using a single or a small number of landscapes, leading to problems with generalising outside of the study area (Vermaat

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et al, 2005; Wiens, 2002). Therefore, innovative approaches are required to address these issues.

By conducting monitoring over time, trends in ecological systems can be derived, potentially reducing uncertainty through increasing temporal replication. This data can then be used to optimise the management and monitoring of the ecological system. The common monitoring method of comparing ecological indicators between sites relies on a black-box approach (Arnold, Lechner and Baumgartl, 2012), whereby the drivers of the differences and similarities between sites are implicit. Such blackbox approaches are used in common statistical approaches, where differences in premining condition and post-mining condition are attributed to impact implicitly, without directly testing the drivers of the differences. This approach is useful and commonly used in ecology, although it is contended that a greater understanding of the system can be gained through a modelling approach using manipulative experimental designs or applying a process model white box approach (Arnold, Lechner and Baumgartl, 2012) to: •• gain certainty in understanding of the ecological system •• ensure the effect sizes used are meaningful •• develop predictive capacity.

Most models are actually a combination of black and white box approaches and can be considered to be grey boxes with varying shades of grey.

Rather than considering monitoring in isolation to modelling, monitoring data can be used as inputs into models and in turn these model outputs can drive monitoring design, such as re‑evaluating effect sizes. This relationship between monitoring and modelling can be applied using the cycle of scientific discovery framework, which describes the beneficial interactions between model development and empirical experiments; whereby the empirical experiments can be considered through different monitoring methods (Savenije, 2009). The model development process can identify knowledge gaps, which can then be addressed through further empirical experiments such as seed germination trials (Arnold, Kailichova and Baumgartl, 2014; Arnold et al, 2014b). The initial model (Figure 5a) is followed by model analysis (Figure 5b), which aims to identify, and if necessary quantify, uncertainties occurring in: •• data sets •• parameter estimates •• model structure.

The latter can be the result of a variety of parameter combinations and model structures. This approach is iterative, whereby model improvements are made through addressing uncertainties, and knowledge gaps are identified in the previous cycle through empirical or manipulative experiments. The model, and therefore understanding of the ecological system, improves every cycle through the reduction of uncertainty. Monitoring programs in mining contexts provide a good opportunity to apply this kind of approach as the life of a mine tends to be greater than ten years and thus there can be numerous iterations of the cycle of science.

Using the cycle of science and/or the related approaches such as the adaptive management framework (for example Auld and Keith, 2009), monitoring can be integrated with modelling, providing a greater understanding of the system that in turn will benefit mining companies and regulators. The application of modelling methods in mining are rare and are not usually specified by regulators. However, outside of the mining context, modelling techniques are being applied for the management of ecological systems for, and by, government agencies. For example, the Victorian Government incorporated the results of simulation modelling to assess the impacts of the growth of Melbourne on threatened vegetation communities (Gordon et al, 2011). In addition, the Victorian Government’s Department of Sustainability and Environment is also including the intensive use

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FIG 5 – Schematic representation of the cycle of scientific discovery. The grey-box model denotes both the start and end point of synthesising scientific knowledge and conducting experiments. The identification of sensitivity to uncertainty can result in the rejection or reconsideration of a model, motivating the building of a restructured model (dashed frame), or the design of an empirical or manipulative experiment (adapted from Arnold, Lechner and Baumgartl, 2012a). of species distribution models and multispecies prioritisation in a generic tool called NaturePrint, aimed to support biodiversity decision-making for multiple purposes including strategic planning and public land management (Department of Sustainability and Environment (DSE), 2011). The Victorian Environmental Assessment Council in conjunction with the Department of Sustainability and Environment used the modelling tool Circuitscape (Mcrae and Shah, 2009) to analyse landscape connectivity for 13 vertebrate species across regional landscapes in Victoria (Victorian Environmental Assessment Council (VEAC), 2010). Finally, a state-of-the-art population viability analysis, undertaken for the wedge-tailed eagle (Aquila audax fleayi), was used in a federal court case to delineate areas for logging in Tasmania (Bekessy et al, 2009).

Using modelling methods site-specific assessments can also potentially incorporate off-site regional-scale impacts and impacts on other forms of natural capital (for example water, soil, productive agricultural land (Lechner et al, 2016)) through connectivity analysis (Lechner et al, 2015a, 2015b), spatial prioritisation tools (Bekessy et al, 2009; Kiesecker et al, 2010; Kiesecker, Copeland and Pocewicz, 2009) and ecosystem services modelling (Lechner et al, 2015; Li et al, 2011). Additionally, modelling can be used to characterise long-term environmental variability that can affect rehabilitation success such as water availability quantified using drought indices (Halwatura et al, 2015a, 2015b).

These modelling methods include a wide range of techniques that have the potential to be applied in the mining context. They can be used to facilitate decision-making in a number of ways by: •

• • •

providing a transparent, quantitative and repeatable approach to systematically investigating management actions identifying important factors affecting the probability of impact or rehabilitation permitting the testing of alternative future scenarios allowing the incorporation of data of varying uncertainty, and determining the consequences of those uncertainties via model outputs (Arnold, Lechner and Baumgartl, 2012; Coulson et al, 2001; Lechner et al, 2012b; Resit Akcakaya and Sjogren-Gulve, 2000; Rodríguez et al, 2007).

It should be noted that modelling techniques, such as those mentioned above, should be treated as additional inputs into the decision-making process and not as a black box that automatically makes the required decisions. They can be particularly useful to help understand environmental phenomena in settings where

data are limited or unavailable, and for synthesising large amounts of information into understandable results. Finally, it should be remembered that there are limitations to all models and thus they can only ever be approximations of the real world. As the old maxim says, ‘all models are wrong, some models are useful’.

CONCLUSIONS

The implementation of power analyses, along with ecological modelling, can be considered best practice. The adoption of best practice environmental management techniques will result in long-term gains for the industry through better relationships with regulatory bodies, communities and other stakeholders, greater certainty for access to land and project approvals, and lower levels of risk to the environment (Environment Australia, 2002). The additional cost to capital and operating costs for new mining projects are likely to be offset through the reduction in the cost of rehabilitation if these relationships and uncertainties are better understood and approached through a rigorous process.

This paper advocates that mining companies conduct more fundamental research to understand ecological systems to improve monitoring methods. Existing monitoring methods using unreplicated comparisons between rehabilitation/impact sites and reference sites will never provide a good understanding of the ecological system in question (Erskine and Fletcher, 2013) and in many cases they may be inadequate due to statistical considerations being ignored. While monitoring without this knowledge may appear to fulfil compliance requirements imposed by the regulator, certainty in our understanding of ecological systems at mine closure is likely to be required by the regulator and society for mine relinquishment.

A critical issue, beyond the scope of this paper, however, which needs to be acknowledged, is that in many cases rehabilitation targets based on historical ecosystems may be difficult to attain in radically disturbed sites and hybrid (reversibly different) or novel (irreversibly different) ecosystems comprising new combinations of physical and biological components may be the only practical rehabilitation goal (Doley and Audet, 2013). Where novel or hybrid ecosystems represent rehabilitation goals, conducting fundamental research using experimental analysis and modelling methods is even more of a pressing issue. Science can never say anything with 100 per cent confidence, and thus it is important to use an adequate modelling and statistical framework to not only describe the system but also to describe the uncertainty in the understanding of that system. A key advantage of using long-term monitoring data along with


modern ecological modelling methods is the possibility of creating predictive models to assess the likelihood of an ecological system staying viable in the near future, and thus allowing for earlier closure of rehabilitated sites with greater confidence about longterm sustainability and reduced future risks.

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Department of Sustainability and Environment (DSE), 2011. Fact sheet: Introduction to nature, Print version 2, Victorian Government Department of Sustainability and Environment.

ACKNOWLEDGEMENTS

Di Stefano, J, 2003. How much power is enough? Against the development of an arbitrary convention for statistical power calculations, Functional Ecology, 17:707–709.

The authors would like to thank the anonymous reviewers for their valuable feedback on the paper and Andrew Fletcher who contributed to a previous version of the paper.

Elzinga, C L, Salzer, D W, Willoughby, J W and Gibbs, J P, 2001. Monitoring Plant and Animal Populations (Blackwell Science: Massachusetts).

Doley, D and Audet, P, 2013. Adopting novel ecosystems as suitable rehabilitation alternatives for former mine sites, Ecological Processes, 2:22.

An earlier version of this paper was originally published in the Life-of-Mine 2012 conference proceedings by the Australasian Institute of Mining and Metallurgy.

Environment Australia, 2002. Overview of best practice environmental management in mining, in Best Practice Environmental Management in Mining, p 43, Commonwealth of Australia.

Arnold, S, Attinger, S, Frank, K, Baxter, P, Possingham, H and Hildebrandt, A, 2014a. Ecosystem management along ephemeral rivers: trading off socio-economic water supply and vegetation conservation under flood regime uncertainty, River Res Appl, 32:219–233.

Fairweather, P G, 1991. Statistical power and design requirements for environmental monitoring, Australian Journal of Marine and Freshwater Research, 42:555–567.

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