The Condition of Australia's Marine Environment is Good but in Decline

8/9/2019 The Condition of Australia's Marine Environment is Good but in Decline

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The condition of Australia's marine environment is good but indecline d An integrated evidence-based national assessment by expertelicitation

Trevor J. Ward a , b , *

a Greenward Consulting, PO Box 493, Wembley, WA 6913, Australiab University of Technology Sydney, NSW, Australia

a r t i c l e i n f o

Article history:Available online 25 August 2014

a b s t r a c t

The condition and trend of 196 marine environment components were assessed for Australia's national2011 State of the Environment Report using professional judgements derived by expert elicitation. The judgements from the diverse group of eld-experienced scientists and practitioners were assembledwithin a single framework common to all components to provide an integrated system-level assessment.Components assessed included the natural environmental assets and values in biodiversity, ecosystemhealth, and the pressures. A high level of agreement was achieved amongst the disciplines and expertson the decision structure and the scores or grades, even though many components are data-poor at thenational scale. The assessment con rmed the clear association between human development history andextant condition of the biodiversity and ecosystem health. Overall, the marine environment wasconsidered to be in good condition relative to conditions prior to European colonisation of the Australianmainland. However, more components of the biodiversity were considered to be deteriorating thanimproving, inferring that the overall quality of ecosystems is in decline. Combined present-day pressureswere considered to be greatest in the temperate and the coastal waters ( < 200 m depth) adjacent to more

developed areas of the coast in the South-east region. Here, condition of biodiversity and ecosystemhealth was considered to be poor, and in some places very poor. Condition in the North region, wherethere is only limited human development, was considered to be good. The process identi ed environ-mental components considered to be in the ‘ best of the best ’ and ‘ worst of the worst ’ condition,providing focus for subsequent prioritisation in national environment policies. Gaps identi ed include alack of national-scale data on the condition and trend in almost all biodiversity components other than

shed species, for which most knowledge is skewed towards resource use. Also, there is a lack of monitoring of the links between national pressures, such as port development, and regional biodiversitycondition in response to policy settings and management activity. The assessment process deployed hereprovides a model for repeatable integrated system-level assessment and reporting at the national andregional scales in data-poor marine situations.

© 2014 The Author. Published by Elsevier Ltd. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/3.0/ ).

1. Introduction

There are many existing frameworks and approaches to envi-ronmental and sustainability assessment and reporting ( Singhet al., 2012; Rombouts et al., 2013 ). However, despite a long his-tory of active research and development there are no uniformglobal approaches to the integrated system-level assessment and

reporting of intrinsic environmental quality in large scale marineecosystems. New approaches continue to be proposed and devel-oped ( de Jonge et al., 2012; Samhouri et al., 2012 ), but criteria thatcomprehensively incorporate system-level structure and functionremain novel ( de Jonge et al., 2012; Keith et al., 2013 ). Some recentlarge-scale marine assessment initiatives ( Kershner et al., 2011;Halpern et al., 2012 ) develop and report on marine systems usingcomplex indices that are informed by available data, but providelimited utility for on-ground management purposes that needbalanced information about ( inter alia ) the intrinsic quality of the* Greenward Consulting, PO Box 493, Wembley, WA 6913, Australia.

E-mail address: t [email protected] .

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structure and functioning of marine ecosystems ( de Jonge et al.,2012 ).

Australia is the world's largest island continent and sixth largestcountry, with jurisdiction and management authority over a ma-rine zone of 13.86 million km 2 stretching from the tropics to sub-Antarctic regions (including the Exclusive Economic Zone (EEZ),Extended Continental Shelf (ECS), and Australian Antarctic Terri-tory (AAT): Symonds et al., 2009; Ward et al., 2014 ). The biodiver-sity of the range of Australia's marine ecosystems is exceptional,including hundreds of habitat types, about 33 000 con rmed spe-cies, and a total marine ora and fauna conservatively estimated tobe of the order of 250 000 macroscopic species, with high levels of endemicity ( Butler et al., 2010 ). Speci c aspects of the Australianmarine environment are managed by the federal (Commonwealth)government, some are managed jointly by the federal and stategovernments, and most nearshore issues are managed by state andlocal governments. The Commonwealth has overarching jurisdic-tional responsibility and delivers some speci c functions under theinternational head of power for Australia's territorial sea within theEEZ and other areas claimed under the United Nations Law of theSea Convention.

Under its Environment Protection and Biodiversity Conservation(EPBC) Act, the Commonwealth requires a 5-yearly State of theEnvironment (SoE) report for the marine environment. A primaryobjective of the SoE report is to inform national and regional policydevelopment, and to guide consequent management imple-mentation and investments that improve environmental outcomes.Nonetheless, the SoE report is restricted to assessing and reportingon environmental conditions and the effectiveness of managementwithout providing recommendations for consequent action orpolicy directives. In this way, Australia's SoE reporting process es-tablishes an agreed and independent national-scaleoverview of theenvironment, providing direction without constraining govern-ments to develop and implement speci c policy and strategies toachieve required environmental outcomes for the assets andvalues.

In other jurisdictions, stateof the marine environment reportingsystems typically utilise selected subsets of data and information,derived from information-rich parameters and spatial models/in-ferences (eg the marine environment assessments of the Baltic Sea;HELCOM, 2009). However, system-level assessments based onnarrowly derived metrics that may also involve complex under-pinning models, risk narrow outcomes for policy-prioritisationpurposes that may not fully represent the system-level conditionsand issues. The approach to system-level assessment reported herefor Australia shifts the focus away from selected local-scale metricsand ne-scale examples which may be unrepresentative to a broadscreening approach that is less dependent on data-richness and ismore suitable for data-poor situations. This approach uses theprofessional judgement of an independent set of experts, summary

aggregation and non-parametric analysis to present simple statis-tical summaries, and avoids model-driven composite indices andmany of their associated issues ( Rogge, 2012 ). The decision modelused here also ‘ hard-wires ’ structure and function attributes of marine biodiversity and ecosystems so that key elements of con-dition quality cannot be overlooked ( Lyashevska and Farnsworth,2012 ). This focuses the assessment on intrinsic and system-levelecological aspects, now widely recognised as being essential tosupport the development of more effective broad-scale environ-mental policy ( de Jonge et al., 2012; Samhouri et al., 2012 ). Thebroad-scale screening approach has a coarse resolution, and is thuspotentially less accurate than individual and local-scale knowledge.However, the screening approach is likely to have more direct high-level relevance for national-scale policy making in large and data-

poor systems, cover a broader spread of the system-level issues

for which policy may be required,be more consistent with the basicconcepts of synthesis and integration of knowledge ( Andrews,2012 ), and reduce the structural model uncertainty ( sensu Walkeret al., 2003 ) surrounding marine environment assessment on thisscale.

The objective of the national assessment reported here was toestablish a system-level evidence-base from a set of informedexpert judgements, and provide a rapid and high-level synthesis of the condition and pressures on the intrinsic assets and values of theAustralian marine environment. To limit structural uncertainty andType III error (the error associated with providing an accurate andprecise answer to an irrelevant question: Bark et al., 2013; Wardet al., 2014 ) the assessment draws on a broad coverage of the as-sets, values, issues and intrinsic ecological qualities. The evidence-base comprises the professional judgement about the environmentqualities elicited from an invited set of experts, based on theirpersonal experience, their understanding of the extant literatureand their estimates of the qualities under assessment. The form of assessment and reporting was developed to provide a clear andsimple interface for consequent policy development, a defendablebasis for estimation of the issues, a transparent process with areadily discoverable information base that is contestable andrepeatable in the context of a data-poor knowledge situation, andwas integrated in the sense that the assessment used a singlestructure for assessment and reporting across a wide range of system attributes ( Ward et al., 2014 ). This approach is consistentwith rapid assessments in other data-poor large-scale marine re-gions ( Feary et al., 2014 ). The ndings are presented here with adescription of the process used to populate the assessment with asecure base of national-scale evidence. The paper summarises theassessment process, presents results at the national-scale and fromtwo marine regions, and brie y discusses the policy relevance of this form of rapid assessment for national-scale environmentalassessment and reporting purposes in the context of Australia'smarine jurisdictional setting.

2. Methods

2.1. Assessment framework

The assessment framework developed for Australia's SoEC 2011report (Common Assessment and Reporting Framework: Wardet al., 2014 ) was applied to secure professional judgement from agroup of experts to assess the condition of biodiversity, ecosystemhealth and environmental pressures affecting the natural assetsand values across the full extent of Australia's marine environment.Setting the frameworkfor the assessment included establishing thespatial boundaries for consideration, identifying the assets andvalues to be reported (the assessment typology), developing pro-cesses for identifying and securing data/information on these as-

pects, and aggregating and reporting the information for thepurposes of national reporting ( Ward et al., 2014 ).The marine system for assessment was spatially bounded on

the landward side by the shoreline around the continent andislands and the penetration of marine waters and their direct in-

uence (such as through tidal movements) into estuaries, lagoonsand bays. The seaward boundary was de ned by the outer extentof Australia's EEZ and claimed ECS ( Fig. 1). A nested set of nationalmarine regions was derived by extending the existing Common-wealth's marine planning regions landward to encompass Aus-tralia's complete marine and the directly marine-in uencedenvironment. This created ve regions for national marine SoEreporting that encompassed offshore waters and seabed underfederal jurisdiction, and inshore waters and seabed under state

jurisdiction. This set of regions was selected to enclose as far as

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possible a complete set of functional marine ecosystems with their

intrinsic assets and values to enable a comprehensive nationalassessment on a functional environmental and ecological basis.The Great Barrier Reef Marine Park was included as part of the Eastregion. The AAT and sub-Antarctic islands were assessed within aseparate SoE process. While jurisdictionally complex, the regionsare less functionally biased than the alternative of following onlythe internal jurisdictional boundaries. The level of resolution (5regions) is coarse, but it is consistent with the established marinebioregional planning and policy frameworks in Australia, andprovides a clear spatial focus on intrinsic ecosystem structure andfunction for invoking region-speci c management actions andinterventions.

2.2. Decision model

2.2.1. Decision processThe decision frame was broad in scope to avoid an assessment

based solely on the extent/availability of knowledge at the expenseof coverage of the intrinsic assets and values of the marine envi-ronment that included matters important in both ecosystemstructure and function. To accept a variety of forms of data andknowledge into the assessment, four quality grades were used forreporting on biodiversity, ecosystem health, and pressures, andthree gradesfor reporting on trendsand con dence (after GBRMPA,2009 ). This permitted both high and low-resolution knowledge tobe used in an equivalent way across a broad range of spatial andtaxonomic coverage as appropriate for national-scale reporting,and to minimise structural model uncertainty and Type III error

(Walker et al., 2003; Bark et al., 2013; Ward et al., 2014 ).

The decision process deployed a multi-metric hierarchical

structure with an unweighted system of aggregation (parametersand components are all equally weighted) and reporting. The in-puts were structured around a set of indicators (see below)designed for policy-level function and effectiveness and based, asfar as possible, on readily available data, information, and knowl-edge that could be substantially populated by expert judgement.

To provide a fully transparent basis for the information syn-thesis and outputs, the process and assumptions used in the deci-sion model were derived from the broader approach toenvironment reporting established for SoE reporting in Australia(SoE, 2014a ) and following an earlier Australian regional-scaleapproach ( GBRMPA, 2009; Dobbs et al., 2011 ). Consistent withthe process of expert elicitation in environmental disciplines ( Knolet al., 2010; Burgman et al., 2011; Martin et al., 2012 ), the draft

structure of the decision model was provided in advance to the setof experts who had agreed to participate in the assessment process,for their review and revision prior to the assessment workshops.The agreed decision model consisted of:

typology for biodiversity, ecosystem health and pressures,including a hierarchy of parameters, components, andindicators;

rapid expert elicitation process for populating the indicatorswith judgement based on data and information consistent withthe minimum resolution required for national-scale policydevelopment;

guidelines for scoring and grading at the indicator level; process for aggregating indicator-levelinformation into relevantforms of summarised outputs.

Fig.1. The reporting regions for the marine environment in Australia's State of the Environment 2011, extending from the most landward in uence of marine waters to the outerlimit of the EEZ and ECS. [Sub-Antarctic islands and Australian Antarctic Territory are not included within this assessment process.] Sources: Environmental Resources InformationNetwork, Australian Government Department of Sustainability, Environment, Water, Populations and Communities (DSEWPaC); Geoscience Australia (after SoEC, 2011.).

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2.2.2. Expert elicitationThe experts involved were selected to represent the disciplines

and areas of eld experience expected to be able to inform thewidespread of components and indicators with well-grounded andunbiased information. Scores and grades were assigned by theexperts in a workshop conducted for each of the ve marine re-gions. At least two experts were invited to each workshop for eachmain discipline area, and a small number of policy specialists alsoattended to maintain a focus on the nexus between scienti cknowledge and policy-relevant knowledge ( Ward, 2011 ). While thedata and knowledge is strongly based in scienti c knowledge andthe personal experience of the participating experts, the overalldecision model was not constrained to only matters of scienti ccertainty, encouraging the personal opinion and judgement of theexperts to be includedin the assessment. Nonetheless,whereit wasavailable and relevant, ne-scale data were used by the experts toassign scores, and examples were documented in the workshoprecord. In this decision process the requirement for technical ac-

curacy in populating the indicators is traded-off against the needfor information of possibly a lower level of con dence but drawnfrom a broader range of assets and values. This both enables amixture of high and low-resolution data to be included in theassessment in an equivalent manneras well as including a broad setof environmental components. As part of the assessment process,the experts also assigned an estimate of their con dence in theindicator data they provided. Triangulation of scores/grades wasachieved through (a) workshop discussion and defence in front of peers, (b) veri cation though example datasets and cited literature,(c) post-workshop circulation of draft outputs to workshop at-tendees, and (d) an anonymous peer review post-workshop pro-cess. Selected examples were also informally checked withindependent experts for the purposes of veri cation.

2.2.3. Typology for assessment The assessment typology for the biodiversity, ecosystem health

and pressures was developed from existing classi cations, mainlyfrom the Great Barrier Reef Outlook Report ( GBRMPA, 2009) and itsprogenitors, and from other SoEreports (eg Ward et al.,1998; Ward,2000; WA SoE, 2007; Victoria SoE, 2008 ). The typology was con-structed on intrinsic assets and values of the marine environmentand resolved indicators at a coarse scale of spatial, temporal andtaxonomic resolution to meet the process objectives for SoEreporting ( Ward et al., 2014 ).

The typology consists of ve biodiversity and ecosystem healthparameters and a single set of pressure components, each with aset of components and indicators, to assess and report on system-

level condition quality and temporal trends ( Table 1 ). The

biodiversity parameters consist of habitats; species and speciesgroups; and ecological processes. The ecosystem health parame-ters consist of physical and chemical processes; and pests, intro-duced species, diseases, and algal blooms (hereafter PIDA). Each of these parameters was attributed with a number of components torepresent structure/function, and six indicators were assigned toeach component. The indicators for both condition quality (threeindicators) and trend (three indicators) were: Most (the modalscore/grade for places, samples, or examples, measured or ex-pected in the spatial distribution of the quality/trend), and theBest10% and Worst10% of the distribution (the score/grade at the90% and 10% points respectively in the estimated spatial frequencydistribution). Each condition indicator was assigned an estimatedscore (range 0 e 10), set within four performance grades d VeryPoor, Poor, Good, Very Good. Trend indicators were assigned asImproving (in current 5 year condition quality of the component:2005 e 2010), Stable, or Deteriorating. For both condition andtrend in each component, experts also were invited to assign a

grade of High, Medium, or Low to their con dence in assigning ascore (condition) or grade (trend). Guidance for interpretation of these terms and their scores/grades (the Grading Statements) wasagreed with the workshop participants in advance of the work-shops ( Table 2 ).

The components of pressure in the typology were set at a highlevel (compared to the biodiversity and ecosystem health equiva-lents), and restricted to the main types of pressures and theirsources. The pressure indicators were assigned scores and gradesin the same manner as for biodiversity and ecosystem health.However, the grading scale assigned to pressures was constructedto re ect the importance of the impact of the pressure on biodi-versity/ecosystem health, so that scores would have a standardisedinference across all indicators d a low score always indicates an

undesirable outcome, and conversely, a high score always indicatesa more desirable outcome from a biodiversity perspective(Table 2 ).

2.2.4. IndicatorsThe indicators were populated with information derived from

expert judgement established through the assessment processdiscussed below. Scores for the Best10% and Worst10% indicatorsfor condition were initially selected (at the workshop) to act asscoring range ‘ anchors ’ , providing an upper and lower bound of thepossible range for their scores. Then the modal score (Most) wasassigned within this range. Rather than choosing the extremes of the range (the most extreme single example of the component), the90% and 10% points in the frequency distribution of scores for a

component were considered to be more appropriate metrics for

Table 1The typology and decision structure. The typology of the decision model developed to provide a nationally-consistent basis for integrated assessment and reporting on thecondition of, and pressures on, the biodiversity and health of Australia's marine ecosystems. This decision model was implemented within the Common Assessment andReporting Framework for Australia's national State of the Environment 2011 report ( Ward et al., 2014 ). The Indicators are metrics assigned to the frequency distribution of spatially estimated scores/grades for each component (see Section 2.2.3 ). (* ¼ all 196 components assessed are identi ed in the Supplementary Material ).

Asset /value assessed Parameter Components* Indicators Quanti ties est imated

Condition Trend Con dence(in grade/score)

Biodiversity Habitats 86 Best10%Most (mode)Worst10%

Scores: 0 e 10 Grades:ImprovingStableDeteriorating

Grades:High MediumLow

Species and species groups 41Ecological processes 21

Ecosystem health Physical and chemical processes 27Pests, introduced species, diseasesand algal blooms (PIDA)

6

Pressures (not used) 15 Scores: 0 e 10 for impact oncondition

Grades: Improving condition Stablecondition Deteriorating condition


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Table 2Grading statements.The Grading Statementsused in the expert elicitationworkshop process to ensure that judgements and scores assigned by the participants wereconsistentamongst and across the parameters and components, and to provide explicit statements about themeaning of each of the four grades: Very Good, Good, Poor, and Very Poor inthe context of each component. The Statements were developed for the qualities of both condition and pressure, and workshop participants considered and agreed to thewording prior to the workshops.

Part 1 condition assessment 1 (a) marine biodiversity: structural and functional aspects

Parameter 1: habitats e quality for species applies to habitat components and what is best understood about their status and trends expressed in terms of habitatquality for species utilisation

Grading scale Grading s ta tement

Very Good ( > 7.5 e 10) All major habitats are essentially structurally and functionally intact and able to support all dependent species.Good ( > 5e 7.5) There is some habitat loss, degradation or alteration in some small areas, leading to minimal degradation but no persistent substantial effects

on populations of dependent species.Poor ( > 2.5 e 5) Habitat loss, degradation or alteration has occurred in a number of areas, leading to persistent substantial effects on populations of some

dependent species.Very Poor (0 e 2.5) There is widespread habitat loss, degradation or alteration, leading to persistent substantial effects on many populations of dependent species.

Parameter 2: populations of species and groups of species applies to the major structural components and what is best understood about their status and trendsexpressed in terms of populations and groups of species. This includes threatened species, which may be assessed by species or as groups of species.

Grading scale Grading statement

Very Good ( > 7.5 e 10) Only a few, if any, species populations have declined as a result of human activities or declining environmental conditions.Good ( > 5e 7.5) Populations of a number of signi cant species but no species groups have declined signi cantly as a result of human activities or

declining environmental conditions.Poor ( > 2.5 e 5) Populations of many species or some species groups have declined signi cantly as a result of human activities or declining

environmental conditions.Very Poor (0 e 2.5) Populations of a large number of species or species groups have declined signi cantly as a result of human activities or declining

environmental conditions.

Parameter 3: Ecological processes applies to what is best understood about the status and trends in the main ecological processes and effects from human activities.


Very Good ( > 7.5 e 10) There are no signi cant changes in ecological processes as a result of human activities.Good ( > 5e 7.5) There are some signi cant changes in ecological processes as a result of human activities in some areas, but these are not to the extent that

they are signi cantly affecting ecosystem functions.Poor ( > 2.5 e 5) There are substantial changes inecologicalprocesses asa resultof human activities, and these are signi cantly affecting ecosystem functions

in some areas.Very Poor (0 e 2.5) There are substantial changes in ecological processes across a wide area of the region as a result of human activities, and ecosystem function

is seriously affected in much of the region.

1 (b) Marine ecosystem health: processes affecting biodiversity

Parameter 4: Physical and chemical processes applies to what is best understood about the status and trends in the main physical and chemical processes as a result of humanactivities. Thegradingscaleis based on a gradient inseverity of change, andapplies theuse of thevaluejudgementgradient( ‘ very good ’ to ‘ very poor ’ ) in relation to theextent/impacts of human activities. For assigning trends, this interpretation was used: I ¼ trend of improving condition e decreasing levels of human-induced change; D ¼ trend of deteriorating condition e increasing levels of human-induced change; S ¼ stable levels of change, accepting natural levels of dynamics


Very Good ( > 7.5 e 10) There are no signi cant changes in physical or chemical processes as a result of human activities.Good ( > 5e 7.5) There are some signi cant changes in physical or chemical processes as a result of human activities in some areas, but these are not

to the extent that they are signi cantly affecting ecosystem functions.Poor ( > 2.5 e 5) There are substantial changes in physical or chemical processes as a result of human activities, and these are signi cantly affecting

ecosystem functions in some areas.Very Poor (0 e 2.5) There are substantial changes in physical or chemical processes across a wide area of the region as a result of human activities, and

ecosystem function is seriously affected in much of the region.

Parameter 5: Pests, introduced species, diseases and algal blooms (PIDA) applies to what is best understood about the status and trends in the main outbreaks.A ‘ pest ’ was de ned as a species on the Australian trigger list of declared marine pests published at http://www.marinepests.gov.au/national-system/how-it-works/Emergency_management (the former CCIMPE list of trigger species, which identi ed 35 species at March 2011). Any other non-indigenous speciesintroduced to the marine environment is considered to be an ‘ introduced species ’ (NIMPIS http://www.marinepests.gov.au ), and may have been additionallynoted in this assessment if it was considered by the workshop participants to be the driver of signi cant impacts on marine ecosystem health.

Grading scale Grading s tatement

Very Good ( > 7.5 e 10) The incidence and extent of diseases and algal blooms are at expected natural levels, there are insigni cant occurrences or numbers of pests,and the numbers and abundance of introduced species is minimal.

Good ( > 5e 7.5) Incidences of diseases or algal blooms occur occasionally above expected occurrences or extent, and recovery is prompt with minimalaffect on ecosystem function. Pests have been found, but there have been limited ecosystem impacts. The occurrence, distribution andabundance of introduced species are limited and have minimal impact on ecosystem function.

Poor ( > 2.5 e 5) Incidences of disease or algal blooms occur regularly in some areas. Occurrences of pests require signi cant intervention or have signi canteffects on ecosystem function. The occurrence, distribution and abundance of introduced species triggers management responses, or haveresulted in signi cant impacts on ecosystem functions.

Very Poor (0 e 2.5) Disease or algal blooms occur regularly across the region. Occurrences of pests or introduced species are uncontrolled in some areas andare seriously affecting ecosystem function.


http://www.marinepests.gov.au/national-system/how-it-works/Emergency_managementhttp://www.marinepests.gov.au/national-system/how-it-works/Emergency_managementhttp://www.marinepests.gov.au/http://www.marinepests.gov.au/http://www.marinepests.gov.au/national-system/how-it-works/Emergency_managementhttp://www.marinepests.gov.au/national-system/how-it-works/Emergency_management


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changed as a result of this nal consultation round, and these wereall minor changes.

2.4. Data analysis

Three data analyses are presented here (i) a summary overviewof all workshop-derived data on condition, trends, pressures and

con dence; (ii) condition and trend in biodiversity and ecosystemhealth parameters; and (iii) regional comparisons of condition andtrend in biodiversity and ecosystem health parameters. The fullworkshop raw datasets are available at SoE, 2014b .

2.4.1. All data overviewAll data for all biodiversity and ecosystem health components

that were assigned a score or grade, including condition scores andtrend and con dence categories (181 of the total 196 components,see Supplementary Material ) were graphically summa-rised d median scores, percentage data densities, frequency anal-ysis, and number of observations. A biodiversity index was createdfor spatial and temporal comparisons within and among regions bysumming the median scores for condition of a set of biodiversityand ecosystem health components and their indicators. Pressures(15 components) were summarised in an equivalent way. Thecombined biodiversity, ecosystem health and pressure dataset(including trend and con dence) of all 196 components was alsosubjected to cluster analysis, to identify national-scale spatial pat-terns in the full dataset of condition, trend and con dence. In thiscluster analysis, all the data were de ned as discrete variables,dissimilarity matrices were generated with Pearsons chi-squarecoef cient, and an unsupervised classi cation generated by thehierarchical clustering routine in the Orange software suite ( Curket al., 2005; Orange, 2012 ). To set the optimal group resolutionfor each cluster, sets of objects were clustered to establish initialgroups based on optimum information gain derived from a pre-liminary k-means cluster analysis (k-means routine in Orange). Therobustness of these groups in each target cluster was subsequentlycon rmed by bootstrapping 50 random resampling 75% subsetswith replacement of data d only target clusters with a groupmisclassi cation rate of 5% or less compared to the bootstrappedsample were used in the data analysis.

2.4.2. Biodiversity and ecosystem healthFor the purposes of a spatial analysis of the condition and trends

in biodiversity and ecosystem health alone (excluding data onpressure and con dence), components that were assigned scoresfor condition in two or more regions were selected, resulting in adataset of 91 components (see Supplementary Material ). Thisexcluded from analysis a large proportion of region-uniquecomponent occurrences in the overall dataset. The dataset is pre-sented as summary statistics and was subjected to cluster analysis

(as above) to further explore the spatial and temporal patterns inthe data free from the possible in uence of the substantial numberof components that either only occurred (or were only scored) inone region, and without the in uence of patterns in pressure andcon dence.

2.4.3. Region comparisons

To examine the patterns of biodiversity and ecosystem health inindividual regions, the North (N) and South-east (SE) regions,which demonstrate the most divergent patterns amongst the re-gions, were analysed in more detail. Data for each region weredrawn from the full dataset, and components were removed thateither do not occur in the region or were not scored, resulting in 92and 89 components for N and SE regions respectively (seeSupplementary Material ). Patterns in the data were examined bysummary statistics, as above.

3. Results

3.1. All data

3.1.1. Biodiversity and ecosystem health 3.1.1.1. Information base. For condition of biodiversity andecosystem health, 1 212 workshop estimates were assigned to in-dicators from 181 biodiversity and ecosystem health componentsin the ve marine regions, representing a data density of approxi-mately 45% of the complete matrix (3 indicators in 181 componentsin 5 regions). Almost all of the condition estimates were assignedwith a trend estimate (1120 estimates, about 41% data density), andthe majority of the estimates of the condition and trend were alsoassigned with con dence information (851 estimates of con dence).

3.1.1.2. Biodiversity and ecosystem health condition. The conditionof all the biodiversity and ecosystem health components assessed,pooled across all regions and all indicators, is Good (medianvalue ¼ 7; Table 2 ). The Best10% of the components is Very Good(median value ¼ 9), Most components is Good (median value ¼ 7)and the Worst10% of components is Poor (median value ¼ 4.5)(Table 3 ).

The distribution of the pooled condition estimates showed aclear spatial pattern d the N region was considered in the bestcondition relative to the other regions, whereas the SE region wasconsidered to be in the worst condition. The highest median scoresfor biodiversity and ecosystem health for each of the three in-dicators (Best10%, Most, Worst10%) and the smallest range of me-dians between Best10% and Worst10% were found in the N region.This suggests a limited extent and amountof degradation, as well ashigh levels of condition quality of the biodiversity and ecosystemhealth components across most of the N region. In contrast, the

Table 3Condition and trend. Condition quality for the three indicators (Best10%, Most, Worst10%) for each biodiversity and ecosystem health parameter (median score), and trend incondition (% observations of improving, stable, deteriorating); all components from all regions (see Table 2 for details of the scoring scale).

Parameter Number of components Median score Condition

Best10% Most Worst10% % Improving % Stable % Deteriorating

Habitats 86 9 8 4 2 72 26Species and species groups 41 8 6 3 16 63 21Ecological processes 21 9 8 5 3 63 33Physical & chemical processes 27 9 9 5 1 63 35Pests, introduced species, diseases

and algal blooms (PIDA)6 9 8 6 4 62 33

All components 181 9 7 4.5 6 66 28


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lowest median scores for the indicators Best10% and Most, and theequal lowest (with East (E) region) for Worst10% were found in theSE region ( Fig. 2a). The biodiversity index is highest in the N regionand lowest in the SE region.

3.1.1.3. Trends in condition. The dominant current trend feature of the regions is that the biodiversity and ecosystem health condition

was broadly stabled

66% of components and their indicators wereassessed as Stable across all the regions ( Table 3 ). However, in theSouth-west (SW), North-west (NW) and E regions more than 30% of trend observations for biodiversity and ecosystem health compo-nents were considered to be Deteriorating ( Fig. 2c). The N regionhas the lowest proportion of biodiversity components in decline(10% observations). The SW region has a high proportion of com-ponents Deteriorating (39% observations), but also demonstratesthe greatest proportion of components (12% observations) that areImproving in condition. In the remaining regions, 6% or less of thecomponent observations were considered to be Improving. Overthe national marine jurisdiction as a whole, many morebiodiversitycomponents are considered to be Deteriorating (28% observations)than are Increasing in condition (6% observations) ( Table 3 ).

Eighty-eight components were found to be in decline in at leastone indicator, and of these, 24 components had a frequency of 5(the 75th percentile of frequency of Deterioration) or more obser-vations of Deterioration across all indicators and all regions(Table 4 ). The components in most extensive decline included arange of habitats (6) and species groups (3), but mainly (propor-tionally) comprised ecological processes (8) and physical andchemical processes (6). The Deterioratingecological processes weredominated by processes related to historic or current shing ac-tivity (5 of the 8 processes d trophic structures and relationships,biological connectivity, pelagic productivity, predation, andrecruitment connectivity). The physical and chemical processes indecline were dominated by global and ocean-basin scale processes(5 of the 6 processes d sea level, ocean acidity, sea temperature,ocean currents dynamics, and ocean-based nutrient supply andcycling).

3.1.1.4. Con dence. The estimates of con dence assigned to theestimates of condition and trend scores were approximatelyequally distributed across the High, Medium or Low grades. EitherHigh or Medium con dence was assigned to the components for68% of condition estimates and 64% of trend estimates ( Fig. 2b, d).However, the scores for condition and trend were assigned in the Eand SE regions with the highest level of con dence, with High andMedium grades assigned to 79% and 78% of components respec-tively. Although high levels of condition and low levels of changewere assigned to the N region, 46% of these grades were assignedwith Low con dence ( Fig. 2b, d).

3.1.2. PressuresThe participants assigned 180 scores to the three condition

indicators for each of 15 pressures (such as climate change,coastal urban development, port facilities) affecting the regions(see Supplementary Material ), resulting in a high level (80%)data density. The combination of pressures currently affectingbiodiversity and ecosystem health components was consideredto be having the greatest impact in the SW region, which had the

Fig. 2. Biodiversity and ecosystem health: (a) condition d median score for all (181)components in the Best10% ( ) Most ( ) and Worst10% ( ) indicators ( n ¼ totalnumber of scores assigned). Median scores of 2.5 or less represent Very Poor condition,median scores greater than 7.5 represent Very Good condition (see Table 2 for grading/scoring scale). (b) condition con dence d levels of High ( ), Medium ( ), or Low ( )con dence assigned to the scores forcondition in all components. Trend in biodiversity

and ecosystem health condition: (c) % of total observations in each region assigned totrend of Improving ( ), Stable ( ) or Deteriorating ( ) condition; all data combined forBest10%, Most and Worst10% indicators. (d) trend con dence d levels of High ( ),

Medium ( ), or Low ( ) con dence assigned to the scores for trends in all components.


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lowest median pressure score (2, Very Poor, in the Worst10%)(Fig. 3a). The SE region also was considered to be affected byhigh levels of combined pressures affecting the biodiversity,with the second lowest median score (3, Worst10%) and sum of pressure scores. Pressures were considered to have the leastcurrent impact on biodiversity and ecosystem health in the N

region. Overall, the pressures having the greatest national levelof current impact on the marine environment were found to bemarine debris, port facilities, shing and shipping, which eachscored a national median of 6 or less. Ports were considered tobe have imposed very high pressures and resulted in Very Poorconditions in the SW, NW and E regions (condition scores of 2 orless).

3.1.2.1. Trend in pressures. All regions demonstrated a dominantpattern of Stable or Deteriorating conditions (increasing impact) inrelation to current pressures ( Fig. 3c). The ve major pressures withmost widespread trend of increases in impact (driving decliningconditions) were climate change, shipping, marine debris, tourism

facilities and coastal development. Only shing, port facilities, andcatchment runoff were considered to be currently reducing aspressures in some places, and thus contributing to some selectiveimprovement in conditions. The impacts of ports is considered tobe currently creating widely declining conditions in the NW, E andSE regions, with Deteriorating trends in both Most and Worst10%indicators in these three regions. Although shing was identi ed asa pressure responsible for Poor condition in at least one indicator inevery region, shing was also considered to be the pressure mostcommonly reducing in impact from high historic levels of impact d having a reducing current impact in four of the regions inmainly the Most indicator. Nonetheless, the effects of shing wereconsidered to be currently increasing and driving continuing

Deterioration in condition in the Best10% of the SW and Worst10%of the E region.

3.1.2.2. Con dence in pressures. About 84% of the scores assigned tothe impacts of pressures were considered to have either a High orMedium level of con dence ( Fig. 3b). This was the dominant

pattern in the E and SE regions, where no pressure scores wereassigned with Low con dence. In contrast, almost half of thepressure estimates assigned for the NW region were graded as Lowin con dence. A similar pattern emerged for con dence in thepressure trends, although the trends in the SW were assigned withmainly Medium con dence, and in the N region with mostly Lowcon dence ( Fig. 3d).

3.1.3. Multivariate overviewCluster analysis of the full dataset (all regions, all components,

all indicator data for condition, trends, con dence and pressures)distinguished the N region from the SE region at a high level in theclassi cation, andthese areseparatefrom the E regionand from theSW and NW regions ( Fig. 4a). This cluster pattern re ects the

substantive spatial differences in biodiversity and ecosystem healthcondition, pressures, information quality (based on con dencegrades), and trends across the national jurisdiction. The primaryseparation of the groups in this cluster is driven by differences incondition and trend in habitats and a numberof species groups, andby differences in con dence.

3.2. Biodiversity and ecosystem health

3.2.1. National condition and trendsThe subset of data containing biodiversity and ecosystem

health components that occur and were scored in more than oneregion (21 habitats; 31 species and species groups; 17 ecological

Table 4Components with the most frequent deteriorating trend. Deteriorating trend for biodiversity and ecosystem health components (frequency of deteriorating observations thatfall into the 75th percentile and above; all indicators, all regions) and their median parameter condition scores.

Parameter Component Frequency of deterioratingtrends > 75thpercentile

Medianconditionscore

Habitats Water column shoreline (0 e 20 m) 11 7

Species and species groups Migratory seabirds/waders 9 4.5Physical and chemical processes Sea level 9 7Physical and chemical processes Ocean acidity 9 8Habitats Water column inner shelf (20 e 50 m) 8 8Species and species groups Dolphins and porpoises 8 6Ecological processes Trophic structures and relationships 8 5Physical and chemical processes Sea temperature 8 6Habitats Water column outer shelf (50 e 200 m) 7 8Ecological processes Connectivity: Biological, migration, yways 7 7Physical and chemical processes Ocean currents, structure and dynamics 7 8Habitats Water column offshore ( > 200 m) 6 9Ecological processes Connectivity: spatial/physical disjunctions 6 8Ecological processes Water column, pelagic productivity 6 9Ecological processes Reef building 6 7Ecological processes Symbiosis: sh, corals, molluscs 6 9Ecological processes Predation 6 6.5Physical and chemical processes Nutrient supply and cycling: land-based 6 8PIDA group Number and abundances of introduced species 6 8Habitats Beaches 5 8Habitats Algal beds 5 8Species and species groups Hard coral species 5 7Ecological processes Connectivity: Recruitment, settlement 5 6Physical and chemical processes Nutrient supply and cycling: ocean-based 5 8



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pressures. Within a single region, the range between the best andworst conditions is greatest in the SE, E and SW regions, adjacent tothe landmass where the majority of Australia's population reside,and where the history of pressures is greatest (eg Hewitt et al.,2004 ). Nonetheless, all regions have areas where there are highlevels of past and current pressure that have substantively affectedthe condition of biodiversity and ecosystem health.

While the national marine environment as a whole and that of each individual regionwas graded as either Good or Very Good, andthe greatest number of biodiversity and ecosystem health compo-nents are considered to be Stable, the number of components thatare considered to be Deteriorating substantially exceed the numberof components that are Improving in condition. System trajectoryoverall thereforeappears to be trending downwards. Also, there areexamples of components where decline in condition is consideredto have been halted, butthere are few exampleswhere componentshave recovered to Good or Very Good condition. Using this infor-mation, priorities for restoration action could therefore be based ona combination of both trends and condition.

A number of the components assessed here were considered tobe at such low condition status that they could not decline furtheron the assessment scale, and so were assessed as Stable. These‘ poorest of the poor ’ include, for example, oyster reefs in the SWand SE regions which are considered for all practical purposes to beextinct ( Beck et al., 2011 ) with likely major historic impacts onbio ltration services in the estuaries and bays, and species that arealmost locally extirpated in some areas, such as some exploitedspecies of sharks or rays and some mangrove habitats and species.While the condition of ve habitats, ve species groups, and oneecological process was scored as zero in the Worst10% of places,these extreme examples each only occurred in a single region,except for mangrove habitats and mangrove species which wereassigned Worst10% condition scores of zero in the E, SE and SWregions. Of the components that occur in more than one region, 14biodiversity or ecosystem healthcomponents arein Poor (orworse)condition. Of these, 10 components are related to the past wide-

spread impacts of shing or hunting activities, and only the con-dition of fur seals (now protected) was assessed as nationallyimproving from a low base. In the Worst10% of examples, 20components (mainly habitats and species groups) were assigned asVery Poor condition in more than one region, indicating a potentialset of issues of high national signi cance. Some of these compo-nents considered to be in Very Poor condition are already protectedunder the EPBC Act and are the subject of formal population re-covery plans (eg the Great White Shark, Carcharodon carcharias ;EPBC, 2014), although most remain to be addressed in a coordi-nated national manner.

The number of such ‘ worst of the worst ’ examples, and thenumber of components that continue to decline in condition,suggest that further and more focused national restoration and

recovery investments will be needed beyond the current programsfor Australia's formally declared threatened species. This is alsoconsistent with the need for a more ecosystem-based approach,where ecosystem structure and function and maintenance of thediversity of species and their natural functional relationships,habitats and productivity are the speci c targets for marineecosystem management ( Rice et al., 2012; de Jonge et al., 2012;Keith et al., 2013 ) rather than only species and habitats at highrisk of extinction, or resource species.

The large number of biodiversity components in poor conditionand declining should provide impetus for a review of nationalpriorities in Australia's ecosystem-based management and moni-toring programs related to the dominant pressures of climate

Table 5Conditionand trendin commoncomponents that arein poorcondition.Condition of biodiversity and ecological health components that occur in more than one region(pooled Best10%, Most and Worst10% scores across regions), with pooled medianscore of 5 or less (condition is Poor or Very Poor) (* ¼ related to shing or huntingactivities; ** ¼ trends may be different in different regions) (Note that this is a morecomplete subset of data than those summarised in Section 2.1.2 of SoEC, 2011 ).

Component Medianscore

Dominantconditiontrend**

Fur seals* 2 ImprovingSouthern blue n tuna* 3 StableGreat white shark* 3.5 DeterioratingInner-shelf reef sh species* 4 StableEast Australian Current 4 DeterioratingMigratory seabirds/waders 4.5 DeterioratingBays and small gulfs 5 StableEstuaries and Lagoons: open

and barred5 Stable,

DeterioratingSharks and rays (exploited and

signi cant by catch)*5 Stable

Tuna and bill sh* 5 StableOuter shelf (50 e 200 m) e demersal

and benthopelagic sh species*5 Stable

Inner shelf e demersal sh species (0 e 50 m)* 5 StableWhales toothed* 5 StableTrophic structures and relat ionships* 5 Deteriorating

Fig. 4. Cluster diagram of scores assigned by expert judgement to the regions (a) alldata d all biodiversity, ecosystem health and pressure components and all indicatorsfor condition, trend, and con dence for each region; (b) condition and trend scores of the subset of biodiversity and ecosystem health components that are broadlydistributed and occur in more than one region. In both clusters, individual metricshave a high delity for their group, with no cross-classi cation of data from any regionand each branch of the cluster containing only data from a single region. The Northregion is clearly separated from the other four regions, when all the scores for allregions are considered in a single cluster analysis. However, when considering only thescores for condition and trend of broadly distributed components, both the North andthe North-west region are clearly distinguished from the other regions.



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Table 6Condition of common components that are in best or worst condition. The pooled scores represent the median of the component scores pooled nationally, derived separatelyfor the Best10% and the Worst10% indicators.Pooled component scores that exceed the 75th percentile of the Best10% scores represent the ‘ best of thebest ’ components.Thosecomponentswith pooledscores less than the 25th percentile of the Worst10% scores represent the ‘ worst of theworst ’ components.Componentsthat are nationally in the bestcondition occur as > 75th percentile in both the Best10% and Worst10% indicators, inferring a consistent national pattern; eg the seabed shelf break and slope is consistentlyfound at or above the 75th percentile for both Best10% and Worst10% indicators, inferring this habitat type is considered to be in good or better condition at the national scale.Other components may occur in both the ‘ best of the best ’ and the ‘ worst of the worst ’ condition groups, inferring a high level of spatial variability eg Estuaries and Lagoons:open and barred habitats are considered in some places to be in very good condition, while in other places to be in very poor condition.

Parameter Component Pooled Best10% scores Pooled Worst10% scores

75thpercentile

25thpercentile

75thpercentile

25thpercentile

Habitat Bays and small gulfs 9 2Estuaries and Lagoons: open andbarred

9 1

Estuaries and Bays 3Beaches 9 3Reefs fringing coasts and islandsFringing coral reefs and rockycoasts (intertidal and subtidal)

9 2

Seabed inner shelf (0 e 50 m) 9 2.5Seabed outer shelf (50 e 200 m) 9Seabed shelf break and slope 9 6Seabed abyss > 1500 m 10 9.5Water column shoreline (0 e 20 m) 9 3Water column inner shelf (20 e 50 m) 9 6

Water column outer shelf (50 e 200 m) 9 7Water column offshore ( > 200 m) 9 7Mangroves 10 0Seagrasses 10 3Algal beds 10Coral reefs ( < 30 m) 9.5 2Deepwater corals and sponges ( > 30 m) 9 2.5Canyons and shelf-break 10 8Offshore banks, shoals, islandsSeamounts ( > 1000 m rise from sea oor) 9.5

Species , groups Sharks and rays (exploited & signi cantby catch)

7

Sharks and rays, non targetted 8Whale shark 7 7Great white shark 4 2Tuna and bill sh 7.5 2.5Southern blue n tuna 3 3

Outer shelf (50 e 200 m) e demersal andbenthopelagic sh species 7.5

Inner shelf - demersal sh species (0 e 50 m) 7 3Meso-pelagic sh species 9 9Small pelagics e inner shelf Inner-shelf reef sh species 8 2Inner shelf e invertebrate species 8 3Outer shelf and inner slope e invertebratespeciesShoreline and intertidal species 8 2Seabirds e resident 8Migratory seabirds/waders 5.5 2.5Hard coral species 9 2Mangrove species 10 0Seagrass species 10 3Dune and saltmarsh plant species 9Dugongs 9Turtles 8 3Seasnakes 9 1Crocodiles 9 9Dolphins and porpoises 9 3Whales baleen (not humpbacks) 8 2.3Humpback whales 8Whales toothed 6Fur seals 3.5 1Seahorses and allies (Families Syngathidae,Solenostomidae)

9 1.5

Herbivorous sh of coral reefs 9.5 7Ecological process Connectivity: Spatial/physical disjunctions 9 7

Connectivity: Biological, migration, yways 8Connectivity: Recruitment, settlement 8Connectivity: Genome structures, genetic adaptation 8 3Nesting, roosting and nursery sites 8Feeding grounds 9 6

(continued on next page )



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change, ports and related coastal development, and shing. Forexample,the impactof ports and port development was found to becreating widely declining conditions in the NW, E and SE regions,and regional scale planning will be needed to mitigate regional-scale impacts from these ongoing coastal developments. Equally,

shing is widespread across regions and affects a number of theintrinsic ecosystem components, many of which are in poor con-dition and demonstrate a high frequency of stability or deteriora-tion. Fishing can therefore be considered to be a dominant pressureon the marine ecosystems, but there is no national synthesis oranalysis of the cumulative impacts of shing on the biodiversitycomponents or indicators assessed in this report, or the interactionwith climate change, or other dominant pressures such as coastalindustrial developments, and there is only very limited relevantknowledge that can be drawn from sheries data reported inAustralia. Collectively, these patterns of pressures infer that a muchmore integrated approach to policy and management is required toachieve more effective ecosystem-based management outcomes. Afocus on both components in poor condition and those in decline,as well as on mitigating the major pressures affecting them, wouldimprove the effectiveness of current policies and managementstrategies in Australia's marine ecosystems. Equally, a focus onthose in very good condition and in recovery would assist in

identifying candidate areas for protection within marinesanctuaries.

Key lessons from the expert elicitation process includeallowing additional time for resolving the issues that arise in theworkshops, providing a set of base literature about the relevantissues well in advance of the assessment workshops, andproviding for a mixture of real-time workshop and moreextended remote review of component scores and analysis. Also,the expert knowledge and experience in marine issues in theglobal oceans is rapidly increasing in the private sector and somescience-based organisations (such as IUCN). Facilitating a moreextensive involvement will be important to continue to enable adiversity of both experts and independent experience to bebrought to future assessments that follow the frameworkdeveloped and applied here.

Environmental policy and management are always likely to bebased on multiple lines of evidence, especially in the context of adata-poor knowledge base and the absence of well formulatednational-scale environmental information systems ( Cook et al.,2012 ). The ‘ wide and shallow ’ assessment used here covers multi-ple lines of evidence related to a wide spectrum of speci c assetsand values. The requirement for veri cation of accuracy at thelocal-scale may need to be invoked after broader priorities are

Table 6 (continued )

Parameter Component Pooled Best10% scores Pooled Worst10% scores

75thpercentile

25thpercentile

75thpercentile

25thpercentile

Trophic structures and relationships 9 3Water column, pelagic productivity 9 7Benthic productivity e inshore 9Benthic productivity e offshore 10 6Reef building 9.5Symbiosis: sh, corals, molluscs 9.5 6Predation 2.5Herbivory 9 7Filter feeding 9.5 3Microbial processes 9 6Wetland ooding cycles 9.5

Physical and chemicalprocess

Ocean currents, structure and dynamics 9.5 6

Storms, cyclones, wind patterns 7.5 6Sediment inputs 9Inshore water turbidity, transparencyand colour

9

Sea temperature 7Sea level 7Nutrient supply and cycling: land-based 9 6Nutrient supply and cycling: ocean-based 9 7

Freshwater in ow, surface andgroundwater runoff

9

Toxins, pesticides, herbicides 9 2Dumped wastes 9 6.5Ocean acidity 8 7Ocean salinity 9 7Low oxygen-dead zones 10 7.5Macro-tidal regimes 10 9.5Sahul carbonate-banks processes 10 8.5East Australian Current 4

PIDA group Number and abundances of NIMPISlisted pests

9 6

Number and abundances of introducedspecies

8 7.5

Virus diseases, parasitic infestations, shkills

9

Algal blooms, jelly sh blooms 9

Crown of Thorns Star sh (COTS):abundance and distribution 6.5 6



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established within the policy-context of a national-scale set of is-

sues, provided these issues are determined through a decisionmodel with low bias in the underlying decision-structure. Thisapproach should serve to enhance the more effective use of speci cindicator sets as policy tools in preference to complex indices(Bauler, 2012 ) by minimising the risk that a small number of sub- jectively determined issues from a ‘ narrow and deep ’ informationbase will leverage national policy out of proportion to their actualimportance for biodiversity. To avoid these and related issues, theintegrated judgement-based approach used here has also beenadopted in other jurisdictions, such as a pilot for the World OceanAssessment ( Feary et al., 2014 ), to provide a defendable frameworkfor the rapid assessment of data-poor ocean ecosystems.

This paper reports the combined personal judgements of thescientists who contributed to the assessments d a diverse and

highly experienced group of independent experts with relevantbackgrounds from various tertiary and science institutions. Their judgements were developed in a structured and peer-groupcontestable process and the ndings are based on all availabledata and knowledge. Although substantial uncertainty remainsaround the accuracy of the ndings for many of the environmentalcomponents, the breadth and robustness of this consultative pro-cess provides a basis for development of improved national-scalemarine policies and strategies that focus on the intrinsic attri-butes of ecosystem structure and function as well as gaps inknowledge. Without such a comprehensive approach, national-scale assessments risk becoming simply reports that re-con rmthe technical detail of what is already known rather than a sys-tematic and balanced analysis of the performance of ocean envi-

ronments as a whole.

Acknowledgements

The national marine condition assessment process and the SoE2011 report was funded and supported by DSEWPaC (now theDepartment of the Environment), and the support and leadershipprovided by the divisions of DSEWPaC, and members of the inde-pendent committee established to prepare State of the Environ-

ment Australia 2011 ( http://www.environment.gov.au/soe SoEC,

Fig. 5. Trends in condition of biodiversity and ecosystem health components (a) Northregion; (b) South-east region: frequency of occurrence (%) of components consideredto be Improving, Stable, or Deteriorating condition in the region.

Fig. 6. Median scores and frequencies for biodiversity and ecosystem health compo-nents in regional groups derived from the cluster analysis at the 3-group level: N, SEand the remaining regions (SW, NW, E): (a) condition d median of all scores for theBest10%, Most or Worst10%; (b) trend d frequency of occurrence as % of all observa-tions for Improving, Stable or Deteriorating; (c) con dence d frequency % of High,Medium or Low.


http://www.environment.gov.au/soehttp://www.environment.gov.au/soe


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2011) is gratefully acknowledged. Earlier drafts of this paper havebeen improved by review, comment and inputs from Nancy DahlTacconi, Boon Lim, Ilse Keissling, Nicole Coombe and CarolynArmstrong (all the Department of the Environment) and externalreviewers of the draft manuscript: Kirstin Dobbs, Great Barrier Reef Marine Park Authority; Richard Kenchington, University of Wol-longong, and Ian Perry, Fisheries and Oceans Canada. The willing-ness and commitment of the 40 experts who openly contributed tothe workshops and the grading process is also very gratefullyacknowledged d these experts are individually identi ed in Ward,2011 . The workshops were facilitated by Richard Stoklosa (E-Sys-tems Pty Ltd, Tasmania). The interpretations of the SoEdata and theopinions in this paper remain those of the author alone, and do notnecessarily represent of cial Australian Government policy, or thespeci c view of any single expert who contributed information.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.ocecoaman.2014.07.012 .

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